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Fonte M, Rôla C, Santana S, Prudêncio M, Almeida J, Ferraz R, Prudêncio C, Teixeira C, Gomes P. Repurposing antiplasmodial leads for cancer: Exploring the antiproliferative effects of N-cinnamoyl-aminoacridines. Bioorg Med Chem Lett 2024; 111:129894. [PMID: 39043264 DOI: 10.1016/j.bmcl.2024.129894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 06/26/2024] [Accepted: 07/20/2024] [Indexed: 07/25/2024]
Abstract
Drug repurposing and rescuing have been widely explored as cost-effective approaches to expand the portfolio of chemotherapeutic agents. Based on the reported antitumor properties of both trans-cinnamic acids and quinacrine, an antimalarial aminoacridine, we explored the antiproliferative properties of two series of N-cinnamoyl-aminoacridines recently identified as multi-stage antiplasmodial leads. The compounds were evaluated in vitro against three cancer cell lines (MKN-28, Huh-7, and HepG2), and human primary dermal fibroblasts. One of the series displayed highly selective antiproliferative activity in the micromolar range against the three cancer cell lines tested, without any toxicity to non-carcinogenic cells.
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Affiliation(s)
- Mélanie Fonte
- LAQV-REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Portugal
| | - Catarina Rôla
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Sofia Santana
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Miguel Prudêncio
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Joana Almeida
- Centro de Investigação em Saúde Translacional e Biotecnologia Médica (TBIO)/Rede de Investigação em Saúde (RISE-Health), Escola Superior de Saúde, Instituto Politécnico do Porto, Portugal; Ciências Químicas e das Biomoléculas, Escola Superior de Saúde, Politécnico do Porto, Portugal
| | - Ricardo Ferraz
- LAQV-REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Portugal; Centro de Investigação em Saúde Translacional e Biotecnologia Médica (TBIO)/Rede de Investigação em Saúde (RISE-Health), Escola Superior de Saúde, Instituto Politécnico do Porto, Portugal; Ciências Químicas e das Biomoléculas, Escola Superior de Saúde, Politécnico do Porto, Portugal
| | - Cristina Prudêncio
- Centro de Investigação em Saúde Translacional e Biotecnologia Médica (TBIO)/Rede de Investigação em Saúde (RISE-Health), Escola Superior de Saúde, Instituto Politécnico do Porto, Portugal; Ciências Químicas e das Biomoléculas, Escola Superior de Saúde, Politécnico do Porto, Portugal
| | - Cátia Teixeira
- LAQV-REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Portugal; Gyros Protein Technologies Inc., Tucson, AZ, USA
| | - Paula Gomes
- LAQV-REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Portugal.
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Ma W, Hu J, Chen Z, Ai Y, Zhang Y, Dong K, Meng X, Liu L. The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform. J Chem Inf Model 2024. [PMID: 39320984 DOI: 10.1021/acs.jcim.4c00595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2024]
Abstract
Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmacology effects and provides unique pharmacological insights for drug repurposing development of existing kinase inhibitor drugs. However, experimental profiling of whole kinome selectivity is still time-consuming and resource-demanding. Here, we report a deep learning classification model using an in-house built data set of inhibitors against 191 well-representative kinases constructed based on a novel strategy by systematically cleaning and integrating six public data sets. This model, a multitask deep neural network, predicts the kinome selectivity profiles of compounds with novel structures. The model demonstrates excellent predictive performance, with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92, 0.90, and 0.37, respectively. It also performs well in a priori testing for inhibitors targeting different categories of proteins from internal compound collections, significantly improving over similar models on data sets from practical application scenarios. Integrated to subsequent machine learning-enhanced virtual screening workflow, novel CDK2 kinase inhibitors with potent kinase inhibitory activity and excellent kinome selectivity profiles are successfully identified. Additionally, we developed a free online web server, KinomePro-DL, to predict the kinome selectivity profiles and kinome-wide polypharmacology effects of small molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users to quickly fine-tune it with their own training data sets, enhancing both prediction accuracy and robustness.
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Affiliation(s)
- Wei Ma
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Jiaqi Hu
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Zhuangzhi Chen
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Yaoqin Ai
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Yihang Zhang
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Keke Dong
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Xiangfei Meng
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Liu Liu
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
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Egger K, Aicher HD, Cumming P, Scheidegger M. Neurobiological research on N,N-dimethyltryptamine (DMT) and its potentiation by monoamine oxidase (MAO) inhibition: from ayahuasca to synthetic combinations of DMT and MAO inhibitors. Cell Mol Life Sci 2024; 81:395. [PMID: 39254764 PMCID: PMC11387584 DOI: 10.1007/s00018-024-05353-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/19/2024] [Accepted: 07/04/2024] [Indexed: 09/11/2024]
Abstract
The potent hallucinogen N,N-dimethyltryptamine (DMT) has garnered significant interest in recent years due to its profound effects on consciousness and its therapeutic psychopotential. DMT is an integral (but not exclusive) psychoactive alkaloid in the Amazonian plant-based brew ayahuasca, in which admixture of several β-carboline monoamine oxidase A (MAO-A) inhibitors potentiate the activity of oral DMT, while possibly contributing in other respects to the complex psychopharmacology of ayahuasca. Irrespective of the route of administration, DMT alters perception, mood, and cognition, presumably through agonism at serotonin (5-HT) 1A/2A/2C receptors in brain, with additional actions at other receptor types possibly contributing to its overall psychoactive effects. Due to rapid first pass metabolism, DMT is nearly inactive orally, but co-administration with β-carbolines or synthetic MAO-A inhibitors (MAOIs) greatly increase its bioavailability and duration of action. The synergistic effects of DMT and MAOIs in ayahuasca or synthetic formulations may promote neuroplasticity, which presumably underlies their promising therapeutic efficacy in clinical trials for neuropsychiatric disorders, including depression, addiction, and post-traumatic stress disorder. Advances in neuroimaging techniques are elucidating the neural correlates of DMT-induced altered states of consciousness, revealing alterations in brain activity, functional connectivity, and network dynamics. In this comprehensive narrative review, we present a synthesis of current knowledge on the pharmacology and neuroscience of DMT, β-carbolines, and ayahuasca, which should inform future research aiming to harness their full therapeutic potential.
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Affiliation(s)
- Klemens Egger
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.
- Department of Nuclear Medicine, Bern University Hospital, Bern, Switzerland.
| | - Helena D Aicher
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
- Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Paul Cumming
- Department of Nuclear Medicine, Bern University Hospital, Bern, Switzerland
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
| | - Milan Scheidegger
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
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Low ZXB, Ng WS, Lim ESY, Goh BH, Kumari Y. The immunomodulatory effects of classical psychedelics: A systematic review of preclinical studies. Prog Neuropsychopharmacol Biol Psychiatry 2024:111139. [PMID: 39251080 DOI: 10.1016/j.pnpbp.2024.111139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 08/27/2024] [Accepted: 09/06/2024] [Indexed: 09/11/2024]
Abstract
Emerging evidence suggests that classical psychedelics possess immunomodulatory and anti-inflammatory properties; however, these effects are yet to be well-established. This systematic review aims to provide a timely and comprehensive overview of the immunomodulatory effects of classical psychedelics in preclinical studies. A systematic search was conducted on six databases, including CINAHL, EMBASE, MEDLINE, PsychINFO, Scopus, and Web of Science. Eligible studies targeting classical psychedelics for evaluation of their effects on inflammatory markers and immunomodulation have been included for analysis. Data was extracted from 40 out of 2822 eligible articles, and their risk of bias was assessed using the Systematic Review Center for Laboratory Animal Experimentation (SYRCLE) tool and Quality Assessment Tool for In Vitro Studies (QUIN). Studies examined 2,5-dimethoxy-4-iodoamphetamine (DOI; n = 18); psilocybin (4-PO-DMT; n = 9); N,N-dimethyltryptamine (DMT; n = 8); lysergic acid diethylamide (LSD; n = 6); 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT; n = 3); psilocin (4-HO-DMT; n = 3); and mescaline (n = 2). In 36 studies where inflammatory cytokine levels were measured following psychedelic administration, a decrease in at least one inflammatory cytokine was observed in 29 studies. Immune cell activity was assessed in 10 studies and findings were mixed, with an equal number of studies (n = 5 out of 10) reporting either an increase or decrease in immune cell activity. Classical psychedelics were found to alleviate pre-existing inflammation but promote inflammation when administered under normal physiological conditions. This information is anticipated to inform future clinical trials, exploring classical psychedelics' potential to alleviate inflammation in various pathologies.
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Affiliation(s)
- Zhen Xuen Brandon Low
- Neurological Disorder and Aging (NDA) Research Group, Neuroscience Research Strength (NRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, 47500, Selangor, Malaysia
| | - Wei Shen Ng
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, 47500, Selangor, Malaysia
| | - Eugene Sheng Yao Lim
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, 47500, Selangor, Malaysia
| | - Bey Hing Goh
- Sunway Biofunctional Molecules Discovery Centre, School of Medical and Life Sciences, Sunway University Malaysia, Bandar Sunway, 47500, Selangor Darul Ehsan, Malaysia; Biofunctional Molecule Exploratory Research Group, School of Pharmacy, Monash University Malaysia, Bandar Sunway, Selangor Darul Ehsan 47500, Malaysia; College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yatinesh Kumari
- Neurological Disorder and Aging (NDA) Research Group, Neuroscience Research Strength (NRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, 47500, Selangor, Malaysia.
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Yaniv D, Mattson B, Talbot S, Gleber-Netto FO, Amit M. Targeting the peripheral neural-tumour microenvironment for cancer therapy. Nat Rev Drug Discov 2024:10.1038/s41573-024-01017-z. [PMID: 39242781 DOI: 10.1038/s41573-024-01017-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/24/2024] [Indexed: 09/09/2024]
Abstract
As the field of cancer neuroscience expands, the strategic targeting of interactions between neurons, cancer cells and other elements in the tumour microenvironment represents a potential paradigm shift in cancer treatment, comparable to the advent of our current understanding of tumour immunology. Cancer cells actively release growth factors that stimulate tumour neo-neurogenesis, and accumulating evidence indicates that tumour neo-innervation propels tumour progression, inhibits tumour-related pro-inflammatory cytokines, promotes neovascularization, facilitates metastasis and regulates immune exhaustion and evasion. In this Review, we give an up-to-date overview of the dynamics of the tumour microenvironment with an emphasis on tumour innervation by the peripheral nervous system, as well as current preclinical and clinical evidence of the benefits of targeting the nervous system in cancer, laying a scientific foundation for further clinical trials. Combining empirical data with a biomarker-driven approach to identify and hone neuronal targets implicated in cancer and its spread can pave the way for swift clinical integration.
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Affiliation(s)
- Dan Yaniv
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Brandi Mattson
- The Neurodegeneration Consortium, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sebastien Talbot
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Sweden
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada
| | - Frederico O Gleber-Netto
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Moran Amit
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Manen-Freixa L, Antolin AA. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery. Expert Opin Drug Discov 2024; 19:1043-1069. [PMID: 39004919 DOI: 10.1080/17460441.2024.2376643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology. AREAS COVERED This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples. EXPERT OPINION Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.
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Affiliation(s)
- Leticia Manen-Freixa
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Albert A Antolin
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Center for Cancer Drug Discovery, The Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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Ye W, Li C, Zhang W, Li J, Liu L, Cheng D, Feng Z. Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions. Methods 2024; 231:15-25. [PMID: 39218170 DOI: 10.1016/j.ymeth.2024.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 08/12/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024] Open
Abstract
Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.
