1
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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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2
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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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3
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Abbas MKG, Rassam A, Karamshahi F, Abunora R, Abouseada M. The Role of AI in Drug Discovery. Chembiochem 2024:e202300816. [PMID: 38735845 DOI: 10.1002/cbic.202300816] [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: 12/03/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/14/2024]
Abstract
The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.
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Affiliation(s)
- M K G Abbas
- Center for Advanced Materials, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Abrar Rassam
- Secondary Education, Educational Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Fatima Karamshahi
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Rehab Abunora
- Faculty of Medicine, General Medicine and Surgery, Helwan University, Cairo, Egypt
| | - Maha Abouseada
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
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4
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Guo B, Chen L, Luo S, Wang C, Feng Y, Li X, Cao C, Zhang L, Yang Q, Zhang X, Yang X. A Potential Multitarget Insect Growth Regulator Candidate: Design, Synthesis, and Biological Activity of Novel Acetamido Derivatives Containing Hexacyclic Pyrazole Carboxamides. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:10271-10281. [PMID: 38655868 DOI: 10.1021/acs.jafc.4c00312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Insect growth regulators (IGRs) are important green insecticides that disrupt normal growth and development in insects to reduce the harm caused by pests to crops. The ecdysone receptor (EcR) and three chitinases OfChtI, OfChtII, and OfChi-h are closely associated with the molting stage of insects. Thus, they are considered promising targets for the development of novel insecticides such as IGRs. Our previous work identified a dual-target compound 6j, which could act simultaneously on both EcR and OfChtI. In the present study, 6j was first found to have inhibitory activities against OfChtII and OfChi-h, too. Subsequently, taking 6j as a lead compound, 19 novel acetamido derivatives were rationally designed and synthesized by introducing an acetamido moiety into the amide bridge based on the flexibility of the binding cavities of 6j with EcR and three chitinases. Then, their insecticidal activities against Plutella xylostella (P. xylostella), Ostrinia furnacalis (O. furnacalis), and Spodoptera frugiperda (S. frugiperda) were carried out. The bioassay results revealed that most of these acetamido derivatives possessed moderate to good larvicidal activities against three lepidopteran pests. Especially, compound I-17 displayed excellent insecticidal activities against P. xylostella (LC50, 93.32 mg/L), O. furnacalis (LC50, 114.79 mg/L), and S. frugiperda (86.1% mortality at 500 mg/L), significantly better than that of 6j. In addition, further protein validation and molecular docking demonstrated that I-17 could act simultaneously on EcR (17.7% binding activity at 8 mg/L), OfChtI (69.2% inhibitory rate at 50 μM), OfChtII (71.5% inhibitory rate at 50 μM), and OfChi-h (73.9% inhibitory rate at 50 μM), indicating that I-17 is a potential lead candidate for novel multitarget IGRs. This work provides a promising starting point for the development of novel types of IGRs as pest management agents.
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Affiliation(s)
- Bingbo Guo
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Lei Chen
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No 97 Buxin Road, Shenzhen 518120, China
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Shihui Luo
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Chunying Wang
- Engineering Research Center of Plant Growth Regulator, Ministry of Education & College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
| | - Yanjiao Feng
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Xiaoyang Li
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Congwang Cao
- 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
| | - Qing Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No 97 Buxin Road, Shenzhen 518120, China
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Xiaoming Zhang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Xinling Yang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
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5
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Li Y, Yang Y, Tong Z, Wang Y, Mi Q, Bai M, Liang G, Li B, Shu K. A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods. Brief Bioinform 2024; 25:bbae172. [PMID: 38647153 PMCID: PMC11033846 DOI: 10.1093/bib/bbae172] [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: 12/10/2023] [Revised: 02/25/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.
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Affiliation(s)
- Yinghong Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yinqi Yang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Zhuohao Tong
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yu Wang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Qin Mi
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, P. R. China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, P. R. China
| | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
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6
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Jimenes-Vargas K, Pazos A, Munteanu CR, Perez-Castillo Y, Tejera E. Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy. J Cheminform 2024; 16:27. [PMID: 38449058 PMCID: PMC10919000 DOI: 10.1186/s13321-024-00816-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 02/15/2024] [Indexed: 03/08/2024] Open
Abstract
For understanding a chemical compound's mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/ . Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling.
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Affiliation(s)
- Karina Jimenes-Vargas
- Bio-Cheminformatics Research Group, Universidad de Las Américas, Quito, 170504, Ecuador.
- Departament of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruña, Campus Elviña s/n, 15071, A Coruña, Spain.
| | - Alejandro Pazos
- Departament of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, 15071, A Coruña, Spain
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruna (CHUAC), 15006, A Coruna, Spain
| | - Cristian R Munteanu
- Departament of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, 15071, A Coruña, Spain
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruna (CHUAC), 15006, A Coruna, Spain
| | | | - Eduardo Tejera
- Bio-Cheminformatics Research Group, Universidad de Las Américas, Quito, 170504, Ecuador.
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Basharat Z, Sattar S, Bahauddin AA, Al Mouslem AK, Alotaibi G. Screening Marine Microbial Metabolites as Promising Inhibitors of Borrelia garinii: A Structural Docking Approach towards Developing Novel Lyme Disease Treatment. BIOMED RESEARCH INTERNATIONAL 2024; 2024:9997082. [PMID: 38456098 PMCID: PMC10919988 DOI: 10.1155/2024/9997082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 01/26/2024] [Accepted: 02/13/2024] [Indexed: 03/09/2024]
Abstract
Lyme disease caused by the Borrelia species is a growing health concern in many parts of the world. Current treatments for the disease may have side effects, and there is also a need for new therapies that can selectively target the bacteria. Pathogens responsible for Lyme disease include B. burgdorferi, B. afzelii, and B. garinii. In this study, we employed structural docking-based screening to identify potential lead-like inhibitors against the bacterium. We first identified the core essential genome fraction of the bacterium, using 37 strains. Later, we screened a library of lead-like marine microbial metabolites (n = 4730) against the arginine deiminase (ADI) protein of Borrelia garinii. This protein plays a crucial role in the survival of the bacteria, and inhibiting it can kill the bacterium. The prioritized lead compounds demonstrating favorable binding energies and interactions with the active site of ADI were then evaluated for their drug-like and pharmacokinetic parameters to assess their suitability for development as drugs. Results from molecular dynamics simulation (100 ns) and other scoring parameters suggest that the compound CMNPD18759 (common name: aureobasidin; IUPAC name: 2-[(4R,6R)-4,6-dihydroxydecanoyl]oxypropan-2-yl (3S,5R)-3,5-dihydroxydecanoate) holds promise as a potential drug candidate for the treatment of Lyme disease, caused by B. garinii. However, further experimental studies are needed to validate the efficacy and safety of this compound in vivo.
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Affiliation(s)
| | - Sadia Sattar
- Molecular Virology Labs, Department of Biosciences, COMSATS University Islamabad, Islamabad Campus, Islamabad 45550, Pakistan
| | | | - Abdulaziz K. Al Mouslem
- Department of Pharmaceutical Sciences, College of Clinical Pharmacy, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Ghallab Alotaibi
- Department of Pharmacology, College of Pharmacy, Al-Dawadmi Campus, Shaqra University, Shaqra, Saudi Arabia
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8
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Cao D, Zhang P, Wang S. Advances in structure-based drug design: The potential for precision therapeutics in psychiatric disorders. Neuron 2024; 112:526-538. [PMID: 38290517 DOI: 10.1016/j.neuron.2024.01.004] [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: 10/21/2023] [Revised: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 02/01/2024]
Abstract
Over the years, the field of GPCR drug design has undergone a remarkable evolution, fueled by advancements in science and technology. This evolution has given rise to a diverse range of ideas and approaches in structure-based drug design, bolstering the versatility and strength of the GPCR drug design toolbox. This review encapsulates the iterative development process, navigating challenges and opportunities in structure-based drug design within GPCRs. With a focused emphasis on its impact on psychiatric disorders, the review accentuates recent advancements and delves into the potentials unlocked by emerging technologies. The review explores the intricate interplay between scientific progress and iterative refinement, offering profound insights into the potential pathways that lie ahead for GPCR drug design.
