Version 1
: Received: 16 August 2024 / Approved: 26 August 2024 / Online: 26 August 2024 (11:31:51 CEST)
How to cite:
Son, A.; Park, J.; Kim, W.; Yoon, Y.; Lee, S.; Kim, H. Utilizing Omics Technologies and Machine Learning to Improve Predictive Toxicology. Preprints2024, 2024081827. https://doi.org/10.20944/preprints202408.1827.v1
Son, A.; Park, J.; Kim, W.; Yoon, Y.; Lee, S.; Kim, H. Utilizing Omics Technologies and Machine Learning to Improve Predictive Toxicology. Preprints 2024, 2024081827. https://doi.org/10.20944/preprints202408.1827.v1
Son, A.; Park, J.; Kim, W.; Yoon, Y.; Lee, S.; Kim, H. Utilizing Omics Technologies and Machine Learning to Improve Predictive Toxicology. Preprints2024, 2024081827. https://doi.org/10.20944/preprints202408.1827.v1
APA Style
Son, A., Park, J., Kim, W., Yoon, Y., Lee, S., & Kim, H. (2024). Utilizing Omics Technologies and Machine Learning to Improve Predictive Toxicology. Preprints. https://doi.org/10.20944/preprints202408.1827.v1
Chicago/Turabian Style
Son, A., Songwoon Lee and Hyunsoo Kim. 2024 "Utilizing Omics Technologies and Machine Learning to Improve Predictive Toxicology" Preprints. https://doi.org/10.20944/preprints202408.1827.v1
Abstract
The topic of predictive toxicology has been greatly influenced by recent progress in comprehending drug toxicity processes and enhancing medication development. The integration of omics technologies, such as transcriptomics, proteomics, and metabolomics, with traditional toxicological assessments has yielded extensive knowledge about the biological pathways implicated in drug-induced toxicity. The utilization of a multi-omics method amplifies the ability to identify biomarkers that can detect toxicity at an early stage, hence enhancing the safety profile of novel therapeutic medicines. Machine learning and in silico models, such as QSAR models and multi-task deep learning algorithms, have become essential tools. They have shown great accuracy in predicting toxicity endpoints and have helped in the identification of new biomarkers and therapeutic targets. The introduction of microphysiological systems and PBPK modeling has enhanced the transfer of preclinical discoveries to clinical results, providing more precise forecasts of human reactions to medications. Notwithstanding these progressions, obstacles such as the diversity of data and the complex nature of omics data require sophisticated computational techniques for efficient analysis. Continued cooperation and established procedures are crucial to fully utilize these technologies, guaranteeing the creation of safer and more efficient medicinal agents.
Keywords
Predictive Toxicology; Omics Technologies; Machine Learning; Structure-Activity Relationship; Fragment-Based Drug Design; Microphysiological Systems; Physiologically Based Pharmacokinetic Modeling; Virtual Screening; Biochemical Targets; Automated De Novo Drug Design
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