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Affiliation(s)
- Wenting Ye
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Chen Li
- Graduate School of Informatic, Nagoya University, Chikusa, Nagoya, 464-8602, Japan
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agric, Wuhan 430070, China; Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agric, Wuhan 430070, China
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Adelaide, 5095, Australia
| | - Lin Liu
- UniSA STEM, University of South Australia, Adelaide, 5095, Australia
| | - Debo Cheng
- UniSA STEM, University of South Australia, Adelaide, 5095, Australia.
| | - Zaiwen Feng
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agric, Wuhan 430070, China; Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agric, Wuhan 430070, China.
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Al-Odat OS, Nelson E, Budak-Alpdogan T, Jonnalagadda SC, Desai D, Pandey MK. Discovering Potential in Non-Cancer Medications: A Promising Breakthrough for Multiple Myeloma Patients. Cancers (Basel) 2024; 16:2381. [PMID: 39001443 PMCID: PMC11240591 DOI: 10.3390/cancers16132381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
MM is a common type of cancer that unfortunately leads to a significant number of deaths each year. The majority of the reported MM cases are detected in the advanced stages, posing significant challenges for treatment. Additionally, all MM patients eventually develop resistance or experience relapse; therefore, advances in treatment are needed. However, developing new anti-cancer drugs, especially for MM, requires significant financial investment and a lengthy development process. The study of drug repurposing involves exploring the potential of existing drugs for new therapeutic uses. This can significantly reduce both time and costs, which are typically a major concern for MM patients. The utilization of pre-existing non-cancer drugs for various myeloma treatments presents a highly efficient and cost-effective strategy, considering their prior preclinical and clinical development. The drugs have shown promising potential in targeting key pathways associated with MM progression and resistance. Thalidomide exemplifies the success that can be achieved through this strategy. This review delves into the current trends, the challenges faced by conventional therapies for MM, and the importance of repurposing drugs for MM. This review highlights a noncomprehensive list of conventional therapies that have potentially significant anti-myeloma properties and anti-neoplastic effects. Additionally, we offer valuable insights into the resources that can help streamline and accelerate drug repurposing efforts in the field of MM.
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Affiliation(s)
- Omar S. Al-Odat
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA; (O.S.A.-O.); (E.N.)
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA;
| | - Emily Nelson
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA; (O.S.A.-O.); (E.N.)
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA;
| | | | | | - Dhimant Desai
- Department of Pharmacology, Penn State Neuroscience Institute, Penn State College of Medicine, Hershey, PA 17033, USA;
| | - Manoj K. Pandey
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA; (O.S.A.-O.); (E.N.)
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Kumar S, Mohan A, Sharma NR, Kumar A, Girdhar M, Malik T, Verma AK. Computational Frontiers in Aptamer-Based Nanomedicine for Precision Therapeutics: A Comprehensive Review. ACS OMEGA 2024; 9:26838-26862. [PMID: 38947800 PMCID: PMC11209897 DOI: 10.1021/acsomega.4c02466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/09/2024] [Accepted: 05/28/2024] [Indexed: 07/02/2024]
Abstract
In the rapidly evolving landscape of nanomedicine, aptamers have emerged as powerful molecular tools, demonstrating immense potential in targeted therapeutics, diagnostics, and drug delivery systems. This paper explores the computational features of aptamers in nanomedicine, highlighting their advantages over antibodies, including selectivity, low immunogenicity, and a simple production process. A comprehensive overview of the aptamer development process, specifically the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process, sheds light on the intricate methodologies behind aptamer selection. The historical evolution of aptamers and their diverse applications in nanomedicine are discussed, emphasizing their pivotal role in targeted drug delivery, precision medicine and therapeutics. Furthermore, we explore the integration of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), Internet of Medical Things (IoMT), and nanotechnology in aptameric development, illustrating how these cutting-edge technologies are revolutionizing the selection and optimization of aptamers for tailored biomedical applications. This paper also discusses challenges in computational methods for advancing aptamers, including reliable prediction models, extensive data analysis, and multiomics data incorporation. It also addresses ethical concerns and restrictions related to AI and IoT use in aptamer research. The paper examines progress in computer simulations for nanomedicine. By elucidating the importance of aptamers, understanding their superiority over antibodies, and exploring the historical context and challenges, this review serves as a valuable resource for researchers and practitioners aiming to harness the full potential of aptamers in the rapidly evolving field of nanomedicine.
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Affiliation(s)
- Shubham Kumar
- School
of Bioengineering and Biosciences, Lovely
Professional University, Phagwara, Punjab 144001, India
| | - Anand Mohan
- School
of Bioengineering and Biosciences, Lovely
Professional University, Phagwara, Punjab 144001, India
| | - Neeta Raj Sharma
- School
of Bioengineering and Biosciences, Lovely
Professional University, Phagwara, Punjab 144001, India
| | - Anil Kumar
- Gene
Regulation Laboratory, National Institute
of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Madhuri Girdhar
- Division
of Research and Development, Lovely Professional
University, Phagwara 144401, Punjab, India
| | - Tabarak Malik
- Department
of Biomedical Sciences, Institute of Health, Jimma University, MVJ4+R95 Jimma, Ethiopia
| | - Awadhesh Kumar Verma
- School
of Bioengineering and Biosciences, Lovely
Professional University, Phagwara, Punjab 144001, India
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10
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Sawada R, Sakajiri Y, Shibata T, Yamanishi Y. Predicting therapeutic and side effects from drug binding affinities to human proteome structures. iScience 2024; 27:110032. [PMID: 38868195 PMCID: PMC11167438 DOI: 10.1016/j.isci.2024.110032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 04/08/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.
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Affiliation(s)
- Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Department of Pharmacology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Yuko Sakajiri
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
| | - Tomokazu Shibata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
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11
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Zhang R, Nolte D, Sanchez-Villalobos C, Ghosh S, Pal R. Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling. Nat Commun 2024; 15:5072. [PMID: 38871711 DOI: 10.1038/s41467-024-49372-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 06/04/2024] [Indexed: 06/15/2024] Open
Abstract
Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learning-based QSAR models and provide better intuitive interpretation by extracting an approximate isometry between the chemical space of the drugs and their activity space.
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Affiliation(s)
- Ruibo Zhang
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Daniel Nolte
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Cesar Sanchez-Villalobos
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Souparno Ghosh
- Department of Statistics, University of Nebraska - Lincoln, Lincoln, NB, 68588, USA.
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
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12
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Yellurkar ML, Prasanna VS, Das P, Sarkar S, Matta R, Dhaked DK, Peraman R, Taraphdar AK, Nanjappan SK, Velayutham R, Arumugam S. Indigenous wisdom of a Kwatha to treat NASH: An insight into the mechanism. JOURNAL OF ETHNOPHARMACOLOGY 2024; 326:117935. [PMID: 38408692 DOI: 10.1016/j.jep.2024.117935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 01/08/2024] [Accepted: 02/17/2024] [Indexed: 02/28/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Nonalcoholic fatty liver disease (NAFLD) is the most common severe liver disease globally, progressing further into nonalcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC). Vasaguduchyadi Kwatha (VK) is an Ayurvedic formulation traditionally used to treat liver diseases and other metabolic complications. This study is an ethnopharmacological approach to unravel this indigenous remedy. AIM OF THE STUDY We aimed to discover the probable mechanism of action of VK against NASH in this study, using network pharmacology, molecular docking, in vitro study, and preclinical investigation. METHODS AND RESULTS Among the 55 components identified, 10 were confirmed based on mass, elution charecteristics, MS/MS analysis data, and fragmentation rules. Computational study indicated 92 targets involved in the central pathways of NASH, out of which only 15 targets and 9 VK constituents have significant docking scores. In vitro and in vivo analysis results showed that VK significantly reduces weight gain and improves insulin sensitivity, dyslipidemia, steatohepatitis and overall histological features of NASH compared to saroglitazar (SGZR). CONCLUSION Our detailed study yielded three signalling pathways related to NASH on which VK has maximum effect, bringing up a probable alternative treatment for NASH.
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Affiliation(s)
- Manoj Limbraj Yellurkar
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Vani Sai Prasanna
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Pamelika Das
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Sulogna Sarkar
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Rakesh Matta
- Department of Natural Products, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Devendra Kumar Dhaked
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Ramalingam Peraman
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Export Promotion Industrial Park (EPIP) Zandaha Road, NH322, Hajipur, Bihar, 844102, India
| | - Amit Kumar Taraphdar
- Department of Dravyaguna (Ayurvedic Pharmacology), Institute of Post Graduate Ayurvedic Education and Research, 294/3/1, Acharya Prafulla Chandra Road, Kolkata, 700009, West Bengal, India
| | - Satheesh Kumar Nanjappan
- Department of Natural Products, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Ravichandiran Velayutham
- Department of Natural Products, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India.
| | - Somasundaram Arumugam
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India.
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13
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Vijayasurya, Gupta S, Shah S, Pappachan A. Drug repurposing for parasitic protozoan diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:23-58. [PMID: 38942539 DOI: 10.1016/bs.pmbts.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Protozoan parasites are major hazards to human health, society, and the economy, especially in equatorial regions of the globe. Parasitic diseases, including leishmaniasis, malaria, and others, contribute towards majority of morbidity and mortality. Around 1.1 million people die from these diseases annually. The lack of licensed vaccinations worsens the worldwide impact of these diseases, highlighting the importance of safe and effective medications for their prevention and treatment. However, the appearance of drug resistance in parasites continuously affects the availability of medications. The demand for novel drugs motivates global antiparasitic drug discovery research, necessitating the implementation of many innovative ways to maintain a continuous supply of promising molecules. Drug repurposing has come out as a compelling tool for drug development, offering a cost-effective and efficient alternative to standard de novo approaches. A thorough examination of drug repositioning candidates revealed that certain drugs may not benefit significantly from their original indications. Still, they may exhibit more pronounced effects in other disorders. Furthermore, certain medications can produce a synergistic effect, resulting in enhanced therapeutic effectiveness when given together. In this chapter, we outline the approaches employed in drug repurposing (sometimes referred to as drug repositioning), propose novel strategies to overcome these hurdles and fully exploit the promise of drug repurposing. We highlight a few major human protozoan diseases and a range of exemplary drugs repurposed for various protozoan infections, providing excellent outcomes for each disease.
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Affiliation(s)
- Vijayasurya
- School of Life Sciences, Central University of Gujarat, Gandhinagar, Gujarat, India
| | - Swadha Gupta
- School of Life Sciences, Central University of Gujarat, Gandhinagar, Gujarat, India
| | - Smit Shah
- School of Life Sciences, Central University of Gujarat, Gandhinagar, Gujarat, India
| | - Anju Pappachan
- School of Life Sciences, Central University of Gujarat, Gandhinagar, Gujarat, India.
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14
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Savva K, Zachariou M, Bourdakou MM, Dietis N, Spyrou GM. D Re Amocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing. Int J Mol Sci 2024; 25:5319. [PMID: 38791356 PMCID: PMC11121186 DOI: 10.3390/ijms25105319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/26/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024] Open
Abstract
In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and, finally, a disease suitability score for each drug from the selected library. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's disease and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimer's Disease include agomelatine, mirtazapine and vortioxetine; for Parkinson's Disease, they include apomorphine, pramipexole and lisuride; for Huntington's, they include chlorpromazine, fluphenazine and perphenazine; and for Multiple Sclerosis, they include zonisamide, disopyramide and priralfimide. Overall, DReAmocracy is a methodology that focuses on leveraging the existing drug-related experimental and/or computational knowledge rather than a predictive model for drug repurposing, offering a quantified aggregation of existing drug discovery results to (1) reveal trends in selected tracks of drug discovery research with increased resolution that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) score new candidate drugs for repurposing against a selected disease.
