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Meng L, Zhou B, Liu H, Chen Y, Yuan R, Chen Z, Luo S, Chen H. Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174201. [PMID: 38936709 DOI: 10.1016/j.scitotenv.2024.174201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 06/29/2024]
Abstract
Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.
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Affiliation(s)
- Lingxuan Meng
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Beihai Zhou
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haijun Liu
- School of Resources and Environment, Anqing Normal University, Anqing, China.
| | - Yuefang Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Rongfang Yuan
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhongbing Chen
- Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic.
| | - Shuai Luo
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Huilun Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
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2
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Waqas M, Ullah S, Ullah A, Halim SA, Rehman NU, Khalid A, Ali A, Khan A, Gibbons S, Csuk R, Al-Harrasi A. Disrupting protease and deubiquitinase activities of SARS-CoV-2 papain-like protease by natural and synthetic products discovered through multiple computational and biochemical approaches. Int J Biol Macromol 2024; 277:134476. [PMID: 39111477 DOI: 10.1016/j.ijbiomac.2024.134476] [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: 12/21/2023] [Revised: 07/31/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024]
Abstract
The single-stranded RNA genome of SARS-CoV-2 encodes several structural and non-structural proteins, among which the papain-like protease (PLpro) is crucial for viral replication and immune evasion and has emerged as a promising therapeutic target. The current study aims to discover new inhibitors of PLpro that can simultaneously disrupt its protease and deubiquitinase activities. Using multiple computational approaches, six compounds (CP1-CP6) were selected from our in-house compounds database, with higher docking scores (-7.97 kcal/mol to -8.14 kcal/mol) and fitted well in the active pocket of PLpro. Furthermore, utilizing microscale molecular dynamics simulations (MD), the dynamic behavior of selected compounds was studied. Those molecules strongly binds at the PLpro active site and forms stable complexes. The dynamic motions suggest that the binding of CP1-CP6 brought the protein to a closed conformational state, thereby altering its normal function. In an in vitro evaluation, CP2 showed the most significant inhibitory potential for PLpro (protease activity = 2.71 ± 0.33 μM and deubiquitinase activity = 3.11 ± 0.75 μM), followed by CP1, CP5, CP4 and CP6. Additionally, CP1-CP6 showed no cytotoxicity at a concentration of 30 μM in the human BJ cell line.
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Affiliation(s)
- Muhammad Waqas
- Department of Biotechnology and Genetic Engineering, Hazara University, Mansehra 2100, Pakistan
| | - Saeed Ullah
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa 616, Sultanate of Oman
| | - Atta Ullah
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa 616, Sultanate of Oman
| | - Sobia Ahsan Halim
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa 616, Sultanate of Oman
| | - Najeeb Ur Rehman
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa 616, Sultanate of Oman
| | - Asaad Khalid
- Health Research Center, Jazan University, P.O. Box: 114, Jazan 45142, Saudi Arabia
| | - Amjad Ali
- Department of Biotechnology and Genetic Engineering, Hazara University, Mansehra 2100, Pakistan.
| | - Ajmal Khan
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa 616, Sultanate of Oman.
| | - Simon Gibbons
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa 616, Sultanate of Oman
| | - Rene Csuk
- Martin-Luther-University Halle-Wittenberg, Organic Chemistry, Kurt-Mothes-Str. 2, D-06120 Halle (Saale), Germany
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa 616, Sultanate of Oman.
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3
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Rudroff T. Artificial Intelligence as a Replacement for Animal Experiments in Neurology: Potential, Progress, and Challenges. Neurol Int 2024; 16:805-820. [PMID: 39195562 DOI: 10.3390/neurolint16040060] [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: 06/05/2024] [Revised: 07/19/2024] [Accepted: 07/26/2024] [Indexed: 08/29/2024] Open
Abstract
Animal experimentation has long been a cornerstone of neurology research, but it faces growing scientific, ethical, and economic challenges. Advances in artificial intelligence (AI) are providing new opportunities to replace animal testing with more human-relevant and efficient methods. This article explores the potential of AI technologies such as brain organoids, computational models, and machine learning to revolutionize neurology research and reduce reliance on animal models. These approaches can better recapitulate human brain physiology, predict drug responses, and uncover novel insights into neurological disorders. They also offer faster, cheaper, and more ethical alternatives to animal experiments. Case studies demonstrate AI's ability to accelerate drug discovery for Alzheimer's, predict neurotoxicity, personalize treatments for Parkinson's, and restore movement in paralysis. While challenges remain in validating and integrating these technologies, the scientific, economic, practical, and moral advantages are driving a paradigm shift towards AI-based, animal-free research in neurology. With continued investment and collaboration across sectors, AI promises to accelerate the development of safer and more effective therapies for neurological conditions while significantly reducing animal use. The path forward requires the ongoing development and validation of these technologies, but a future in which they largely replace animal experiments in neurology appears increasingly likely. This transition heralds a new era of more humane, human-relevant, and innovative brain research.
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Affiliation(s)
- Thorsten Rudroff
- Department of Health and Human Physiology, University of Iowa, Iowa City, IA 52242, USA
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA
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4
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Rao M, McDuffie E, Srivastava S, Plaisted W, Sachs C. Safety Implications of Modulating Nuclear Receptors: A Comprehensive Analysis from Non-Clinical and Clinical Perspectives. Pharmaceuticals (Basel) 2024; 17:875. [PMID: 39065726 PMCID: PMC11279859 DOI: 10.3390/ph17070875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 06/13/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024] Open
Abstract
The unintended modulation of nuclear receptor (NR) activity by drugs can lead to toxicities amongst the endocrine, gastrointestinal, hepatic cardiovascular, and central nervous systems. While secondary pharmacology screening assays include NRs, safety risks due to unintended interactions of small molecule drugs with NRs remain poorly understood. To identify potential nonclinical and clinical safety effects resulting from functional interactions with 44 of the 48 human-expressed NRs, we conducted a systematic narrative review of the scientific literature, tissue expression data, and used curated databases (OFF-X™) (Off-X, Clarivate) to organize reported toxicities linked to the functional modulation of NRs in a tabular and machine-readable format. The top five NRs associated with the highest number of safety alerts from peer-reviewed journals, regulatory agency communications, congresses/conferences, clinical trial registries, and company communications were the Glucocorticoid Receptor (GR, 18,328), Androgen Receptor (AR, 18,219), Estrogen Receptor (ER, 12,028), Retinoic acid receptors (RAR, 10,450), and Pregnane X receptor (PXR, 8044). Toxicities associated with NR modulation include hepatotoxicity, cardiotoxicity, endocrine disruption, carcinogenicity, metabolic disorders, and neurotoxicity. These toxicities often arise from the dysregulation of receptors like Peroxisome proliferator-activated receptors (PPARα, PPARγ), the ER, PXR, AR, and GR. This dysregulation leads to various health issues, including liver enlargement, hepatocellular carcinoma, heart-related problems, hormonal imbalances, tumor growth, metabolic syndromes, and brain function impairment. Gene expression analysis using heatmaps for human and rat tissues complemented the functional modulation of NRs associated with the reported toxicities. Interestingly, certain NRs showed ubiquitous expression in tissues not previously linked to toxicities, suggesting the potential utilization of organ-specific NR interactions for therapeutic purposes.
