1
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Kollár L, Grabrijan K, Hrast Rambaher M, Bozovičar K, Imre T, Ferenczy GG, Gobec S, Keserű GM. Boronic acid inhibitors of penicillin-binding protein 1b: serine and lysine labelling agents. J Enzyme Inhib Med Chem 2024; 39:2305833. [PMID: 38410950 PMCID: PMC10901194 DOI: 10.1080/14756366.2024.2305833] [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: 08/29/2023] [Accepted: 01/08/2024] [Indexed: 02/28/2024] Open
Abstract
Penicillin-binding proteins (PBPs) contribute to bacterial cell wall biosynthesis and are targets of antibacterial agents. Here, we investigated PBP1b inhibition by boronic acid derivatives. Chemical starting points were identified by structure-based virtual screening and aliphatic boronic acids were selected for further investigations. Structure-activity relationship studies focusing on the branching of the boron-connecting carbon and quantum mechanical/molecular mechanical simulations showed that reaction barrier free energies are compatible with fast reversible covalent binding and small or missing reaction free energies limit the inhibitory activity of the investigated boronic acid derivatives. Therefore, covalent labelling of the lysine residue of the catalytic dyad was also investigated. Compounds with a carbonyl warhead and an appropriately positioned boronic acid moiety were shown to inhibit and covalently label PBP1b. Reversible covalent labelling of the catalytic lysine by imine formation and the stabilisation of the imine by dative N-B bond is a new strategy for PBP1b inhibition.
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Affiliation(s)
- Levente Kollár
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Budapest, Hungary
| | | | | | | | - Tímea Imre
- MS Metabolomics Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - György G Ferenczy
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - Stanislav Gobec
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Budapest, Hungary
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2
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Shi W, Yang H, Xie L, Yin XX, Zhang Y. A review of machine learning-based methods for predicting drug-target interactions. Health Inf Sci Syst 2024; 12:30. [PMID: 38617016 PMCID: PMC11014838 DOI: 10.1007/s13755-024-00287-6] [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: 09/11/2023] [Accepted: 03/04/2024] [Indexed: 04/16/2024] Open
Abstract
The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.
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Affiliation(s)
- Wen Shi
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Linhai Xie
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing, 102206 China
| | - Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000 China
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3
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Wang Y, Shen Z, Chen R, Chi X, Li W, Xu D, Lu Y, Ding J, Dong X, Zheng X. Discovery and characterization of novel FGFR1 inhibitors in triple-negative breast cancer via hybrid virtual screening and molecular dynamics simulations. Bioorg Chem 2024; 150:107553. [PMID: 38901279 DOI: 10.1016/j.bioorg.2024.107553] [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: 04/26/2024] [Revised: 06/08/2024] [Accepted: 06/08/2024] [Indexed: 06/22/2024]
Abstract
The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schrödinger's Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p-FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound's specific binding interactions with FGFR1.
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Affiliation(s)
- Yuchen Wang
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou 310015, China; Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zheyuan Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Roufen Chen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xinglong Chi
- Affiliated Yongkang First People's Hospital and School of Pharmacy, Hangzhou Medical College, Hangzhou 310014, Zhejiang, China
| | - Wenjie Li
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou 310015, China
| | - Donghang Xu
- Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yan Lu
- Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianjun Ding
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Xiaowu Dong
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Xiaoli Zheng
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou 310015, China.
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4
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Zhong Y, Wen W, Luo Z, Cheng N. A multi-component, multi-target, and multi-pathway prediction method for Chinese medicine based on the combination of mass spectrometry analysis and network analysis: An example using Weifuchun. J Chromatogr A 2024; 1731:465164. [PMID: 39043100 DOI: 10.1016/j.chroma.2024.465164] [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: 04/02/2024] [Revised: 07/03/2024] [Accepted: 07/12/2024] [Indexed: 07/25/2024]
Abstract
Weifuchun, a Chinese medicinal prescription made from herbs of natural origin including Hongshen (Ginseng Radix et Rhizoma Rubra), Xiangchacai (Rabdosia Amethystoides), and Zhiqiao (Aurantii Fructus), has attracted increasing attention for clinically treating chronic atrophic gastritis, which is characterized by the chronic inflammation of the gastric mucosa leading to progressive loss of gastric glandular cells. To investigate the active ingredients and potential mechanisms of WFC, it was analyzed using a novel multi-component, multi-target, and multi-pathway prediction method. High/ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC/UPLC-Q-TOF-MS) was employed to separate and profile the chemical constituents of WFC with high precision and efficiency. Network analysis and molecular docking were used to predict bioactive compounds and their interactions with biological targets. The results highlight 42 significant compounds potentially contributing to the therapeutic effects of WFC by effecting on several key pathways, including proved PI3K/Akt, NF-κB, and JAK/STAT signaling pathways. This study showcases the efficacy of combining advanced chromatographic techniques with computational methods to elucidate the pharmacological mechanisms of complex botanical drugs.
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Affiliation(s)
- Yi Zhong
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China
| | - Wu Wen
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China
| | - Zhenyu Luo
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China
| | - Ningtao Cheng
- School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China.
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5
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Yang H, Luo X, Wang X, Peng Y, Li Z, He Y, Cong J, Xie T, Zhang W. The PP2A inhibitor LB-100 mitigates lupus nephritis by suppressing tertiary lymphoid structure formation. Eur J Pharmacol 2024; 977:176703. [PMID: 38839028 DOI: 10.1016/j.ejphar.2024.176703] [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: 01/05/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024]
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by multi-organ involvement and autoantibody production. Patients with SLE face a substantial risk of developing lupus nephritis (LN), which imposes a substantial burden on both patients and their families. Protein phosphatase 2A (PP2A) is a widely distributed serine/threonine phosphatase that participates in regulating multiple signaling pathways. Inhibition of PP2A has been implicated in the treatment of various diseases. LB-100, a small molecule inhibitor of PP2A, has demonstrated anti-tumor therapeutic effects and high safety profile in preclinical experiments. However, the role of PP2A and its inhibitor has been insufficiently studied in LN. In this study, we assessed the potential effects of LB-100 in both MRL/lpr mice and R848-induced BALB/c mice. Our findings indicated that LB-100 administration led to reduced spleen enlargement, decreased deposition of immune complexes, ameliorated renal damage, and improved kidney function in both spontaneous and R848-induced lupus mouse models. Importantly, we observed the formation of tertiary lymphoid structures (TLSs) in the kidneys of two distinct lupus mouse models. The levels of signature genes of TLS were elevated in the kidneys of lupus mice, whereas LB-100 mitigated chemokine production and inhibited TLS formation. In addition, we confirmed that inhibition or knockdown of PP2A reduced the production of T cell-related chemokines by renal tubular epithelial cells (RTEC). In summary, our study highlighted the renal protective potential of the PP2A inhibitor LB-100 in two distinct lupus mouse models, suggesting its potential as a novel strategy for treating LN and other autoimmune diseases.
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Affiliation(s)
- Hongyu Yang
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Xiaoyu Luo
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Xuan Wang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Department of General Medicine, Xiangya Hospital, Central South University, Changsha, 410008, China; National Medical Metabolomics International Collaborative Research Center, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Yi Peng
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Zhilan Li
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Yifei He
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Jing Cong
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Tingting Xie
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Department of General Medicine, Xiangya Hospital, Central South University, Changsha, 410008, China; National Medical Metabolomics International Collaborative Research Center, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Weiru Zhang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Department of General Medicine, Xiangya Hospital, Central South University, Changsha, 410008, China; National Medical Metabolomics International Collaborative Research Center, Xiangya Hospital, Central South University, Changsha, 410008, China.
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6
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Beckers M, Sirockin F, Fechner N, Stiefl N. Balancing Molecular Size, Activity, Permeability, and Other Properties: Drug Candidates in the Context of Their Chemical Structure Optimization. J Chem Inf Model 2024. [PMID: 39137447 DOI: 10.1021/acs.jcim.4c00898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Chemical structure optimization is a vital part of early drug discovery projects. Starting with compounds that show activity on the target of interest, the chemical structures are subsequently optimized toward a development candidate (DC) molecule with the best chances of clinical success. However, the DCs in the context of such optimization programs, as well as detailed characterization of major limiting factors, have not been investigated in detail so far. Here, we report an analysis of the historical DC molecules at Novartis since 2005 in the context of their optimization projects. Mapping the DCs into their respective chemical optimization series, we find that these tend to be synthesized rather early in a substantial number of cases. Further analysis of structural properties, ADMET, and potency-related readouts revealed that DC compounds tend to be generally significantly smaller, more permeable, and have higher ligand efficiency than other compounds sent to in vivo PK studies, which we also show for compounds from the same chemical series. Although this might seem obvious to most practitioners in medicinal chemistry, for all of these properties, we could show that they tend to evolve in an undesired direction during structure optimization. This highlights the difficulty of successfully translating our knowledge to medicinal chemistry optimizations.
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Affiliation(s)
- Maximilian Beckers
- Biomedical Research, Novartis Pharma AG, Postfach, Basel 4002, Switzerland
| | - Finton Sirockin
- Biomedical Research, Novartis Pharma AG, Postfach, Basel 4002, Switzerland
| | - Nikolas Fechner
- Biomedical Research, Novartis Pharma AG, Postfach, Basel 4002, Switzerland
| | - Nikolaus Stiefl
- Biomedical Research, Novartis Pharma AG, Postfach, Basel 4002, Switzerland
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7
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Dorsey MA, Dsouza K, Ranganath D, Harris JS, Lane TR, Urbina F, Ekins S. Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery. J Chem Inf Model 2024; 64:5922-5930. [PMID: 39013438 DOI: 10.1021/acs.jcim.4c00953] [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: 07/18/2024]
Abstract
Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.
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Affiliation(s)
- Matthew A Dorsey
- Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Kelvin Dsouza
- Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Dhruv Ranganath
- Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, United States
| | - Joshua S Harris
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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8
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Urbina F, Jones T, Harris JS, Snyder SH, Lane TR, Ekins S. Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence. ACS Chem Neurosci 2024. [PMID: 39092989 DOI: 10.1021/acschemneuro.4c00405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024] Open
Abstract
The development of new drugs addressing serious mental health and other disorders should avoid the psychedelic experience. Analogs of psychedelic drugs can have clinical utility and are termed "psychoplastogens". These represent promising candidates for treating opioid use disorder to reduce drug dependence, with rarely reported serious adverse effects. This drug abuse cessation is linked to the induction of neuritogenesis and increased neuroplasticity, a hallmark of psychedelic molecules, such as lysergic acid diethylamine. Some, but not all psychoplastogens may act through the G-protein coupled receptor (GPCR) 5HT2A whereas others may display very different polypharmacology making prediction of hallucinogenic potential challenging. In the process of developing tools to help design new psychoplastogens, we have used artificial intelligence in the form of machine learning classification models for predicting psychedelic effects using a published in vitro data set from PsychLight (support vector classification (SVC), area under the curve (AUC) 0.74) and in vivo human data derived from books from Shulgin and Shulgin (SVC, AUC, 0.72) with nested five-fold cross validation. We have also explored conformal predictors with ECFP6 and electrostatic descriptors in an effort to optimize them. These models have been used to predict known 5HT2A agonists to assess their potential to act as psychedelics and induce hallucinations for PsychLight (SVC, AUC 0.97) and Shulgin and Shulgin (random forest, AUC 0.71). We have tested these models with head twitch data from the mouse. This predictive capability is desirable to reliably design new psychoplastogens that lack in vivo hallucinogenic potential and help assess existing and future molecules for this potential. These efforts also provide useful insights into understanding the psychedelic structure activity relationship.