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Affiliation(s)
- Kyriaki Savva
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
| | - Margarita Zachariou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
| | - Marilena M. Bourdakou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
| | - Nikolas Dietis
- Experimental Pharmacology Laboratory, Medical School, University of Cyprus, Nicosia 2115, Cyprus;
| | - George M. Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
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15
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Vu TD, Luong DT, Ho TT, Nguyen Thi TM, Singh V, Chu DT. Drug repurposing for regenerative medicine and cosmetics: Scientific, technological and economic issues. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:337-353. [PMID: 38942543 DOI: 10.1016/bs.pmbts.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Regenerative medicine and cosmetics are currently two outstanding fields for drug discovery. Although many pharmaceutical products for regenerative medicine and cosmetics have received approval by official agencies, several challenges are still needed to overcome, especially financial and time issues. As a result, drug repositioning, which is the usage of previously approved drugs for new treatment, stands out as a promising approach to tackle these problems. Recently, increasing scientific evidence is collected to demonstrate the applicability of this novel method in the field of regenerative medicine and cosmetics. Experts in drug development have also taken advantage of novel technologies to discover new candidates for repositioning purposes following computational approach, one of two main approaches of drug repositioning. Therefore, numerous repurposed candidates have obtained approval to enter the market and have witnessed financial success such as minoxidil and fingolimod. The benefits of drug repositioning are undeniable for regenerative medicine and cosmetics. However, some aspects still need to be carefully considered regarding this method including actual effectiveness during clinical trials, patent regulations, data integration and analysis, publicly unavailable databases as well as environmental concerns and more effort are required to overcome these obstacles.
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Affiliation(s)
- Thuy-Duong Vu
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Duc Tri Luong
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Thuy-Tien Ho
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Thuy-My Nguyen Thi
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, India
| | - Dinh-Toi Chu
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam; Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Vietnam.
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16
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Daina A, Zoete V. Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning. Commun Chem 2024; 7:105. [PMID: 38724725 PMCID: PMC11082207 DOI: 10.1038/s42004-024-01179-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Estimating protein targets of compounds based on the similarity principle-similar molecules are likely to show comparable bioactivity-is a long-standing strategy in drug research. Having previously quantified this principle, we present here a large-scale evaluation of its predictive power for inferring macromolecular targets by reverse screening an unprecedented vast external test set of more than 300,000 active small molecules against another bioactivity set of more than 500,000 compounds. We show that machine-learning can predict the correct targets, with the highest probability among 2069 proteins, for more than 51% of the external molecules. The strong enrichment thus obtained demonstrates its usefulness in supporting phenotypic screens, polypharmacology, or repurposing. Moreover, we quantified the impact of the bioactivity knowledge available for proteins in terms of number and diversity of actives. Finally, we advise that developers of such approaches follow an application-oriented benchmarking strategy and use large, high-quality, non-overlapping datasets as provided here.
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Affiliation(s)
- Antoine Daina
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland
| | - Vincent Zoete
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland.
- Computer-Aided Molecular Engineering, Department of Oncology UNIL-CHUV, Ludwig Institute for Cancer Research Lausanne Branch, University of Lausanne, Lausanne, Switzerland.
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17
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Pham T, Ghafoor M, Grañana-Castillo S, Marzolini C, Gibbons S, Khoo S, Chiong J, Wang D, Siccardi M. DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy. NPJ Syst Biol Appl 2024; 10:48. [PMID: 38710671 PMCID: PMC11074332 DOI: 10.1038/s41540-024-00374-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 04/17/2024] [Indexed: 05/08/2024] Open
Abstract
Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.
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Affiliation(s)
- Thao Pham
- Institute of Systems, Molecular & Integrative Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Mohamed Ghafoor
- Department of Computer Science, University of Liverpool, Liverpool, UK
| | - Sandra Grañana-Castillo
- Institute of Systems, Molecular & Integrative Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Catia Marzolini
- Institute of Systems, Molecular & Integrative Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Department of Infectious Diseases and Hospital Epidemiology, Departments of Medicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Sara Gibbons
- Institute of Systems, Molecular & Integrative Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Saye Khoo
- Institute of Systems, Molecular & Integrative Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Justin Chiong
- Institute of Systems, Molecular & Integrative Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Dennis Wang
- National Heart and Lung Institute, Imperial College London, London, UK.
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
| | - Marco Siccardi
- Institute of Systems, Molecular & Integrative Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
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18
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Nada H, Kim S, Lee K. PT-Finder: A multi-modal neural network approach to target identification. Comput Biol Med 2024; 174:108444. [PMID: 38636325 DOI: 10.1016/j.compbiomed.2024.108444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/20/2024]
Abstract
Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.
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Affiliation(s)
- Hossam Nada
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea
| | - Sungdo Kim
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea
| | - Kyeong Lee
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea.
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19
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Glynos NG, Huels ER, Nelson A, Kim Y, Kennedy RT, Mashour GA, Pal D. Neurochemical and Neurophysiological Effects of Intravenous Administration of N,N-dimethyltryptamine in Rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.589047. [PMID: 38712161 PMCID: PMC11071436 DOI: 10.1101/2024.04.19.589047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
N,N-dimethyltryptamine (DMT) is a serotonergic psychedelic that is being investigated clinically for the treatment of psychiatric disorders. Although the neurophysiological effects of DMT in humans are well-characterized, similar studies in animal models as well as data on the neurochemical effects of DMT are generally lacking, which are critical for mechanistic understanding. In the current study, we combined behavioral analysis, high-density (32-channel) electroencephalography, and ultra-high-performance liquid chromatography-tandem mass spectrometry to simultaneously quantify changes in behavior, cortical neural dynamics, and levels of 17 neurochemicals in medial prefrontal and somatosensory cortices before, during, and after intravenous administration of three different doses of DMT (0.75 mg/kg, 3.75 mg/kg, 7.5 mg/kg) in male and female adult rats. All three doses of DMT produced head twitch response with most twitches observed after the low dose. DMT caused dose-dependent increases in serotonin and dopamine levels in both cortical sites along with a reduction in EEG spectral power in theta (4-10 Hz) and low gamma (25-55 Hz), and increase in power in delta (1-4 Hz), medium gamma (65-115), and high gamma (125-155 Hz) bands. Functional connectivity decreased in the delta band and increased across the gamma bands. In addition, we provide the first measurements of endogenous DMT in these cortical sites at levels comparable to serotonin and dopamine, which together with a previous study in occipital cortex, suggests a physiological role for endogenous DMT. This study represents one of the most comprehensive characterizations of psychedelic drug action in rats and the first to be conducted with DMT.
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Affiliation(s)
- Nicolas G. Glynos
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Anesthesiology, University of Michigan, Ann Abor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emma R. Huels
- Department of Anesthesiology, University of Michigan, Ann Abor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Amanda Nelson
- Department of Anesthesiology, University of Michigan, Ann Abor, MI 48109, USA
| | - Youngsoo Kim
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robert T. Kennedy
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - George A. Mashour
- Department of Anesthesiology, University of Michigan, Ann Abor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Dinesh Pal
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Anesthesiology, University of Michigan, Ann Abor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
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20