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Affiliation(s)
- Mohan Rao
- Toxicology Department, Neurocrine Biosciences, Inc., San Diego, CA 92130, USA (C.S.)
| | - Eric McDuffie
- Toxicology Department, Neurocrine Biosciences, Inc., San Diego, CA 92130, USA (C.S.)
| | - Sanjay Srivastava
- Chemistry Department, Neurocrine Biosciences, Inc., San Diego, CA 92130, USA
| | - Warren Plaisted
- Biology Department, Neurocrine Biosciences, Inc., San Diego, CA 92130, USA
| | - Clifford Sachs
- Toxicology Department, Neurocrine Biosciences, Inc., San Diego, CA 92130, USA (C.S.)
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Liu J, Gui Y, Rao J, Sun J, Wang G, Ren Q, Qu N, Niu B, Chen Z, Sheng X, Wang Y, Zheng M, Li X. In silico off-target profiling for enhanced drug safety assessment. Acta Pharm Sin B 2024; 14:2927-2941. [PMID: 39027254 PMCID: PMC11252485 DOI: 10.1016/j.apsb.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/21/2024] [Accepted: 02/29/2024] [Indexed: 07/20/2024] Open
Abstract
Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.
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Affiliation(s)
- Jin Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
| | - Yike Gui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingjing Sun
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Gang Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qun Ren
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ning Qu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Buying Niu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhiyi Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, Hangzhou 330106, China
| | - Xia Sheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yitian Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingyue Zheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Nanjing University of Chinese Medicine, Nanjing 210023, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, Hangzhou 330106, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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6
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Kaito S, Takeshita JI, Iwata M, Sasaki T, Hosaka T, Shizu R, Yoshinari K. Utility of human cytochrome P450 inhibition data in the assessment of drug-induced liver injury. Xenobiotica 2024; 54:411-419. [PMID: 38315106 DOI: 10.1080/00498254.2024.2312505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 01/28/2024] [Indexed: 02/07/2024]
Abstract
Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis.The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc.Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data.These results suggest that P450 inhibition assay data are useful for DILI risk evaluation.
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Affiliation(s)
- Shunnosuke Kaito
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Jun-Ichi Takeshita
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Misaki Iwata
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Takamitsu Sasaki
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Takuomi Hosaka
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Ryota Shizu
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Kouichi Yoshinari
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
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7
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Amorim AM, Piochi LF, Gaspar AT, Preto A, Rosário-Ferreira N, Moreira IS. Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction. Chem Res Toxicol 2024; 37:827-849. [PMID: 38758610 PMCID: PMC11187637 DOI: 10.1021/acs.chemrestox.3c00352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.
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Affiliation(s)
- Ana M.
B. Amorim
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD
Programme in Biosciences, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PURR.AI,
Rua Pedro Nunes, IPN Incubadora, Ed C, 3030-199 Coimbra, Portugal
| | - Luiz F. Piochi
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Ana T. Gaspar
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - António
J. Preto
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD Programme
in Experimental Biology and Biomedicine, Institute for Interdisciplinary
Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789 Coimbra, Portugal
| | - Nícia Rosário-Ferreira
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Irina S. Moreira
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
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8
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Abady MM, Jeong JS, Kwon HJ, Assiri AM, Cho J, Saadeldin IM. The reprotoxic adverse side effects of neurogenic and neuroprotective drugs: current use of human organoid modeling as a potential alternative to preclinical models. Front Pharmacol 2024; 15:1412188. [PMID: 38948466 PMCID: PMC11211546 DOI: 10.3389/fphar.2024.1412188] [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: 04/04/2024] [Accepted: 05/29/2024] [Indexed: 07/02/2024] Open
Abstract
The management of neurological disorders heavily relies on neurotherapeutic drugs, but notable concerns exist regarding their possible negative effects on reproductive health. Traditional preclinical models often fail to accurately predict reprotoxicity, highlighting the need for more physiologically relevant systems. Organoid models represent a promising approach for concurrently studying neurotoxicity and reprotoxicity, providing insights into the complex interplay between neurotherapeutic drugs and reproductive systems. Herein, we have examined the molecular mechanisms underlying neurotherapeutic drug-induced reprotoxicity and discussed experimental findings from case studies. Additionally, we explore the utility of organoid models in elucidating the reproductive complications of neurodrug exposure. Have discussed the principles of organoid models, highlighting their ability to recapitulate neurodevelopmental processes and simulate drug-induced toxicity in a controlled environment. Challenges and future perspectives in the field have been addressed with a focus on advancing organoid technologies to improve reprotoxicity assessment and enhance drug safety screening. This review underscores the importance of organoid models in unraveling the complex relationship between neurotherapeutic drugs and reproductive health.
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Affiliation(s)
- Mariam M. Abady
- Organic Metrology Group, Division of Chemical and Material Metrology, Korea Research Institute of Standards and Science, Daejeon, Republic of Korea
- Department of Bio-Analytical Science, University of Science and Technology, Daejeon, Republic of Korea
- Department of Nutrition and Food Science, National Research Centre, Cairo, Egypt
| | - Ji-Seon Jeong
- Organic Metrology Group, Division of Chemical and Material Metrology, Korea Research Institute of Standards and Science, Daejeon, Republic of Korea
- Department of Bio-Analytical Science, University of Science and Technology, Daejeon, Republic of Korea
| | - Ha-Jeong Kwon
- Organic Metrology Group, Division of Chemical and Material Metrology, Korea Research Institute of Standards and Science, Daejeon, Republic of Korea
| | - Abdullah M. Assiri
- Deperament of Comparative Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Jongki Cho
- College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, Republic of Korea
| | - Islam M. Saadeldin
- Deperament of Comparative Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
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9
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Zhang S, Zhao D, Cui Q. Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity. ACS OMEGA 2024; 9:17839-17847. [PMID: 38680329 PMCID: PMC11044234 DOI: 10.1021/acsomega.3c07682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 05/01/2024]
Abstract
Molecular toxicity is a critical feature of drug development. It is thus very important to develop computational models to evaluate the toxicity of small molecules. The accuracy of toxicity prediction largely depends on the quality of molecular representation; however, current methods for this purpose do not address this issue well. Here, we introduce a new metric, gap-Δenergy, which is designed to quantify the intermolecular bond energy difference with atom distance. We next find significant variations in the gap-Δenergy distribution among different types of molecules. Moreover, we show that this metric is able to distinguish the toxic small molecules. We collected data sets of toxic and exogenous small molecules and presented a novel index, namely, global toxicity, to evaluate the overall toxicity of molecules. Based on molecular descriptors and the proposed gap-Δenergy metric, we further constructed machine learning models that were trained with 7816 small molecules. The XGBoost-based model achieved the best performance with an AUC score of 0.965 and an F1 score of 0.849 on the test set (1954 small molecules), which outperformed the model that did not use gap-Δenergy features, with a sensitivity score increase of 3.2%.