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Affiliation(s)
- Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thane Jones
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Joshua S Harris
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Scott H Snyder
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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9
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Saifi I, Bhat BA, Hamdani SS, Bhat UY, Lobato-Tapia CA, Mir MA, Dar TUH, Ganie SA. Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science. J Biomol Struct Dyn 2024; 42:6523-6541. [PMID: 37434311 DOI: 10.1080/07391102.2023.2234039] [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: 02/12/2023] [Accepted: 07/03/2023] [Indexed: 07/13/2023]
Abstract
In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ifra Saifi
- Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India
| | - Basharat Ahmad Bhat
- Department of Bioresources, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Syed Suhail Hamdani
- Department of Bioresources, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Umar Yousuf Bhat
- Department of Zoology, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | | | - Mushtaq Ahmad Mir
- Department of Clinical Laboratory Sciences, College of Applied Medical Science, King Khalid University, KSA, Saudi Arabia
| | - Tanvir Ul Hasan Dar
- Department of Biotechnology, School of Biosciences and Biotechnology, BGSB University, Rajouri, India
| | - Showkat Ahmad Ganie
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
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10
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Masand VH, Al-Hussain S, Alzahrani AY, Al-Mutairi AA, Sultan Alqahtani A, Samad A, Alafeefy AM, Jawarkar RD, Zaki MEA. Unveiling dynamics of nitrogen content and selected nitrogen heterocycles in thrombin inhibitors: a ceteris paribus approach. Expert Opin Drug Discov 2024; 19:991-1009. [PMID: 38898679 DOI: 10.1080/17460441.2024.2368743] [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: 01/26/2024] [Accepted: 06/12/2024] [Indexed: 06/21/2024]
Abstract
BACKGROUND Despite the progress in comprehending molecular design principles and biochemical processes associated with thrombin inhibition, there is a crucial need to optimize efforts and curtail the recurrence of synthesis-testing cycles. Nitrogen and N-heterocycles are key features of many anti-thrombin drugs. Hence, a pragmatic analysis of nitrogen and N-heterocycles in thrombin inhibitors is important throughout the drug discovery pipeline. In the present work, the authors present an analysis with a specific focus on understanding the occurrence and distribution of nitrogen and selected N-heterocycles in the realm of thrombin inhibitors. RESEARCH DESIGN AND METHODS A dataset comprising 4359 thrombin inhibitors is used to scrutinize various categories of nitrogen atoms such as ring, non-ring, aromatic, and non-aromatic. In addition, selected aromatic and aliphatic N-heterocycles have been analyzed. RESULTS The analysis indicates that ~62% of thrombin inhibitors possess five or fewer nitrogen atoms. Substituted N-heterocycles have a high occurrence, like pyrrolidine (23.24%), pyridine (20.56%), piperidine (16.10%), thiazole (9.61%), imidazole (7.36%), etc. in thrombin inhibitors. CONCLUSIONS The majority of active thrombin inhibitors contain nitrogen atoms close to 5 and a combination of N-heterocycles like pyrrolidine, pyridine, piperidine, etc. This analysis provides crucial insights to optimize the transformation of lead compounds into potential anti-thrombin inhibitors.
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Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, India
| | - Sami Al-Hussain
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdullah Y Alzahrani
- Department of Chemistry, Faculty of Science and Arts, King Khalid University, Mohail Asser, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Arwa Sultan Alqahtani
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Iraq
| | - Ahmed M Alafeefy
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Universiti Teknologi MARA [UiTM], Bandar Puncak Alam, Selangor, Malaysia
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr Rajendra Gode Institute of Pharmacy, Amravati, India
| | - Magdi E A Zaki
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
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11
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Zahid H, Tayara H, Chong KT. Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties. Arch Toxicol 2024; 98:2647-2658. [PMID: 38619593 DOI: 10.1007/s00204-024-03756-9] [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: 02/23/2024] [Accepted: 03/27/2024] [Indexed: 04/16/2024]
Abstract
Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabolism of xenobiotics. Inhibition of any of these five CYP isozymes causes drug-drug interactions with high pharmacological and toxicological effects. So, the inhibition or non-inhibition prediction of these isozymes is of great importance. Many techniques based on machine learning and deep learning algorithms are currently being used to predict whether these isozymes will be inhibited or not. In this study, three different molecular or substructural properties that include Morgan, MACCS and Morgan (combined) and RDKit of the various molecules are used to train a distinct SVM model against each isozyme (1A2, 2C9, 2C19, 2D6, and 3A4). On the independent dataset, Morgan fingerprints provided the best results, while MACCS and Morgan (combined) achieved comparable results in terms of balanced accuracy (BA), sensitivity (Sn), and Mathews correlation coefficient (MCC). For the Morgan fingerprints, balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4 on an independent dataset ranged between 0.81 and 0.85, 0.61 and 0.70, 0.72 and 0.83, respectively. Similarly, on the independent dataset, MACCS and Morgan (combined) fingerprints achieved competitive results in terms of balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4, which ranged between 0.79 and 0.85, 0.59 and 0.69, 0.69 and 0.82, respectively.
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Affiliation(s)
- Hamza Zahid
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
- Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea.
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12
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Agea MI, Čmelo I, Dehaen W, Chen Y, Kirchmair J, Sedlák D, Bartůněk P, Šícho M, Svozil D. Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library. Mol Inform 2024; 43:e202300316. [PMID: 38979783 DOI: 10.1002/minf.202300316] [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: 11/10/2023] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 07/10/2024]
Abstract
Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized. The composition of the targeted GR ligand library was compared with a reference library that randomly samples chemical space. A random forest model was used to determine the biological activity of ligands, incorporating its applicability domain using conformal prediction. It was demonstrated that the GR library is significantly enriched with GR ligands compared to the random library. Furthermore, a prospective analysis demonstrated that Molpher successfully designed compounds, which were subsequently experimentally confirmed to be active on the GR. A collection of 34 potential new GR ligands was also identified. Moreover, an important contribution of this study is the establishment of a comprehensive workflow for evaluating computationally generated ligands, particularly those with potential activity against targets that are challenging to dock.
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Affiliation(s)
- M Isabel Agea
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
| | - Ivan Čmelo
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
| | - Wim Dehaen
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
- Department of Organic Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
| | - Ya Chen
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146, Hamburg, Germany
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090, Vienna, Austria
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146, Hamburg, Germany
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090, Vienna, Austria
| | - David Sedlák
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, 14220, Czech Republic
| | - Petr Bartůněk
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, 14220, Czech Republic
| | - Martin Šícho
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
| | - Daniel Svozil
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, 14220, Czech Republic
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13
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Nerella S, Bandyopadhyay S, Zhang J, Contreras M, Siegel S, Bumin A, Silva B, Sena J, Shickel B, Bihorac A, Khezeli K, Rashidi P. Transformers and large language models in healthcare: A review. Artif Intell Med 2024; 154:102900. [PMID: 38878555 DOI: 10.1016/j.artmed.2024.102900] [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: 09/01/2023] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 08/09/2024]
Abstract
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of healthcare data, including clinical NLP, medical imaging, structured Electronic Health Records (EHR), social media, bio-physiological signals, biomolecular sequences. Furthermore, which have also include the articles that used the transformer architecture for generating surgical instructions and predicting adverse outcomes after surgeries under the umbrella of critical care. Under diverse settings, these models have been used for clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. Finally, we also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.
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Affiliation(s)
- Subhash Nerella
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | | | - Jiaqing Zhang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, United States
| | - Miguel Contreras
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | - Scott Siegel
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | - Aysegul Bumin
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, United States
| | - Brandon Silva
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, United States
| | - Jessica Sena
- Department Of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Benjamin Shickel
- Department of Medicine, University of Florida, Gainesville, United States
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, United States
| | - Kia Khezeli
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, United States.
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14
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Shan M, Zhao X, Sun P, Qu X, Cheng G, Qin LP. Revisiting Structure-activity Relationships: Unleashing the potential of selective Janus kinase 1 inhibitors. Bioorg Chem 2024; 149:107506. [PMID: 38833989 DOI: 10.1016/j.bioorg.2024.107506] [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: 03/20/2024] [Revised: 05/13/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024]
Abstract
Janus kinases (JAKs), a kind of non-receptor tyrosine kinases, the function has been implicated in the regulation of cell proliferation, differentiation and apoptosis, immune, inflammatory response and malignancies. Among them, JAK1 represents an essential target for modulating cytokines involved in inflammation and immune function. Rheumatoid arthritis, atopic dermatitis, ulcerative colitis and psoriatic arthritis are areas where approved JAK1 drugs have been applied for the treatment. In the review, we provided a brief introduction to JAK1 inhibitors in market and clinical trials. The structures of high active JAK1 compounds (IC50 ≤ 0.1 nM) were highlighted, with primary focus on structure-activity relationship and selectivity. Moreover, the druggability processes of approved drugs and high active compounds were analyzed. In addition, the issues involved in JAK1 compounds clinical application as well as strategies to surmount these challenges, were discussed.
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Affiliation(s)
- Mengyi Shan
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
| | - Xuan Zhao
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
| | - Peng Sun
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
| | - Xinhao Qu
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
| | - Gang Cheng
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China.
| | - Lu-Ping Qin
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China.
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15
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Lejmi M, Geslin D, Bureau R, Cuissart B, Ben Slima I, Meddouri N, Borgi A, Lamotte JL, Lepailleur A. Navigating pharmacophore space to identify activity discontinuities: A case study with BCR-ABL. Mol Inform 2024; 43:e202400050. [PMID: 38979846 DOI: 10.1002/minf.202400050] [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: 02/07/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 07/10/2024]
Abstract
The exploration of chemical space is a fundamental aspect of chemoinformatics, particularly when one explores a large compound data set to relate chemical structures with molecular properties. In this study, we extend our previous work on chemical space visualization at the pharmacophoric level. Instead of using conventional binary classification of affinity (active vs inactive), we introduce a refined approach that categorizes compounds into four distinct classes based on their activity levels: super active, very active, active, and inactive. This classification enriches the color scheme applied to pharmacophore space, where the color representation of a pharmacophore hypothesis is driven by the associated compounds. Using the BCR-ABL tyrosine kinase as a case study, we identified intriguing regions corresponding to pharmacophore activity discontinuities, providing valuable insights for structure-activity relationships analysis.