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Affiliation(s)
- Dongmei Cao
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Pei Zhang
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Sheng Wang
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
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9
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Liang Z, Lin C, Tan G, Li J, He Y, Cai S. A low-cost machine learning framework for predicting drug-drug interactions based on fusion of multiple features and a parameter self-tuning strategy. Phys Chem Chem Phys 2024; 26:6300-6315. [PMID: 38305788 DOI: 10.1039/d4cp00039k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Poly-drug therapy is now recognized as a crucial treatment, and the analysis of drug-drug interactions (DDIs) offers substantial theoretical support and guidance for its implementation. Predicting potential DDIs using intelligent algorithms is an emerging approach in pharmacological research. However, the existing supervised models and deep learning-based techniques still have several limitations. This paper proposes a novel DDI analysis and prediction framework called the Multi-View Semi-supervised Graph-based (MVSG) framework, which provides a comprehensive judgment by integrating multiple DDI features and functions without any time-consuming training process. Unlike conventional approaches, MVSG can search for the most suitable similarity (or distance) measurement among DDI data and construct graph structures for each feature. By employing a parameter self-tuning strategy, MVSG fuses multiple graphs according to the contributions of features' information. The actual anticancer drug data are extracted from the authoritative public database for evaluating the effectiveness of our framework, including 904 drugs, 7730 DDI records and 19 types of drug interactions. Validation results indicate that the prediction is more accurate when multiple features are adopted by our framework. In comparison to conventional machine learning techniques, MVSG can achieve higher performance even with less labeled data and without a training process. Finally, MVSG is employed to narrow down the search for potential valuable combinations.
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Affiliation(s)
- Zexiao Liang
- School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China.
| | - Canxin Lin
- School of Computer Science and Technology, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China
| | - Guoliang Tan
- School of Automation, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China
| | - Jianzhong Li
- School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China.
| | - Yan He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China
| | - Shuting Cai
- School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China.
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10
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Bhandari R, Varma M, Rana P, Dhingra N, Kuhad A. Taurine as a potential therapeutic agent interacting with multiple signaling pathways implicated in autism spectrum disorder (ASD): An in-silico analysis. IBRO Neurosci Rep 2023; 15:170-177. [PMID: 37711998 PMCID: PMC10497788 DOI: 10.1016/j.ibneur.2023.08.2191] [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: 05/12/2023] [Revised: 08/04/2023] [Accepted: 08/23/2023] [Indexed: 09/16/2023] Open
Abstract
Autism spectrum disorders (ASD) are a complex sequelae of neurodevelopmental disorders which manifest in the form of communication and social deficits. Currently, only two agents, namely risperidone and aripiprazole have been approved for the treatment of ASD, and there is a dearth of more drugs for the disorder. The exact pathophysiology of autism is not understood clearly, but research has implicated multiple pathways at different points in the neuronal circuitry, suggesting their role in ASD. Among these, the role played by neuroinflammatory cascades like the NF-KB and Nrf2 pathways, and the excitotoxic glutamatergic system, are said to have a bearing on the development of ASD. Similarly, the GPR40 receptor, present in both the gut and the blood brain barrier, has also been said to be involved in the disorder. Consequently, molecules which can act by interacting with one or multiple of these targets might have a potential in the therapy of the disorder, and for this reason, this study was designed to assess the binding affinity of taurine, a naturally-occurring amino acid, with these target molecules. The same was scored against these targets using in-silico docking studies, with Risperidone and Aripiprazole being used as standard comparators. Encouraging docking scores were obtained for taurine across all the selected targets, indicating promising target interaction. But the affinity for targets actually varied in the order NRF-KEAP > NF-κB > NMDA > Calcium channel > GPR 40. Given the potential implication of these targets in the pathogenesis of ASD, the drug might show promising results in the therapy of the disorder if subjected to further evaluations.
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Affiliation(s)
- Ranjana Bhandari
- Pharmacology Research Laboratory, University Institute of Pharmaceutical Sciences, UGC-Centre of Advanced Study, Panjab University, Chandigarh 160 014, India
| | - Manasi Varma
- Pharmacology Research Laboratory, University Institute of Pharmaceutical Sciences, UGC-Centre of Advanced Study, Panjab University, Chandigarh 160 014, India
- Pharmaceutical Chemistry & CADD-Lab, University Institute of Pharmaceutical Sciences, UGC, Centre of Advanced Study, Panjab University, Chandigarh 160 014, India
| | - Priyanka Rana
- Pharmacology Research Laboratory, University Institute of Pharmaceutical Sciences, UGC-Centre of Advanced Study, Panjab University, Chandigarh 160 014, India
- Pharmaceutical Chemistry & CADD-Lab, University Institute of Pharmaceutical Sciences, UGC, Centre of Advanced Study, Panjab University, Chandigarh 160 014, India
| | - Neelima Dhingra
- Pharmaceutical Chemistry & CADD-Lab, University Institute of Pharmaceutical Sciences, UGC, Centre of Advanced Study, Panjab University, Chandigarh 160 014, India
| | - Anurag Kuhad
- Pharmacology Research Laboratory, University Institute of Pharmaceutical Sciences, UGC-Centre of Advanced Study, Panjab University, Chandigarh 160 014, India
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11
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Rao M, Nassiri V, Alhambra C, Snoeys J, Van Goethem F, Irrechukwu O, Aleo MD, Geys H, Mitra K, Will Y. AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules. Chem Res Toxicol 2023. [PMID: 37294641 DOI: 10.1021/acs.chemrestox.3c00098] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp3, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.
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Affiliation(s)
- Mohan Rao
- Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States
| | - Vahid Nassiri
- Open Analytics, Jupiterstraat 20, 2600 Antwerpen, Belgium
| | - Cristóbal Alhambra
- Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States
| | - Jan Snoeys
- Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States
| | - Freddy Van Goethem
- Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States
| | - Onyi Irrechukwu
- Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States
| | - Michael D Aleo
- TOXinsights LLC, Boiling Springs, Pennsylvania 17007, United States
| | - Helena Geys
- Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States
| | - Kaushik Mitra
- Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States
| | - Yvonne Will
- Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States
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12
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Singh R, Kashif M, Srivastava P, Manna PP. Recent Advances in Chemotherapeutics for Leishmaniasis: Importance of the Cellular Biochemistry of the Parasite and Its Molecular Interaction with the Host. Pathogens 2023; 12:pathogens12050706. [PMID: 37242374 DOI: 10.3390/pathogens12050706] [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: 12/20/2022] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023] Open
Abstract
Leishmaniasis, a category 1 neglected protozoan disease caused by a kinetoplastid pathogen called Leishmania, is transmitted through dipteran insect vectors (phlebotomine, sand flies) in three main clinical forms: fatal visceral leishmaniasis, self-healing cutaneous leishmaniasis, and mucocutaneous leishmaniasis. Generic pentavalent antimonials have long been the drug of choice against leishmaniasis; however, their success is plagued with limitations such as drug resistance and severe side effects, which makes them redundant as frontline therapy for endemic visceral leishmaniasis. Alternative therapeutic regimens based on amphotericin B, miltefosine, and paromomycin have also been approved. Due to the unavailability of human vaccines, first-line chemotherapies such as pentavalent antimonials, pentamidine, and amphotericin B are the only options to treat infected individuals. The higher toxicity, adverse effects, and perceived cost of these pharmaceutics, coupled with the emergence of parasite resistance and disease relapse, makes it urgent to identify new, rationalized drug targets for the improvement in disease management and palliative care for patients. This has become an emergent need and more relevant due to the lack of information on validated molecular resistance markers for the monitoring and surveillance of changes in drug sensitivity and resistance. The present study reviewed the recent advances in chemotherapeutic regimens by targeting novel drugs using several strategies including bioinformatics to gain new insight into leishmaniasis. Leishmania has unique enzymes and biochemical pathways that are distinct from those of its mammalian hosts. In light of the limited number of available antileishmanial drugs, the identification of novel drug targets and studying the molecular and cellular aspects of these drugs in the parasite and its host is critical to design specific inhibitors targeting and controlling the parasite. The biochemical characterization of unique Leishmania-specific enzymes can be used as tools to read through possible drug targets. In this review, we discuss relevant metabolic pathways and novel drugs that are unique, essential, and linked to the survival of the parasite based on bioinformatics and cellular and biochemical analyses.