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Affiliation(s)
- Senpeng Zhang
- Department of Biomedical
Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling,
School of Basic Medical Sciences, Peking
University, 38 Xueyuan Rd, Beijing 100191, People’s Republic
of China
| | - Dongyu Zhao
- Department of Biomedical
Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling,
School of Basic Medical Sciences, Peking
University, 38 Xueyuan Rd, Beijing 100191, People’s Republic
of China
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10
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Hang NT, Long NT, Duy ND, Chien NN, Van Phuong N. Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility. Int J Pharm 2024; 653:123884. [PMID: 38341049 DOI: 10.1016/j.ijpharm.2024.123884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 01/28/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024]
Abstract
Predicting drug-excipient compatibility is a critical aspect of pharmaceutical formulation design. In this study, we introduced an innovative approach that leverages machine learning techniques to improve the accuracy of drug-excipient compatibility predictions. Mol2vec and 2D molecular descriptors combined with the stacking technique were used to improve the performance of the model. This approach achieved a significant advancement in the predictive capacity as demonstrated by the accuracy, precision, recall, AUC, and MCC of 0.98, 0.87, 0.88, 0.93 and 0.86, respectively. Using the DE-INTERACT model as the benchmark, our stacking model could remarkably detect drug-excipient incompatibility in 10/12 tested cases, while DE-INTERACT managed to recognize only 3 out of 12 incompatibility cases in the validation experiments. To ensure user accessibility, the trained model was deployed to a user-friendly web platform (URL: https://decompatibility.streamlit.app/). This interactive interface accommodated inputs through various types, including names, PubChem CID, or SMILES strings. It promptly generated compatibility predictions alongside corresponding probability scores. However, the continual refinement of model performance is crucial before applying this model in practice.
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Affiliation(s)
- Nguyen Thu Hang
- Department of Pharmacognosy, Hanoi University of Pharmacy, Hanoi, Viet Nam
| | - Nguyen Thanh Long
- Department of Pharmacognosy, Hanoi University of Pharmacy, Hanoi, Viet Nam
| | - Nguyen Dang Duy
- Department of Pharmacognosy, Hanoi University of Pharmacy, Hanoi, Viet Nam
| | - Nguyen Ngoc Chien
- National Institute of Pharmaceutical Technology, Hanoi University of Pharmacy, Hanoi, Viet Nam
| | - Nguyen Van Phuong
- Department of Pharmacognosy, Hanoi University of Pharmacy, Hanoi, Viet Nam.
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11
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McCorkindale W, Filep M, London N, Lee AA, King-Smith E. Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning. RSC Med Chem 2024; 15:1015-1021. [PMID: 38516605 PMCID: PMC10953487 DOI: 10.1039/d3md00719g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/12/2024] [Indexed: 03/23/2024] Open
Abstract
High throughput and rapid biological evaluation of small molecules is an essential factor in drug discovery and development. Direct-to-biology (D2B), whereby compound purification is foregone, has emerged as a viable technique in time efficient screening, specifically for PROTAC design and biological evaluation. However, one notable limitation is the prerequisite of high yielding reactions to ensure the desired compound is indeed the compound responsible for biological activity. Herein, we report a machine learning based yield-assay deconfounder capable of deconvoluting low yield from low potency to identify false negatives. We validated this approach by identifying promising SARS-CoV-2 main protease inhibitors with nanomolar activity that rivaled potency observed from the standard D2B workflow. Furthermore, we show how our framework can be utilized in a broad, in silico screen to produce compounds of similar potency as a D2B assay.
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Affiliation(s)
| | - Mihajlo Filep
- Department of Chemical and Structural Biology, The Weizmann Institute of Science Israel
| | - Nir London
- Department of Chemical and Structural Biology, The Weizmann Institute of Science Israel
| | - Alpha A Lee
- Cavendish Laboratory, University of Cambridge UK
| | - Emma King-Smith
- Yusuf Hamied Department of Chemistry, University of Cambridge UK
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12
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Li F, Wang P, Fan T, Zhang N, Zhao L, Zhong R, Sun G. Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133410. [PMID: 38185092 DOI: 10.1016/j.jhazmat.2023.133410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/09/2024]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.
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Affiliation(s)
- Feifan Li
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment, Beijing 100029, China
| | - Peng Wang
- Department of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Na Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
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13
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Kleinstreuer N, Hartung T. Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine). Arch Toxicol 2024; 98:735-754. [PMID: 38244040 PMCID: PMC10861653 DOI: 10.1007/s00204-023-03666-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/12/2023] [Indexed: 01/22/2024]
Abstract
The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured-a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.
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Affiliation(s)
| | - Thomas Hartung
- Bloomberg School of Public Health, Doerenkamp-Zbinden Chair for Evidence-Based Toxicology, Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, MD, USA.
- CAAT-Europe, University of Konstanz, Constance, Germany.
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14
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Xiao F, Ding X, Shi Y, Wang D, Wang Y, Cui C, Zhu T, Chen K, Xiang P, Luo X. Application of ensemble learning for predicting GABA A receptor agonists. Comput Biol Med 2024; 169:107958. [PMID: 38194778 DOI: 10.1016/j.compbiomed.2024.107958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 12/29/2023] [Accepted: 01/01/2024] [Indexed: 01/11/2024]
Abstract
BACKGROUND Over the past few decades, agonists binding to the benzodiazepine site of the GABAA receptor have been successfully developed as clinical drugs. Different modulators (agonist, antagonist, and reverse agonist) bound to benzodiazepine sites exhibit different or even opposite pharmacological effects, however, their structures are so similar that it is difficult to distinguish them based solely on molecular skeleton. This study aims to develop classification models for predicting the agonists. METHODS 306 agonists or non-agonists were collected from literature. Six machine learning algorithms including RF, XGBoost, AdaBoost, GBoost, SVM, and ANN algorithms were employed for model development. Using six descriptors including 1D/2D Descriptors, ECFP4, 2D-Pharmacophore, MACCS, PubChem, and Estate fingerprint to characterize chemical structures. The model interpretability was explored by SHAP method. RESULTS The best model demonstrated an AUC value of 0.905 and an MCC value of 0.808 for the test set. The PubMac-based model (PubMac-GB) achieved best AUC values of 0.935 for test set. The SHAP analysis results emphasized that MaccsFP62, ECFP_624, ECFP_724, and PubchemFP213 were the crucial molecular features. Applicability domain analysis was also performed to determine reliable prediction boundaries for the model. The PubMac-GB model was applied to virtual screening for potential GABAA agonists and the top 100 compounds were given. CONCLUSION Overall, our ensemble learning-based model (PubMac-GB) achieved comparable performance and would be helpful in effectively identifying agonists of GABAA receptors.