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Affiliation(s)
- Maroua Lejmi
- Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen, UNICAEN, ENSICAEN, CNRS - UMR GREYC, Normandie Univ, Caen, France
- Laboratoire en Informatique, Programmation Algorithmique et Heuristique, LIPAH, Université de Tunis El Manar, Tunis, Tunisia
| | - Damien Geslin
- Centre d'Etudes et de Recherche sur le Médicament de Normandie, UNICAEN, CERMN, Normandie Univ, Caen, France
| | - Ronan Bureau
- Centre d'Etudes et de Recherche sur le Médicament de Normandie, UNICAEN, CERMN, Normandie Univ, Caen, France
| | - Bertrand Cuissart
- Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen, UNICAEN, ENSICAEN, CNRS - UMR GREYC, Normandie Univ, Caen, France
| | - Ilef Ben Slima
- ISMAI, University of Kairouan, Kairouan, Tunisia
- Laboratory of Signals, systeMs, aRtificial Intelligence and neTworkS, SM@RTS, Digital Research Center of Sfax, Sfax, Tunisia
| | - Nida Meddouri
- Laboratoire de Recherche de l'EPITA, LRE, Le Kremlin-Bicêtre, Paris, France
| | - Amel Borgi
- Laboratoire en Informatique, Programmation Algorithmique et Heuristique, LIPAH, Université de Tunis El Manar, Tunis, Tunisia
| | - Jean-Luc Lamotte
- Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen, UNICAEN, ENSICAEN, CNRS - UMR GREYC, Normandie Univ, Caen, France
| | - Alban Lepailleur
- Centre d'Etudes et de Recherche sur le Médicament de Normandie, UNICAEN, CERMN, Normandie Univ, Caen, France
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16
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Razuvayevskaya O, Lopez I, Dunham I, Ochoa D. Genetic factors associated with reasons for clinical trial stoppage. Nat Genet 2024:10.1038/s41588-024-01854-z. [PMID: 39075208 DOI: 10.1038/s41588-024-01854-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/02/2024] [Indexed: 07/31/2024]
Abstract
Many drug discovery projects are started but few progress fully through clinical trials to approval. Previous work has shown that human genetics support for the therapeutic hypothesis increases the chance of trial progression. Here, we applied natural language processing to classify the free-text reasons for 28,561 clinical trials that stopped before their endpoints were met. We then evaluated these classes in light of the underlying evidence for the therapeutic hypothesis and target properties. We found that trials are more likely to stop because of a lack of efficacy in the absence of strong genetic evidence from human populations or genetically modified animal models. Furthermore, certain trials are more likely to stop for safety reasons if the drug target gene is highly constrained in human populations and if the gene is broadly expressed across tissues. These results support the growing use of human genetics to evaluate targets for drug discovery programs.
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Affiliation(s)
- Olesya Razuvayevskaya
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Irene Lopez
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Ian Dunham
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - David Ochoa
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK.
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17
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Wu T, Zhou M, Zou J, Chen Q, Qian F, Kurths J, Liu R, Tang Y. AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria. Nat Commun 2024; 15:6288. [PMID: 39060236 PMCID: PMC11282099 DOI: 10.1038/s41467-024-50533-4] [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: 09/26/2023] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers (<102), much smaller than public polymer datasets (>105), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking β-amino acid polymers, we successfully simulate predictions of over 105 polymers and identify 83 optimal polymers. Furthermore, we synthesize an optimal polymer DM0.8iPen0.2 and find that this polymer exhibits broad-spectrum and potent antibacterial activity against multiple clinically isolated antibiotic-resistant pathogens, validating the effectiveness of AI-guided design strategy.
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Affiliation(s)
- Tianyu Wu
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, China
| | - Min Zhou
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Jingcheng Zou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Key Laboratory for Ultrafine Materials of Ministry of Education, Research Center for Biomedical Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Qi Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Key Laboratory for Ultrafine Materials of Ministry of Education, Research Center for Biomedical Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Feng Qian
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, China
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, 14473, Germany
- Institut für Physik, Humboldt-Universität zu Berlin, Berlin, 10115, Germany
- The Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China
| | - Runhui Liu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China.
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Key Laboratory for Ultrafine Materials of Ministry of Education, Research Center for Biomedical Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China.
| | - Yang Tang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, China.
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18
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He S, Nader K, Abarrategi JS, Bediaga H, Nocedo-Mena D, Ascencio E, Casanola-Martin GM, Castellanos-Rubio I, Insausti M, Rasulev B, Arrasate S, González-Díaz H. NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study. J Nanobiotechnology 2024; 22:435. [PMID: 39044265 PMCID: PMC11267683 DOI: 10.1186/s12951-024-02660-9] [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: 04/18/2024] [Accepted: 06/24/2024] [Indexed: 07/25/2024] Open
Abstract
Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.
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Affiliation(s)
- Shan He
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº 6, Leioa, 48940, Greater Bilbao, Basque Country, Spain
| | - Karam Nader
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
| | - Julen Segura Abarrategi
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
| | - Harbil Bediaga
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº 6, Leioa, 48940, Greater Bilbao, Basque Country, Spain
| | - Deyani Nocedo-Mena
- Faculty of Physical Mathematical Sciences, Autonomous University of Nuevo León, San Nicolás de los Garza, 66455, Nuevo León, México
| | - Estefania Ascencio
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº 6, Leioa, 48940, Greater Bilbao, Basque Country, Spain
| | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
| | - Idoia Castellanos-Rubio
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain.
| | - Maite Insausti
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Leioa, 48940, Spain
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- BIOFISIKA: Basque Center for Biophysics CSIC, University of The Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, 48940, Bizkaia, Basque Country, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48011, Biscay, Spain
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Wang F, Cheng X, Xia X, Zheng C, Su Y. Adaptive Space Search-based Molecular Evolution Optimization Algorithm. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae446. [PMID: 39041594 DOI: 10.1093/bioinformatics/btae446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/30/2024] [Accepted: 07/22/2024] [Indexed: 07/24/2024]
Abstract
MOTIVATION In drug development process, a significant portion of budget and research time are dedicated to the lead compound optimization procedure in order to identify potential drugs. This procedure focuses on enhancing the pharmacological and bioactive properties of compounds by optimizing their local substructures. However, due to the vast and discrete chemical structure space and the unpredictable element combinations within this space, the optimization process is inherently complex. Various structure enumeration-based combinatorial optimization methods have shown certain advantages. However, they still have limitations. Those methods fail to consider the differences between molecules and struggle to explore the unknown outer search space. RESULTS In this study, we propose an adaptive space search-based molecular evolution optimization algorithm (ASSMOEA). It consists of three key modules: construction of molecule-specific search space, molecular evolutionary optimization, and adaptive expansion of molecule-specific search space. Specifically, we design a fragment similarity tree in molecule-specific search space, and apply a dynamic mutation strategy in this space to guide molecular optimization. Then we utilize an encoder-encoder structure to adaptively expand the space. Those three modules are circled iteratively to optimize molecules. Our experiments demonstrate that ASSMOEA outperforms existing methods in terms of molecular optimization. It not only enhances the efficiency of the molecular optimization process, but also exhibits a robust ability to search for correct solutions. AVAILABILITY AND IMPLEMENTATION The code is freely available on the web at https://github.com/bbbbb-b/MEOAFST. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fei Wang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, Hefei, 230601, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
| | - Xianglong Cheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, Hefei, 230601, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
| | - Xin Xia
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, Hefei, 230601, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, Hefei, 230601, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
| | - Yansen Su
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, Hefei, 230601, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
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20
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Chen S, Jung Y. Estimating the synthetic accessibility of molecules with building block and reaction-aware SAScore. J Cheminform 2024; 16:83. [PMID: 39044299 PMCID: PMC11267797 DOI: 10.1186/s13321-024-00879-0] [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: 04/20/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024] Open
Abstract
Synthetic accessibility prediction is a task to estimate how easily a given molecule might be synthesizable in the laboratory, playing a crucial role in computer-aided molecular design. Although synthesis planning programs can determine synthesis routes, their slow processing times make them impractical for large-scale molecule screening. On the other hand, existing rapid synthesis accessibility estimation methods offer speed but typically lack integration with actual synthesis routes and building block information. In this work, we introduce BR-SAScore, an enhanced version of SAScore that integrates the available building block information (B) and reaction knowledge (R) from synthesis planning programs into the scoring process. In particular, we differentiate fragments inherent in building blocks and fragments to be derived from synthesis (reactions) when scoring synthetic accessibility. Compared to existing methods, our experimental findings demonstrate that BR-SAScore offers more accurate and precise identification of a molecule's synthetic accessibility by the synthesis planning program with a fast calculation time. Moreover, we illustrate how BR-SAScore provides chemically interpretable results, aligning with the capability of the synthesis planning program embedded with the same reaction knowledge and available building blocks.Scientific contributionWe introduce BR-SAScore, an extension of SAScore, to estimate the synthetic accessibility of molecules by leveraging known building-block and reactivity information. In our experiments, BR-SAScore shows superior prediction performance on predicting molecule synthetic accessibility compared to previous methods, including SAScore and deep-learning models, while requiring significantly less computation time. In addition, we show that BR-SAScore is able to precisely identify the chemical fragment contributing to the synthetic infeasibility, holding great potential for future molecule synthesizability optimization.
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Affiliation(s)
- Shuan Chen
- Department of Chemical and Biological Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
- Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Yousung Jung
- Department of Chemical and Biological Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
- Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
- Institute of Engineering Research, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
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21
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Özçelik R, de Ruiter S, Criscuolo E, Grisoni F. Chemical language modeling with structured state space sequence models. Nat Commun 2024; 15:6176. [PMID: 39039051 PMCID: PMC11263548 DOI: 10.1038/s41467-024-50469-9] [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: 09/22/2023] [Accepted: 07/05/2024] [Indexed: 07/24/2024] Open
Abstract
Generative deep learning is reshaping drug design. Chemical language models (CLMs) - which generate molecules in the form of molecular strings - bear particular promise for this endeavor. Here, we introduce a recent deep learning architecture, termed Structured State Space Sequence (S4) model, into de novo drug design. In addition to its unprecedented performance in various fields, S4 has shown remarkable capabilities to learn the global properties of sequences. This aspect is intriguing in chemical language modeling, where complex molecular properties like bioactivity can 'emerge' from separated portions in the molecular string. This observation gives rise to the following question: Can S4 advance chemical language modeling for de novo design? To provide an answer, we systematically benchmark S4 with state-of-the-art CLMs on an array of drug discovery tasks, such as the identification of bioactive compounds, and the design of drug-like molecules and natural products. S4 shows a superior capacity to learn complex molecular properties, while at the same time exploring diverse scaffolds. Finally, when applied prospectively to kinase inhibition, S4 designs eight of out ten molecules that are predicted as highly active by molecular dynamics simulations. Taken together, these findings advocate for the introduction of S4 into chemical language modeling - uncovering its untapped potential in the molecular sciences.
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Affiliation(s)
- Rıza Özçelik
- Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, The Netherlands
| | - Sarah de Ruiter
- Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Emanuele Criscuolo
- Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Francesca Grisoni
- Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, The Netherlands.
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22
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Gurnani R, Shukla S, Kamal D, Wu C, Hao J, Kuenneth C, Aklujkar P, Khomane A, Daniels R, Deshmukh AA, Cao Y, Sotzing G, Ramprasad R. AI-assisted discovery of high-temperature dielectrics for energy storage. Nat Commun 2024; 15:6107. [PMID: 39030220 PMCID: PMC11271506 DOI: 10.1038/s41467-024-50413-x] [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: 02/07/2024] [Accepted: 07/01/2024] [Indexed: 07/21/2024] Open
Abstract
Electrostatic capacitors play a crucial role as energy storage devices in modern electrical systems. Energy density, the figure of merit for electrostatic capacitors, is primarily determined by the choice of dielectric material. Most industry-grade polymer dielectrics are flexible polyolefins or rigid aromatics, possessing high energy density or high thermal stability, but not both. Here, we employ artificial intelligence (AI), established polymer chemistry, and molecular engineering to discover a suite of dielectrics in the polynorbornene and polyimide families. Many of the discovered dielectrics exhibit high thermal stability and high energy density over a broad temperature range. One such dielectric displays an energy density of 8.3 J cc-1 at 200 °C, a value 11 × that of any commercially available polymer dielectric at this temperature. We also evaluate pathways to further enhance the polynorbornene and polyimide families, enabling these capacitors to perform well in demanding applications (e.g., aerospace) while being environmentally sustainable. These findings expand the potential applications of electrostatic capacitors within the 85-200 °C temperature range, at which there is presently no good commercial solution. More broadly, this research demonstrates the impact of AI on chemical structure generation and property prediction, highlighting the potential for materials design advancement beyond electrostatic capacitors.