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Wallach I, Bernard D, Nguyen K, Ho G, Morrison A, Stecula A, Rosnik A, O’Sullivan AM, Davtyan A, Samudio B, Thomas B, Worley B, Butler B, Laggner C, Thayer D, Moharreri E, Friedland G, Truong H, van den Bedem H, Ng HL, Stafford K, Sarangapani K, Giesler K, Ngo L, Mysinger M, Ahmed M, Anthis NJ, Henriksen N, Gniewek P, Eckert S, de Oliveira S, Suterwala S, PrasadPrasad SVK, Shek S, Contreras S, Hare S, Palazzo T, O’Brien TE, Van Grack T, Williams T, Chern TR, Kenyon V, Lee AH, Cann AB, Bergman B, Anderson BM, Cox BD, Warrington JM, Sorenson JM, Goldenberg JM, Young MA, DeHaan N, Pemberton RP, Schroedl S, Abramyan TM, Gupta T, Mysore V, Presser AG, Ferrando AA, Andricopulo AD, Ghosh A, Ayachi AG, Mushtaq A, Shaqra AM, Toh AKL, Smrcka AV, Ciccia A, de Oliveira AS, Sverzhinsky A, de Sousa AM, Agoulnik AI, Kushnir A, Freiberg AN, Statsyuk AV, Gingras AR, Degterev A, Tomilov A, Vrielink A, Garaeva AA, Bryant-Friedrich A, Caflisch A, Patel AK, Rangarajan AV, Matheeussen A, Battistoni A, Caporali A, Chini A, Ilari A, Mattevi A, Foote AT, Trabocchi A, Stahl A, Herr AB, Berti A, Freywald A, Reidenbach AG, Lam A, Cuddihy AR, White A, Taglialatela A, Ojha AK, Cathcart AM, Motyl AAL, Borowska A, D’Antuono A, Hirsch AKH, Porcelli AM, Minakova A, Montanaro A, Müller A, Fiorillo A, Virtanen A, O’Donoghue AJ, Del Rio Flores A, Garmendia AE, Pineda-Lucena A, Panganiban AT, Samantha A, Chatterjee AK, Haas AL, Paparella AS, John ALS, Prince A, ElSheikh A, Apfel AM, Colomba A, O’Dea A, Diallo BN, Ribeiro BMRM, Bailey-Elkin BA, Edelman BL, Liou B, Perry B, Chua BSK, Kováts B, Englinger B, Balakrishnan B, Gong B, Agianian B, Pressly B, Salas BPM, Duggan BM, Geisbrecht BV, Dymock BW, Morten BC, Hammock BD, Mota BEF, Dickinson BC, Fraser C, Lempicki C, Novina CD, Torner C, Ballatore C, Bon C, Chapman CJ, Partch CL, Chaton CT, Huang C, Yang CY, Kahler CM, Karan C, Keller C, Dieck CL, Huimei C, Liu C, Peltier C, Mantri CK, Kemet CM, Müller CE, Weber C, Zeina CM, Muli CS, Morisseau C, Alkan C, Reglero C, Loy CA, Wilson CM, Myhr C, Arrigoni C, Paulino C, Santiago C, Luo D, Tumes DJ, Keedy DA, Lawrence DA, Chen D, Manor D, Trader DJ, Hildeman DA, Drewry DH, Dowling DJ, Hosfield DJ, Smith DM, Moreira D, Siderovski DP, Shum D, Krist DT, Riches DWH, Ferraris DM, Anderson DH, Coombe DR, Welsbie DS, Hu D, Ortiz D, Alramadhani D, Zhang D, Chaudhuri D, Slotboom DJ, Ronning DR, Lee D, Dirksen D, Shoue DA, Zochodne DW, Krishnamurthy D, Duncan D, Glubb DM, Gelardi ELM, Hsiao EC, Lynn EG, Silva EB, Aguilera E, Lenci E, Abraham ET, Lama E, Mameli E, Leung E, Giles E, Christensen EM, Mason ER, Petretto E, Trakhtenberg EF, Rubin EJ, Strauss E, Thompson EW, Cione E, Lisabeth EM, Fan E, Kroon EG, Jo E, García-Cuesta EM, Glukhov E, Gavathiotis E, Yu F, Xiang F, Leng F, Wang F, Ingoglia F, van den Akker F, Borriello F, Vizeacoumar FJ, Luh F, Buckner FS, Vizeacoumar FS, Bdira FB, Svensson F, Rodriguez GM, Bognár G, Lembo G, Zhang G, Dempsey G, Eitzen G, Mayer G, Greene GL, Garcia GA, Lukacs GL, Prikler G, Parico GCG, Colotti G, De Keulenaer G, Cortopassi G, Roti G, Girolimetti G, Fiermonte G, Gasparre G, Leuzzi G, Dahal G, Michlewski G, Conn GL, Stuchbury GD, Bowman GR, Popowicz GM, Veit G, de Souza GE, Akk G, Caljon G, Alvarez G, Rucinski G, Lee G, Cildir G, Li H, Breton HE, Jafar-Nejad H, Zhou H, Moore HP, Tilford H, Yuan H, Shim H, Wulff H, Hoppe H, Chaytow H, Tam HK, Van Remmen H, Xu H, Debonsi HM, Lieberman HB, Jung H, Fan HY, Feng H, Zhou H, Kim HJ, Greig IR, Caliandro I, Corvo I, Arozarena I, Mungrue IN, Verhamme IM, Qureshi IA, Lotsaris I, Cakir I, Perry JJP, Kwiatkowski J, Boorman J, Ferreira J, Fries J, Kratz JM, Miner J, Siqueira-Neto JL, Granneman JG, Ng J, Shorter J, Voss JH, Gebauer JM, Chuah J, Mousa JJ, Maynes JT, Evans JD, Dickhout J, MacKeigan JP, Jossart JN, Zhou J, Lin J, Xu J, Wang J, Zhu J, Liao J, Xu J, Zhao J, Lin J, Lee J, Reis J, Stetefeld J, Bruning JB, Bruning JB, Coles JG, Tanner JJ, Pascal JM, So J, Pederick JL, Costoya JA, Rayman JB, Maciag JJ, Nasburg JA, Gruber JJ, Finkelstein JM, Watkins J, Rodríguez-Frade JM, Arias JAS, Lasarte JJ, Oyarzabal J, Milosavljevic J, Cools J, Lescar J, Bogomolovas J, Wang J, Kee JM, Kee JM, Liao J, Sistla JC, Abrahão JS, Sishtla K, Francisco KR, Hansen KB, Molyneaux KA, Cunningham KA, Martin KR, Gadar K, Ojo KK, Wong KS, Wentworth KL, Lai K, Lobb KA, Hopkins KM, Parang K, Machaca K, Pham K, Ghilarducci K, Sugamori KS, McManus KJ, Musta K, Faller KME, Nagamori K, Mostert KJ, Korotkov KV, Liu K, Smith KS, Sarosiek K, Rohde KH, Kim KK, Lee KH, Pusztai L, Lehtiö L, Haupt LM, Cowen LE, Byrne LJ, Su L, Wert-Lamas L, Puchades-Carrasco L, Chen L, Malkas LH, Zhuo L, Hedstrom L, Hedstrom L, Walensky LD, Antonelli L, Iommarini L, Whitesell L, Randall LM, Fathallah MD, Nagai MH, Kilkenny ML, Ben-Johny M, Lussier MP, Windisch MP, Lolicato M, Lolli ML, Vleminckx M, Caroleo MC, Macias MJ, Valli M, Barghash MM, Mellado M, Tye MA, Wilson MA, Hannink M, Ashton MR, Cerna MVC, Giorgis M, Safo MK, Maurice MS, McDowell MA, Pasquali M, Mehedi M, Serafim MSM, Soellner MB, Alteen MG, Champion MM, Skorodinsky M, O’Mara ML, Bedi M, Rizzi M, Levin M, Mowat M, Jackson MR, Paige M, Al-Yozbaki M, Giardini MA, Maksimainen MM, De Luise M, Hussain MS, Christodoulides M, Stec N, Zelinskaya N, Van Pelt N, Merrill NM, Singh N, Kootstra NA, Singh N, Gandhi NS, Chan NL, Trinh NM, Schneider NO, Matovic N, Horstmann N, Longo N, Bharambe N, Rouzbeh N, Mahmoodi N, Gumede NJ, Anastasio NC, Khalaf NB, Rabal O, Kandror O, Escaffre O, Silvennoinen O, Bishop OT, Iglesias P, Sobrado P, Chuong P, O’Connell P, Martin-Malpartida P, Mellor P, Fish PV, Moreira POL, Zhou P, Liu P, Liu P, Wu P, Agogo-Mawuli P, Jones PL, Ngoi P, Toogood P, Ip P, von Hundelshausen P, Lee PH, Rowswell-Turner RB, Balaña-Fouce R, Rocha REO, Guido RVC, Ferreira RS, Agrawal RK, Harijan RK, Ramachandran R, Verma R, Singh RK, Tiwari RK, Mazitschek R, Koppisetti RK, Dame RT, Douville RN, Austin RC, Taylor RE, Moore RG, Ebright RH, Angell RM, Yan R, Kejriwal R, Batey RA, Blelloch R, Vandenberg RJ, Hickey RJ, Kelm RJ, Lake RJ, Bradley RK, Blumenthal RM, Solano R, Gierse RM, Viola RE, McCarthy RR, Reguera RM, Uribe RV, do Monte-Neto RL, Gorgoglione R, Cullinane RT, Katyal S, Hossain S, Phadke S, Shelburne SA, Geden SE, Johannsen S, Wazir S, Legare S, Landfear SM, Radhakrishnan SK, Ammendola S, Dzhumaev S, Seo SY, Li S, Zhou S, Chu S, Chauhan S, Maruta S, Ashkar SR, Shyng SL, Conticello SG, Buroni S, Garavaglia S, White SJ, Zhu S, Tsimbalyuk S, Chadni SH, Byun SY, Park S, Xu SQ, Banerjee S, Zahler S, Espinoza S, Gustincich S, Sainas S, Celano SL, Capuzzi SJ, Waggoner SN, Poirier S, Olson SH, Marx SO, Van Doren SR, Sarilla S, Brady-Kalnay SM, Dallman S, Azeem SM, Teramoto T, Mehlman T, Swart T, Abaffy T, Akopian T, Haikarainen T, Moreda TL, Ikegami T, Teixeira TR, Jayasinghe TD, Gillingwater TH, Kampourakis T, Richardson TI, Herdendorf TJ, Kotzé TJ, O’Meara TR, Corson TW, Hermle T, Ogunwa TH, Lan T, Su T, Banjo T, O’Mara TA, Chou T, Chou TF, Baumann U, Desai UR, Pai VP, Thai VC, Tandon V, Banerji V, Robinson VL, Gunasekharan V, Namasivayam V, Segers VFM, Maranda V, Dolce V, Maltarollo VG, Scoffone VC, Woods VA, Ronchi VP, Van Hung Le V, Clayton WB, Lowther WT, Houry WA, Li W, Tang W, Zhang W, Van Voorhis WC, Donaldson WA, Hahn WC, Kerr WG, Gerwick WH, Bradshaw WJ, Foong WE, Blanchet X, Wu X, Lu X, Qi X, Xu X, Yu X, Qin X, Wang X, Yuan X, Zhang X, Zhang YJ, Hu Y, Aldhamen YA, Chen Y, Li Y, Sun Y, Zhu Y, Gupta YK, Pérez-Pertejo Y, Li Y, Tang Y, He Y, Tse-Dinh YC, Sidorova YA, Yen Y, Li Y, Frangos ZJ, Chung Z, Su Z, Wang Z, Zhang Z, Liu Z, Inde Z, Artía Z, Heifets A. AI is a viable alternative to high throughput screening: a 318-target study. Sci Rep 2024; 14:7526. [PMID: 38565852 PMCID: PMC10987645 DOI: 10.1038/s41598-024-54655-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 02/15/2024] [Indexed: 04/04/2024] Open
Abstract
High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.
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21
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Ghandikota SK, Jegga AG. Application of artificial intelligence and machine learning in drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:171-211. [PMID: 38789178 DOI: 10.1016/bs.pmbts.2024.03.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.
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Affiliation(s)
- Sudhir K Ghandikota
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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22
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Jobe A, Vijayan R. Orphan G protein-coupled receptors: the ongoing search for a home. Front Pharmacol 2024; 15:1349097. [PMID: 38495099 PMCID: PMC10941346 DOI: 10.3389/fphar.2024.1349097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/15/2024] [Indexed: 03/19/2024] Open
Abstract
G protein-coupled receptors (GPCRs) make up the largest receptor superfamily, accounting for 4% of protein-coding genes. Despite the prevalence of such transmembrane receptors, a significant number remain orphans, lacking identified endogenous ligands. Since their conception, the reverse pharmacology approach has been used to characterize such receptors. However, the multifaceted and nuanced nature of GPCR signaling poses a great challenge to their pharmacological elucidation. Considering their therapeutic relevance, the search for native orphan GPCR ligands continues. Despite limited structural input in terms of 3D crystallized structures, with advances in machine-learning approaches, there has been great progress with respect to accurate ligand prediction. Though such an approach proves valuable given that ligand scarcity is the greatest hurdle to orphan GPCR deorphanization, the future pairings of the remaining orphan GPCRs may not necessarily take a one-size-fits-all approach but should be more comprehensive in accounting for numerous nuanced possibilities to cover the full spectrum of GPCR signaling.
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Affiliation(s)
- Amie Jobe
- Department of Biology, College of Science, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Ranjit Vijayan
- Department of Biology, College of Science, United Arab Emirates University, Al Ain, United Arab Emirates
- The Big Data Analytics Center, United Arab Emirates University, Al Ain, United Arab Emirates
- Zayed Bin Sultan Center for Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
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23
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Yao H, Wang X, Chi J, Chen H, Liu Y, Yang J, Yu J, Ruan Y, Xiang X, Pi J, Xu JF. Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures. Molecules 2024; 29:964. [PMID: 38474476 DOI: 10.3390/molecules29050964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 02/09/2024] [Indexed: 03/14/2024] Open
Abstract
Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.
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Affiliation(s)
- Hanbo Yao
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Xiaodong Wang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaxin Chi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Haorong Chen
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yilin Liu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiayi Yang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaqi Yu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yongdui Ruan
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
| | - Xufu Xiang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jiang Pi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jun-Fa Xu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
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24
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Katz LS, Visser EJ, Plitzko KF, Pennings M, Cossar PJ, Tse IL, Kaiser M, Brunsveld L, Scott DK, Ottmann C. Molecular glues of the regulatory ChREBP/14-3-3 complex protect beta cells from glucolipotoxicity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.16.580675. [PMID: 38405965 PMCID: PMC10888794 DOI: 10.1101/2024.02.16.580675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
The Carbohydrate Response Element Binding Protein (ChREBP) is a glucose-responsive transcription factor (TF) that is characterized by two major splice isoforms (α and β). In acute hyperglycemia, both ChREBP isoforms regulate adaptive β-expansion; however, during chronic hyperglycemia and glucolipotoxicity, ChREBPβ expression surges, leading to β-cell dedifferentiation and death. 14-3-3 binding to ChREBPα results in its cytoplasmic retention and concomitant suppression of transcriptional activity, suggesting that small molecule-mediated stabilization of this protein-protein interaction (PPI) via molecular glues may represent an attractive entry for the treatment of metabolic disease. Here, we show that structure-based optimizations of a molecular glue tool compound led not only to more potent ChREBPα/14-3-3 PPI stabilizers but also for the first time cellular active compounds. In primary human β-cells, the most active compound stabilized the ChREBPα/14-3-3 interaction and thus induced cytoplasmic retention of ChREBPα, resulting in highly efficient β-cell protection from glucolipotoxicity while maintaining β-cell identity. This study may thus not only provide the basis for the development of a unique class of compounds for the treatment of Type 2 Diabetes but also showcases an alternative 'molecular glue' approach for achieving small molecule control of notoriously difficult targetable TFs.