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Affiliation(s)
- Ranjeet Singh
- Immunobiology Laboratory, Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi 221005, India
| | - Mohammad Kashif
- Immunobiology Laboratory, Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi 221005, India
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Prateek Srivastava
- Immunobiology Laboratory, Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi 221005, India
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Partha Pratim Manna
- Immunobiology Laboratory, Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi 221005, India
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13
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Ji KY, Liu C, Liu ZQ, Deng YF, Hou TJ, Cao DS. Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? Brief Bioinform 2023; 24:6995377. [PMID: 36681902 DOI: 10.1093/bib/bbad014] [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: 11/07/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/23/2023] Open
Abstract
Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.
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Affiliation(s)
- Kai-Yue Ji
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Chong Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zhao-Qian Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Ya-Feng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
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14
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Wang JN, Fan H, Song JT. Targeting purinergic receptors to attenuate inflammation of dry eye. Purinergic Signal 2023; 19:199-206. [PMID: 35218451 PMCID: PMC9984584 DOI: 10.1007/s11302-022-09851-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/04/2022] [Indexed: 11/28/2022] Open
Abstract
Inflammation is one of the potential factors to cause the damage of ocular surface in dry eye disease (DED). Increasing evidence indicated that purinergic A1, A2A, A3, P2X4, P2X7, P2Y1, P2Y2, and P2Y4 receptors play an important role in the regulation of inflammation in DED: A1 adenosine receptor (A1R) is a systemic pro-inflammatory factor; A2AR is involved in the activation of the MAPK/NF-kB pathway; A3R combined with inhibition of adenylate cyclase and regulation of the mitogen-activated protein kinase (MAPK) pathway leads to regulation of transcription; P2X4 promotes receptor-associated activation of pro-inflammatory cytokines and inflammatory vesicles; P2X7 promotes inflammasome activation and release of pro-inflammatory cytokines IL-1β and IL-18; P2Y receptors affect the phospholipase C(PLC)/IP3/Ca2+ signaling pathway and mucin secretion. These suggested that purinergic receptors would be promising targets to control the inflammation of DED in the future.
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Affiliation(s)
- Jia-Ning Wang
- Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Hua Fan
- Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jian-Tao Song
- Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
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15
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Gao H, Dai R, Su R. Computer-aided drug design for the pain-like protease (PL pro) inhibitors against SARS-CoV-2. Biomed Pharmacother 2023; 159:114247. [PMID: 36689835 PMCID: PMC9841087 DOI: 10.1016/j.biopha.2023.114247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
A new coronavirus, known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a highly contagious virus and has caused a massive worldwide health crisis. While large-scale vaccination efforts are underway, the management of population health, economic impact and asof-yet unknown long-term effects on physical and mental health will be a key challenge for the next decade. The papain-like protease (PLpro) of SARS-CoV-2 is a promising target for antiviral drugs. This report used pharmacophore-based drug design technology to identify potential compounds as PLpro inhibitors against SARS-CoV-2. The optimal pharmacophore model was fully validated using different strategies and then was employed to virtually screen out 10 compounds with inhibitory. Molecular docking and non-bonding interactions between the targeted protein PLpro and compounds showed that UKR1129266 was the best compound. These results provided a theoretical foundation for future studies of PLpro inhibitors against SARS-CoV-2.
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Affiliation(s)
- Hongwei Gao
- School of Life Science, Ludong University, Yantai, Shandong 264025, China.
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16
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An S, Hwang SY, Gong J, Ahn S, Park IG, Oh S, Chin YW, Noh M. Computational Prediction of the Phenotypic Effect of Flavonoids on Adiponectin Biosynthesis. J Chem Inf Model 2023; 63:856-869. [PMID: 36716271 DOI: 10.1021/acs.jcim.3c00033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In silico machine learning applications for phenotype-based screening have primarily been limited due to the lack of machine-readable data related to disease phenotypes. Adiponectin, a nuclear receptor (NR)-regulated adipocytokine, is relatively downregulated in human metabolic diseases. Here, we present a machine-learning model to predict the adiponectin-secretion-promoting activity of flavonoid-associated phytochemicals (FAPs). We modeled a structure-activity relationship between the chemical similarity of FAPs and their bioactivities using a random forest-based classifier, which provided the NR activity of each FAP as a probability. To link the classifier-predicted NR activity to the phenotype, we next designed a single-cell transcriptomics-based multiple linear regression model to generate the relative adiponectin score (RAS) of FAPs. In experimental validation, estimated RAS values of FAPs isolated from Scutellaria baicalensis exhibited a significant correlation with their adiponectin-secretion-promoting activity. The combined cheminformatics and bioinformatics approach enables the computational reconstruction of phenotype-based screening systems.
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Affiliation(s)
- Seungchan An
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Seok Young Hwang
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Junpyo Gong
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Sungjin Ahn
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - In Guk Park
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Soyeon Oh
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Young-Won Chin
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Minsoo Noh
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
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17
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Cancela F, Rendon-Marin S, Quintero-Gil C, Houston DR, Gumbis G, Panzera Y, Pérez R, Arbiza J, Mirazo S. Modelling of Hepatitis E virus RNA-dependent RNA polymerase genotype 3 from a chronic patient and in silico interaction analysis by molecular docking with Ribavirin. J Biomol Struct Dyn 2023; 41:705-721. [PMID: 34861797 DOI: 10.1080/07391102.2021.2011416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Hepatitis E Virus (HEV) infection is an emergent zoonotic disease, where chronic hepatitis E associated to solid organ transplant (SOT) recipients, related to genotype 3, is the clinical manifestation of major concern. In this setting, ribavirin (RBV) treatment is the only available therapy, though drug-resistant variants could emerge leading to a therapeutic failure. Crystallographic structures have not been reported for most of the HEV proteins, including the RNA-polymerase (RdRp). Therefore, the mechanism of action of RBV against HEV and the molecular interactions between this drug and RdRp are largely unknown. In this work, we aimed to model in silico the 3 D structure of a novel HEV3 RdRp (HEV_C1_Uy) from a chronically HEV infected-SOT recipient treated with RBV and to perform a molecular docking simulation between RBV triphosphate (RBVT), 7-methyl-guanosine-5'-triphosphate and the modelled protein. The models were generated using I-TASSER server and validated with multiple bioinformatics tools. The docking analysis were carried out with AutoDock Vina and LeDock software. We obtained a suitable model for HEV_C1_Uy (C-Score=-1.33, RMSD = 10.4 ± 4.6 Å). RBVT displayed a binding affinity of -7.6 ± 0.2 Kcal/mol by molecular docking, mediated by 6 hydrogen-bonds (Q195-O14, S198-O11, E257-O13, S260-O2, O3, S311-O11) between the finger's-palm-domains and a free binding energy of 31.26 ± 16.81 kcal/mol by molecular dynamics simulations. We identified the possible HEV RdRp interacting region for incoming nucleotides or analogs and provide novel insights that will contribute to better understand the molecular interactions of RBV and the enzyme and the mechanism of action of this antiviral drug.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Florencia Cancela
- Sección Virología, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - Santiago Rendon-Marin
- Grupo de Investigación en Ciencias Animales - GRICA, Facultad de Medicina Veterinaria y Zootecnia, Universidad Cooperativa de Colombia, sede Bucaramanga, Bucaramanga, Colombia
| | - Carolina Quintero-Gil
- Grupo de Investigación en Ciencias Animales - GRICA, Facultad de Medicina Veterinaria y Zootecnia, Universidad Cooperativa de Colombia, sede Bucaramanga, Bucaramanga, Colombia
| | - Douglas R Houston
- Institute of Quantitative Biology, Biochemistry and Biotechnology, The University of Edinburgh, Edinburgh, UK
| | - Gediminas Gumbis
- Institute of Quantitative Biology, Biochemistry and Biotechnology, The University of Edinburgh, Edinburgh, UK
| | - Yanina Panzera
- Sección Genética Evolutiva, Instituto de Biología, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - Ruben Pérez
- Sección Genética Evolutiva, Instituto de Biología, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - Juan Arbiza
- Sección Virología, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - Santiago Mirazo
- Sección Virología, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay.,Departamento de Bacteriología y Virología, Instituto de Higiene, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
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18
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Exploring different computational approaches for effective diagnosis of breast cancer. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 177:141-150. [PMID: 36509230 DOI: 10.1016/j.pbiomolbio.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/28/2022] [Accepted: 11/10/2022] [Indexed: 12/13/2022]
Abstract
Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.