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Affiliation(s)
- Fu Xiao
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, 210023, China; Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Xiaoyu Ding
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Yan Shi
- Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, 200063, China
| | - Dingyan Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Yitian Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Chen Cui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Tingfei Zhu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Kaixian Chen
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, 210023, China; Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Ping Xiang
- Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, 200063, China.
| | - Xiaomin Luo
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, 210023, China; Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
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15
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Abstract
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
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Affiliation(s)
- Catrin Hasselgren
- Safety Assessment, Genentech, Inc., South San Francisco, California, USA
| | - Tudor I Oprea
- Expert Systems Inc., San Diego, California, USA;
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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16
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Hase T, Ghosh S, Aisaki KI, Kitajima S, Kanno J, Kitano H, Yachie A. DTox: A deep neural network-based in visio lens for large scale toxicogenomics data. J Toxicol Sci 2024; 49:105-115. [PMID: 38432953 DOI: 10.2131/jts.49.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
With the advancement of large-scale omics technologies, particularly transcriptomics data sets on drug and treatment response repositories available in public domain, toxicogenomics has emerged as a key field in safety pharmacology and chemical risk assessment. Traditional statistics-based bioinformatics analysis poses challenges in its application across multidimensional toxicogenomic data, including administration time, dosage, and gene expression levels. Motivated by the visual inspection workflow of field experts to augment their efficiency of screening significant genes to derive meaningful insights, together with the ability of deep neural architectures to learn the image signals, we developed DTox, a deep neural network-based in visio approach. Using the Percellome toxicogenomics database, instead of utilizing the numerical gene expression values of the transcripts (gene probes of the microarray) for dose-time combinations, DTox learned the image representation of 3D surface plots of distinct time and dosage data points to train the classifier on the experts' labels of gene probe significance. DTox outperformed statistical threshold-based bioinformatics and machine learning approaches based on numerical expression values. This result shows the ability of image-driven neural networks to overcome the limitations of classical numeric value-based approaches. Further, by augmenting the model with explainability modules, our study showed the potential to reveal the visual analysis process of human experts in toxicogenomics through the model weights. While the current work demonstrates the application of the DTox model in toxicogenomic studies, it can be further generalized as an in visio approach for multi-dimensional numeric data with applications in various fields in medical data sciences.
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Affiliation(s)
- Takeshi Hase
- The Systems Biology Institute, Saisei Ikedayama Bldg
- SBX BioSciences, Inc, Canada
- Institute of Education, Tokyo Medical and Dental University
- Faculty of Pharmacy, Keio University
- Center for Mathematical Modelling and Data Science, Osaka University
| | - Samik Ghosh
- The Systems Biology Institute, Saisei Ikedayama Bldg
| | - Ken-Ichi Aisaki
- Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research (CBSR), National Institute of Health Sciences (NIHS)
| | - Satoshi Kitajima
- Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research (CBSR), National Institute of Health Sciences (NIHS)
| | - Jun Kanno
- The Systems Biology Institute, Saisei Ikedayama Bldg
- Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research (CBSR), National Institute of Health Sciences (NIHS)
- Faculty of Medicine, University of Tsukuba
| | - Hiroaki Kitano
- The Systems Biology Institute, Saisei Ikedayama Bldg
- Integrated Open Systems Unit, Okinawa Institute of Science and Technology (OIST)
| | - Ayako Yachie
- The Systems Biology Institute, Saisei Ikedayama Bldg
- SBX BioSciences, Inc, Canada
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17
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Smajić A, Grandits M, Ecker GF. Privacy-preserving techniques for decentralized and secure machine learning in drug discovery. Drug Discov Today 2023; 28:103820. [PMID: 37935330 DOI: 10.1016/j.drudis.2023.103820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/17/2023] [Accepted: 11/01/2023] [Indexed: 11/09/2023]
Abstract
Data availability, data security, and privacy concerns often hamper optimal performance efficiency of machine learning (ML) techniques. Therefore, novel techniques for the utilization of private/sensitive data in the field of drug discovery have been proposed for ML model-building tasks. Some examples of the different techniques are secure multiparty computation, distributed deep learning, homomorphic encryption, blockchain-based peer-to-peer networking, differential privacy, and federated learning, as well as combinations of such techniques. In this paper, we present an overview of these techniques for decentralized ML to illustrate its benefits and drawbacks in the field of drug discovery.
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Affiliation(s)
- Aljoša Smajić
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Melanie Grandits
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Gerhard F Ecker
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
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18
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Stevenson GA, Kirshner D, Bennion BJ, Yang Y, Zhang X, Zemla A, Torres MW, Epstein A, Jones D, Kim H, Bennett WFD, Wong SE, Allen JE, Lightstone FC. Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method. J Chem Inf Model 2023; 63:6655-6666. [PMID: 37847557 PMCID: PMC10647021 DOI: 10.1021/acs.jcim.3c00722] [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: 05/11/2023] [Indexed: 10/18/2023]
Abstract
Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.
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Affiliation(s)
- Garrett A. Stevenson
- Computational
Engineering Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Dan Kirshner
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Brian J. Bennion
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Yue Yang
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Xiaohua Zhang
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Adam Zemla
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Marisa W. Torres
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Aidan Epstein
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Derek Jones
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
- Department
of Computer Science and Engineering, University
of California, San Diego, La Jolla, California 92093, United States
| | - Hyojin Kim
- Center
for Applied Scientific Computing, Lawrence
Livermore National Laboratory, Livermore, California 94550, United States
| | - W. F. Drew Bennett
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Sergio E. Wong
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Jonathan E. Allen
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Felice C. Lightstone
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
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19
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Bao Z, Bufton J, Hickman RJ, Aspuru-Guzik A, Bannigan P, Allen C. Revolutionizing drug formulation development: The increasing impact of machine learning. Adv Drug Deliv Rev 2023; 202:115108. [PMID: 37774977 DOI: 10.1016/j.addr.2023.115108] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023]
Abstract
Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.
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Affiliation(s)
- Zeqing Bao
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Jack Bufton
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Riley J Hickman
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada; Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, ON M5S 1M1, Canada; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada; Department of Materials Science & Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada; CIFAR Artificial Intelligence Research Chair, Vector Institute, Toronto, ON M5S 1M1, Canada; Acceleration Consortium, Toronto, ON M5S 3H6, Canada
| | - Pauric Bannigan
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada.
| | - Christine Allen
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada; Acceleration Consortium, Toronto, ON M5S 3H6, Canada.
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20
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Ferreira RC, do Nascimento YM, de Araújo Loureiro PB, Martins RX, de Souza Maia ME, Farias DF, Tavares JF, Gonçalves JCR, da Silva MS, Sobral MV. Chemical Composition, In Vitro Antitumor Effect, and Toxicity in Zebrafish of the Essential Oil from Conyza bonariensis (L.) Cronquist (Asteraceae). Biomolecules 2023; 13:1439. [PMID: 37892120 PMCID: PMC10604947 DOI: 10.3390/biom13101439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 09/13/2023] [Accepted: 09/17/2023] [Indexed: 10/29/2023] Open
Abstract
The essential oil from Conyza bonariensis (Asteraceae) aerial parts (CBEO) was extracted by hydrodistillation in a Clevenger-type apparatus and was characterized by gas chromatography-mass spectrometry. The antitumor potential was evaluated against human tumor cell lines (melanoma, cervical, colorectal, and leukemias), as well as non-tumor keratinocyte lines using the MTT assay. The effect of CBEO on the production of Reactive Oxygen Species (ROS) was evaluated by DCFH-DA assay, and a protection assay using the antioxidant N-acetyl-L-cysteine (NAC) was also performed. Moreover, the CBEO toxicity in the zebrafish model was assessed. The majority of the CBEO compound was (Z)-2-lachnophyllum ester (57.24%). The CBEO exhibited selectivity towards SK-MEL-28 melanoma cells (half maximal inhibitory concentration, IC50 = 18.65 ± 1.16 µg/mL), and induced a significant increase in ROS production. In addition, the CBEO's cytotoxicity against SK-MEL-28 cells was reduced after pretreatment with NAC. Furthermore, after 96 h of exposure, 1.5 µg/mL CBEO induced death of all zebrafish embryos. Non-lethal effects were observed after exposure to 0.50-1.25 µg/mL CBEO. Additionally, significant alterations in the activity of enzymes associated with oxidative stress in zebrafish larvae were observed. These results provide evidence that CBEO has a significant in vitro antimelanoma effect by increasing ROS production and moderate embryotoxicity in zebrafish.
