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Sun D, Macedonia C, Chen Z, Chandrasekaran S, Najarian K, Zhou S, Cernak T, Ellingrod VL, Jagadish HV, Marini B, Pai M, Violi A, Rech JC, Wang S, Li Y, Athey B, Omenn GS. Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival? J Med Chem 2024; 67:16035-16055. [PMID: 39253942 DOI: 10.1021/acs.jmedchem.4c01684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.
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Affiliation(s)
| | | | - Zhigang Chen
- LabBotics.ai, Palo Alto, California 94303, United States
| | | | | | - Simon Zhou
- Aurinia Pharmaceuticals Inc., Rockville, Maryland 20850, United States
| | | | | | | | | | | | | | | | | | - Yan Li
- Translational Medicine and Clinical Pharmacology, Bristol Myers Squibb, Summit, New Jersey 07901, United States
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Yucel MA, Adal E, Aktekin MB, Hepokur C, Gambacorta N, Nicolotti O, Algul O. From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors. ChemMedChem 2024; 19:e202400108. [PMID: 38726553 DOI: 10.1002/cmdc.202400108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/24/2024] [Indexed: 07/21/2024]
Abstract
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 μM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.
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Affiliation(s)
- Mehmet Ali Yucel
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Erzincan Binali Yildirim University, 24002, Erzincan, Türkiye
| | - Ercan Adal
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mersin University, 33160, Mersin, Türkiye
| | - Mine Buga Aktekin
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mersin University, 33160, Mersin, Türkiye
| | - Ceylan Hepokur
- Department of Biochemistry, Faculty of Pharmacy, Sivas Cumhuriyet University, 58140, Sivas, Türkiye
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Universita 'degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, Bari I, 70125, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Universita 'degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, Bari I, 70125, Italy
| | - Oztekin Algul
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Erzincan Binali Yildirim University, 24002, Erzincan, Türkiye
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mersin University, 33160, Mersin, Türkiye
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Le NQK, Tran TX, Nguyen PA, Ho TT, Nguyen VN. Recent progress in machine learning approaches for predicting carcinogenicity in drug development. Expert Opin Drug Metab Toxicol 2024; 20:621-628. [PMID: 38742542 DOI: 10.1080/17425255.2024.2356162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 05/13/2024] [Indexed: 05/16/2024]
Abstract
INTRODUCTION This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance. AREAS COVERED The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency. EXPERT OPINION Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.
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Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Thi-Xuan Tran
- University of Economics and Business Administration, Thai Nguyen University, Thai Nguyen, Vietnam
| | - Phung-Anh Nguyen
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Vietnam
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Vietnam
| | - Trang-Thi Ho
- Department of Computer Science and Information Engineering, TamKang University, New Taipei, Taiwan
| | - Van-Nui Nguyen
- University of Information and Communication Technology, Thai Nguyen University, Thai Nguyen, Vietnam
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Mastrolorito F, Togo MV, Gambacorta N, Trisciuzzi D, Giannuzzi V, Bonifazi F, Liantonio A, Imbrici P, De Luca A, Altomare CD, Ciriaco F, Amoroso N, Nicolotti O. TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity. Chem Res Toxicol 2024; 37:323-339. [PMID: 38200616 DOI: 10.1021/acs.chemrestox.3c00310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.
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Affiliation(s)
- Fabrizio Mastrolorito
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Viviana Giannuzzi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Fedele Bonifazi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Antonella Liantonio
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Paola Imbrici
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Annamaria De Luca
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
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