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Späth J, Wang R, Humphrey M, Baumbach J, Loscalzo J. Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis. CPT Pharmacometrics Syst Pharmacol 2024; 13:257-269. [PMID: 37950385 PMCID: PMC10864927 DOI: 10.1002/psp4.13076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/12/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.
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Affiliation(s)
- Julian Späth
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Institute of Computational Systems BiologyUniversity of HamburgHamburgGermany
| | - Rui‐Sheng Wang
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Maeve Humphrey
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jan Baumbach
- Institute of Computational Systems BiologyUniversity of HamburgHamburgGermany
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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2
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Kate A, Seth E, Singh A, Chakole CM, Chauhan MK, Singh RK, Maddalwar S, Mishra M. Artificial Intelligence for Computer-Aided Drug Discovery. Drug Res (Stuttg) 2023; 73:369-377. [PMID: 37276884 DOI: 10.1055/a-2076-3359] [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: 06/07/2023]
Abstract
The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.
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Affiliation(s)
- Aditya Kate
- Amity Institute of Biotechnology, Amity University, Chhattisgarh, India
| | - Ekkita Seth
- Amity Institute of Biotechnology, Amity University, Chhattisgarh, India
| | - Ananya Singh
- Amity Institute of Biotechnology, Amity University, Chhattisgarh, India
| | - Chandrashekhar Mahadeo Chakole
- Bajiraoji Karanjekar college of Pharmacy, Sakoli, Dist-Bhandara, India
- NDDS Research Lab, Delhi Institute of Pharmaceutical Sciences and Research, DPSR-University, New Delhi
| | - Meenakshi Kanwar Chauhan
- NDDS Research Lab, Delhi Institute of Pharmaceutical Sciences and Research, DPSR-University, New Delhi
| | - Ravi Kant Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | | | - Mohit Mishra
- Amity Institute of Biotechnology, Amity University, Chhattisgarh, India
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Gupta Y, Savytskyi OV, Coban M, Venugopal A, Pleqi V, Weber CA, Chitale R, Durvasula R, Hopkins C, Kempaiah P, Caulfield TR. Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics. Mol Aspects Med 2023; 91:101151. [PMID: 36371228 PMCID: PMC9613808 DOI: 10.1016/j.mam.2022.101151] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022]
Abstract
With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the first documented pandemic due to a coronavirus that continues to be a major health challenge. Despite being rapid, uncontrollable, and highly infectious in its spread, it also created incentives for technology development and redefined public health needs and research agendas to fast-track innovations to be translated. Breakthroughs in computational biology peaked during the pandemic with renewed attention to making all cutting-edge technology deliver agents to combat the disease. The demand to develop effective treatments yielded surprising collaborations from previously segregated fields of science and technology. The long-standing pharmaceutical industry's aversion to repurposing existing drugs due to a lack of exponential financial gain was overrun by the health crisis and pressures created by front-line researchers and providers. Effective vaccine development even at an unprecedented pace took more than a year to develop and commence trials. Now the emergence of variants and waning protections during the booster shots is resulting in breakthrough infections that continue to strain health care systems. As of now, every protein of SARS-CoV-2 has been structurally characterized and related host pathways have been extensively mapped out. The research community has addressed the druggability of a multitude of possible targets. This has been made possible due to existing technology for virtual computer-assisted drug development as well as new tools and technologies such as artificial intelligence to deliver new leads. Here in this article, we are discussing advances in the drug discovery field related to target-based drug discovery and exploring the implications of known target-specific agents on COVID-19 therapeutic management. The current scenario calls for more personalized medicine efforts and stratifying patient populations early on for their need for different combinations of prognosis-specific therapeutics. We intend to highlight target hotspots and their potential agents, with the ultimate goal of using rational design of new therapeutics to not only end this pandemic but also uncover a generalizable platform for use in future pandemics.
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Affiliation(s)
- Yash Gupta
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | - Oleksandr V Savytskyi
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; In Vivo Biosystems, Eugene, OR, USA
| | - Matt Coban
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Vasili Pleqi
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | - Caleb A Weber
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Rohit Chitale
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA; The Council on Strategic Risks, 1025 Connecticut Ave NW, Washington, DC, USA
| | - Ravi Durvasula
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | | | - Prakasha Kempaiah
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | - Thomas R Caulfield
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Department of QHS Computational Biology, Mayo Clinic, Jacksonville, FL, USA; Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA; Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA; Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.
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Singh D, Singh A, Chawla PA. An overview of current strategies and future prospects in drug repurposing in tuberculosis. EXPLORATION OF MEDICINE 2023. [DOI: 10.37349/emed.2023.00125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
A large number of the population faces mortality as an effect of tuberculosis (TB). The line of treatment in the management of TB faces a jolt with ever-increasing multi-drug resistance (DR) cases. Further, the drugs engaged in the treatment of TB are associated with different toxicities, such as renal and hepatic toxicity. Different combinations are sought for effective anti-tuberculosis (anti-TB) effects with a decrease in toxicity. In this regard, drug repurposing has been very promising in improving the efficacy of drugs by enhancement of bioavailability and widening the safety margin. The success in drug repurposing lies in specified binding and inhibition of a particular target in the drug molecule. Different drugs have been repurposed for various ailments like cancer, Alzheimer’s disease, acquired immunodeficiency syndrome (AIDS), hair loss, etc. Repurposing in anti-TB drugs holds great potential too. The use of whole-cell screening assays and the availability of large chemical compounds for testing against Mycobacterium tuberculosis poses a challenge in this development. The target-based discovery of sites has emerged in the form of phenotypic screening as ethionamide R (EthR) and malate synthase inhibitors are similar to pharmaceuticals. In this review, the authors have thoroughly described the drug repurposing techniques on the basis of pharmacogenomics and drug metabolism, pathogen-targeted therapy, host-directed therapy, and bioinformatics approaches for the identification of drugs. Further, the significance of repurposing of drugs elaborated on large databases has been revealed. The role of genomics and network-based methods in drug repurposing has been also discussed in this article.