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19
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In Combo Studies for the Optimization of 5-Aminoanthranilic Acid Derivatives as Potential Multitarget Drugs for the Management of Metabolic Syndrome. Pharmaceuticals (Basel) 2022; 15:ph15121461. [PMID: 36558912 PMCID: PMC9784827 DOI: 10.3390/ph15121461] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
Metabolic syndrome is a set of risk factors that consist of abdominal obesity, arterial hypertension, alterations in the lipid profile, and hyperglycemia. The current therapeutic strategy includes polypharmacy, using three or more drugs to control each syndrome component. However, this approach has drawbacks that could lead to therapeutic failure. Multitarget drugs are molecules with the ability to act on different targets simultaneously and are an attractive alternative for treating complex diseases such as metabolic syndrome. Previously, we identified a triamide derivative of 5-aminoanthranilic acid that exhibited hypoglycemic, hypolipemic, and antihypertensive activities simultaneously. In the present study, we report the synthesis and in combo evaluation of new derivatives of anthranilic acid, intending to identify the primary structural factors that improve the activity over metabolic syndrome-related parameters. We found that substitution on position 5, incorporation of 3,4-dimethoxyphenyl substituents, and having a free carboxylic acid group lead to the in vitro inhibition of HMG-CoA reductase, and simultaneously the diminution of the serum levels of glucose, triglycerides, and cholesterol in a diet-induced in vivo model.
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20
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Kanapeckaitė A, Mažeikienė A, Geris L, Burokienė N, Cottrell GS, Widera D. Computational pharmacology: New avenues for COVID-19 therapeutics search and better preparedness for future pandemic crises. Biophys Chem 2022; 290:106891. [PMID: 36137310 PMCID: PMC9464258 DOI: 10.1016/j.bpc.2022.106891] [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: 07/07/2022] [Revised: 09/03/2022] [Accepted: 09/04/2022] [Indexed: 01/07/2023]
Abstract
The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.
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Affiliation(s)
- Austė Kanapeckaitė
- AK Consulting, Laisvės g. 7, LT 12007 Vilnius, Lithuania,Corresponding author
| | - Asta Mažeikienė
- Department of Physiology, Biochemistry, Microbiology and Laboratory Medicine, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, M. K. Čiurlionio g. 21, LT-03101 Vilnius, Lithuania
| | - Liesbet Geris
- Biomechanics Research Unit, GIGA In Silico Medicine, University of Liège, Quartier Hôpital, Avenue de l'Hôpital 11 (B34), Liège 4000, Belgium,Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300C (2419), Leuven 3001, Belgium,Skeletel Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Herestraat 49 (813), Leuven 3000, Belgium
| | - Neringa Burokienė
- Clinics of Internal Diseases, Family Medicine and Oncology, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, M. K. Čiurlionio str. 21/27, LT-03101 Vilnius, Lithuania
| | - Graeme S. Cottrell
- University of Reading, School of Pharmacy, Hopkins Building, Reading RG6 6UB, United Kingdom
| | - Darius Widera
- University of Reading, School of Pharmacy, Hopkins Building, Reading RG6 6UB, United Kingdom
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21
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Multi-target-based polypharmacology prediction (mTPP): An approach using virtual screening and machine learning for multi-target drug discovery. Chem Biol Interact 2022; 368:110239. [DOI: 10.1016/j.cbi.2022.110239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/19/2022] [Accepted: 10/21/2022] [Indexed: 11/23/2022]
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22
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Jiang Z, Shi D, Li H, He D, Zhu K, Li J, Zi Y, Xu Z, Huang J, Duan H, Yang Q. Rational Design and Identification of Novel Piperine Derivatives as Multichitinase Inhibitors. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:10326-10336. [PMID: 35960858 DOI: 10.1021/acs.jafc.2c03751] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Asian corn borer (Ostrinia furnacalis) is one of the most destructive pests in agriculture. Three chitinases OfChtI, OfChtII, and OfChi-h are regarded as potential targets for discovering novel agrochemicals to control O. furnacalis. In this study, piperine (Ki = 43.78∼83.03 μM) was first shown to exhibit inhibitory activities against all three chitinases. Subsequently, 19 novel piperine derivatives were rationally designed based on the conserved aromatic residues of three chitinases and then synthesized. Among them, Compound 5k (Ki = 11.78∼22.82 μM) was identified as the most effective multichitinase inhibitor and indeed displayed higher insecticidal activity against O. furnacalis than dual- or single-chitinase inhibitors. Molecular mechanism studies clarified that Compound 5k interacted with two conserved TRP and TYR of three chitinases in identical modes through hydrogen bonds, hydrophobic, and π-π interactions. Moreover, the microinjection experiment indicated that Compound 5k exhibited substantial sublethal effects against O. furnacalis by regulating its growth and development. This study provides evidence of multichitinase inhibitors to be applied in the control of O. furnacalis.
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Affiliation(s)
- Zhiyang Jiang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Dongmei Shi
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Huilin Li
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Danchan He
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection and Shenzhen Agricultural Genome Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Kai Zhu
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Jingyi Li
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Yunjiang Zi
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Jiaxing Huang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Hongxia Duan
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Qing Yang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection and Shenzhen Agricultural Genome Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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Aprepitant as plausible inhibitor of MAPK/ERK2 pathway to ameliorate neurological deficits post traumatic brain injury. Med Hypotheses 2022. [DOI: 10.1016/j.mehy.2022.110909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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24
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Identification of Effective and Nonpromiscuous Antidiabetic Drug Molecules from Penicillium Species. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:7040547. [PMID: 35722152 PMCID: PMC9200499 DOI: 10.1155/2022/7040547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/08/2022] [Indexed: 12/13/2022]
Abstract
Diabetes mellitus (DM) is a very common metabolic disorder/disease. The deterioration of β-cells by autoimmune system is the hallmark of this disease. Thioredoxin-Interacting Protein (TXNIP) is responsible for β-cells degradation by T-cells in the pancreas. This protein had been declared a good drug target for controlling DM. Lots of side effects have been reported as a result of long-time consumption of conventional antidiabetic drugs. The development of new and effective drugs with the minimal side effects needs time. TXNIP was selected as a target for Computer-Aided Drug Design. The antidiabetic fungal metabolite compounds were selected from the literature. The compounds were screened for their drug-likeness properties by DruLiTo and DataWarior tools. Twenty-two drug-like fungal compounds were subjected to Quantitative Structure-Activity Relationship (QSAR) analysis by using CheS-Mapper 2.0. The lowest (0.01) activity cliff was found for three compounds: Pinazaphilone A, Pinazaphilone B, and Chermesinone A. The highest value for apol (81.76) was shown by Asperphenamate, while Albonoursin and Sterenin L showed highest score (40.66) for bpol. The lowest value (0.46) for fractional molecular frame (FMF) was calculated for Pinazaphilone A and Pinazaphilone B. TPSA for Pinazaphilone A and Pinazaphilone B was 130.51 Å2.
was observed for all the twenty-two compounds. Molecular docking of fungal compounds with TXNIP was done by AutoDock Vina. The binding energy for complexes ranged between −9.2 and −4.6 kcal/mol. Four complexes, TXNIP-Pinazaphilone A, TXNIP-Pinazaphilone B, TXNIP-Asperphenamate, and TXNIP-Sterenin L, were selected for MD simulation to find out the best lead molecule. Only one complex, TXNIP-Pinazaphilone B, showed a stable conformation throughout the 80 ns run of MD simulation. Pinazaphilone B derived from the Penicillium species fungi was selected as the lead molecule for development of antidiabetic drug having the least side effects.
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25
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Ruiz Puentes P, Rueda-Gensini L, Valderrama N, Hernández I, González C, Daza L, Muñoz-Camargo C, Cruz JC, Arbeláez P. Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery. Sci Rep 2022; 12:8434. [PMID: 35589824 PMCID: PMC9119967 DOI: 10.1038/s41598-022-12180-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/05/2022] [Indexed: 02/08/2023] Open
Abstract
Drug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein–ligand with adversarial augmentations network (PLA-Net), a deep learning-based approach to predict target–ligand interactions. PLA-Net consists of a two-module deep graph convolutional network that considers ligands’ and targets’ most relevant chemical information, successfully combining them to find their binding capability. Moreover, we generate adversarial data augmentations that preserve relevant biological backgrounds and improve the interpretability of our model, highlighting the relevant substructures of the ligands reported to interact with the protein targets. Our experiments demonstrate that the joint ligand–target information and the adversarial augmentations significantly increase the interaction prediction performance. PLA-Net achieves 86.52% in mean average precision for 102 target proteins with perfect performance for 30 of them, in a curated version of actives as decoys dataset. Lastly, we accurately predict pharmacologically-relevant molecules when screening the ligands of ChEMBL and drug repurposing Hub datasets with the perfect-scoring targets.
