1
|
Liu J, Khan MKH, Guo W, Dong F, Ge W, Zhang C, Gong P, Patterson TA, Hong H. Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study. Expert Opin Drug Metab Toxicol 2024; 20:665-684. [PMID: 38968091 DOI: 10.1080/17425255.2024.2377593] [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: 02/27/2024] [Accepted: 06/26/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identifying cardiac safety issues. Various QSAR models exist, but their performance varies. Ongoing improvements show promise, necessitating continued efforts to enhance accuracy using emerging deep learning algorithms in predicting potential hERG blockade. STUDY DESIGN AND METHOD Using a large training dataset, six individual QSAR models were developed. Additionally, three ensemble models were constructed. All models were evaluated using 10-fold cross-validations and two external datasets. RESULTS The 10-fold cross-validations resulted in Mathews correlation coefficient (MCC) values from 0.682 to 0.730, surpassing the best-reported model on the same dataset (0.689). External validations yielded MCC values from 0.520 to 0.715 for the first dataset, exceeding those of previously reported models (0-0.599). For the second dataset, MCC values fell between 0.025 and 0.215, aligning with those of reported models (0.112-0.220). CONCLUSIONS The developed models can assist the pharmaceutical industry and regulatory agencies in predicting hERG blockage activity, thereby enhancing safety assessments and reducing the risk of adverse cardiac events associated with new drug candidates.
Collapse
Affiliation(s)
- Jie Liu
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Md Kamrul Hasan Khan
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Wenjing Guo
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Fan Dong
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Weigong Ge
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Chaoyang Zhang
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Ping Gong
- Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| |
Collapse
|
2
|
Lynch C, Sakamuru S, Ooka M, Huang R, Klumpp-Thomas C, Shinn P, Gerhold D, Rossoshek A, Michael S, Casey W, Santillo MF, Fitzpatrick S, Thomas RS, Simeonov A, Xia M. High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions. Annu Rev Pharmacol Toxicol 2024; 64:191-209. [PMID: 37506331 PMCID: PMC10822017 DOI: 10.1146/annurev-pharmtox-112122-104310] [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] [Indexed: 07/30/2023]
Abstract
Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.
Collapse
Affiliation(s)
- Caitlin Lynch
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - Srilatha Sakamuru
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - Masato Ooka
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - Carleen Klumpp-Thomas
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - Paul Shinn
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - David Gerhold
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - Anna Rossoshek
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - Sam Michael
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - Warren Casey
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Michael F Santillo
- Division of Toxicology, Office of Applied Research and Safety Assessment, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, Laurel, Maryland, USA
| | - Suzanne Fitzpatrick
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, Maryland, USA
| | - Russell S Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Anton Simeonov
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| | - Menghang Xia
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA; ,
| |
Collapse
|