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Affiliation(s)
- Liora S Katz
- Diabetes, Obesity and Metabolism Institute and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1152, New York, 10029, USA
| | - Emira J Visser
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Kathrin F Plitzko
- Chemical Biology, Center of Medical Biotechnology, Faculty of Biology, University of Duisburg-Essen, Duisburg, Germany
| | - Marloes Pennings
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Peter J Cossar
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Isabelle L Tse
- Diabetes, Obesity and Metabolism Institute and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1152, New York, 10029, USA
| | - Markus Kaiser
- Chemical Biology, Center of Medical Biotechnology, Faculty of Biology, University of Duisburg-Essen, Duisburg, Germany
| | - Luc Brunsveld
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Donald K Scott
- Diabetes, Obesity and Metabolism Institute and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1152, New York, 10029, USA
| | - Christian Ottmann
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
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25
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De Filippo R, Schmitz D. Synthetic surprise as the foundation of the psychedelic experience. Neurosci Biobehav Rev 2024; 157:105538. [PMID: 38220035 PMCID: PMC10839673 DOI: 10.1016/j.neubiorev.2024.105538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/16/2024]
Abstract
Psychedelic agents, such as LSD and psilocybin, induce marked alterations in consciousness via activation of the 5-HT2A receptor (5-HT2ARs). We hypothesize that psychedelics enforce a state of synthetic surprise through the biased activation of the 5-HTRs system. This idea is informed by recent insights into the role of 5-HT in signaling surprise. The effects on consciousness, explained by the cognitive penetrability of perception, can be described within the predictive coding framework where surprise corresponds to prediction error, the mismatch between predictions and actual sensory input. Crucially, the precision afforded to the prediction error determines its effect on priors, enabling a dynamic interaction between top-down expectations and incoming sensory data. By integrating recent findings on predictive coding circuitry and 5-HT2ARs transcriptomic data, we propose a biological implementation with emphasis on the role of inhibitory interneurons. Implications arise for the clinical use of psychedelics, which may rely primarily on their inherent capacity to induce surprise in order to disrupt maladaptive patterns.
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Affiliation(s)
- Roberto De Filippo
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, Neuroscience Research Center, 10117 Berlin, Germany.
| | - Dietmar Schmitz
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, Neuroscience Research Center, 10117 Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, Einstein Center for Neuroscience, 10117 Berlin, Germany; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, NeuroCure Cluster of Excellence, 10117 Berlin, Germany; Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience, Philippstr. 13, 10115 Berlin, Germany
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26
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Fordyce BA, Roth BL. Making Sense of Psychedelics in the CNS. Int J Neuropsychopharmacol 2024; 27:pyae007. [PMID: 38289825 PMCID: PMC10888522 DOI: 10.1093/ijnp/pyae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
For centuries, ancient lineages have consumed psychedelic compounds from natural sources. In the modern era, scientists have since harnessed the power of computational tools, cellular assays, and behavioral metrics to study how these compounds instigate changes on molecular, cellular, circuit-wide, and system levels. Here, we provide a brief history of psychedelics and their use in science, medicine, and culture. We then outline current techniques for studying psychedelics from a pharmacological perspective. Finally, we address known gaps in the field and potential avenues of further research to broaden our collective understanding of physiological changes induced by psychedelics, the limits of their therapeutic capabilities, and how researchers can improve and inform treatments that are rapidly becoming accessible worldwide.
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Affiliation(s)
- Blake A Fordyce
- Department of Neuroscience, UNC Chapel Hill Medical School Chapel Hill, North Carolina, USA
| | - Bryan L Roth
- Department of Pharmacology, UNC Chapel Hill Medical School Chapel Hill, North Carolina, USA
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27
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Marchant JS. Progress interrogating TRPMPZQ as the target of praziquantel. PLoS Negl Trop Dis 2024; 18:e0011929. [PMID: 38358948 PMCID: PMC10868838 DOI: 10.1371/journal.pntd.0011929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024] Open
Abstract
The drug praziquantel (PZQ) has served as the long-standing drug therapy for treatment of infections caused by parasitic flatworms. These encompass diseases caused by parasitic blood, lung, and liver flukes, as well as various tapeworm infections. Despite a history of clinical usage spanning over 4 decades, the parasite target of PZQ has long resisted identification. However, a flatworm transient receptor potential ion channel from the melastatin subfamily (TRPMPZQ) was recently identified as a target for PZQ action. Here, recent experimental progress interrogating TRPMPZQ is evaluated, encompassing biochemical, pharmacological, genetic, and comparative phylogenetic data that highlight the properties of this ion channel. Various lines of evidence that support TRPMPZQ being the therapeutic target of PZQ are presented, together with additional priorities for further research into the mechanism of action of this important clinical drug.
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Affiliation(s)
- Jonathan S. Marchant
- Department of Cell Biology, Neurobiology & Anatomy, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
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28
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Wu J, Chen Y, Wu J, Zhao D, Huang J, Lin M, Wang L. Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors. J Cheminform 2024; 16:13. [PMID: 38291477 PMCID: PMC10829268 DOI: 10.1186/s13321-023-00799-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 12/22/2023] [Indexed: 02/01/2024] Open
Abstract
Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.
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Affiliation(s)
- Jiangxia Wu
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Yihao Chen
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Jingxing Wu
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Duancheng Zhao
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Jindi Huang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - MuJie Lin
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China.
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James E, Erritzoe D, Benway T, Joel Z, Timmermann C, Good M, Agnorelli C, Weiss BM, Barba T, Campbell G, Baker Jones M, Hughes C, Topping H, Boyce M, Routledge C. Safety, tolerability, pharmacodynamic and wellbeing effects of SPL026 (dimethyltryptamine fumarate) in healthy participants: a randomized, placebo-controlled phase 1 trial. Front Psychiatry 2024; 14:1305796. [PMID: 38274414 PMCID: PMC10810248 DOI: 10.3389/fpsyt.2023.1305796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
Background Due to their potential impact on mood and wellbeing there has been increasing interest in the potential of serotonergic psychedelics such as N,N-dimethyltryptamine (DMT) in the treatment of major depressive disorder (MDD). Aim The aim of Part A of this study was to evaluate the safety, tolerability, pharmacokinetics (PK) and pharmacodynamic (PD) profile of escalating doses of SPL026 (DMT fumarate) in psychedelic-naïve healthy participants to determine a dose for administration to patients with MDD in the subsequent Phase 2a part of the trial (Part B: not presented in this manuscript). Methods In the Phase 1, randomized, double-blind, placebo-controlled, parallel-group, single dose-escalation trial, psychedelic-naïve participants were randomized to placebo (n = 8) or four different escalating doses [9, 12, 17 and 21.5 mg intravenously (IV)] of SPL026 (n = 6 for each dose) together with psychological support from 2 therapy team members. PK and acute (immediately following dosing experience) psychometric measures [including mystical experience questionnaire (MEQ), ego dissolution inventory (EDI), and intensity rating visual analogue scale (IRVAS)] were determined. Additional endpoints were measured as longer-term change from baseline to days 8, 15, 30 and 90. These measures included the Warwick and Edinburgh mental wellbeing scale and Spielberger's state-trait anxiety inventory. Results SPL026 was well tolerated, with an acceptable safety profile, with no serious adverse events. There was some evidence of a correlation between maximum plasma concentration and increased IRVAS, MEQ, and EDI scores. These trends are likely to require confirmation in a larger sample size. Using the analysis of the safety, tolerability, PD, PK results, doses of 21.5 mg SPL026 were the most likely to provide an intense, tolerated experience. Conclusion Based on the data obtained from this part of the trial, a dose of 21.5 mg SPL026 given as a 2-phase IV infusion over 10 min (6 mg/5 min and 15.5 mg/5 min) was selected as the dose to be taken into patients in Part B (to be presented in a future manuscript).Clinical trial registration:www.clinicaltrials.gov, identifier NCT04673383; https://www.clinicaltrialsregister.eu, identifier 2020-000251-13; https://www.isrctn.com/, identifier ISRCTN63465876.
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Affiliation(s)
| | - David Erritzoe
- The Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | | | - Zelah Joel
- Small Pharma Ltd., London, United Kingdom
| | - Christopher Timmermann
- The Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | | | - Claudio Agnorelli
- The Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Brandon M. Weiss
- The Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Tommaso Barba
- The Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | | | | | | | - Helen Topping
- Hammersmith Medicines Research, London, United Kingdom
| | - Malcolm Boyce
- Hammersmith Medicines Research, London, United Kingdom
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Duan W, Cao D, Wang S, Cheng J. Serotonin 2A Receptor (5-HT 2AR) Agonists: Psychedelics and Non-Hallucinogenic Analogues as Emerging Antidepressants. Chem Rev 2024; 124:124-163. [PMID: 38033123 DOI: 10.1021/acs.chemrev.3c00375] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Psychedelics make up a group of psychoactive compounds that induce hallucinogenic effects by activating the serotonin 2A receptor (5-HT2AR). Clinical trials have demonstrated the traditional psychedelic substances like psilocybin as a class of rapid-acting and long-lasting antidepressants. However, there is a pressing need for rationally designed 5-HT2AR agonists that possess optimal pharmacological profiles in order to fully reveal the therapeutic potential of these agonists and identify safer drug candidates devoid of hallucinogenic effects. This Perspective provides an overview of the structure-activity relationships of existing 5-HT2AR agonists based on their chemical classifications and discusses recent advancements in understanding their molecular pharmacology at a structural level. The encouraging clinical outcomes of psychedelics in depression treatment have sparked drug discovery endeavors aimed at developing novel 5-HT2AR agonists with improved subtype selectivity and signaling bias properties, which could serve as safer and potentially nonhallucinogenic antidepressants. These efforts can be significantly expedited through the utilization of structure-based methods and functional selectivity-directed screening.
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Affiliation(s)
- Wenwen Duan
- iHuman Institute, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Dongmei Cao
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences; University of Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
| | - Sheng Wang
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences; University of Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Jianjun Cheng
- iHuman Institute, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
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31
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Wei J, Lu L, Shen T. Predicting drug-protein interactions by preserving the graph information of multi source data. BMC Bioinformatics 2024; 25:10. [PMID: 38177981 PMCID: PMC10768380 DOI: 10.1186/s12859-023-05620-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/15/2023] [Indexed: 01/06/2024] Open
Abstract
Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.
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Affiliation(s)
- Jiahao Wei
- School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China
| | - Linzhang Lu
- School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China.
- School of Mathematical Sciences, Xiamen University, Xiamen, 361005, China.
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guizhou, 550001, China.
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Fatemi N, Karimpour M, Bahrami H, Zali MR, Chaleshi V, Riccio A, Nazemalhosseini-Mojarad E, Totonchi M. Current trends and future prospects of drug repositioning in gastrointestinal oncology. Front Pharmacol 2024; 14:1329244. [PMID: 38239190 PMCID: PMC10794567 DOI: 10.3389/fphar.2023.1329244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 12/11/2023] [Indexed: 01/22/2024] Open
Abstract
Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.
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Affiliation(s)
- Nayeralsadat Fatemi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Karimpour
- Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hoda Bahrami
- Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Vahid Chaleshi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Andrea Riccio
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies (DiSTABiF), Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy
- Institute of Genetics and Biophysics (IGB) “Adriano Buzzati-Traverso”, Consiglio Nazionale delle Ricerche (CNR), Naples, Italy
| | - Ehsan Nazemalhosseini-Mojarad
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehdi Totonchi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies (DiSTABiF), Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
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Ling C, Zeng T, Dang Q, Liang Y, Liu X, Xie S. Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information. Technol Health Care 2024; 32:49-64. [PMID: 38759038 PMCID: PMC11191455 DOI: 10.3233/thc-248005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
BACKGROUND Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately. OBJECTIVE We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance. METHODS Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning. RESULTS Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods. CONCLUSION The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.