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Affiliation(s)
- Dilpreet Singh
- Department of Pharmaceutics, ISF College of Pharmacy, Moga 142001, Punjab, India
| | - Amrinder Singh
- Department of Pharmaceutics, ISF College of Pharmacy, Moga 142001, Punjab, India
| | - Pooja A. Chawla
- Department of Pharmaceutical Chemistry, ISF College of Pharmacy, Moga 142001, Punjab, India
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Cong Y, Endo T. Multi-Omics and Artificial Intelligence-Guided Drug Repositioning: Prospects, Challenges, and Lessons Learned from COVID-19. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:361-371. [PMID: 35759424 DOI: 10.1089/omi.2022.0068] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Drug repurposing is of interest for therapeutics innovation in many human diseases including coronavirus disease 2019 (COVID-19). Methodological innovations in drug repurposing are currently being empowered by convergence of omics systems science and digital transformation of life sciences. This expert review article offers a systematic summary of the application of artificial intelligence (AI), particularly machine learning (ML), to drug repurposing and classifies and introduces the common clustering, dimensionality reduction, and other methods. We highlight, as a present-day high-profile example, the involvement of AI/ML-based drug discovery in the COVID-19 pandemic and discuss the collection and sharing of diverse data types, and the possible futures awaiting drug repurposing in an era of AI/ML and digital technologies. The article provides new insights on convergence of multi-omics and AI-based drug repurposing. We conclude with reflections on the various pathways to expedite innovation in drug development through drug repurposing for prompt responses to the current COVID-19 pandemic and future ecological crises in the 21st century.
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Affiliation(s)
- Yi Cong
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Toshinori Endo
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
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Cong Y, Shintani M, Imanari F, Osada N, Endo T. A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learning, and Unsupervised Clustering of Gene Expression. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:339-347. [PMID: 35666246 PMCID: PMC9245788 DOI: 10.1089/omi.2022.0026] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Drug repurposing has broad importance in planetary health for therapeutics innovation in infectious diseases as well as common or rare chronic human diseases. Drug repurposing has also proved important to develop interventions against the COVID-19 pandemic. We propose a new approach for drug repurposing involving two-stage prediction and machine learning. First, diseases are clustered by gene expression on the premise that similar patterns of altered gene expression imply critical pathways shared in different disease conditions. Next, drug efficacy is assessed by the reversibility of abnormal gene expression, and results are clustered to identify repurposing targets. To cluster similar diseases, gene expression data from 262 cases of 31 diseases and 268 controls were analyzed by Uniform Manifold Approximation and Projection for Dimension Reduction followed by k-means to optimize the number of clusters. For evaluation, we examined disease-specific gene expression data for inclusion, body myositis, polymyositis, and dermatomyositis (DM), and used LINCS L1000 characteristic direction signatures search engine (L1000CDS2) to obtain lists of small-molecule compounds that reversed the expression patterns of these specifically altered genes as candidates for drug repurposing. Finally, the functions of affected genes were analyzed by Gene Set Enrichment Analysis to examine consistency with expected drug efficacy. Consequently, we found disease-specific gene expression, and importantly, identified 20 drugs such as BMS-387032, phorbol-12-myristate-13-acetate, mitoxantrone, alvocidib, and vorinostat as candidates for repurposing. These were previously noted to be effective against two of the three diseases, and have a high probability of being effective against the other. That is, inclusion body myositis and DM. The two-stage prediction approach to drug repurposing presented here offers innovation to inform future drug discovery and clinical trials in a variety of human diseases.
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Affiliation(s)
- Yi Cong
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Misaki Shintani
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Fuga Imanari
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Naoki Osada
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Toshinori Endo
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
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Lee C, Lin J, Prokop A, Gopalakrishnan V, Hanna RN, Papa E, Freeman A, Patel S, Yu W, Huhn M, Sheikh AS, Tan K, Sellman BR, Cohen T, Mangion J, Khan FM, Gusev Y, Shameer K. StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit. Front Genet 2022; 13:868015. [PMID: 35711912 PMCID: PMC9197487 DOI: 10.3389/fgene.2022.868015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/29/2022] [Indexed: 01/26/2023] Open
Abstract
Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.
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Affiliation(s)
- Chiyun Lee
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Junxia Lin
- Georgetown University, Washington, DC, United States
| | | | | | - Richard N. Hanna
- Early Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Eliseo Papa
- Research Data and Analytics, R&D IT, AstraZeneca, Cambridge, United Kingdom
| | - Adrian Freeman
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Saleha Patel
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Wen Yu
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Monika Huhn
- Biometrics and Information Sciences, BioPharmaceuticals R&D, AstraZeneca, Mölndal, Sweden
| | - Abdul-Saboor Sheikh
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Keith Tan
- Neuroscience, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Bret R. Sellman
- Discovery Microbiome, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Taylor Cohen
- Discovery Microbiome, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Jonathan Mangion
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Faisal M. Khan
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Yuriy Gusev
- Georgetown University, Washington, DC, United States
| | - Khader Shameer
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States,*Correspondence: Khader Shameer,
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