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Affiliation(s)
- Rafael Carlos Ferreira
- Postgraduate Program in Natural Products and Bioactive Synthetics, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
| | - Yuri Mangueira do Nascimento
- Postgraduate Program in Natural Products and Bioactive Synthetics, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
| | - Paulo Bruno de Araújo Loureiro
- Postgraduate Program in Natural Products and Bioactive Synthetics, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
| | - Rafael Xavier Martins
- Laboratory of Risk Assessment for Novel Technologies (LabRisk), Department of Molecular Biology, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
| | - Maria Eduarda de Souza Maia
- Laboratory of Risk Assessment for Novel Technologies (LabRisk), Department of Molecular Biology, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
| | - Davi Felipe Farias
- Laboratory of Risk Assessment for Novel Technologies (LabRisk), Department of Molecular Biology, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
| | - Josean Fechine Tavares
- Postgraduate Program in Natural Products and Bioactive Synthetics, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
| | - Juan Carlos Ramos Gonçalves
- Postgraduate Program in Natural Products and Bioactive Synthetics, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
| | - Marcelo Sobral da Silva
- Postgraduate Program in Natural Products and Bioactive Synthetics, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
| | - Marianna Vieira Sobral
- Postgraduate Program in Natural Products and Bioactive Synthetics, Federal University of Paraíba, João Pessoa 58051-970, PB, Brazil
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21
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Williams AH, Zhan CG. Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management. BioDrugs 2023; 37:649-674. [PMID: 37464099 DOI: 10.1007/s40259-023-00611-8] [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] [Accepted: 05/28/2023] [Indexed: 07/20/2023]
Abstract
In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.
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Affiliation(s)
- Alexander H Williams
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- GSK Upper Providence, 1250 S. Collegeville Road, Collegeville, PA, 19426, USA
| | - Chang-Guo Zhan
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
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22
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Smajić A, Rami I, Sosnin S, Ecker GF. Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets. Chem Res Toxicol 2023; 36:1300-1312. [PMID: 37439496 PMCID: PMC10445286 DOI: 10.1021/acs.chemrestox.3c00042] [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: 02/13/2023] [Indexed: 07/14/2023]
Abstract
Each year, publicly available databases are updated with new compounds from different research institutions. Positive experimental outcomes are more likely to be reported; therefore, they account for a considerable fraction of these entries. Established publicly available databases such as ChEMBL allow researchers to use information without constrictions and create predictive tools for a broad spectrum of applications in the field of toxicology. Therefore, we investigated the distribution of positive and nonpositive entries within ChEMBL for a set of off-targets and its impact on the performance of classification models when applied to pharmaceutical industry data sets. Results indicate that models trained on publicly available data tend to overpredict positives, and models based on industry data sets predict negatives more often than those built using publicly available data sets. This is strengthened even further by the visualization of the prediction space for a set of 10,000 compounds, which makes it possible to identify regions in the chemical space where predictions converge. Finally, we highlight the utilization of these models for consensus modeling for potential adverse events prediction.
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Affiliation(s)
- Aljoša Smajić
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Iris Rami
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Sergey Sosnin
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
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23
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Muniyandi P, O’Hern C, Popa MA, Aguirre A. Biotechnological advances and applications of human pluripotent stem cell-derived heart models. Front Bioeng Biotechnol 2023; 11:1214431. [PMID: 37560538 PMCID: PMC10407810 DOI: 10.3389/fbioe.2023.1214431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 07/12/2023] [Indexed: 08/11/2023] Open
Abstract
In recent years, significant biotechnological advancements have been made in engineering human cardiac tissues and organ-like models. This field of research is crucial for both basic and translational research due to cardiovascular disease being the leading cause of death in the developed world. Additionally, drug-associated cardiotoxicity poses a major challenge for drug development in the pharmaceutical and biotechnological industries. Progress in three-dimensional cell culture and microfluidic devices has enabled the generation of human cardiac models that faithfully recapitulate key aspects of human physiology. In this review, we will discuss 3D pluripotent stem cell (PSC)-models of the human heart, such as engineered heart tissues and organoids, and their applications in disease modeling and drug screening.
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Affiliation(s)
- Priyadharshni Muniyandi
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, United States
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, United States
| | - Colin O’Hern
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, United States
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, United States
| | - Mirel Adrian Popa
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, United States
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, United States
- Institute of Cellular Biology and Pathology Nicolae Simionescu, Bucharest, Romania
| | - Aitor Aguirre
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, United States
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, United States
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24
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Wong F, de la Fuente-Nunez C, Collins JJ. Leveraging artificial intelligence in the fight against infectious diseases. Science 2023; 381:164-170. [PMID: 37440620 PMCID: PMC10663167 DOI: 10.1126/science.adh1114] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/05/2023] [Indexed: 07/15/2023]
Abstract
Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.
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Affiliation(s)
- Felix Wong
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James J. Collins
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
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25
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Lunghini F, Fava A, Pisapia V, Sacco F, Iaconis D, Beccari AR. ProfhEX: AI-based platform for small molecules liability profiling. J Cheminform 2023; 15:60. [PMID: 37296454 DOI: 10.1186/s13321-023-00728-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R2 determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .
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Affiliation(s)
- Filippo Lunghini
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Anna Fava
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Vincenzo Pisapia
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Francesco Sacco
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Daniela Iaconis
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
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26
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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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27
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Recio-Vega R, Facio-Campos RA, Hernández-González SI, Olivas-Calderón E. State of the Art of Genomic Technology in Toxicology: A Review. Int J Mol Sci 2023; 24:ijms24119618. [PMID: 37298568 DOI: 10.3390/ijms24119618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/23/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
The rapid growth of genomics techniques has revolutionized and impacted, greatly and positively, the knowledge of toxicology, ushering it into a "new era": the era of genomic technology (GT). This great advance permits us to analyze the whole genome, to know the gene response to toxicants and environmental stressors, and to determine the specific profiles of gene expression, among many other approaches. The aim of this work was to compile and narrate the recent research on GT during the last 2 years (2020-2022). A literature search was managed using the PubMed and Medscape interfaces on the Medline database. Relevant articles published in peer-reviewed journals were retrieved and their main results and conclusions are mentioned briefly. It is quite important to form a multidisciplinary taskforce on GT with the aim of designing and implementing a comprehensive, collaborative, and a strategic work plan, prioritizing and assessing the most relevant diseases, so as to decrease human morbimortality due to exposure to environmental chemicals and stressors.