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Affiliation(s)
- Caijin Ling
- Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macao, China
| | - Ting Zeng
- Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Taipa, Macao, China
| | - Qi Dang
- Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macao, China
| | - Yong Liang
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Xiaoying Liu
- Computer Engineering Technical College, Guangdong Polytechnic of Science and Technology, Zhuhai, Guangdong, China
| | - Shengli Xie
- Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, Guangdong, China
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Liu Y, Sang G, Liu Z, Pan Y, Cheng J, Zhang Y. MPTN: A message-passing transformer network for drug repurposing from knowledge graph. Comput Biol Med 2024; 168:107800. [PMID: 38043469 DOI: 10.1016/j.compbiomed.2023.107800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/09/2023] [Accepted: 11/29/2023] [Indexed: 12/05/2023]
Abstract
Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses knowledge graph reasoning models to predict new therapeutic pathways for existing drugs. With the rapid development of computing technology and the growing availability of validated biomedical data, various knowledge graph-based methods have been widely used to analyze and process complex and novel data to discover new indications for given drugs. However, existing methods need to be improved in extracting semantic information from contextual triples of biomedical entities. In this study, we propose a message-passing transformer network named MPTN based on knowledge graph for drug repurposing. Firstly, CompGCN is used as precoder to jointly aggregate entity and relation embeddings. Then, to fully capture the semantic information of entity context triples, the message propagating transformer module is designed. The module integrates the transformer into the message passing mechanism and incorporates the attention weight information of computing entity context triples into the entity embedding to update the entity embedding. Next, the residual connection is introduced to retain information as much as possible and improve prediction accuracy. Finally, MPTN utilizes the InteractE module as the decoder to obtain heterogeneous feature interactions in entity and relation representations and predict new pathways for drug treatment. Experiments on two datasets show that the model is superior to the existing knowledge graph embedding (KGE) learning methods.
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Affiliation(s)
- Yuanxin Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Guoming Sang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Zhi Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yilin Pan
- School of Artificial Intelligence, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Junkai Cheng
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yijia Zhang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China.
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Qiu W, Liang Q, Yu L, Xiao X, Qiu W, Lin W. LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach. Curr Pharm Des 2024; 30:468-476. [PMID: 38323613 PMCID: PMC11071654 DOI: 10.2174/0113816128282837240130102817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/14/2024] [Accepted: 01/19/2024] [Indexed: 02/08/2024]
Abstract
INTRODUCTION Drug development is a challenging and costly process, yet it plays a crucial role in improving healthcare outcomes. Drug development requires extensive research and testing to meet the demands for economic efficiency, cures, and pain relief. METHODS Drug development is a vital research area that necessitates innovation and collaboration to achieve significant breakthroughs. Computer-aided drug design provides a promising avenue for drug discovery and development by reducing costs and improving the efficiency of drug design and testing. RESULTS In this study, a novel model, namely LSTM-SAGDTA, capable of accurately predicting drug-target binding affinity, was developed. We employed SeqVec for characterizing the protein and utilized the graph neural networks to capture information on drug molecules. By introducing self-attentive graph pooling, the model achieved greater accuracy and efficiency in predicting drug-target binding affinity. CONCLUSION Moreover, LSTM-SAGDTA obtained superior accuracy over current state-of-the-art methods only by using less training time. The results of experiments suggest that this method represents a highprecision solution for the DTA predictor.
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Affiliation(s)
- Wenjing Qiu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Qianle Liang
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Liyi Yu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Xuan Xiao
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Wangren Qiu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Weizhong Lin
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
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Ramos S, Vicente-Blázquez A, López-Rubio M, Gallego-Yerga L, Álvarez R, Peláez R. Frentizole, a Nontoxic Immunosuppressive Drug, and Its Analogs Display Antitumor Activity via Tubulin Inhibition. Int J Mol Sci 2023; 24:17474. [PMID: 38139302 PMCID: PMC10744269 DOI: 10.3390/ijms242417474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/04/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Antimitotic agents are one of the more successful types of anticancer drugs, but they suffer from toxicity and resistance. The application of approved drugs to new indications (i.e., drug repurposing) is a promising strategy for the development of new drugs. It relies on finding pattern similarities: drug effects to other drugs or conditions, similar toxicities, or structural similarity. Here, we recursively searched a database of approved drugs for structural similarity to several antimitotic agents binding to a specific site of tubulin, with the expectation of finding structures that could fit in it. These searches repeatedly retrieved frentizole, an approved nontoxic anti-inflammatory drug, thus indicating that it might behave as an antimitotic drug devoid of the undesired toxic effects. We also show that the usual repurposing approach to searching for targets of frentizole failed in most cases to find such a relationship. We synthesized frentizole and a series of analogs to assay them as antimitotic agents and found antiproliferative activity against HeLa tumor cells, inhibition of microtubule formation within cells, and arrest at the G2/M phases of the cell cycle, phenotypes that agree with binding to tubulin as the mechanism of action. The docking studies suggest binding at the colchicine site in different modes. These results support the repurposing of frentizole for cancer treatment, especially for glioblastoma.
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Affiliation(s)
- Sergio Ramos
- Laboratorio de Química Orgánica y Farmacéutica, Departamento de Ciencias Farmacéuticas, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain; (S.R.); (M.L.-R.); (L.G.-Y.); (R.Á.)
- Instituto de Investigación Biomédica de Salamanca (IBSAL), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
- Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca (CIETUS), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
| | - Alba Vicente-Blázquez
- Laboratorio de Química Orgánica y Farmacéutica, Departamento de Ciencias Farmacéuticas, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain; (S.R.); (M.L.-R.); (L.G.-Y.); (R.Á.)
- Instituto de Investigación Biomédica de Salamanca (IBSAL), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
- Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca (CIETUS), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
| | - Marta López-Rubio
- Laboratorio de Química Orgánica y Farmacéutica, Departamento de Ciencias Farmacéuticas, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain; (S.R.); (M.L.-R.); (L.G.-Y.); (R.Á.)
- Instituto de Investigación Biomédica de Salamanca (IBSAL), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
- Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca (CIETUS), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
| | - Laura Gallego-Yerga
- Laboratorio de Química Orgánica y Farmacéutica, Departamento de Ciencias Farmacéuticas, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain; (S.R.); (M.L.-R.); (L.G.-Y.); (R.Á.)
- Instituto de Investigación Biomédica de Salamanca (IBSAL), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
- Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca (CIETUS), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
| | - Raquel Álvarez
- Laboratorio de Química Orgánica y Farmacéutica, Departamento de Ciencias Farmacéuticas, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain; (S.R.); (M.L.-R.); (L.G.-Y.); (R.Á.)
- Instituto de Investigación Biomédica de Salamanca (IBSAL), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
- Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca (CIETUS), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
| | - Rafael Peláez
- Laboratorio de Química Orgánica y Farmacéutica, Departamento de Ciencias Farmacéuticas, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain; (S.R.); (M.L.-R.); (L.G.-Y.); (R.Á.)
- Instituto de Investigación Biomédica de Salamanca (IBSAL), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
- Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca (CIETUS), Facultad de Farmacia, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
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Doan J, Defaix C, Mendez-David I, Gardier AM, Colle R, Corruble E, McGowan JC, David DJ, Guilloux JP, Tritschler L. Intrahippocampal injection of a selective blocker of NMDA receptors containing the GluN2B subunit, Ro25-6981, increases glutamate neurotransmission and induces antidepressant-like effects. Fundam Clin Pharmacol 2023; 37:1119-1128. [PMID: 37161789 DOI: 10.1111/fcp.12917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 04/07/2023] [Accepted: 05/09/2023] [Indexed: 05/11/2023]
Abstract
Major depressive disorder (MDD) is a serious public health problem, as it is the most common psychiatric disorder worldwide. Antidepressant drugs increase adult hippocampal neurogenesis, which is required to induce some behavioral effects of antidepressants. Adult-born granule cells in the dentate gyrus (DG) and the glutamate receptors subunits 2 (GluN2B) subunit of N-methyl-D-aspartate (NMDA) ionotropic receptors play an important role in these effects. However, the precise neurochemical role of the GluN2B subunit of the NMDA receptor on adult-born GCs for antidepressant-like effects has yet to be elucidated. The present study aims to explore the contribution of the GluN2B-containing NMDA receptors in the ventral dentate gyrus (vDG) to the antidepressant drug treatment using a pharmacological approach. Thus, (αR)-(4-hydroxyphenyl)-(βS)-methyl-4-(phenylmethyl)-1-piperidinepropanol (Ro25-6981), a selective antagonist of the GluN2B subunit, was acutely administered locally into the ventral DG (vDG, 1 μg each side) following a chronic fluoxetine (18 mg/kg/day) treatment-known to increase adult hippocampal neurogenesis-in a mouse model of anxiety/depression. Responses in a neurogenesis-dependent task, the novelty suppressed feeding (NSF), and neurochemical consequences on extracellular glutamate and gamma-aminobutyric acid (GABA) levels in the vDG were measured. Here, we show a rapid-acting antidepressant-like effect of local Ro25-6981 administration in the NSF independent of fluoxetine treatment. Furthermore, we revealed a fluoxetine-independent increase in the glutamatergic transmission in the vDG. Our results suggest behavioral and neurochemical effects of GluN2B subunit independent of serotonin reuptake inhibition.
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Affiliation(s)
- Julie Doan
- Université Paris-Saclay, Faculté de Pharmacie, UMR 1018 CESP, INSERM MOODS Team, Orsay, France
| | - Céline Defaix
- Université Paris-Saclay, Faculté de Pharmacie, UMR 1018 CESP, INSERM MOODS Team, Orsay, France
| | - Indira Mendez-David
- Université Paris-Saclay, Faculté de Pharmacie, UMR 1018 CESP, INSERM MOODS Team, Orsay, France
| | - Alain M Gardier
- Université Paris-Saclay, Faculté de Pharmacie, UMR 1018 CESP, INSERM MOODS Team, Orsay, France
| | - Romain Colle
- Université Paris-Saclay, Faculté de Médecine, UMR 1018 CESP, INSERM MOODS Team, Le Kremlin Bicêtre, France
- Service Hospitalo-Universitaire de Psychiatrie de Bicêtre, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, Hôpital de Bicêtre, Le Kremlin Bicêtre, France
| | - Emmanuelle Corruble
- Université Paris-Saclay, Faculté de Médecine, UMR 1018 CESP, INSERM MOODS Team, Le Kremlin Bicêtre, France
- Service Hospitalo-Universitaire de Psychiatrie de Bicêtre, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, Hôpital de Bicêtre, Le Kremlin Bicêtre, France
| | - Josephine C McGowan
- Doctoral Program in Neurobiology and Behavior, Columbia University, New York, New York, USA
| | - Denis J David
- Université Paris-Saclay, Faculté de Pharmacie, UMR 1018 CESP, INSERM MOODS Team, Orsay, France
| | - Jean-Philippe Guilloux
- Université Paris-Saclay, Faculté de Pharmacie, UMR 1018 CESP, INSERM MOODS Team, Orsay, France
| | - Laurent Tritschler
- Université Paris-Saclay, Faculté de Pharmacie, UMR 1018 CESP, INSERM MOODS Team, Orsay, France
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Oh KK, Choi I, Gupta H, Raja G, Sharma SP, Won SM, Jeong JJ, Lee SB, Cha MG, Kwon GH, Jeong MK, Min BH, Hyun JY, Eom JA, Park HJ, Yoon SJ, Choi MR, Kim DJ, Suk KT. New insight into gut microbiota-derived metabolites to enhance liver regeneration via network pharmacology study. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:1-12. [PMID: 36562095 DOI: 10.1080/21691401.2022.2155661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We intended to identify favourable metabolite(s) and pharmacological mechanism(s) of gut microbiota (GM) for liver regeneration (LR) through network pharmacology. We utilized the gutMGene database to obtain metabolites of GM, and targets associated with metabolites as well as LR-related targets were identified using public databases. Furthermore, we performed a molecular docking assay on the active metabolite(s) and target(s) to verify the network pharmacological concept. We mined a total of 208 metabolites in the gutMGene database and selected 668 targets from the SEA (1,256 targets) and STP (947 targets) databases. Finally, 13 targets were identified between 61 targets and the gutMGene database (243 targets). Protein-protein interaction network analysis showed that AKT1 is a hub target correlated with 12 additional targets. In this study, we describe the potential microbe from the microbiota (E. coli), chemokine signalling pathway, AKT1 and myricetin that accelerate LR, providing scientific evidence for further clinical trials.
