Son Ahrum, Park Jongham, Kim Woojin, Yoon Yoonki, Lee Sangwoon, Ji Jaeho, Kim Hyunsoo
Department of Molecular Medicine, Scripps Research, San Diego, CA 92037, USA.
Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea.
Toxics. 2024 Nov 16;12(11):822. doi: 10.3390/toxics12110822.
It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure-activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer.
为提高新型治疗药物的疗效和安全性,必须了解药物毒性的潜在机制。组学技术(如转录组学、蛋白质组学和代谢组学)的最新进展显著增强了识别导致药物诱导毒性的分子途径的能力。这使得能够早期识别潜在的不良反应。计算工具(包括定量构效关系(QSAR)分析和机器学习模型)进一步深化了这些见解,这些工具能够准确预测毒性终点。此外,基于生理学的药代动力学(PBPK)建模和微生理系统(MPS)等技术提供了更精确的临床前到临床的转化,从而改善了药物安全性评估。本综述强调了先进的筛选技术、计算机模拟和组学数据之间的协同作用,强调了它们在减少后期药物开发失败方面的作用。尽管已经取得了进展,但在整合各种数据类型和解释复杂的生物相互作用方面仍然存在挑战。进一步加强预测毒理学的标准化方法的开发取决于研究人员、临床医生和监管机构之间的持续合作。这种合作确保开发出更有效、更安全的治疗药物。