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Affiliation(s)
| | - Rolando Adair Facio-Campos
- Laboratory of Environmental Health, School of Chemical Sciences, Juarez University of Durango State, Gomez Palacio 35010, Mexico
| | - Sandra Isabel Hernández-González
- Laboratory of Environmental Health, School of Chemical Sciences, Juarez University of Durango State, Gomez Palacio 35010, Mexico
| | - Edgar Olivas-Calderón
- Laboratory of Environmental Health, School of Chemical Sciences, Juarez University of Durango State, Gomez Palacio 35010, Mexico
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28
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Pomykala KL, Hadaschik BA, Sartor O, Gillessen S, Sweeney CJ, Maughan T, Hofman MS, Herrmann K. Next generation radiotheranostics promoting precision medicine. Ann Oncol 2023; 34:507-519. [PMID: 36924989 DOI: 10.1016/j.annonc.2023.03.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 03/03/2023] [Indexed: 03/17/2023] Open
Abstract
Radiotheranostics is a field of rapid growth with some approved treatments including 131I for thyroid cancer, 223Ra for osseous metastases, 177Lu-DOTATATE for neuroendocrine tumors, and 177Lu-PSMA (prostate-specific membrane antigen) for prostate cancer, and several more under investigation. In this review, we will cover the fundamentals of radiotheranostics, the key clinical studies that have led to current success, future developments with new targets, radionuclides and platforms, challenges with logistics and reimbursement and, lastly, forthcoming considerations regarding dosimetry, identifying the right line of therapy, artificial intelligence and more.
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Affiliation(s)
- K L Pomykala
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - B A Hadaschik
- Department of Urology, University Hospital Essen, Essen, Germany
| | - O Sartor
- School of Medicine, Tulane University, New Orleans, USA
| | - S Gillessen
- Oncology Institute of Southern Switzerland, Bellinzona, Switzerland; Università della Svizzera Italiana, Lugano, Switzerland; Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - C J Sweeney
- Dana-Farber Cancer Institute, Boston, USA; Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - T Maughan
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | - M S Hofman
- Prostate Cancer Theranostics and Imaging Centre of Excellence (ProsTIC), Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - K Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
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29
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Bhattacharya S, Sahoo A, Baitalik S. Human brain-inspired chemical artificial intelligence tools for the analysis and prediction of the anion-sensing characteristics of an imidazole-based luminescent Os(II)-bipyridine complex. Dalton Trans 2023; 52:6749-6762. [PMID: 37129261 DOI: 10.1039/d3dt00327b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Neural network and decision tree-based soft computing techniques are implemented in this work for the thorough analysis of the multichannel anion-sensing characteristics of an Os(II)-bipyridine complex derived from imidazole-4,5-bis(benzimidazole) ligand. With the aid of three imidazole NH protons in its outer coordination sphere, a substantial change in the spectral response as well as OsII/OsIII potential is made possible upon treating with anions of varying basicity. Initial hydrogen bonding between NH protons and anions and thereafter complete proton transfer from the complex backbone probably take place in the process. The deprotonation of the complex by specific anions and restoration to its original form by acid is also reversible. The responsiveness of the new compound is complex enough to imitate multiple sophisticated binary and ternary Boolean logic (BL) functions (NOT logic, combinational logic, traffic signal, set-reset flip-flop logic, and ternary NOR logic) by employing its spectral and redox outputs upon the action of suitable anions and acid in a proper sequence. Executing sensing investigations on altering the amount of the anions within a widespread range is often time-consuming and tedious. To overcome the lacuna, we implemented multiple soft computing techniques, viz., fuzzy logic (FL), artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and decision tree (DT) regression, for the thorough analysis and prediction of the experimentally observed results. The outcomes obtained from different techniques were compared among themselves as well as with the experimental data and utilized for the proper modeling of the anion-sensing behaviors of the complex.
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Affiliation(s)
- Sohini Bhattacharya
- Department of Chemistry, Inorganic Chemistry Section, Jadavpur University, Kolkata-700032, India.
| | - Anik Sahoo
- Department of Chemistry, Inorganic Chemistry Section, Jadavpur University, Kolkata-700032, India.
| | - Sujoy Baitalik
- Department of Chemistry, Inorganic Chemistry Section, Jadavpur University, Kolkata-700032, India.
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30
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Liu JYH, Rudd JA. Predicting drug adverse effects using a new Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD). Sci Rep 2023; 13:6935. [PMID: 37117211 PMCID: PMC10147650 DOI: 10.1038/s41598-023-33655-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/17/2023] [Indexed: 04/30/2023] Open
Abstract
Electrical data could be a new source of big-data for training artificial intelligence (AI) for drug discovery. A Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) was built using a standardized methodology to test drug effects on electrical gastrointestinal (GI) pacemaker activity. The current report used data obtained from 89 drugs with 4867 datasets to evaluate the potential use of the GIPADD for predicting drug adverse effects (AEs) using a machine-learning (ML) approach and to explore correlations between AEs and GI pacemaker activity. Twenty-four "electrical" features (EFs) were extracted using an automated analytical pipeline from the electrical signals recorded before and after acute drug treatment at three concentrations (or more) on four-types of GI tissues (stomach, duodenum, ileum and colon). Extracted features were normalized and merged with an online side-effect resource (SIDER) database. Sixty-six common AEs were selected. Different algorithms of classification ML models, including Naïve Bayes, discriminant analysis, classification tree, k-nearest neighbors, support vector machine and an ensemble model were tested. Separated tissue models were also tested. Averaging experimental repeats and dose adjustment were performed to refine the prediction results. Random datasets were created for model validation. After model validation, nine AEs classification ML model were constructed with accuracy ranging from 67 to 80%. EF can be further grouped into 'excitatory' and 'inhibitory' types of AEs. This is the first time drugs are being clustered based on EF. Drugs acting on similar receptors share similar EF profile, indicating potential use of the database to predict drug targets too. GIPADD is a growing database, where prediction accuracy is expected to improve. The current approach provides novel insights on how EF may be used as new source of big-data in health and disease.
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Affiliation(s)
- Julia Yuen Hang Liu
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, 704, Lo Kwee-Seong Integrated Biomedical Sciences Building, Shatin, New Territories, Hong Kong, SAR, People's Republic of China.
| | - John A Rudd
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, 704, Lo Kwee-Seong Integrated Biomedical Sciences Building, Shatin, New Territories, Hong Kong, SAR, People's Republic of China
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31
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Tran TTV, Surya Wibowo A, Tayara H, Chong KT. Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives. J Chem Inf Model 2023; 63:2628-2643. [PMID: 37125780 DOI: 10.1021/acs.jcim.3c00200] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University - Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Agung Surya Wibowo
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Department of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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32
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Naidu A, Nayak SS, Lulu S S, Sundararajan V. Advances in computational frameworks in the fight against TB: The way forward. Front Pharmacol 2023; 14:1152915. [PMID: 37077815 PMCID: PMC10106641 DOI: 10.3389/fphar.2023.1152915] [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: 01/28/2023] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.