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Affiliation(s)
- Ki-Kwang Oh
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ickwon Choi
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Haripriya Gupta
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ganesan Raja
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Satya Priya Sharma
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Sung-Min Won
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Jin-Ju Jeong
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Su-Been Lee
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Min-Gi Cha
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Goo-Hyun Kwon
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Min-Kyo Jeong
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Byeong-Hyun Min
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ji-Ye Hyun
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Jung-A Eom
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Hee-Jin Park
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Sang-Jun Yoon
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Mi-Ran Choi
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Dong Joon Kim
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ki-Tae Suk
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
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Li Y, Fan Z, Rao J, Chen Z, Chu Q, Zheng M, Li X. An overview of recent advances and challenges in predicting compound-protein interaction (CPI). MEDICAL REVIEW (2021) 2023; 3:465-486. [PMID: 38282802 PMCID: PMC10808869 DOI: 10.1515/mr-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/30/2023] [Indexed: 01/30/2024]
Abstract
Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.
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Affiliation(s)
- Yanbei Li
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhehuan Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiyi Chen
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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Amiri R, Razmara J, Parvizpour S, Izadkhah H. A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks. BMC Bioinformatics 2023; 24:442. [PMID: 37993777 PMCID: PMC10664633 DOI: 10.1186/s12859-023-05572-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.
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Affiliation(s)
- Ramin Amiri
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
| | - Jafar Razmara
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran.
| | - Sepideh Parvizpour
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Habib Izadkhah
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
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Somers J, Fenner M, Kong G, Thirumalaisamy D, Yashar WM, Thapa K, Kinali M, Nikolova O, Babur Ö, Demir E. A framework for considering prior information in network-based approaches to omics data analysis. Proteomics 2023; 23:e2200402. [PMID: 37986684 DOI: 10.1002/pmic.202200402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 11/22/2023]
Abstract
For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.
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Affiliation(s)
- Julia Somers
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Madeleine Fenner
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Garth Kong
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Dharani Thirumalaisamy
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - William M Yashar
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Kisan Thapa
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Meric Kinali
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Olga Nikolova
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Özgün Babur
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Emek Demir
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
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da Silva CR, do Amaral Valente Sá LG, Ferreira TL, Leitão AC, de Farias Cabral VP, Rodrigues DS, Barbosa AD, Moreira LEA, Filho HLP, de Andrade Neto JB, Rios MEF, Cavalcanti BC, Magalhães HIF, de Moraes MO, Vitoriano Nobre H. Antifungal activity of selective serotonin reuptake inhibitors against Cryptococcus spp. and their possible mechanism of action. J Mycol Med 2023; 33:101431. [PMID: 37666030 DOI: 10.1016/j.mycmed.2023.101431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/22/2023] [Accepted: 08/28/2023] [Indexed: 09/06/2023]
Abstract
Fungal infections caused by Cryptococcus spp. pose a threat to health, especially in immunocompromised individuals. The available arsenal of drugs against cryptococcosis is limited, due to their toxicity and/or lack of accessibility in low-income countries, requiring more therapeutic alternatives. Selective serotonin reuptake inhibitors (SSRIs), through drug repositioning, are a promising alternative to broaden the range of new antifungals against Cryptococcus spp. This study evaluates the antifungal activity of three SSRIs, sertraline, paroxetine, and fluoxetine, against Cryptococcus spp. strains, as well as assesses their possible mechanism of action. Seven strains of Cryptococcus spp. were used. Sensitivity to SSRIs, fluconazole, and itraconazole was evaluated using the broth microdilution assay. The interactions resulting from combinations of SSRIs and azoles were investigated using the checkerboard assay. The possible action mechanism of SSRIs against Cryptococcus spp. was evaluated through flow cytometry assays. The SSRIs exhibited in vitro antifungal activity against Cryptococcus spp. strains, with minimum inhibitory concentrations ranging from 2 to 32 μg/mL, and had synergistic and additive interactions with azoles. The mechanism of action of SSRIs against Cryptococcus spp. involved damage to the mitochondrial membrane and increasing the production of reactive oxygen species, resulting in loss of cellular viability and apoptotic cell death. Fluoxetine also was able to cause significant damage to yeast DNA. These findings demonstrate the in vitro antifungal potential of SSRIs against Cryptococcus spp. strains.
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Affiliation(s)
- Cecília Rocha da Silva
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Livia Gurgel do Amaral Valente Sá
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil; Christus University Center, Fortaleza, Ceará, Brazil
| | - Thais Lima Ferreira
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Amanda Cavalcante Leitão
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Vitória Pessoa de Farias Cabral
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Daniel Sampaio Rodrigues
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Amanda Dias Barbosa
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Lara Elloyse Almeida Moreira
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Hugo Leonardo Pereira Filho
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - João Batista de Andrade Neto
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil; Christus University Center, Fortaleza, Ceará, Brazil
| | | | - Bruno Coêlho Cavalcanti
- Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | | | - Manoel Odorico de Moraes
- Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Hélio Vitoriano Nobre
- Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Laboratory of Bioprospection in Antimicrobial Molecules, Federal University of Ceará, Fortaleza, Ceará, Brazil; Center for Research and Development of Medicines, Federal University of Ceará, Fortaleza, Ceará, Brazil.
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Rao M, McDuffie E, Sachs C. Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics. TOXICS 2023; 11:875. [PMID: 37888725 PMCID: PMC10611213 DOI: 10.3390/toxics11100875] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/12/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023]
Abstract
The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.
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Affiliation(s)
- Mohan Rao
- Neurocrine Biosciences, Inc., Nonclinical Toxicology, San Diego, CA 92130, USA; (E.M.); (C.S.)
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Pogorelov VM, Rodriguiz RM, Roth BL, Wetsel WC. The G protein biased serotonin 5-HT2A receptor agonist lisuride exerts anti-depressant drug-like activities in mice. Front Mol Biosci 2023; 10:1233743. [PMID: 37900918 PMCID: PMC10603247 DOI: 10.3389/fmolb.2023.1233743] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 09/19/2023] [Indexed: 10/31/2023] Open
Abstract
There is now evidence from multiple Phase II clinical trials that psychedelic drugs can exert long-lasting anxiolytic, anti-depressant, and anti-drug abuse (nicotine and ethanol) effects in patients. Despite these benefits, the hallucinogenic actions of these drugs at the serotonin 2A receptor (5-HT2AR) limit their clinical use in diverse settings. Activation of the 5-HT2AR can stimulate both G protein and β-arrestin (βArr) -mediated signaling. Lisuride is a G protein biased agonist at the 5-HT2AR and, unlike the structurally-related lysergic acid diethylamide (LSD), the drug does not typically produce hallucinations in normal subjects at routine doses. Here, we examined behavioral responses to lisuride, in wild-type (WT), βArr1-knockout (KO), and βArr2-KO mice. In the open field, lisuride reduced locomotor and rearing activities, but produced a U-shaped function for stereotypies in both βArr lines of mice. Locomotion was decreased overall in βArr1-KOs and βArr2-KOs relative to wild-type controls. Incidences of head twitches and retrograde walking to lisuride were low in all genotypes. Grooming was decreased in βArr1 mice, but was increased then decreased in βArr2 animals with lisuride. Serotonin syndrome-associated responses were present at all lisuride doses in WTs, but they were reduced especially in βArr2-KO mice. Prepulse inhibition (PPI) was unaffected in βArr2 mice, whereas 0.5 mg/kg lisuride disrupted PPI in βArr1 animals. The 5-HT2AR antagonist MDL100907 failed to restore PPI in βArr1 mice, whereas the dopamine D2/D3 antagonist raclopride normalized PPI in WTs but not in βArr1-KOs. Clozapine, SCH23390, and GR127935 restored PPI in both βArr1 genotypes. Using vesicular monoamine transporter 2 mice, lisuride reduced immobility times in tail suspension and promoted a preference for sucrose that lasted up to 2 days. Together, it appears βArr1 and βArr2 play minor roles in lisuride's actions on many behaviors, while this drug exerts anti-depressant drug-like responses without hallucinogenic-like activities.
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Affiliation(s)
- Vladimir M. Pogorelov
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Ramona M. Rodriguiz
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
- Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical Center, Durham, NC, United States
| | - Bryan L. Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, United States
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, National Institute of Mental Health Psychoactive Drug Screening Program, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, United States
| | - William C. Wetsel
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
- Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical Center, Durham, NC, United States
- Departments of Cell Biology and Neurobiology, Duke University Medical Center, Durham, NC, United States
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Nguyen M, Aslam MA, Nguyen Y, Javaid HM, Pham L, Huh JY, Kim G. Design and Synthesis of l-1'-Homologated Adenosine Derivatives as Potential Anti-inflammatory Agents. ACS OMEGA 2023; 8:36361-36369. [PMID: 37810713 PMCID: PMC10552512 DOI: 10.1021/acsomega.3c05029] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/08/2023] [Indexed: 10/10/2023]
Abstract
Inflammatory responses are fundamental protective warning mechanisms. However, in certain instances, they contribute significantly to the development of several chronic diseases such as cancer. Based on previous studies of truncated 1'-homologated adenosine derivatives, l-nucleosides and their nucleobase-modified quinolone analogues were designed, synthesized, and evaluated for anti-inflammatory activities. The target molecules were synthesized via the key intramolecular cyclization of monotosylate and Mitsunobu condensation from the natural product, d-ribose. All compounds tested and showed potent anti-inflammatory activities, as indicated by their inhibition of LPS-induced IL-1β secretion from the RAW 264.7 macrophages. Gene expressions of pro-inflammatory cytokines showed that all compounds, except 3a and 3b, significantly reduced LPS-induced IL-1β and IL-6 mRNA expressions. The half-maximal inhibitory concentrations (IC50) of 2g and 2h against IL-1β were 1.08 and 2.28 μM, respectively. In contrast, only 2d, 2g, and 3d effectively reversed LPS-induced TNFα mRNA expression. Our mechanistic study revealed that LPS-induced phosphorylation of NF-κB was significantly downregulated by all compounds tested, providing evidence that the NF-κB signaling pathway is involved in their anti-inflammatory activities. Among the compounds tested, 2g and 2h had the most potent anti-inflammatory effects, as shown by the extent of decrease in pro-inflammatory gene expression, protein secretion, and NF-κB phosphorylation. These findings suggest that the l-truncated 1'-homologated adenosine skeleton and its nucleobase-modified analogues have therapeutic potential as treatments for various human diseases by mediating inflammatory processes.