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Affiliation(s)
- Rishi Gurnani
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Matmerize Inc., Atlanta, GA, USA
| | - Stuti Shukla
- Materials Science Program, Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | - Deepak Kamal
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Chao Wu
- Electrical Insulation Research Center, Institute of Materials Science, University of Connecticut, Storrs, CT, USA
- Department of Electrical Engineering, Tsinghua University, Beijing, China
| | - Jing Hao
- Electrical Insulation Research Center, Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | - Christopher Kuenneth
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Faculty of Engineering Science, University of Bayreuth, Bayreuth, Germany
| | - Pritish Aklujkar
- Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | - Ashish Khomane
- Materials Science Program, Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | - Robert Daniels
- Materials Science Program, Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | | | - Yang Cao
- Electrical Insulation Research Center, Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | - Gregory Sotzing
- Materials Science Program, Institute of Materials Science, University of Connecticut, Storrs, CT, USA
- Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | - Rampi Ramprasad
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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23
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Wang N, Wu X, Liang J, Liu B, Wang B. Molecular design of hydroxamic acid-based derivatives as urease inhibitors of Helicobacter pylori. Mol Divers 2024:10.1007/s11030-024-10914-9. [PMID: 39020133 DOI: 10.1007/s11030-024-10914-9] [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: 04/27/2024] [Accepted: 06/08/2024] [Indexed: 07/19/2024]
Abstract
Helicobacter pylori is the main causative agent of gastric cancer, especially non-cardiac gastric cancers. This bacterium relies on urease producing much ammonia to colonize the host. Herein, the study provides valuable insights into structural patterns driving urease inhibition for high-activity molecules designed via exploring known inhibitors. Firstly, an ensemble model was devised to predict the inhibitory activity of novel compounds in an automated workflow (R2 = 0.761) that combines four machine learning approaches. The dataset was characterized in terms of chemical space, including molecular scaffolds, clustering analysis, distribution for physicochemical properties, and activity cliffs. Through these analyses, the hydroxamic acid group and the benzene ring responsible for distinct activity were highlighted. Activity cliff pairs uncovered substituents of the benzene ring on hydroxamic acid derivatives are key structures for substantial activity enhancement. Moreover, 11 hydroxamic acid derivatives were designed, named mol1-11. Results of molecular dynamic simulations showed that the mol9 exhibited stabilization of the active site flap's closed conformation and are expected to be promising drug candidates for Helicobacter pylori infection and further in vitro, in vivo, and clinical trials to demonstrate in future.
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Affiliation(s)
- Na Wang
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China
| | - Xiaoyan Wu
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China
| | - Jianhuai Liang
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China
| | - Boping Liu
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China.
| | - Bingfeng Wang
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China.
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24
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Huang W, Huang S, Fang Y, Zhu T, Chu F, Liu Q, Yu K, Chen F, Dong J, Zeng W. AI-Powered Mining of Highly Customized and Superior ESIPT-Based Fluorescent Probes. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2405596. [PMID: 39021325 DOI: 10.1002/advs.202405596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/18/2024] [Indexed: 07/20/2024]
Abstract
Excited-state intramolecular proton transfer (ESIPT) has attracted great attention in fluorescent sensors and luminescent materials due to its unique photobiological and photochemical features. However, the current structures are far from meeting the specific demands for ESIPT molecules in different scenarios; the try-and-error development method is labor-intensive and costly. Therefore, it is imperative to devise novel approaches for the exploration of promising ESIPT fluorophores. This research proposes an artificial intelligence approach aiming at exploring ESIPT molecules efficiently. The first high-quality ESIPT dataset and a multi-level prediction system are constructed that realized accurate identification of ESIPT molecules from a large number of compounds under a stepwise distinguishing from conventional molecules to fluorescent molecules and then to ESIPT molecules. Furthermore, key structural features that contributed to ESIPT are revealed by using the SHapley Additive exPlanations (SHAP) method. Then three strategies are proposed to ensure the ESIPT process while keeping good safety, pharmacokinetic properties, and novel structures. With these strategies, >700 previously unreported ESIPT molecules are screened from a large pool of 570 000 compounds. The ESIPT process and biosafety of optimal molecules are successfully validated by quantitative calculation and experiment. This novel approach is expected to bring a new paradigm for exploring ideal ESIPT molecules.
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Affiliation(s)
- Wenzhi Huang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China
| | - Shuai Huang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China
| | - Yanpeng Fang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China
| | - Tianyu Zhu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China
| | - Feiyi Chu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China
| | - Qianhui Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China
| | - Kunqian Yu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, P. R. China
| | - Fei Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China
| | - Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China
| | - Wenbin Zeng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China
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25
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Xin J, Wang T, Hou B, Lu X, Han N, He Y, Zhang D, Wang X, Wei C, Jia Z. Tongxinluo capsule as a multi-functional traditional Chinese medicine in treating cardiovascular disease: A review of components, pharmacological mechanisms, and clinical applications. Heliyon 2024; 10:e33309. [PMID: 39040283 PMCID: PMC11261786 DOI: 10.1016/j.heliyon.2024.e33309] [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: 11/07/2023] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 07/24/2024] Open
Abstract
Cardiovascular diseases (CVDs) are one of the most significant diseases that pose a threat to human health. The innovative traditional Chinese medicine Tongxinluo Capsule, developed under the guidance of the theory of traditional Chinese medicine, has good clinical efficacy in various cardiovascular diseases, this medicine has effects such as blood protection, vascular protection, myocardial protection, stabilizing vulnerable plaques, and vasodilation. However, CVDs are a multifactorial disease, and their underlying mechanisms are not fully understood. Therefore, exploring the mechanism of action and clinical application of Tongxinluo Capsule in the treatment of various cardiovascular diseases is beneficial for exerting its therapeutic effect from multiple components, targets, and pathways. At the same time, it provides broader treatment ideas for other difficult to treat diseases in the cardiovascular event chain, and has significant theoretical and clinical significance for improving the treatment of cardiovascular diseases with traditional Chinese medicine.
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Affiliation(s)
- Jingjing Xin
- Graduate School, Hebei Medical University, Shijiazhuang, 050017, China
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Tongxing Wang
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Bin Hou
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Xuan Lu
- Graduate School, Hebei Medical University, Shijiazhuang, 050017, China
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Ningxin Han
- Graduate School, Hebei Medical University, Shijiazhuang, 050017, China
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Yanling He
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050090, Hebei, China
| | - Dan Zhang
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050090, Hebei, China
| | - Xiaoqi Wang
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Cong Wei
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Zhenhua Jia
- Graduate School, Hebei Medical University, Shijiazhuang, 050017, China
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Affiliated Yiling Hospital of Hebei Medical University, High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Shijiazhuang, 050091, Hebei, China
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26
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Martin HJ, Hossain MA, Wellnitz J, Kelestemur E, Hochuli JE, Parveen S, Arrowsmith C, Willson TM, Muratov E, Tropsha A. Heli-SMACC: Helicase-targeting SMAll Molecule Compound Collection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.04.602122. [PMID: 39026851 PMCID: PMC11257486 DOI: 10.1101/2024.07.04.602122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Helicases have emerged as promising targets for the development of antiviral drugs; however, the family remains largely undrugged. To support the focused development of viral helicase inhibitors we identified, collected, and integrated all chemogenomics data for all available helicases from the ChEMBL database. After thoroughly curating and enriching the data with relevant annotations we have created a derivative database of helicase inhibitors which we dubbed Heli-SMACC (Helicase-targeting SMAll Molecule Compound Collection). The current version of Heli-SMACC contains 20,432 bioactivity entries for viral, human, and bacterial helicases. We have selected 30 compounds with promising viral helicase activity and tested them in a SARS-CoV-2 NSP13 ATPase assay. Twelve compounds demonstrated ATPase inhibition and a consistent dose-response curve. The Heli-SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel helicase inhibitors. Heli-SMACC is publicly available at https://smacc.mml.unc.edu.
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Affiliation(s)
- Holli-Joi Martin
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Mohammad A. Hossain
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - James Wellnitz
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Enes Kelestemur
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Joshua E. Hochuli
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Sumera Parveen
- The Structural Genomics Consortium, University of Toronto, Canada
| | | | - Timothy M. Willson
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Eugene Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
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27
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Li Z, Zhang Q, Gao Y, Wan F, Wang Y, Hou B, Cui W, Wang Y, Feng W, Hou Y. Luobitong Potentiates MTX's Anti-Rheumatoid Arthritis Activity via Targeting Multiple Inflammatory Pathways. J Inflamm Res 2024; 17:4389-4403. [PMID: 38994468 PMCID: PMC11236762 DOI: 10.2147/jir.s461093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 06/18/2024] [Indexed: 07/13/2024] Open
Abstract
Background The LuoBiTong (LBT) capsule, a novel traditional Chinese medicine formulation, is currently in Phase III clinical trials. Preliminary preclinical and Phase II clinical studies suggest its efficacy and safety in treating rheumatoid arthritis (RA). However, the underlying mechanisms of its action remain to be elucidated.This research aims to explore the effects and mechanisms of LBT in conjunction with a maintenance dose of methotrexate (M-MTX) on RA. Methods A Collagen-Induced Arthritis (CIA) mouse model was used to evaluate the anti-RA effects of LBT combined with M-MTX. Assessments included foot swelling, arthritis scoring, serum inflammatory factor analysis, and histopathological examination of the foot. These effects were compared with those of high-dose MTX (H-MTX). Network pharmacology was employed to construct a compound-target network for RA, based on drug composition, to predict its potential mechanism of action. Flow cytometry, Western Blot, and immunohistochemical analyses in animal models identified multiple inflammatory pathways targeted by LBT to augment the anti-RA effects of MTX. Results The study revealed that LBT combined with M-MTX significantly alleviated CIA-induced arthritis without adverse effects. The combination of LBT and M-MTX showed similar or superior efficacy in regulating macrophage polarization, NF-κB, MAPK signaling pathways, and in the suppression of TH-17 expression in proinflammatory cells. These findings suggest that LBT may exert a multi-pathway therapeutic effect in RA treatment. The predicted pharmacological targets and mechanisms align well with this hypothesis. Conclusion LBT, when combined with MTX, enhances the anti-RA effect by targeting multiple inflammatory pathways, demonstrating significant therapeutic potential.