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Affiliation(s)
| | | | | | - Vino Sundararajan
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, India
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Pognan F, Beilmann M, Boonen HCM, Czich A, Dear G, Hewitt P, Mow T, Oinonen T, Roth A, Steger-Hartmann T, Valentin JP, Van Goethem F, Weaver RJ, Newham P. The evolving role of investigative toxicology in the pharmaceutical industry. Nat Rev Drug Discov 2023; 22:317-335. [PMID: 36781957 PMCID: PMC9924869 DOI: 10.1038/s41573-022-00633-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2022] [Indexed: 02/15/2023]
Abstract
For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.
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Affiliation(s)
- Francois Pognan
- Discovery and Investigative Safety, Novartis Pharma AG, Basel, Switzerland.
| | - Mario Beilmann
- Nonclinical Drug Safety Germany, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Harrie C M Boonen
- Drug Safety, Dept of Exploratory Toxicology, Lundbeck A/S, Valby, Denmark
| | | | - Gordon Dear
- In Vitro In Vivo Translation, GlaxoSmithKline David Jack Centre for Research, Ware, UK
| | - Philip Hewitt
- Chemical and Preclinical Safety, Merck Healthcare KGaA, Darmstadt, Germany
| | - Tomas Mow
- Safety Pharmacology and Early Toxicology, Novo Nordisk A/S, Maaloev, Denmark
| | - Teija Oinonen
- Preclinical Safety, Orion Corporation, Espoo, Finland
| | - Adrian Roth
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | | | | | - Freddy Van Goethem
- Predictive, Investigative & Translational Toxicology, Nonclinical Safety, Janssen Research & Development, Beerse, Belgium
| | - Richard J Weaver
- Innovation Life Cycle Management, Institut de Recherches Internationales Servier, Suresnes, France
| | - Peter Newham
- Clinical Pharmacology and Safety Sciences, AstraZeneca R&D, Cambridge, UK.
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Liao M, Wu F, Yu X, Zhao L, Wu H, Zhou J. Random Forest Algorithm-Based Prediction of Solvation Gibbs Energies. J SOLUTION CHEM 2023. [DOI: 10.1007/s10953-023-01247-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Wang N, Zhang Y, Wang W, Ye Z, Chen H, Hu G, Ouyang D. How can machine learning and multiscale modeling benefit ocular drug development? Adv Drug Deliv Rev 2023; 196:114772. [PMID: 36906232 DOI: 10.1016/j.addr.2023.114772] [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: 12/16/2022] [Revised: 02/06/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023]
Abstract
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.
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Affiliation(s)
- Nannan Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Yunsen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hongyu Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Guanghui Hu
- Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences (FHS), University of Macau, Macau, China.
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Kour S, Biswas I, Sheoran S, Arora S, Sheela P, Duppala SK, Murthy DK, Pawar SC, Singh H, Kumar D, Prabhu D, Vuree S, Kumar R. Artificial intelligence and nanotechnology for cervical cancer treatment: Current status and future perspectives. J Drug Deliv Sci Technol 2023. [DOI: 10.1016/j.jddst.2023.104392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023; 75:3-18. [PMID: 36624355 PMCID: PMC9838466 DOI: 10.1007/s43440-022-00445-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/11/2023]
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
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Affiliation(s)
- Mandeep Kumar
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
| | - T P Nhung Nguyen
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
- Department of Pharmacy, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Jasleen Kaur
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Lucknow, Uttar Pradesh, 226002, India
| | | | - Divya Soni
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Randhir Singh
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Puneet Kumar
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India.
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Aljarf R, Tang S, Pires DEV, Ascher DB. embryoTox: Using Graph-Based Signatures to Predict the Teratogenicity of Small Molecules. J Chem Inf Model 2023; 63:432-441. [PMID: 36595441 DOI: 10.1021/acs.jcim.2c00824] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Teratogenic drugs can lead to extreme fetal malformation and consequently critically influence the fetus's health, yet the teratogenic risks associated with most approved drugs are unknown. Here, we propose a novel predictive tool, embryoTox, which utilizes a graph-based signature representation of the chemical structure of a small molecule to predict and classify molecules likely to be safe during pregnancy. embryoTox was trained and validated using in vitro bioactivity data of over 700 small molecules with characterized teratogenicity effects. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.96 on 10-fold cross-validation and 0.82 on nonredundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a practical resource for optimizing screening libraries to determine effective and safe molecules to use during pregnancy. To provide a simple and integrated platform to rapidly screen for potential safe molecules and their risk factors, we made embryoTox freely available online at https://biosig.lab.uq.edu.au/embryotox/.
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Affiliation(s)
- Raghad Aljarf
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
| | - Simon Tang
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
- School of Computing and Information Systems, University of Melbourne, Parkville 3052, Victoria, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia
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Xiao ZJ, Chen JW, Wang Y, Wang ZY. In silico package models for deriving values of solute parameters in linear solvation energy relationships. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:21-37. [PMID: 36625152 DOI: 10.1080/1062936x.2022.2162576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Environmental partitioning influences fate, exposure and ecological risks of chemicals. Linear solvation energy relationship (LSER) models may serve as efficient tools for estimating environmental partitioning parameter values that are commonly deficient for many chemicals. Nonetheless, scarcities of empirical solute parameter values of LSER models restricted the application. This study developed and evaluated in silico methods and models to derive the values, in which excess molar refraction, molar volume and logarithm of hexadecane/air partition coefficient were computed from density functional theory; dipolarity/polarizability parameter, solute H-bond acidity and basicity parameters were predicted by quantitative structure-activity relationship models developed with theoretical molecular descriptors. New LSER models on four physicochemical properties relevant with environmental partitioning (n-octanol/water partition coefficients, n-octanol/air partition coefficients, water solubilities, sub-cooled liquid vapour pressures) were constructed using the in silico solute parameter values, which exhibited comparable performance with conventional LSER models using the empirical solute parameter values. The package models for deriving the LSER solute parameter values, with advantages that they are free of instrumental determinations, may lay the foundation for high-throughput estimating environmental partition parameter values of diverse organic chemicals.
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Affiliation(s)
- Z J Xiao
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - J W Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Y Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Z Y Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
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40
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Wu L, Yan B, Han J, Li R, Xiao J, He S, Bo X. TOXRIC: a comprehensive database of toxicological data and benchmarks. Nucleic Acids Res 2022; 51:D1432-D1445. [PMID: 36400569 PMCID: PMC9825425 DOI: 10.1093/nar/gkac1074] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/10/2022] [Accepted: 10/26/2022] [Indexed: 11/20/2022] Open
Abstract
The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.
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Affiliation(s)
| | | | - Junshan Han
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Ruijiang Li
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jian Xiao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China,Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Song He
- Correspondence may also be addressed to Song He. Tel: +86 01066931450;
| | - Xiaochen Bo
- To whom correspondence should be addressed. Tel: +86 01066931207; ;
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Machine Learning Models to Predict Protein-Protein Interaction Inhibitors. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27227986. [PMID: 36432086 PMCID: PMC9694076 DOI: 10.3390/molecules27227986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/09/2022] [Accepted: 11/16/2022] [Indexed: 11/19/2022]
Abstract
Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.