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Affiliation(s)
- Paola Ruiz Puentes
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Laura Rueda-Gensini
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Natalia Valderrama
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Isabela Hernández
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Cristina González
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Laura Daza
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Carolina Muñoz-Camargo
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Juan C Cruz
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Pablo Arbeláez
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia. .,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia.
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26
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Chen N, Wan G, Zeng X. Integrated Whole-Transcriptome Profiling and Bioinformatics Analysis of the Polypharmacological Effects of Ganoderic Acid Me in Colorectal Cancer Treatment. Front Oncol 2022; 12:833375. [PMID: 35574354 PMCID: PMC9093067 DOI: 10.3389/fonc.2022.833375] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 03/29/2022] [Indexed: 11/29/2022] Open
Abstract
Ganoderic acid Me (GA-Me) is a natural bioactive compound derived from Ganoderma lucidum. Our present results suggested that GA-Me inhibited proliferation, induced DNA fragmentation and significantly activated caspase-9 and caspase-3 in HCT116 cells. As shown in our previous studies, GA-Me targets several genes to prevent cancer, including colorectal cancer (CRC). Thus, we hypothesized that GA-Me might be a multitarget ligand against cancer. However, its exact mechanism in CRC remains unclear. Here, whole-transcriptome sequencing was employed to assess the long noncoding RNA (lncRNA), circular RNA (circRNA), microRNA (miRNA), and messenger RNA (mRNA) profiles of GA-Me-treated HCT116 cells. In total, 1572 differentially expressed (DE) lncRNAs, 123 DEcircRNAs, 87 DEmiRNAs, and 1508 DEmRNAs were identified. DCBLD2 and RAPGEF5 were validated as two core mRNAs in the DElncRNA, DEcircRNA, and DEmiRNA networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed the biological functions and potential mechanisms of TCONS-00008997, XR-925056.2, circRNA-07908, hsa-miR-100-3p, hsa-miR-1257, hsa-miR-3182, NAV3, ADAM20, and STARD4, which were altered after GA-Me treatment. The regulatory relationships of the XR-925056.2-hsa-miR-3182-NAV3/ADAM20/STARD4, circRNA-07908|Chr22:38986298-39025349-hsa-miR-3182-NAV3/ADAM20, ENST00000414039/ENST00000419190-novel874_mature-MMP9 and circRNA-00314|Chr1:35470863-35479212/circRNA-05460|Chr17:72592203-72649268-novel874_mature-MMP9 immune-regulatory networks involved both noncoding RNAs (ncRNAs) and mRNAs. Molecular docking studies showed that Zn2+ and the His201, His205, His211, Glu202, and Ala165 residues of MMP2 contributed to its high affinity for GA-Me. Zn2+ and the Glu402 and Gly186 residues of MMP9 are important for its interaction with GA-Me. Our results suggested and confirmed that GA-Me is a potential multitarget lead compound for CRC treatment with unique polypharmacological advantages.
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Affiliation(s)
- Nianhong Chen
- Center Lab of Longhua Branch and Department of Infectious Disease, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen,China.,Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Medicine School of Shenzhen University, Shenzhen, China.,Laboratory of Signal Transduction, Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY, United States
| | - Guoqing Wan
- Center Lab of Longhua Branch and Department of Infectious Disease, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen,China.,Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Medicine School of Shenzhen University, Shenzhen, China
| | - Xiaobin Zeng
- Center Lab of Longhua Branch and Department of Infectious Disease, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen,China.,Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Medicine School of Shenzhen University, Shenzhen, China
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27
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PLATO: A Predictive Drug Discovery Web Platform for Efficient Target Fishing and Bioactivity Profiling of Small Molecules. Int J Mol Sci 2022; 23:ijms23095245. [PMID: 35563636 PMCID: PMC9103655 DOI: 10.3390/ijms23095245] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/03/2022] [Accepted: 05/06/2022] [Indexed: 02/05/2023] Open
Abstract
PLATO (Polypharmacology pLATform predictiOn) is an easy-to-use drug discovery web platform, which has been designed with a two-fold objective: to fish putative protein drug targets and to compute bioactivity values of small molecules. Predictions are based on the similarity principle, through a reverse ligand-based screening, based on a collection of 632,119 compounds known to be experimentally active on 6004 protein targets. An efficient backend implementation allows to speed-up the process that returns results for query in less than 20 s. The graphical user interface is intuitive to give practitioners easy input and transparent output, which is available as a standard report in portable document format. PLATO has been validated on thousands of external data, with performances better than those of other parallel approaches. PLATO is available free of charge (http://plato.uniba.it/ accessed on 13 April 2022).
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28
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Kumar D, Sharma P, Mahajan A, Dhawan R, Dua K. Pharmaceutical interest of in-silico approaches. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2018-0157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
The virtual environment within the computer using software performed on the computer is known as in-silico studies. These drugs designing software play a vital task in discovering new drugs in the field of pharmaceuticals. These designing programs and software are employed in gene sequencing, molecular modeling, and in assessing the three-dimensional structure of the molecule, which can further be used in drug designing and development. Drug development and discovery is not only a powerful, extensive, and an interdisciplinary system but also a very complex and time-consuming method. This book chapter mainly focused on different types of in-silico approaches along with their pharmaceutical applications in numerous diseases.
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Affiliation(s)
- Dinesh Kumar
- Sri Sai College of Pharmacy , Manawala , Amritsar 143001 , Punjab , India
| | - Pooja Sharma
- Department of Pharmaceutical Sciences and Drug Research , Punjabi University , Patiala 147002 , Punjab , India
- Khalsa College of Pharmacy , Amritsar 143001 , Punjab , India
| | - Ayush Mahajan
- Sri Sai College of Pharmacy , Manawala , Amritsar 143001 , Punjab , India
| | - Ravi Dhawan
- Khalsa College of Pharmacy , Amritsar 143001 , Punjab , India
| | - Kamal Dua
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney , Ultimo 2007 , NSW , Australia
- Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney , Ultimo 2007 , New South Wales , Australia
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29
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Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach. Int J Mol Sci 2021; 22:ijms222413259. [PMID: 34948055 PMCID: PMC8703488 DOI: 10.3390/ijms222413259] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/09/2021] [Accepted: 11/14/2021] [Indexed: 12/12/2022] Open
Abstract
Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.
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30
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Lagoutte-Renosi J, Allemand F, Ramseyer C, Yesylevskyy S, Davani S. Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives. Drug Discov Today 2021; 27:985-1007. [PMID: 34863931 DOI: 10.1016/j.drudis.2021.11.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/02/2021] [Accepted: 11/25/2021] [Indexed: 01/10/2023]
Abstract
Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.
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Affiliation(s)
- Jennifer Lagoutte-Renosi
- EA 3920 Université Bourgogne Franche-Comté, 25000 Besançon, France; Laboratoire de Pharmacologie Clinique et Toxicologie-CHU de Besançon, 25000 Besançon, France
| | - Florentin Allemand
- EA 3920 Université Bourgogne Franche-Comté, 25000 Besançon, France; Laboratoire Chrono Environnement UMR CNRS 6249, Université de Bourgogne Franche-Comté, 16 route de Gray, 25000 Besançon, France
| | - Christophe Ramseyer
- Laboratoire Chrono Environnement UMR CNRS 6249, Université de Bourgogne Franche-Comté, 16 route de Gray, 25000 Besançon, France
| | - Semen Yesylevskyy
- Laboratoire Chrono Environnement UMR CNRS 6249, Université de Bourgogne Franche-Comté, 16 route de Gray, 25000 Besançon, France; Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine, Nauky Sve. 46, Kyiv, Ukraine; Receptor.ai inc, 16192 Coastal Highway, Lewes, DE, USA
| | - Siamak Davani
- EA 3920 Université Bourgogne Franche-Comté, 25000 Besançon, France; Laboratoire de Pharmacologie Clinique et Toxicologie-CHU de Besançon, 25000 Besançon, France.