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Affiliation(s)
- Ziyu Li
- Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, People’s Republic of China
| | - Qiuyan Zhang
- New Drug Evaluation Center, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
| | - Yuhe Gao
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, People’s Republic of China
| | - Fang Wan
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, People’s Republic of China
| | - Yincang Wang
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, People’s Republic of China
| | - Bin Hou
- New Drug Evaluation Center, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
| | - Wenwen Cui
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral VesselCollateral Disease), Shijiazhuang, People’s Republic of China
| | - Yanan Wang
- New Drug Evaluation Center, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
| | - Wei Feng
- New Drug Evaluation Center, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
| | - Yunlong Hou
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral VesselCollateral Disease), Shijiazhuang, People’s Republic of China
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28
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Kırboğa KK, Işık M. Explainable artificial intelligence in the design of selective carbonic anhydrase I-II inhibitors via molecular fingerprinting. J Comput Chem 2024; 45:1530-1539. [PMID: 38491535 DOI: 10.1002/jcc.27335] [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: 12/20/2023] [Revised: 02/04/2024] [Accepted: 02/10/2024] [Indexed: 03/18/2024]
Abstract
Inhibiting the enzymes carbonic anhydrase I (CA I) and carbonic anhydrase II (CA II) presents a potential avenue for addressing nervous system ailments such as glaucoma and Alzheimer's disease. Our study explored harnessing explainable artificial intelligence (XAI) to unveil the molecular traits inherent in CA I and CA II inhibitors. The PubChem molecular fingerprints of these inhibitors, sourced from the ChEMBL database, were subjected to detailed XAI analysis. The study encompassed training 10 regression models using IC50 values, and their efficacy was gauged using metrics including R2, RMSE, and time taken. The Decision Tree Regressor algorithm emerged as the optimal performer (R2: 0.93, RMSE: 0.43, time-taken: 0.07). Furthermore, the PFI method unveiled key molecular features for CA I inhibitors, notably PubChemFP432 (C(O)N) and PubChemFP6978 (C(O)O). The SHAP analysis highlighted the significance of attributes like PubChemFP539 (C(O)NCC), PubChemFP601 (C(O)OCC), and PubChemFP432 (C(O)N) in CA I inhibitiotable n. Likewise, features for CA II inhibitors encompassed PubChemFP528(C(O)OCCN), PubChemFP791 (C(O)OCCC), PubChemFP696 (C(O)OCCCC), PubChemFP335 (C(O)NCCN), PubChemFP580 (C(O)NCCCN), and PubChemFP180 (C(O)NCCC), identified through SHAP analysis. The sulfonamide group (S), aromatic ring (A), and hydrogen bonding group (H) exert a substantial impact on CA I and CA II enzyme activities and IC50 values through the XAI approach. These insights into the CA I and CA II inhibitors are poised to guide future drug discovery efforts, serving as a beacon for innovative therapeutic interventions.
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Affiliation(s)
- Kevser Kübra Kırboğa
- Faculty of Engineering, Department of Bioengineering, Bilecik Seyh Edebali University, Bilecik, Turkey
- Bioengineering Department, Süleyman Demirel University, Isparta, Turkey
| | - Mesut Işık
- Faculty of Engineering, Department of Bioengineering, Bilecik Seyh Edebali University, Bilecik, Turkey
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29
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Peng J, Yi J, Yang G, Huang Z, Cao D. ISTransbase: an online database for inhibitor and substrate of drug transporters. Database (Oxford) 2024; 2024:baae053. [PMID: 38943608 PMCID: PMC11214160 DOI: 10.1093/database/baae053] [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: 01/02/2024] [Revised: 03/17/2024] [Accepted: 06/17/2024] [Indexed: 07/01/2024]
Abstract
Drug transporters, integral membrane proteins found throughout the human body, play critical roles in physiological and biochemical processes through interactions with ligands, such as substrates and inhibitors. The extensive and disparate data on drug transporters complicate understanding their complex relationships with ligands. To address this challenge, it is essential to gather and summarize information on drug transporters, inhibitors and substrates, and simultaneously develop a comprehensive and user-friendly database. Current online resources often provide fragmented information and have limited coverage of drug transporter substrates and inhibitors, highlighting the need for a specialized, comprehensive and openly accessible database. ISTransbase addresses this gap by amassing a substantial amount of data from literature, government documents and open databases. It includes 16 528 inhibitors and 4465 substrates of 163 drug transporters from 18 different species, resulting in a total of 93 841 inhibitor records and 51 053 substrate records. ISTransbase provides detailed insights into drug transporters and their inhibitors/substrates, encompassing transporter and molecule structure, transporter function and distribution, as well as experimental methods and results from transport or inhibition experiments. Furthermore, ISTransbase offers three search strategies that allow users to retrieve drugs and transporters based on multiple selectable constraints, as well as perform checks for drug-drug interactions. Users can also browse and download data. In summary, ISTransbase (https://istransbase.scbdd.com/) serves as a valuable resource for accurately and efficiently accessing information on drug transporter inhibitors and substrates, aiding researchers in exploring drug transporter mechanisms and assisting clinicians in mitigating adverse drug reactions Database URL: https://istransbase.scbdd.com/.
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Affiliation(s)
- Jinfu Peng
- Xiangya School of Pharmaceutical Sciences, Central South University, No.172 Tongzipo Road, Changsha, Hunan 410031, China
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, Hunan 410031, China
| | - Jiacai Yi
- School of Computer Science, National University of Defense Technology, No.869 Furong Middle Road, Changsha, Hunan 410073, China
| | - Guoping Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, No.172 Tongzipo Road, Changsha, Hunan 410031, China
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, Hunan 410031, China
| | - Zhijun Huang
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, Hunan 410031, China
- XiangYa School of Medicine, Central South University, No.172 Tongzipo Road, Changsha, Hunan 410031, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, No.172 Tongzipo Road, Changsha, Hunan 410031, China
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30
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Ju M, Deng T, Jia X, Gong M, Li Y, Liu F, Yin Y. The causal relationship between anti-diabetic drugs and gastrointestinal disorders: a drug-targeted mendelian randomization study. Diabetol Metab Syndr 2024; 16:141. [PMID: 38918852 PMCID: PMC11201305 DOI: 10.1186/s13098-024-01359-z] [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: 04/08/2024] [Accepted: 05/24/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND The incidence of diabetic gastrointestinal diseases is increasing year by year. This study aimed to investigate the causal relationship between antidiabetic medications and gastrointestinal disorders, with the goal of reducing the incidence of diabetes-related gastrointestinal diseases and exploring the potential repurposing of antidiabetic drugs. METHODS We employed a two-sample Mendelian randomization (TSMR) design to investigate the causal association between antidiabetic medications and gastrointestinal disorders, including gastroesophageal reflux disease (GERD), gastric ulcer (GU), chronic gastritis, acute gastritis, Helicobacter pylori infection, gastric cancer (GC), functional dyspepsia (FD), irritable bowel syndrome (IBS), ulcerative colitis (UC), Crohn's disease (CD), diverticulosis, and colorectal cancer (CRC). To identify potential inhibitors of antidiabetic drug targets, we collected single-nucleotide polymorphisms (SNPs) associated with metformin, GLP-1 receptor agonists, SGLT2 inhibitors, DPP-4 inhibitors, insulin, and its analogs, thiazolidinediones, sulfonylureas, and alpha-glucosidase inhibitors from published genome-wide association study statistics. We then conducted a drug-target Mendelian randomization (MR) analysis using inverse variance weighting (IVW) as the primary analytical method to assess the impact of these inhibitors on gastrointestinal disorders. Additionally, diabetes was selected as a positive control. RESULTS Sulfonylureas were found to significantly reduce the risk of CD (IVW: OR [95% CI] = 0.986 [0.978, 0.995], p = 1.99 × 10- 3), GERD (IVW: OR [95% CI] = 0.649 [0.452, 0.932], p = 1.90 × 10- 2), and chronic gastritis (IVW: OR [95% CI] = 0.991 [0.982, 0.999], p = 4.50 × 10- 2). However, they were associated with an increased risk of GU development (IVW: OR [95%CI] = 2 0.761 [1.259, 6.057], p = 1 0.12 × 10- 2). CONCLUSIONS The results indicated that sulfonylureas had a positive effect on the prevention of CD, GERD, and chronic gastritis but a negative effect on the development of gastric ulcers. However, our research found no causal evidence for the impact of metformin, GLP-1 agonists, SGLT2 inhibitors, DPP 4 inhibitors, insulin and its analogs, thiazolidinediones, or alpha-glucosidase inhibitors on gastrointestinal diseases.
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Affiliation(s)
- Mingyan Ju
- College of Acupuncture and moxibustion, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Tingting Deng
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xuemin Jia
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Menglin Gong
- College of Acupuncture and moxibustion, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yuying Li
- College of Acupuncture and moxibustion, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Fanjie Liu
- Bone Biomechanics Engineering Laboratory of Shandong Province, Shandong Medicinal Biotechnology Center (School of Biomedical Sciences), Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
| | - Ying Yin
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
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31
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Wang QX, Cai J, Chen ZJ, Liu JC, Wang JJ, Zhou H, Li QQ, Wang ZX, Wang YB, Tong ZJ, Yang J, Wei TH, Zhang MY, Zhou Y, Dai WC, Ding N, Leng XJ, Yin XY, Sun SL, Yu YC, Li NG, Shi ZH. Exploring drug repositioning possibilities of kinase inhibitors via molecular simulation. Mol Inform 2024:e202300336. [PMID: 39031899 DOI: 10.1002/minf.202300336] [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: 12/06/2023] [Revised: 04/09/2024] [Accepted: 04/28/2024] [Indexed: 07/22/2024]
Abstract
Kinases, a class of enzymes controlling various substrates phosphorylation, are pivotal in both physiological and pathological processes. Although their conserved ATP binding pockets pose challenges for achieving selectivity, this feature offers opportunities for drug repositioning of kinase inhibitors (KIs). This study presents a cost-effective in silico prediction of KIs drug repositioning via analyzing cross-docking results. We established the KIs database (278 unique KIs, 1834 bioactivity data points) and kinases database (357 kinase structures categorized by the DFG motif) for carrying out cross-docking. Comparative analysis of the docking scores and reported experimental bioactivity revealed that the Atypical, TK, and TKL superfamilies are suitable for drug repositioning. Among these kinase superfamilies, Olverematinib, Lapatinib, and Abemaciclib displayed enzymatic activity in our focused AKT-PI3K-mTOR pathway with IC50 values of 3.3, 3.2 and 5.8 μM. Further cell assays showed IC50 values of 0.2, 1.2 and 0.6 μM in tumor cells. The consistent result between prediction and validation demonstrated that repositioning KIs via in silico method is feasible.