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Affiliation(s)
| | | | - Yen Nguyen
- College of Pharmacy and Research Institute
of Drug Development, Chonnam National University, Gwangju 61186, Korea
| | - Hafiz Muhammad
Ahmad Javaid
- College of Pharmacy and Research Institute
of Drug Development, Chonnam National University, Gwangju 61186, Korea
| | - Linh Pham
- College of Pharmacy and Research Institute
of Drug Development, Chonnam National University, Gwangju 61186, Korea
| | - Joo Young Huh
- College of Pharmacy and Research Institute
of Drug Development, Chonnam National University, Gwangju 61186, Korea
| | - Gyudong Kim
- College of Pharmacy and Research Institute
of Drug Development, Chonnam National University, Gwangju 61186, Korea
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Zeng L, Gu R, Li W, Shao Y, Zhu Y, Xie Z, Liu H, Zhou Y. Ataluren prevented bone loss induced by ovariectomy and aging in mice through the BMP-SMAD signaling pathway. Biomed Pharmacother 2023; 166:115332. [PMID: 37597324 DOI: 10.1016/j.biopha.2023.115332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/02/2023] [Accepted: 08/13/2023] [Indexed: 08/21/2023] Open
Abstract
Both estrogen deficiency and aging may lead to osteoporosis. Developing novel drugs for treating osteoporosis is a popular research direction. We screened several potential therapeutic agents through a new deep learning-based efficacy prediction system (DLEPS) using transcriptional profiles for osteoporosis. DLEPS screening led to a potential novel drug examinee, ataluren, for treating osteoporosis. Ataluren significantly reversed bone loss in ovariectomized mice. Next, ataluren significantly increased human bone marrow-derived mesenchymal stem cell (hBMMSC) osteogenic differentiation without cytotoxicity, indicated by the high expression index of osteogenic differentiation genes (OCN , BGLAP, ALP, COL1A, BMP2, RUNX2). Mechanistically, ataluren exerted its function through the BMP-SMAD pathway. Furthermore, it activated SMAD phosphorylation but osteogenic differentiation was attenuated by BMP2-SMAD inhibitors or small interfering RNA of BMP2. Finally, ataluren significantly reversed bone loss in aged mice. In summary, our findings suggest that the DLEPS-screened ataluren may be a therapeutic agent against osteoporosis by aiding hBMMSC osteogenic differentiation.
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Affiliation(s)
- Lijun Zeng
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Ranli Gu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Wei Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Yuzi Shao
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Yuan Zhu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Zhengwei Xie
- Peking University International Cancer Institute, Peking University Health Science Center, Peking University, 38 Xueyuan Lu, Haidian District, Beijing 100191, China.
| | - Hao Liu
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China.
| | - Yongsheng Zhou
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China.
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Zhai H, Hou H, Luo J, Liu X, Wu Z, Wang J. DGDTA: dynamic graph attention network for predicting drug-target binding affinity. BMC Bioinformatics 2023; 24:367. [PMID: 37777712 PMCID: PMC10543834 DOI: 10.1186/s12859-023-05497-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/23/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND Obtaining accurate drug-target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive. RESULTS Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA . CONCLUSIONS The experimental results show that DGDTA can predict DTA more accurately than some other methods.
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Affiliation(s)
- Haixia Zhai
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
| | - Hongli Hou
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
| | - Junwei Luo
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China.
| | - Xiaoyan Liu
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
| | - Zhengjiang Wu
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
| | - Junfeng Wang
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
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Egger K, Gudmundsen F, Jessen NS, Baun C, Poetzsch SN, Shalgunov V, Herth MM, Quednow BB, Martin-Soelch C, Dornbierer D, Scheidegger M, Cumming P, Palner M. A pilot study of cerebral metabolism and serotonin 5-HT 2A receptor occupancy in rats treated with the psychedelic tryptamine DMT in conjunction with the MAO inhibitor harmine. Front Pharmacol 2023; 14:1140656. [PMID: 37841918 PMCID: PMC10568461 DOI: 10.3389/fphar.2023.1140656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 09/19/2023] [Indexed: 10/17/2023] Open
Abstract
Rationale: The psychedelic effects of the traditional Amazonian botanical decoction known as ayahuasca are often attributed to agonism at brain serotonin 5-HT2A receptors by N,N-dimethyltryptamine (DMT). To reduce first pass metabolism of oral DMT, ayahuasca preparations additionally contain reversible monoamine oxidase A (MAO-A) inhibitors, namely β-carboline alkaloids such as harmine. However, there is lacking biochemical evidence to substantiate this pharmacokinetic potentiation of DMT in brain via systemic MAO-A inhibition. Objectives: We measured the pharmacokinetic profile of harmine and/or DMT in rat brain, and tested for pharmacodynamic effects on brain glucose metabolism and DMT occupancy at brain serotonin 5-HT2A receptors. Methods: We first measured brain concentrations of harmine and DMT after treatment with harmine and/or DMT at low sub-cutaneous doses (1 mg/kg each) or harmine plus DMT at moderate doses (3 mg/kg each). In the same groups of rats, we also measured ex vivo the effects of these treatments on the availability of serotonin 5-HT2A receptors in frontal cortex. Finally, we explored effects of DMT and/or harmine (1 mg/kg each) on brain glucose metabolism with [18F]FDG-PET. Results: Results confirmed that co-administration of harmine inhibited the formation of the DMT metabolite indole-3-acetic acid (3-IAA) in brain, while correspondingly increasing the cerebral availability of DMT. However, we were unable to detect any significant occupancy by DMT at 5-HT2A receptors measured ex vivo, despite brain DMT concentrations as high as 11.3 µM. We did not observe significant effects of low dose DMT and/or harmine on cerebral [18F]FDG-PET uptake. Conclusion: These preliminary results call for further experiments to establish the dose-dependent effects of harmine/DMT on serotonin receptor occupancy and cerebral metabolism.
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Affiliation(s)
- Klemens Egger
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
- Department of Nuclear Medicine, Bern University Hospital, Bern, Switzerland
| | - Frederik Gudmundsen
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Naja Støckel Jessen
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Christina Baun
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Sandra N. Poetzsch
- Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland
| | - Vladimir Shalgunov
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital, Copenhagen, Denmark
| | - Matthias M. Herth
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital, Copenhagen, Denmark
| | - Boris B. Quednow
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | | | - Dario Dornbierer
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Milan Scheidegger
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Paul Cumming
- Department of Nuclear Medicine, Bern University Hospital, Bern, Switzerland
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
| | - Mikael Palner
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
- Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
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49
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Chiu YT, Deutch AY, Wang W, Schmitz GP, Huang KL, Kocak DD, Llorach P, Bowyer K, Liu B, Sciaky N, Hua K, Chen C, Mott SE, Niehaus J, DiBerto JF, English J, Walsh JJ, Scherrer G, Herman MA, Wu Z, Wetsel WC, Roth BL. A suite of engineered mice for interrogating psychedelic drug actions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559347. [PMID: 37808655 PMCID: PMC10557740 DOI: 10.1101/2023.09.25.559347] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Psychedelic drugs like lysergic acid diethylamide (LSD) and psilocybin have emerged as potentially transformative therapeutics for many neuropsychiatric diseases, including depression, anxiety, post-traumatic stress disorder, migraine, and cluster headaches. LSD and psilocybin exert their psychedelic effects via activation of the 5-hydroxytryptamine 2A receptor (HTR2A). Here we provide a suite of engineered mice useful for clarifying the role of HTR2A and HTR2A-expressing neurons in psychedelic drug actions. We first generated Htr2a-EGFP-CT-IRES-CreERT2 mice (CT:C-terminus) to independently identify both HTR2A-EGFP-CT receptors and HTR2A-containing cells thereby providing a detailed anatomical map of HTR2A and identifying cell types that express HTR2A. We also generated a humanized Htr2a mouse line and an additional constitutive Htr2A-Cre mouse line. Psychedelics induced a variety of known behavioral changes in our mice validating their utility for behavioral studies. Finally, electrophysiology studies revealed that extracellular 5-HT elicited a HTR2A-mediated robust increase in firing of genetically-identified pyramidal neurons--consistent with a plasma membrane localization and mode of action. These mouse lines represent invaluable tools for elucidating the molecular, cellular, pharmacological, physiological, behavioral, and other actions of psychedelic drugs in vivo.
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Affiliation(s)
- Yi-Ting Chiu
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Ariel Y. Deutch
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Wei Wang
- Appel Alzheimer’s Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, 10021, USA
| | - Gavin P Schmitz
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Karen Lu Huang
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - D. Dewran Kocak
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Pierre Llorach
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kasey Bowyer
- Appel Alzheimer’s Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, 10021, USA
| | - Bei Liu
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Noah Sciaky
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Kunjie Hua
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Chongguang Chen
- Center for Substance Abuse Research, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA
| | - Sarah E. Mott
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Jesse Niehaus
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jeffrey F. DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Justin English
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Jessica J. Walsh
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Grégory Scherrer
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- New York Stem Cell Foundation ‒ Robertson Investigator, Chapel Hill, NC 27599, USA
| | - Melissa A Herman
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
- Bowles Center for Alcohol Studies, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Zhuhao Wu
- Appel Alzheimer’s Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, 10021, USA
| | - William C Wetsel
- Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurobiology, Duke University Medical Center, Durham, NC 27710, USA
- Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical Center, Durham, NC 27710, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill, NC 27599, USA
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50
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Xiaolin X, Xiaozhi L, Guoping H, Hongwei L, Jinkuo G, Xiyun B, Zhen T, Xiaofang M, Yanxia L, Na X, Chunyan Z, Rui G, Kuan W, Cheng Z, Cuancuan W, Mingyong L, Xinping D. Overfit deep neural network for predicting drug-target interactions. iScience 2023; 26:107646. [PMID: 37680476 PMCID: PMC10480310 DOI: 10.1016/j.isci.2023.107646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 06/28/2023] [Accepted: 08/11/2023] [Indexed: 09/09/2023] Open
Abstract
Drug-target interactions (DTIs) prediction is an important step in drug discovery. As traditional biological experiments or high-throughput screening are high cost and time-consuming, many deep learning models have been developed. Overfitting must be avoided when training deep learning models. We propose a simple framework, called OverfitDTI, for DTI prediction. In OverfitDTI, a deep neural network (DNN) model is overfit to sufficiently learn the features of the chemical space of drugs and the biological space of targets. The weights of trained DNN model form an implicit representation of the nonlinear relationship between drugs and targets. Performance of OverfitDTI on three public datasets showed that the overfit DNN models fit the nonlinear relationship with high accuracy. We identified fifteen compounds that interacted with TEK, a receptor tyrosine kinase contributing to vascular homeostasis, and the predicted AT9283 and dorsomorphin were experimentally demonstrated as inhibitors of TEK in human umbilical vein endothelial cells (HUVECs).
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Affiliation(s)
- Xiao Xiaolin
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Liu Xiaozhi
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - He Guoping
- Geriatrics Department, Traditional Chinese Medicine Hospital of Binhai New Area, Tianjin, China
| | - Liu Hongwei
- School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei, China
- Department of Anesthesiology, Tangshan Maternal and Child Health Hospital, Tangshan, Hebei, China
| | - Guo Jinkuo
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin, China
| | - Bian Xiyun
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Tian Zhen
- Deepwater Technology Research Institute, China National Offshore Oil Corporation, Tianjin, China
| | - Ma Xiaofang
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Li Yanxia
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Xue Na
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Zhang Chunyan
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Gao Rui
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
| | - Wang Kuan
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Zhang Cheng
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Wang Cuancuan
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Liu Mingyong
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Department of Urology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Du Xinping
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin, China
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