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31
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Ciriaco F, Gambacorta N, Alberga D, Nicolotti O. Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach. J Chem Inf Model 2021; 61:4868-4876. [PMID: 34570498 DOI: 10.1021/acs.jcim.1c00498] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. Quantitative bioactivity predictions are made on the basis of the statistical distributions of multiple Tanimoto similarity θ values, calculated through 13 different molecular fingerprints, and of the variation of the measured biological activity, reported as ΔpIC50, for all of the ligands sharing a given protein drug target. The application data set comprises as much as 4241 protein drug targets as well as 418 485 ligands selected from ChEMBL (release 25) by employing a set of well-defined filtering rules. Several large internal and external validation studies were carried out to demonstrate the robustness and the predictive potential of the herein proposed method. Additional comparative studies, carried out on two freely available and well-known ligand-target prediction platforms, demonstrated the reliability of our proposed approach for accurate ligand-target matching. Moreover, two applicative cases were also discussed to practically describe how to use our predictive algorithm, which is freely available as a user-friendly web platform. The user can screen single or multiple queries at a time and retrieve the output as a terse html table or as a json file including all of the information concerning the explored similarities to obtain a deeper understanding of the results. High-throughput virtual reverse screening campaigns, allowing for a given query compound the quick detection of the potential drug target from a large collection of them, can be carried out in batch on demand.
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Affiliation(s)
- Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
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32
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Mathai N, Chen Y, Kirchmair J. Validation strategies for target prediction methods. Brief Bioinform 2021; 21:791-802. [PMID: 31220208 PMCID: PMC7299289 DOI: 10.1093/bib/bbz026] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 01/14/2019] [Accepted: 02/17/2019] [Indexed: 12/11/2022] Open
Abstract
Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.
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Affiliation(s)
- Neann Mathai
- Department of Chemistry, University of Bergen, Bergen, Norway.,Computational Biology Unit (CBU), University of Bergen, Bergen, Norway.,Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany
| | - Ya Chen
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany
| | - Johannes Kirchmair
- Department of Chemistry, University of Bergen, Bergen, Norway.,Computational Biology Unit (CBU), University of Bergen, Bergen, Norway.,Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany
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33
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QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs. Molecules 2021; 26:molecules26175270. [PMID: 34500703 PMCID: PMC8434483 DOI: 10.3390/molecules26175270] [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: 07/02/2021] [Revised: 08/05/2021] [Accepted: 08/16/2021] [Indexed: 11/16/2022] Open
Abstract
S2R overexpression is associated with various forms of cancer as well as both neuropsychiatric disorders (e.g., schizophrenia) and neurodegenerative diseases (Alzheimer’s disease: AD). In the present study, three ligand-based methods (QSAR modeling, pharmacophore mapping, and shape-based screening) were implemented to select putative S2R ligands from the DrugBank library comprising 2000+ entries. Four separate optimization algorithms (i.e., stepwise regression, Lasso, genetic algorithm (GA), and a customized extension of GA called GreedGene) were adapted to select descriptors for the QSAR models. The subsequent biological evaluation of selected compounds revealed that three FDA-approved drugs for unrelated therapeutic indications exhibited sub-1 uM binding affinity for S2R. In particular, the antidepressant drug nefazodone elicited a S2R binding affinity Ki = 140 nM. A total of 159 unique S2R ligands were retrieved from 16 publications for model building, validation, and testing. To our best knowledge, the present report represents the first case to develop comprehensive QSAR models sourced by pooling and curating a large assemblage of structurally diverse S2R ligands, which should prove useful for identifying new drug leads and predicting their S2R binding affinity prior to the resource-demanding tasks of chemical synthesis and biological evaluation.
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34
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Cai L, Lu C, Xu J, Meng Y, Wang P, Fu X, Zeng X, Su Y. Drug repositioning based on the heterogeneous information fusion graph convolutional network. Brief Bioinform 2021; 22:6347207. [PMID: 34378011 DOI: 10.1093/bib/bbab319] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/30/2021] [Accepted: 07/21/2021] [Indexed: 11/13/2022] Open
Abstract
In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines. Therefore, there is a pressing need for computational drug repurposing methodologies to facilitate drug discovery. In this study, we propose a new method, called DRHGCN (Drug Repositioning based on the Heterogeneous information fusion Graph Convolutional Network), to discover potential drugs for a certain disease. To make full use of different topology information in different domains (i.e. drug-drug similarity, disease-disease similarity and drug-disease association networks), we first design inter- and intra-domain feature extraction modules by applying graph convolution operations to the networks to learn the embedding of drugs and diseases, instead of simply integrating the three networks into a heterogeneous network. Afterwards, we parallelly fuse the inter- and intra-domain embeddings to obtain the more representative embeddings of drug and disease. Lastly, we introduce a layer attention mechanism to combine embeddings from multiple graph convolution layers for further improving the prediction performance. We find that DRHGCN achieves high performance (the average AUROC is 0.934 and the average AUPR is 0.539) in four benchmark datasets, outperforming the current approaches. Importantly, we conducted molecular docking experiments on DRHGCN-predicted candidate drugs, providing several novel approved drugs for Alzheimer's disease (e.g. benzatropine) and Parkinson's disease (e.g. trihexyphenidyl and haloperidol).
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Affiliation(s)
- Lijun Cai
- Hunan University, Changsha, Hunan, 410082, China
| | | | - Junlin Xu
- Hunan University, Changsha, Hunan, 410082, China
| | - Yajie Meng
- Hunan University, Changsha, Hunan, 410082, China
| | - Peng Wang
- Hunan University, Changsha, Hunan, 410082, China
| | | | | | - Yansen Su
- Anhui University, Changsha, Hunan, 410082, China
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35
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 253] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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Yue Z, Li L, Fu H, Yin Y, Du B, Wang F, Ding Y, Liu Y, Zhao R, Zhang Z, Yu S. Effect of dapagliflozin on diabetic patients with cardiovascular disease via MAPK signalling pathway. J Cell Mol Med 2021; 25:7500-7512. [PMID: 34258872 PMCID: PMC8335696 DOI: 10.1111/jcmm.16786] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/22/2021] [Accepted: 06/28/2021] [Indexed: 12/25/2022] Open
Abstract
Clinical studies have shown that dapagliflozin can reduce cardiovascular outcome in patients with type 2 diabetes mellitus (T2DM), but the exact mechanism is unclear. In this study, we used the molecular docking and network pharmacology methods to explore the potential mechanism of dapagliflozin on T2DM complicated with cardiovascular diseases (CVD). Dapagliflozin's potential targets were predicted via the Swiss Target Prediction platform. The pathogenic targets of T2DM and CVD were screened by the Online Mendelian Inheritance in Man (OMIM) and Gene Cards databases. The common targets of dapagliflozin, T2DM and CVD were used to establish a protein-protein interaction (PPI) network; the potential protein functional modules in the PPI network were found out by MCODE. Metascape tool was used for Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis. A potential protein functional module with the best score was obtained from the PPI network and 9 targets in the protein functional module all showed good binding properties when docking with dapagliflozin. The results of KEGG pathway enrichment analysis showed that the underlying mechanism mainly involved AGE-RAGE signalling pathway in diabetic complications, TNF signalling pathway and MAPK signalling pathway. Significantly, the MAPK signalling pathway was considered as the key pathway. In conclusion, we speculated that dapagliflozin played a therapeutic role in T2DM complicated with CVD mainly through MAPK signalling pathway. This study preliminarily reveals the possible mechanism of dapagliflozin in the treatment of T2DM complicated with CVD and provides a theoretical basis for future clinical research.