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Affiliation(s)
- Qing-Xin Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jiao Cai
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zi-Jun Chen
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jia-Chuan Liu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jing-Jing Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Hai Zhou
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Qing-Qing Li
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zi-Xuan Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Yi-Bo Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zhen-Jiang Tong
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jin Yang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Tian-Hua Wei
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Meng-Yuan Zhang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Yun Zhou
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Wei-Chen Dai
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, 211198, Nanjing, Jiangsu, China
| | - Ning Ding
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Xue-Jiao Leng
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Xiao-Ying Yin
- School of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, 201620, Shanghai, China
| | - Shan-Liang Sun
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Yan-Cheng Yu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Nian-Guang Li
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zhi-Hao Shi
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, 211198, Nanjing, Jiangsu, China
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32
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Adessi TG, Wagner PM, Bisogno FR, Nicotra VE, Guido ME, García ME. Enhancing structural diversity through chemical engineering of Ambrosia tenuifolia extract for novel anti-glioblastoma compounds. Sci Rep 2024; 14:14229. [PMID: 38902325 PMCID: PMC11190268 DOI: 10.1038/s41598-024-63639-y] [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: 12/29/2023] [Accepted: 05/30/2024] [Indexed: 06/22/2024] Open
Abstract
Natural products are an unsurpassed source of leading structures in drug discovery. The biosynthetic machinery of the producing organism offers an important source for modifying complex natural products, leading to analogs that are unattainable by chemical semisynthesis or total synthesis. In this report, through the combination of natural products chemistry and diversity-oriented synthesis, a diversity-enhanced extracts approach is proposed using chemical reactions that remodel molecular scaffolds directly on extracts of natural resources. This method was applied to subextract enriched in sesquiterpene lactones from Ambrosia tenuifolia (Fam. Asteraceae) using acid media conditions (p-toluenesulfonic acid) to change molecular skeletons. The chemically modified extract was then fractionated by a bioguided approach to obtain the pure compounds responsible for the anti-glioblastoma (GBM) activity in T98G cell cultures. Indeed, with the best candidate, chronobiological experiments were performed to evaluate temporal susceptibility to the treatment on GBM cell cultures to define the best time to apply the therapy. Finally, bioinformatics tools were used to supply qualitative and quantitative information on the physicochemical properties, chemical space, and structural similarity of the compound library obtained. As a result, natural products derivatives containing new molecular skeletons were obtained, with possible applications as chemotherapeutic agents against human GBM T98G cell cultures.
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Affiliation(s)
- Tonino G Adessi
- Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Edificio de Ciencias Químicas 2, Haya de la Torre y Medina Allende, Ciudad Universitaria, CP X5000HUA, Córdoba, Argentina
- Instituto Multidisciplinario de Biología Vegetal (IMBIV), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina
| | - Paula M Wagner
- Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Edificio de Ciencias Químicas 2, Haya de la Torre y Medina Allende, Ciudad Universitaria, CP X5000HUA, Córdoba, Argentina
- Departamento de Química Biológica Ranwel Caputto, Centro de Investigaciones en Química Biológica de Córdoba (CIQUIBIC-CONICET), Córdoba, Argentina
| | - Fabricio R Bisogno
- Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Edificio de Ciencias Químicas 2, Haya de la Torre y Medina Allende, Ciudad Universitaria, CP X5000HUA, Córdoba, Argentina
- Instituto de Investigaciones en Físico-Química de Córdoba (INFIQC-CONICET), Córdoba, Argentina
| | - Viviana E Nicotra
- Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Edificio de Ciencias Químicas 2, Haya de la Torre y Medina Allende, Ciudad Universitaria, CP X5000HUA, Córdoba, Argentina
- Instituto Multidisciplinario de Biología Vegetal (IMBIV), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina
| | - Mario E Guido
- Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Edificio de Ciencias Químicas 2, Haya de la Torre y Medina Allende, Ciudad Universitaria, CP X5000HUA, Córdoba, Argentina
- Departamento de Química Biológica Ranwel Caputto, Centro de Investigaciones en Química Biológica de Córdoba (CIQUIBIC-CONICET), Córdoba, Argentina
| | - Manuela E García
- Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Edificio de Ciencias Químicas 2, Haya de la Torre y Medina Allende, Ciudad Universitaria, CP X5000HUA, Córdoba, Argentina.
- Instituto Multidisciplinario de Biología Vegetal (IMBIV), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina.
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Amorim AM, Piochi LF, Gaspar AT, Preto A, Rosário-Ferreira N, Moreira IS. Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction. Chem Res Toxicol 2024; 37:827-849. [PMID: 38758610 PMCID: PMC11187637 DOI: 10.1021/acs.chemrestox.3c00352] [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: 11/06/2023] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.
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Affiliation(s)
- Ana M.
B. Amorim
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD
Programme in Biosciences, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PURR.AI,
Rua Pedro Nunes, IPN Incubadora, Ed C, 3030-199 Coimbra, Portugal
| | - Luiz F. Piochi
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Ana T. Gaspar
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - António
J. Preto
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD Programme
in Experimental Biology and Biomedicine, Institute for Interdisciplinary
Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789 Coimbra, Portugal
| | - Nícia Rosário-Ferreira
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Irina S. Moreira
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
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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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35
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Shu J, Wang Y, Guo W, Liu T, Cai S, Shi T, Hu W. Carbenoid-involved reactions integrated with scaffold-based screening generates a Nav1.7 inhibitor. Commun Chem 2024; 7:135. [PMID: 38866907 PMCID: PMC11169417 DOI: 10.1038/s42004-024-01213-3] [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: 11/21/2023] [Accepted: 05/30/2024] [Indexed: 06/14/2024] Open
Abstract
The discovery of selective Nav1.7 inhibitors is a promising approach for developing anti-nociceptive drugs. In this study, we present a novel oxindole-based readily accessible library (OREAL), which is characterized by readily accessibility, unique chemical space, ideal drug-like properties, and structural diversity. We used a scaffold-based approach to screen the OREAL and discovered compound C4 as a potent Nav1.7 inhibitor. The bioactivity characterization of C4 reveals that it is a selective Nav1.7 inhibitor and effectively reverses Paclitaxel-induced neuropathic pain (PINP) in rodent models. Preliminary toxicology study shows C4 is negative to hERG. The consistent results of molecular docking and molecular simulations further support the reasonability of the in-silico screening and show the insight of the binding mode of C4. Our discovery of C4 paves the way for pushing the Nav1.7-based anti-nociceptive drugs forward to the clinic.
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Affiliation(s)
- Jirong Shu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yuwei Wang
- Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Weijie Guo
- Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Tao Liu
- Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Song Cai
- Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Taoda Shi
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
| | - Wenhao Hu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China
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36
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Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov Today 2024; 29:103992. [PMID: 38663579 DOI: 10.1016/j.drudis.2024.103992] [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: 02/09/2024] [Revised: 03/22/2024] [Accepted: 04/18/2024] [Indexed: 05/04/2024]
Abstract
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule 424001, Maharashtra, India
| | - Antonio Lavecchia
- "Drug Discovery" Laboratory, Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy.
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Caña-Bozada VH, Ovando-Vázquez C, Flores-Méndez LC, Martínez-Brown JM, Morales-Serna FN. Identifying potential drug targets in the kinomes of two monogenean species. Helminthologia 2024; 61:142-150. [PMID: 39040804 PMCID: PMC11260314 DOI: 10.2478/helm-2024-0020] [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: 09/12/2023] [Accepted: 05/24/2024] [Indexed: 07/24/2024] Open
Abstract
Protein kinases are enzymes involved in essential biological processes such as signal transduction, transcription, metabolism, and the cell cycle. Human kinases are targets for several drugs approved by the US Food and Drug Administration. Therefore, the identification and classification of kinases in other organisms, including pathogenic parasites, is an interesting subject of study. Monogeneans are platyhelminths, mainly ectoparasites, capable of causing health problems in farmed fish. Although some genomes and transcriptomes are available for monogenean species, their full repertoire of kinases is unknown. The aim of this study was to identify and classify the putative kinases in the transcriptomes of two monogeneans, Rhabdosynochus viridisi and Scutogyrus longicornis, and then to predict potential monogenean drug targets (MDTs) and selective inhibitor drugs using computational approaches. Monogenean kinases having orthologs in the lethal phenotype of C. elegans but not in fish or humans were considered MDTs. A total of 160 and 193 kinases were identified in R. viridisi and S. longicornis, respectively. Of these, 22 kinases, belonging mainly to the major groups CAMK, AGC, and TK, were classified as MDTs, five of which were evaluated further. Molecular docking analysis indicated that dihydroergotamine, ergotamine, and lomitapide have the highest affinity for the kinases BRSK and MEKK1. These well-known drugs could be evaluated in future studies for potential repurposing as anti-monogenean agents. The present study contributes valuable data for the development of new antiparasitic candidates for finfish aquaculture.
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Affiliation(s)
- V. H. Caña-Bozada
- Centro de Investigación en Alimentación y Desarrollo, A.C., Mazatlán, Sinaloa82112, Mexico
| | - C. Ovando-Vázquez
- Centro Nacional de Supercómputo, Instituto Potosino de investigación Científica y Tecnológica, San Luis Potosí78216, Mexico
- Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT), Ciudad de México, Mexico
| | - L. C. Flores-Méndez
- Centro de Investigación en Alimentación y Desarrollo, A.C., Mazatlán, Sinaloa82112, Mexico
- Present address:Universidad Autónoma de Occidente, Unidad Regional Mazatlán, Mazatlán, 82100, Sinaloa, Mexico
| | - J. M. Martínez-Brown
- Centro de Investigación en Alimentación y Desarrollo, A.C., Mazatlán, Sinaloa82112, Mexico
| | - F. N. Morales-Serna
- Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Mazatlán82040, Sinaloa, Mexico
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Thribhuvan Reddy D, Grewal I, García Pinzon LF, Latchireddy B, Goraya S, Ali Alansari B, Gadwal A. The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management. Cureus 2024; 16:e61523. [PMID: 38957241 PMCID: PMC11218716 DOI: 10.7759/cureus.61523] [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] [Accepted: 06/02/2024] [Indexed: 07/04/2024] Open
Abstract
This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.
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Affiliation(s)
| | - Inayat Grewal
- Department of Medicine, Government Medical College and Hospital, Chandigarh, IND
| | | | | | - Simran Goraya
- Department of Medicine, Kharkiv National Medical University, Kharkiv, UKR
| | | | - Aishwarya Gadwal
- Department of Radiodiagnosis, St. John's Medical College and Hospital, Bengaluru, IND
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Hemasree GNS, Satish KS, Rajalekshmi SG, Burri RR, Murthy TPK. Exploration of interaction interface of TRKβ/BDNF through fingerprint analysis to disinter potential agonists. Mol Divers 2024; 28:1531-1549. [PMID: 37389778 DOI: 10.1007/s11030-023-10673-z] [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: 02/21/2023] [Accepted: 06/12/2023] [Indexed: 07/01/2023]
Abstract
Tyrosine Kinase beta (TRKβ), is a type I membrane receptor which plays a major role in various signalling pathways. TRKβ was found to be upregulated in various cancers and contrastingly downregulated in various neurodegenerative disorders. Hitherto, contemporary drug research is oriented towards discovery of TRKβ inhibitors, thus neglecting the development of TRKβ agonists. This research is aimed at identifying FDA approved drugs exhibiting repurposable potential as TRKβ agonists by mapping them with fingerprints of the BDNF/TRKβ interaction interface. Initially, crucial interacting residues were retrieved and a receptor grid was generated around it. TRKβ agonists were retrieved from literature search and a drug library was created for each agonist based on its structural and side effect similarities. Subsequently, molecular docking and dynamics were performed for each library to identify the drugs possessing affinity towards the binding pocket of TRKβ. The study revealed molecular interactions of Perospirone, Droperidol, Urapidil, and Clobenzorex with the crucial amino acids lining the active binding pocket of TRKβ. Subsequent network pharmacological analysis of the above drugs revealed their interactions with key proteins involved in neurotransmitter signalling pathways. Clobenzorex displayed high stability in dynamics simulation and therefore this drug is recommended for further experimental evaluations to attain better mechanistic insights and predict its implications in correcting neuropathological aberrations. This study's focus on the interaction interface between TRKβ and BDNF, combined with the utilization of fingerprint analysis for drug repurposing, contributes to our understanding of neurotrophic signalling and holds potential for identifying new therapeutic options for neurological disorders.