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Bhandari N, Walambe R, Kotecha K, Khare SP. A comprehensive survey on computational learning methods for analysis of gene expression data. Front Mol Biosci 2022; 9:907150. [PMID: 36458095 PMCID: PMC9706412 DOI: 10.3389/fmolb.2022.907150] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 09/28/2022] [Indexed: 09/19/2023] Open
Abstract
Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.
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Affiliation(s)
- Nikita Bhandari
- Computer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
| | - Rahee Walambe
- Electronics and Telecommunication Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
- Symbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, India
| | - Ketan Kotecha
- Computer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
- Symbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, India
| | - Satyajeet P. Khare
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
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Lai TT, Kuntz D, Wilson AK. Molecular Screening and Toxicity Estimation of 260,000 Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) through Machine Learning. J Chem Inf Model 2022; 62:4569-4578. [PMID: 36154169 DOI: 10.1021/acs.jcim.2c00374] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are a class of chemicals widely used in industrial applications due to their exceptional properties and stability. However, they do not readily degrade in the environment and are linked to contamination and adverse health effects in humans and wildlife. To find alternatives for the most commonly used PFAS molecules that maintain their desirable chemical properties but are not adverse to biological lifeforms, a novel approach based upon machine learning is utilized. The machine learning model is trained on an existing set of PFAS molecules to generate over 260,000 novel PFAS molecules, which we dub PFAS-AI-Gen. Using molecular descriptors with known relationships to toxicity and industrial suitability followed by molecular docking and molecular dynamics simulations, this set of molecules is screened. In this manner, increasingly complex calculations are performed only for candidate molecules that are most likely to yield the desired properties of low binding affinity toward two selected protein receptors, the human pregnane x receptor (hPXR) and peroxisome proliferator-activated receptor γ (PPAR-γ), and high industrial suitability, defined by critical micelle concentration (CMC). The selection criteria of low binding affinity and high industrial suitability are relative to the popular PFAS alternative GenX. hPXR and PPAR-γ are selected as they are PFAS targets and facilitate a variety of functions, such as drug metabolism and glucose regulation, respectively. Through this approach, 22 promising new PFAS substitutes that may warrant experimental investigation are identified. This integrated approach of molecular screening and toxicity estimation may be applicable to other chemical classes.
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Affiliation(s)
- Thanh T Lai
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48823, United States
| | - David Kuntz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48823, United States
| | - Angela K Wilson
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48823, United States
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Wu X, Zhou Q, Mu L, Hu X. Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives. JOURNAL OF HAZARDOUS MATERIALS 2022; 438:129487. [PMID: 35816807 DOI: 10.1016/j.jhazmat.2022.129487] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/16/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.
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Affiliation(s)
- Xiaotong Wu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Qixing Zhou
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Li Mu
- Tianjin Key Laboratory of Agro-environment and Safe-product, Key Laboratory for Environmental Factors Control of Agro-product Quality Safety (Ministry of Agriculture and Rural Affairs), Institute of Agro-environmental Protection, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China.
| | - Xiangang Hu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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Smajić A, Grandits M, Ecker GF. Using Jupyter Notebooks for re-training machine learning models. J Cheminform 2022; 14:54. [PMID: 35964049 PMCID: PMC9375336 DOI: 10.1186/s13321-022-00635-2] [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: 12/26/2021] [Accepted: 07/31/2022] [Indexed: 11/10/2022] Open
Abstract
Machine learning (ML) models require an extensive, user-driven selection of molecular descriptors in order to learn from chemical structures to predict actives and inactives with a high reliability. In addition, privacy concerns often restrict the access to sufficient data, leading to models with a narrow chemical space. Therefore, we propose a framework of re-trainable models that can be transferred from one local instance to another, and further allow a less extensive descriptor selection. The models are shared via a Jupyter Notebook, allowing the evaluation and implementation of a broader chemical space by keeping most of the tunable parameters pre-defined. This enables the models to be updated in a decentralized, facile, and fast manner. Herein, the method was evaluated with six transporter datasets (BCRP, BSEP, OATP1B1, OATP1B3, MRP3, P-gp), which revealed the general applicability of this approach.
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Affiliation(s)
- Aljoša Smajić
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Melanie Grandits
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria.
| | - Gerhard F Ecker
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
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46
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Lim S, Lee S, Piao Y, Choi M, Bang D, Gu J, Kim S. On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Comput Struct Biotechnol J 2022; 20:4288-4304. [PMID: 36051875 PMCID: PMC9399946 DOI: 10.1016/j.csbj.2022.07.049] [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: 04/02/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 11/22/2022] Open
Abstract
A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.
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Affiliation(s)
- Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sangseon Lee
- Institute of Computer Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Yinhua Piao
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - MinGyu Choi
- Department of Chemistry, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- MOGAM Institute for Biomedical Research, Yong-in 16924, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
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47
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Sajjan M, Li J, Selvarajan R, Sureshbabu SH, Kale SS, Gupta R, Singh V, Kais S. Quantum machine learning for chemistry and physics. Chem Soc Rev 2022; 51:6475-6573. [PMID: 35849066 DOI: 10.1039/d2cs00203e] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.
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Affiliation(s)
- Manas Sajjan
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA
| | - Junxu Li
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA.,Department of Physics and Astronomy, Purdue University, West Lafayette, IN-47907, USA
| | - Raja Selvarajan
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA.,Department of Physics and Astronomy, Purdue University, West Lafayette, IN-47907, USA
| | - Shree Hari Sureshbabu
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA.,Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN-47907, USA
| | - Sumit Suresh Kale
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA
| | - Rishabh Gupta
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA
| | - Vinit Singh
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA
| | - Sabre Kais
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA.,Department of Physics and Astronomy, Purdue University, West Lafayette, IN-47907, USA.,Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN-47907, USA
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48
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Gorgulla C, Jayaraj A, Fackeldey K, Arthanari H. Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches. Curr Opin Chem Biol 2022; 69:102156. [PMID: 35576813 PMCID: PMC9990419 DOI: 10.1016/j.cbpa.2022.102156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/16/2022] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.
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Affiliation(s)
- Christoph Gorgulla
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA
| | | | - Konstantin Fackeldey
- Institute of Mathematics, Technical University Berlin, Berlin, Germany; Zuse Institute Berlin, Berlin, Germany
| | - Haribabu Arthanari
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA.
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49
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Yang YD, Yang BB, Li L. A nonneglectable stereochemical factor in drug development: Atropisomerism. Chirality 2022; 34:1355-1370. [PMID: 35904531 DOI: 10.1002/chir.23497] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 11/07/2022]
Abstract
Chirality is one of the key factors affecting the medicinal efficacy of compounds. In addition to central chirality, sterically hindered chiral axes commonly appear in drugs and the resulting chirality is known as atropisomerism. With developments in medicinal chemistry, atropisomerism has attracted increasing attention. This review discusses the classification, biological activity, pharmacokinetics, toxicity and side effects of atropisomers, and can serve as a reference in the research and development of potential chiral drugs.
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Affiliation(s)
- Ya-Dong Yang
- Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Bei-Bei Yang
- Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Li Li
- Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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50
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Recent trends in pharmaceutical analysis to foster modern drug discovery by comparative in-silico profiling of drugs and related substances. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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