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Affiliation(s)
- Zhaodi Yue
- Department of rehabilitation medicine, Department of Endocrinology and Metabology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational medicine, Shandong Institute of Nephrology, Jinan, China.,College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Li Li
- Department of rehabilitation medicine, Department of Endocrinology and Metabology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Hui Fu
- The Clinical Medical College, Cheeloo Medical College of Shandong University, Jinan, China
| | - Yanyan Yin
- Department of rehabilitation medicine, Department of Endocrinology and Metabology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational medicine, Shandong Institute of Nephrology, Jinan, China.,College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Bingyu Du
- Department of rehabilitation medicine, Department of Endocrinology and Metabology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational medicine, Shandong Institute of Nephrology, Jinan, China.,College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Fangqi Wang
- College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yi Ding
- Department of rehabilitation medicine, Department of Endocrinology and Metabology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yibo Liu
- Department of rehabilitation medicine, Department of Endocrinology and Metabology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational medicine, Shandong Institute of Nephrology, Jinan, China.,College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Renjie Zhao
- Department of rehabilitation medicine, Department of Endocrinology and Metabology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational medicine, Shandong Institute of Nephrology, Jinan, China.,College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhongwen Zhang
- Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational medicine, Shandong Institute of Nephrology, Jinan, China
| | - Shaohong Yu
- Department of rehabilitation medicine, Department of Endocrinology and Metabology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
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Li S, Cai C, Gong J, Liu X, Li H. A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing. Proteins 2021; 89:1541-1556. [PMID: 34245187 DOI: 10.1002/prot.26176] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/26/2021] [Accepted: 06/30/2021] [Indexed: 01/18/2023]
Abstract
The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.
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Affiliation(s)
- Shiliang Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Chaoqian Cai
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.,School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Jiayu Gong
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.,School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Xiaofeng Liu
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Honglin Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.,School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China.,Research and Development Department, Jiangzhong Pharmaceutical Co., Ltd., Nanchang, China
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38
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Nayarisseri A. Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery. Curr Top Med Chem 2021; 20:1651-1660. [PMID: 32614747 DOI: 10.2174/156802662019200701164759] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
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39
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Off-Target-Based Design of Selective HIV-1 PROTEASE Inhibitors. Int J Mol Sci 2021; 22:ijms22116070. [PMID: 34199858 PMCID: PMC8200130 DOI: 10.3390/ijms22116070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 05/28/2021] [Accepted: 06/02/2021] [Indexed: 11/17/2022] Open
Abstract
The approval of the first HIV-1 protease inhibitors (HIV-1 PRIs) marked a fundamental step in the control of AIDS, and this class of agents still represents the mainstay therapy for this illness. Despite the undisputed benefits, the necessary lifelong treatment led to numerous severe side-effects (metabolic syndrome, hepatotoxicity, diabetes, etc.). The HIV-1 PRIs are capable of interacting with "secondary" targets (off-targets) characterized by different biological activities from that of HIV-1 protease. In this scenario, the in-silico techniques undoubtedly contributed to the design of new small molecules with well-fitting selectivity against the main target, analyzing possible undesirable interactions that are already in the early stages of the research process. The present work is focused on a new mixed-hierarchical, ligand-structure-based protocol, which is centered on an on/off-target approach, to identify the new selective inhibitors of HIV-1 PR. The use of the well-established, ligand-based tools available in the DRUDIT web platform, in combination with a conventional, structure-based molecular docking process, permitted to fast screen a large database of active molecules and to select a set of structure with optimal on/off-target profiles. Therefore, the method exposed herein, could represent a reliable help in the research of new selective targeted small molecules, permitting to design new agents without undesirable interactions.
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40
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Blaschke T, Bajorath J. Fine-tuning of a generative neural network for designing multi-target compounds. J Comput Aided Mol Des 2021; 36:363-371. [PMID: 34046745 PMCID: PMC9325839 DOI: 10.1007/s10822-021-00392-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/23/2021] [Indexed: 12/20/2022]
Abstract
Exploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.
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Affiliation(s)
- Thomas Blaschke
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, 53115, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, 53115, Bonn, Germany.
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41
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Kanapeckaitė A, Beaurivage C, Jančorienė L, Mažeikienė A. In silico drug discovery for a complex immunotherapeutic target - human c-Rel protein. Biophys Chem 2021; 276:106593. [PMID: 34087524 DOI: 10.1016/j.bpc.2021.106593] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/28/2021] [Accepted: 04/12/2021] [Indexed: 12/22/2022]
Abstract
Target evaluation and rational drug design rely on identifying and characterising small-molecule binding sites on therapeutically relevant target proteins. Immunotherapeutics development is especially challenging because of complex disease etiology and heterogenous nature of targets. c-Rel protein, a promising target in many human inflammatory and cancer pathologies, was selected as a case study for an effective in silico screening platform development since this transcription factor currently has no successful therapeutic inhibitors or modulators. This study introduces a novel in silico screening approach to probe binding sites using structural validation sets, molecular modelling and describes a method of a computer-aided drug design when a crystal structure is not available for the target of interest. In addition, we showed that binding sites can be analysed with the machine learning as well as molecular simulation approaches to help assess and systematically analyse how drug candidates can exert their mode of action. Finally, this cutting-edge approach was subjected to a high through-put virtual screen of selected 34 M drug-like compounds filtered from a library of 659 M compounds by identifying the most promising structures and proposing potential action mechanisms for the future development of highly selective human c-Rel inhibitors and/or modulators.
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Affiliation(s)
| | | | - Ligita Jančorienė
- Vilnius University Medical Faculty InsTtute of Clinical Medicine, Clinic of InfecTous Diseases and Dermatovenerology, Santariškių str. 14, 08406 Vilnius, Lithuania
| | - Asta Mažeikienė
- Department of Physiology, Biochemistry, Microbiology and Laboratory Medicine, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, M. K. Čiurlionio g. 21, LT-03101, Vilnius, Lithuania
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42
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Li S, Zhang F, Xiao X, Guo Y, Wen Z, Li M, Pu X. Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics. Front Pharmacol 2021; 12:634097. [PMID: 33986671 PMCID: PMC8112211 DOI: 10.3389/fphar.2021.634097] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/04/2021] [Indexed: 12/26/2022] Open
Abstract
Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.
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Affiliation(s)
- Shiqi Li
- College of Chemistry, Sichuan University, Chengdu, China
| | - Fuhui Zhang
- College of Chemistry, Sichuan University, Chengdu, China
| | - Xiuchan Xiao
- School of Material Science and Environmental Engineering, Chengdu Technological University, Chengdu, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, China
| | - Zhining Wen
- College of Chemistry, Sichuan University, Chengdu, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, China
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Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network. Biomolecules 2021; 11:biom11040546. [PMID: 33917905 PMCID: PMC8068312 DOI: 10.3390/biom11040546] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/05/2021] [Accepted: 04/06/2021] [Indexed: 01/08/2023] Open
Abstract
Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze the interactions between the chemicals in the medicinal plants and multiple targets based on the complex multipartite network of the medicinal plants, multi-chemicals, and multiple targets. The multipartite network was constructed via the conjunction of two relationships: chemicals in plants and the biological actions of those chemicals on the targets. In doing so, we introduced an index of the efficacy of chemicals in a plant on a protein target of interest, called target potency score (TPS). We showed that the analysis can identify specific chemical profiles from each group of plants, which can then be employed for discovering new alternative therapeutic agents. Furthermore, specific clusters of plants and chemicals acting on specific targets were retrieved using TPS that suggested potential drug candidates with high probability of clinical success. We expect that this approach may open a way to predict the biological functions of multi-chemicals and multi-plants on the targets of interest and enable repositioning of the plants and chemicals.
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Fontana F, Figueiredo P, Martins JP, Santos HA. Requirements for Animal Experiments: Problems and Challenges. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2004182. [PMID: 33025748 DOI: 10.1002/smll.202004182] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Indexed: 05/27/2023]
Abstract
In vivo models remain a principle screening tool in the drug discovery pipeline. Here, the challenges associated with the need for animal experiments, as well as their impact on research, individual/societal, and economic contexts are discussed. A number of alternatives that, with further development, optimization, and investment, may replace animal experiments are also revised.
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Affiliation(s)
- Flavia Fontana
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, FI-00014, Finland
| | - Patrícia Figueiredo
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, FI-00014, Finland
| | - João P Martins
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, FI-00014, Finland
| | - Hélder A Santos
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, FI-00014, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, FI-00014, Finland
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Zeigler AC, Chandrabhatla AS, Christiansen SL, Nelson AR, Holmes JW, Saucerman JJ. Network model-based screen for FDA-approved drugs affecting cardiac fibrosis. CPT Pharmacometrics Syst Pharmacol 2021; 10:377-388. [PMID: 33571402 PMCID: PMC8099443 DOI: 10.1002/psp4.12599] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/08/2020] [Accepted: 01/14/2021] [Indexed: 12/30/2022] Open
Abstract
Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.