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Affiliation(s)
- G N S Hemasree
- Faculty of Pharmacy, M.S.Ramaiah University of Applied Sciences, Bangalore, Karnataka, 560054, India
| | - Kshreeraja S Satish
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S.Ramaiah University of Applied Sciences, Bangalore, Karnataka, 560054, India
| | - Saraswathy Ganesan Rajalekshmi
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S.Ramaiah University of Applied Sciences, Bangalore, Karnataka, 560054, India.
- Pharmacological Modelling and Simulation Centre, Faculty of Pharmacy, M.S.Ramaiah University of Applied Sciences, Bangalore, Karnataka, 560054, India.
| | | | - T P Krishna Murthy
- Department of Biotechnology, M S Ramaiah Institute of Technology, Bangalore, Karnataka, 560054, India
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40
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Huangfu X, Zhang C, Li H, Li S, Li Y. SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations. Comput Biol Chem 2024; 110:108054. [PMID: 38522389 DOI: 10.1016/j.compbiolchem.2024.108054] [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: 10/25/2023] [Revised: 02/22/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Network-based Synergy prediction (SNSynergy), for predicting synergistic effects towards new cell lines and new drug combinations by two locally weighted models CLSN and DCSN. This framework only requires a small amount of auxiliary data, like genomics information of cell lines and the molecular fingerprints or targets of drugs. Based on the assumption that similar cell lines and similar drug combinations have similar synergistic effects, CLSN and DCSN predict synergy scores through capturing individual synergy contributions of nearest cell line and drug combination neighbors, respectively. High correlations between predicted and measured synergy scores on two leading cancer cell line pharmacogenomic screening datasets (the O'Neil dataset and the NCI-ALMANAC dataset) demonstrate the effectiveness and robustness of SNSynergy. Many of the identified drug combinations are consistent with previous studies, or have been explored in clinical settings against the specific cancer type, showing that SNSynergy has the potential to supply cost-saving and effective high-throughput screening for prioritizing the most applicable cell lines and the most promising drug combinations.
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Affiliation(s)
- Xiaosheng Huangfu
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Chengwei Zhang
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Hualong Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China; School of mathematics and statistics, Guangdong University of Technology, Guangzhou 510520, China
| | - Sile Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Yushuang Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China.
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Chen R, Wang Y, Shen Z, Ye C, Guo Y, Lu Y, Ding J, Dong X, Xu D, Zheng X. Discovery of potent CSK inhibitors through integrated virtual screening and molecular dynamic simulation. Arch Pharm (Weinheim) 2024:e2400066. [PMID: 38809025 DOI: 10.1002/ardp.202400066] [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: 01/24/2024] [Revised: 04/23/2024] [Accepted: 05/08/2024] [Indexed: 05/30/2024]
Abstract
Oncogenic overexpression or activation of C-terminal Src kinase (CSK) has been shown to play an important role in triple-negative breast cancer (TNBC) progression, including tumor initiation, growth, metastasis, drug resistance. This revelation has pivoted the focus toward CSK as a potential target for novel treatments. However, until now, there are few inhibitors designed to target the CSK protein. Responding to this, our research has implemented a comprehensive virtual screening protocol. By integrating energy-based screening methods with AI-driven scoring functions, such as Attentive FP, and employing rigorous rescoring methods like Glide docking and molecular mechanics generalized Born surface area (MM/GBSA), we have systematically sought out inhibitors of CSK. This approach led to the discovery of a compound with a potent CSK inhibitory activity, reflected by an IC50 value of 1.6 nM under a homogeneous time-resolved fluorescence (HTRF) bioassay. Subsequently, molecule 2 exhibits strong growth inhibition of MD anderson - metastatic breast (MDA-MB) -231, Hs578T, and SUM159 cells, showing a level of growth inhibition comparable to that observed with dasatinib. Treatment with molecule 2 also induced significant G1 phase accumulation and cell apoptosis. Furthermore, we have explored the explicit binding interactions of the compound with CSK using molecular dynamics simulations, providing valuable insights into its mechanism of action.
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Affiliation(s)
- Roufen Chen
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yuchen Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Zheyuan Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Chenyi Ye
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China
| | - Yu Guo
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yan Lu
- Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianjun Ding
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Xiaowu Dong
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Donghang Xu
- Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoli Zheng
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China
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Cui JL, Li H, He Q, Jin BY, Liu Z, Zhang XM, Zhang L. Integrating classic AI and agriculture: A novel model for predicting insecticide-likeness to enhance efficiency in insecticide development. Comput Biol Chem 2024; 112:108113. [PMID: 38851150 DOI: 10.1016/j.compbiolchem.2024.108113] [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: 03/03/2024] [Revised: 05/10/2024] [Accepted: 05/25/2024] [Indexed: 06/10/2024]
Abstract
The integration of artificial intelligence (AI) into smart agriculture boosts production and management efficiency, facilitating sustainable agricultural development. In intensive agricultural management, adopting eco-friendly and effective pesticides is crucial to promote green agricultural practices. However, exploring new insecticides species is a difficult and time-consuming task that involves significant risks. Enhancing compound druggability in the lead discovery phase could considerably shorten the discovery cycle, accelerating insecticides research and development. The Insecticide Activity Prediction (IAPred) model, a novel classic artificial intelligence-based method for evaluating the potential insecticidal activity of unknown functional compounds, is introduced in this study. The IAPred model utilized 27 insecticide-likeness features from PaDEL descriptors and employed an ensemble of Support Vector Machine (SVM) and Random Forest (RF) algorithms using the hard-vote mechanism, achieving an accuracy rate of 86 %. Notably, the IAPred model outperforms current models by accurately predicting the efficacy of novel insecticides such as nicofluprole, overcoming the limitations inherent in existing insecticide structures. Our research presents a practical approach for discovering and optimizing novel insecticide lead compounds quickly and efficiently.
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Affiliation(s)
- Jia-Lin Cui
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Hua Li
- Beijing Key Laboratory for Agricultural Application and New Technique, College of Plant Science and Technology, Beijing University of Agriculture, Beijing 102206, China
| | - Qi He
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Bin-Yan Jin
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Zhe Liu
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Xiao-Ming Zhang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Li Zhang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China.
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Luchini G, Paton RS. Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters. ACS PHYSICAL CHEMISTRY AU 2024; 4:259-267. [PMID: 38800724 PMCID: PMC11117679 DOI: 10.1021/acsphyschemau.3c00045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 05/29/2024]
Abstract
The ability to relate substituent electronic effects to chemical reactivity is a cornerstone of physical organic chemistry and Linear Free Energy Relationships. The computation of electronic parameters is increasingly attractive since they can be obtained rapidly for structures and substituents without available experimental data and can be applied beyond aromatic substituents, for example, in studies of transition metal complexes and aliphatic and radical systems. Nevertheless, the description of "top-down" macroscopic observables, such as Hammett parameters using a "bottom-up" computational approach, poses several challenges for the practitioner. We have examined and benchmarked the performance of various computational charge schemes encompassing quantum mechanical methods that partition charge density, methods that fit charge to physical observables, and methods enhanced by semiempirical adjustments alongside NMR values. We study the locations of the atoms used to obtain these descriptors and their correlation with empirical Hammett parameters and rate differences resulting from electronic effects. These seemingly small choices have a much more significant impact than previously imagined, which outweighs the level of theory or basis set used. We observe a wide range of performance across the different computational protocols and observe stark and surprising differences in the ability of computational parameters to capture para- vs meta-electronic effects. In general, σm predictions fare much worse than σp. As a result, the choice of where to compute these descriptors-for the ring carbons or the attached H or other substituent atoms-affects their ability to capture experimental electronic differences. Density-based schemes, such as Hirshfeld charges, are more stable toward unphysical charge perturbations that result from nearby functional groups and outperform all other computational descriptors, including several commonly used basis set based schemes such as Natural Population Analysis. Using attached atoms also improves the statistical correlations. We obtained general linear relationships for the global prediction of experimental Hammett parameters from computed descriptors for use in statistical modeling studies.
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Affiliation(s)
- Guilian Luchini
- Department
of Chemistry, Colorado State University, 1301 Center Ave., Ft. Collins, Colorado 80523-1872, United States
| | - Robert S. Paton
- Department
of Chemistry, Colorado State University, 1301 Center Ave., Ft. Collins, Colorado 80523-1872, United States
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Wei Z, Zhao C, Zhang M, Xu J, Xu N, Wu S, Xin X, Yu L, Feng W. PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning. BMC Bioinformatics 2024; 25:197. [PMID: 38769505 PMCID: PMC11106915 DOI: 10.1186/s12859-024-05804-8] [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: 02/06/2024] [Accepted: 05/08/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. RESULTS We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. CONCLUSIONS Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS . The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html .
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Affiliation(s)
- Zhenyu Wei
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China.
| | - Chengkui Zhao
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
- Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China
| | - Min Zhang
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
| | - Jiayu Xu
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
| | - Nan Xu
- School of Chemical and Molecular Engineering, East China Normal University, Zhongshan North Street, Shanghai, 200000, People's Republic of China
- Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China
| | - Shiwei Wu
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
| | - Xiaohui Xin
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
| | - Lei Yu
- School of Chemical and Molecular Engineering, East China Normal University, Zhongshan North Street, Shanghai, 200000, People's Republic of China.
- Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China.
| | - Weixing Feng
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China.
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Hu H, Serra C, Zhang W, Scrivo A, Fernández-Carasa I, Consiglio A, Aytes A, Pujana MA, Llebaria A, Antolin AA. Identification of differential biological activity and synergy between the PARP inhibitor rucaparib and its major metabolite. Cell Chem Biol 2024; 31:973-988.e4. [PMID: 38335967 DOI: 10.1016/j.chembiol.2024.01.007] [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: 11/21/2022] [Revised: 08/16/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
The (poly)pharmacology of drug metabolites is seldom comprehensively characterized in drug discovery. However, some drug metabolites can reach high plasma concentrations and display in vivo activity. Here, we use computational and experimental methods to comprehensively characterize the kinase polypharmacology of M324, the major metabolite of the PARP1 inhibitor rucaparib. We demonstrate that M324 displays unique PLK2 inhibition at clinical concentrations. This kinase activity could have implications for the efficacy and safety of rucaparib and therefore warrants further clinical investigation. Importantly, we identify synergy between the drug and the metabolite in prostate cancer models and a complete reduction of α-synuclein accumulation in Parkinson's disease models. These activities could be harnessed in the clinic or open new drug discovery opportunities. The study reported here highlights the importance of characterizing the activity of drug metabolites to comprehensively understand drug response in the clinic and exploit our current drug arsenal in precision medicine.
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Affiliation(s)
- Huabin Hu
- Center for Cancer Drug Discovery, Division of Cancer Therapeutics, The Institute of Cancer Research, London SM2 5NG, UK
| | - Carme Serra
- Medicinal Chemistry and Synthesis (MCS) Laboratory, Institut de Química Avançada de Catalunya (IQAC-CSIC), 08034 Barcelona, Spain; Synthesis of High Added Value Molecules (SIMChem), Institut de Química Avançada de Catalunya (IQAC-CSIC), 08034 Barcelona, Spain
| | - Wenjie Zhang
- ProCURE, Catalan Institute of Oncology (ICO), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, Catalonia, Spain
| | - Aurora Scrivo
- Department of Pathology and Experimental Therapeutics, Bellvitge University Hospital-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain; Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
| | - Irene Fernández-Carasa
- Department of Pathology and Experimental Therapeutics, Bellvitge University Hospital-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain; Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
| | - Antonella Consiglio
- Department of Pathology and Experimental Therapeutics, Bellvitge University Hospital-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain; Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Alvaro Aytes
- ProCURE, Catalan Institute of Oncology (ICO), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, Catalonia, Spain
| | - Miguel Angel Pujana
- ProCURE, Catalan Institute of Oncology (ICO), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, Catalonia, Spain
| | - Amadeu Llebaria
- Medicinal Chemistry and Synthesis (MCS) Laboratory, Institut de Química Avançada de Catalunya (IQAC-CSIC), 08034 Barcelona, Spain; Synthesis of High Added Value Molecules (SIMChem), Institut de Química Avançada de Catalunya (IQAC-CSIC), 08034 Barcelona, Spain.
| | - Albert A Antolin
- Center for Cancer Drug Discovery, Division of Cancer Therapeutics, The Institute of Cancer Research, London SM2 5NG, UK; ProCURE, Catalan Institute of Oncology (ICO), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, Catalonia, Spain.
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He S, Segura Abarrategi J, Bediaga H, Arrasate S, González-Díaz H. On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:535-555. [PMID: 38774585 PMCID: PMC11106676 DOI: 10.3762/bjnano.15.47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/23/2024] [Indexed: 05/24/2024]
Abstract
Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood-brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.
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Affiliation(s)
- Shan He
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
| | - Julen Segura Abarrategi
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Harbil Bediaga
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
- Painting Department, Fine Arts Faculty, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, Basque Country, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- Instituto Biofisika (UPV/EHU-CSIC), 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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47
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Sun X, Ping J, Guo X, Long J, Cai Q, Shu XO, Shu X. Drug-target Mendelian randomization revealed a significant association of genetically proxied metformin effects with increased prostate cancer risk. Mol Carcinog 2024; 63:849-858. [PMID: 38517045 PMCID: PMC11014764 DOI: 10.1002/mc.23692] [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: 11/02/2023] [Revised: 01/04/2024] [Accepted: 01/16/2024] [Indexed: 03/23/2024]
Abstract
The association between metformin use and risk of prostate cancer remains controversial, while data from randomized trials is lacking. We aim to evaluate the association of genetically proxied metformin effects with prostate cancer risk using a drug-target Mendelian randomization (MR) approach. Summary statistics for prostate cancer were obtained from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome Consortium (79,148 cases and 61,106 controls). Cis-expression quantitative trait loci (cis-eQTL) variants in the gene targets of metformin were identified in the GTEx project and eQTLGen consortium. We also obtained male-specific genome-wide association study data for type 2 diabetes, body mass index (BMI), total testosterone, bioavailable testosterone, estradiol, and sex hormone binding globulin for mediation analysis. Inverse-variance weighted (IVW) regression, weighted median, MR-Egger regression, and MR-PRESSO were performed in the main MR analysis. Multivariable MR was used to identify potential mediators and genetic colocalization analysis was performed to assess any shared genetic basis between two traits of interest. We found that genetically proxied metformin effects (1-SD HbA1c reduction, equivalent to 6.75 mmol/mol) were associated with higher risk of prostate cancer (odds ratioIVW [ORIVW]: 1.55, 95% confidence interval, CI: 1.23-1.96, p = 3.0 × 10-3). Two metformin targets, mitochondrial complex I (ORIVW: 1.48, 95% CI: 1.07-2.03, p = 0.016) and gamma-secretase complex (ORIVW: 2.58, 95%CI :1.47-4.55, p = 0.001), showed robust associations with prostate cancer risk, and their effects were partly mediated through BMI (16.4%) and total testosterone levels (34.3%), respectively. These results were further supported by colocalization analysis that expressions of NDUFA13 and BMI, APH1A, and total testosterone may be influenced by shared genetic factors, respectively. In summary, our study indicated that genetically proxied metformin effects may be associated with an increased risk of prostate cancer. Repurposing metformin for prostate cancer prevention in general populations is not supported by our findings.
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Affiliation(s)
- Xiaohui Sun
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology, Zhejiang Chinese Medical University, Zhejiang, China
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiang Shu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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48
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Yang JJ, Goff A, Wild DJ, Ding Y, Annis A, Kerber R, Foote B, Passi A, Duerksen JL, London S, Puhl AC, Lane TR, Braunstein M, Waddell SJ, Ekins S. Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus. Tuberculosis (Edinb) 2024; 146:102500. [PMID: 38432118 PMCID: PMC10978224 DOI: 10.1016/j.tube.2024.102500] [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: 11/30/2023] [Revised: 02/20/2024] [Accepted: 02/24/2024] [Indexed: 03/05/2024]
Abstract
Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 μM; M. abscessus IC90 59.1 μM) and tribromsalan (M. tuberculosis IC90 76.92 μM; M. abscessus IC90 147.4 μM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.
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Affiliation(s)
- Jeremy J Yang
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; Department of Internal Medicine Translational Informatics Division, University of New Mexico, Albuquerque, NM, USA
| | - Aaron Goff
- Department of Global Health and Infection, Brighton & Sussex Medical School, University of Sussex, UK
| | - David J Wild
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA
| | - Ying Ding
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; School of Information, Dell Medical School, University of Texas, Austin, TX, USA
| | - Ayano Annis
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, NC, 27599, USA
| | | | | | - Anurag Passi
- Department of Pediatrics, UC San Diego, San Diego, CA, USA
| | | | | | - Ana C Puhl
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Thomas R Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Miriam Braunstein
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Simon J Waddell
- Department of Global Health and Infection, Brighton & Sussex Medical School, University of Sussex, UK
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
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49
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Kędzierski J, Jäger MC, Naeem S, Odermatt A, Smieško M. In silico and in vitro assessment of drugs potentially causing adverse effects by inhibiting CYP17A1. Toxicol Appl Pharmacol 2024; 486:116945. [PMID: 38688424 DOI: 10.1016/j.taap.2024.116945] [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: 01/18/2024] [Revised: 04/11/2024] [Accepted: 04/25/2024] [Indexed: 05/02/2024]
Abstract
Cytochrome P450 enzymes (CYPs) play a crucial role in the metabolism and synthesis of various compound classes. While drug-metabolizing CYP enzymes are frequently investigated as anti-targets, the inhibition of CYP enzymes involved in adrenal steroidogenesis is not well studied. The steroidogenic enzyme CYP17A1 is a dual-function enzyme catalyzing hydroxylase and lyase reactions relevant for the biosynthesis of adrenal glucocorticoids and androgens. Inhibition of CYP17A1-hydroxylase leads to pseudohyperaldosteronism with subsequent excessive mineralocorticoid receptor activation, hypertension and hypokalemia. In contrast, specific inhibition of the lyase function might be beneficial for the treatment of prostate cancer by decreasing adrenal androgen levels. This study combined in silico and in vitro methods to identify drugs inhibiting CYP17A1. The most potent CYP17A1 inhibitors identified are serdemetan, mocetinostat, nolatrexed, liarozole, and talarozole. While some of these drugs are currently under investigation for the treatment of various cancers, their potential for the treatment of prostate cancer is yet to be explored. The DrugBank database was screened for CYP17A1 inhibitors, to increase the awareness for the risk of drug-induced pseudohyperaldosteronism and to highlight drugs so far unknown for their potential to cause side effects resulting from CYP17A1 inhibition.
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Affiliation(s)
- Jacek Kędzierski
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland.
| | - Marie-Christin Jäger
- Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland.
| | - Sadaf Naeem
- Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Department of Biochemistry, University of Karachi, KU, Circular Road, Karachi, Pakistan
| | - Alex Odermatt
- Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland.
| | - Martin Smieško
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland.
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50
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Baltasar-Marchueta M, Llona L, M-Alicante S, Barbolla I, Ibarluzea MG, Ramis R, Salomon AM, Fundora B, Araujo A, Muguruza-Montero A, Nuñez E, Pérez-Olea S, Villanueva C, Leonardo A, Arrasate S, Sotomayor N, Villarroel A, Bergara A, Lete E, González-Díaz H. Identification of Riluzole derivatives as novel calmodulin inhibitors with neuroprotective activity by a joint synthesis, biosensor, and computational guided strategy. Biomed Pharmacother 2024; 174:116602. [PMID: 38636396 DOI: 10.1016/j.biopha.2024.116602] [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: 01/19/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024] Open
Abstract
The development of new molecules for the treatment of calmodulin related cardiovascular or neurodegenerative diseases is an interesting goal. In this work, we introduce a novel strategy with four main steps: (1) chemical synthesis of target molecules, (2) Förster Resonance Energy Transfer (FRET) biosensor development and in vitro biological assay of new derivatives, (3) Cheminformatics models development and in vivo activity prediction, and (4) Docking studies. This strategy is illustrated with a case study. Firstly, a series of 4-substituted Riluzole derivatives 1-3 were synthetized through a strategy that involves the construction of the 4-bromoriluzole framework and its further functionalization via palladium catalysis or organolithium chemistry. Next, a FRET biosensor for monitoring Ca2+-dependent CaM-ligands interactions has been developed and used for the in vitro assay of Riluzole derivatives. In particular, the best inhibition (80%) was observed for 4-methoxyphenylriluzole 2b. Besides, we trained and validated a new Networks Invariant, Information Fusion, Perturbation Theory, and Machine Learning (NIFPTML) model for predicting probability profiles of in vivo biological activity parameters in different regions of the brain. Next, we used this model to predict the in vivo activity of the compounds experimentally studied in vitro. Last, docking study conducted on Riluzole and its derivatives has provided valuable insights into their binding conformations with the target protein, involving calmodulin and the SK4 channel. This new combined strategy may be useful to reduce assay costs (animals, materials, time, and human resources) in the drug discovery process of calmodulin inhibitors.
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Affiliation(s)
- Maider Baltasar-Marchueta
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Leire Llona
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | | | - Iratxe Barbolla
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Markel Garcia Ibarluzea
- Donostia International Physics Center, Donostia, Spain; Departament of Physics, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Rafael Ramis
- Donostia International Physics Center, Donostia, Spain; Departament of Physics, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Ane Miren Salomon
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Brenda Fundora
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Ariane Araujo
- Biofisika Institute, CSIC-UPV/EHU, Leioa 48940, Spain
| | | | - Eider Nuñez
- Biofisika Institute, CSIC-UPV/EHU, Leioa 48940, Spain
| | - Scarlett Pérez-Olea
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Christian Villanueva
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Aritz Leonardo
- Donostia International Physics Center, Donostia, Spain; Departament of Physics, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Nuria Sotomayor
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | | | - Aitor Bergara
- Donostia International Physics Center, Donostia, Spain; Departament of Physics, University of the Basque Country, UPV/EHU, Leioa, Spain.
| | - Esther Lete
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain; Biofisika Institute, CSIC-UPV/EHU, Leioa 48940, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao 48011, Spain.
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