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Affiliation(s)
- Angela C. Zeigler
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
| | | | | | - Anders R. Nelson
- Department of PharmacologyUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Jeffrey W. Holmes
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
- Division of Cardiovascular MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Jeffrey J. Saucerman
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
- Division of Cardiovascular MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA
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46
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Broni E, Kwofie SK, Asiedu SO, Miller WA, Wilson MD. A Molecular Modeling Approach to Identify Potential Antileishmanial Compounds Against the Cell Division Cycle (cdc)-2-Related Kinase 12 (CRK12) Receptor of Leishmania donovani. Biomolecules 2021; 11:458. [PMID: 33803906 PMCID: PMC8003136 DOI: 10.3390/biom11030458] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 03/12/2021] [Accepted: 03/14/2021] [Indexed: 12/11/2022] Open
Abstract
The huge burden of leishmaniasis caused by the trypanosomatid protozoan parasite Leishmania is well known. This illness was included in the list of neglected tropical diseases targeted for elimination by the World Health Organization. However, the increasing evidence of resistance to existing antimonial drugs has made the eradication of the disease difficult to achieve, thus warranting the search for new drug targets. We report here studies that used computational methods to identify inhibitors of receptors from natural products. The cell division cycle-2-related kinase 12 (CRK12) receptor is a plausible drug target against Leishmania donovani. This study modelled the 3D molecular structure of the L. donovani CRK12 (LdCRK12) and screened for small molecules with potential inhibitory activity from African flora. An integrated library of 7722 African natural product-derived compounds and known inhibitors were screened against the LdCRK12 using AutoDock Vina after performing energy minimization with GROMACS 2018. Four natural products, namely sesamin (NANPDB1649), methyl ellagic acid (NANPDB1406), stylopine (NANPDB2581), and sennecicannabine (NANPDB6446) were found to be potential LdCRK12 inhibitory molecules. The molecular docking studies revealed two compounds NANPDB1406 and NANPDB2581 with binding affinities of -9.5 and -9.2 kcal/mol, respectively, against LdCRK12 which were higher than those of the known inhibitors and drugs, including GSK3186899, amphotericin B, miltefosine, and paromomycin. All the four compounds were predicted to have inhibitory constant (Ki) values ranging from 0.108 to 0.587 μM. NANPDB2581, NANPDB1649 and NANPDB1406 were also predicted as antileishmanial with Pa and Pi values of 0.415 and 0.043, 0.391 and 0.052, and 0.351 and 0.071, respectively. Molecular dynamics simulations coupled with molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations reinforced their good binding mechanisms. Most compounds were observed to bind in the ATP binding pocket of the kinase domain. Lys488 was predicted as a key residue critical for ligand binding in the ATP binding pocket of the LdCRK12. The molecules were pharmacologically profiled as druglike with inconsequential toxicity. The identified molecules have scaffolds that could form the backbone for fragment-based drug design of novel leishmanicides but warrant further studies to evaluate their therapeutic potential.
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Affiliation(s)
- Emmanuel Broni
- Department of Biomedical Engineering, School of Engineering Sciences, College of Basic & Applied Sciences, University of Ghana, Legon, Accra LG 77, Ghana;
| | - Samuel K. Kwofie
- Department of Biomedical Engineering, School of Engineering Sciences, College of Basic & Applied Sciences, University of Ghana, Legon, Accra LG 77, Ghana;
- West African Center for Cell Biology of Infectious Pathogens, Department of Biochemistry, Cell and Molecular Biology, College of Basic and Applied Sciences, University of Ghana, Legon, Accra LG 54, Ghana
| | - Seth O. Asiedu
- Department of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, Legon, Accra LG 581, Ghana; (S.O.A.); (M.D.W.)
| | - Whelton A. Miller
- Department of Medicine, Loyola University Medical Center, Maywood, IL 60153, USA;
- Department of Molecular Pharmacology and Neuroscience, Loyola University Medical Center, Maywood, IL 60153, USA
- Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, IL 19104, USA
| | - Michael D. Wilson
- Department of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, Legon, Accra LG 581, Ghana; (S.O.A.); (M.D.W.)
- Department of Medicine, Loyola University Medical Center, Maywood, IL 60153, USA;
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Tanoli Z, Vähä-Koskela M, Aittokallio T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opin Drug Discov 2021; 16:977-989. [PMID: 33543671 DOI: 10.1080/17460441.2021.1883585] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication.Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland.,Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway.,Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
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Vanhaelen Q. Web-based Tools for Drug Repurposing: Successful Examples of Collaborative Research. Curr Med Chem 2021; 28:181-195. [PMID: 32003659 DOI: 10.2174/0929867327666200128111925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 11/23/2019] [Accepted: 11/30/2019] [Indexed: 11/22/2022]
Abstract
Computational approaches have been proven to be complementary tools of interest in identifying potential candidates for drug repurposing. However, although the methods developed so far offer interesting opportunities and could contribute to solving issues faced by the pharmaceutical sector, they also come with their constraints. Indeed, specific challenges ranging from data access, standardization and integration to the implementation of reliable and coherent validation methods must be addressed to allow systematic use at a larger scale. In this mini-review, we cover computational tools recently developed for addressing some of these challenges. This includes specific databases providing accessibility to a large set of curated data with standardized annotations, web-based tools integrating flexible user interfaces to perform fast computational repurposing experiments and standardized datasets specifically annotated and balanced for validating new computational drug repurposing methods. Interestingly, these new databases combined with the increasing number of information about the outcomes of drug repurposing studies can be used to perform a meta-analysis to identify key properties associated with successful drug repurposing cases. This information could further be used to design estimation methods to compute a priori assessment of the repurposing possibilities.
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Affiliation(s)
- Quentin Vanhaelen
- Insilico Medicine, 307A, Core Building 1, 1 Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
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Spinozzi E, Baldassarri C, Acquaticci L, Del Bello F, Grifantini M, Cappellacci L, Riccardo P. Adenosine receptors as promising targets for the management of ocular diseases. Med Chem Res 2021; 30:353-370. [PMID: 33519168 PMCID: PMC7829661 DOI: 10.1007/s00044-021-02704-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 12/01/2020] [Indexed: 12/15/2022]
Abstract
The ocular drug discovery arena has undergone a significant improvement in the last few years culminating in the FDA approvals of 8 new drugs. However, despite a large number of drugs, generics, and combination products available, it remains an urgent need to find breakthrough strategies and therapies for tackling ocular diseases. Targeting the adenosinergic system may represent an innovative strategy for discovering new ocular therapeutics. This review focused on the recent advance in the field and described the numerous nucleoside and non-nucleoside modulators of the four adenosine receptors (ARs) used as potential tools or clinical drug candidates.
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Affiliation(s)
- Eleonora Spinozzi
- School of Pharmacy Medicinal Chemistry Unit, University of Camerino, Via S. Agostino 1, 62032 Camerino, Italy
| | - Cecilia Baldassarri
- School of Pharmacy Medicinal Chemistry Unit, University of Camerino, Via S. Agostino 1, 62032 Camerino, Italy
| | - Laura Acquaticci
- School of Pharmacy Medicinal Chemistry Unit, University of Camerino, Via S. Agostino 1, 62032 Camerino, Italy
| | - Fabio Del Bello
- School of Pharmacy Medicinal Chemistry Unit, University of Camerino, Via S. Agostino 1, 62032 Camerino, Italy
| | - Mario Grifantini
- School of Pharmacy Medicinal Chemistry Unit, University of Camerino, Via S. Agostino 1, 62032 Camerino, Italy
| | - Loredana Cappellacci
- School of Pharmacy Medicinal Chemistry Unit, University of Camerino, Via S. Agostino 1, 62032 Camerino, Italy
| | - Petrelli Riccardo
- School of Pharmacy Medicinal Chemistry Unit, University of Camerino, Via S. Agostino 1, 62032 Camerino, Italy
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50
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Wang C, Kurgan L. Survey of Similarity-Based Prediction of Drug-Protein Interactions. Curr Med Chem 2021; 27:5856-5886. [PMID: 31393241 DOI: 10.2174/0929867326666190808154841] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 04/16/2018] [Accepted: 10/23/2018] [Indexed: 12/20/2022]
Abstract
Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.
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Affiliation(s)
- Chen Wang
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
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