Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, MO, USA.
Expert Opin Drug Discov. 2023 Jul-Dec;18(12):1333-1348. doi: 10.1080/17460441.2023.2264770. Epub 2023 Nov 1.
Drug-binding kinetics has been increasingly recognized as an important factor to be considered in drug discovery. Long residence time could prolong the action of some drugs while produce toxicity on others. Early evaluation of the binding kinetics of drug candidates could reduce attrition rate late in the drug discovery process. Computational prediction of drug-binding kinetics is useful as compounds can be evaluated even before they are made. However, simulation of drug-binding kinetics is a challenging problem because of the long-time scale involved. Nevertheless, significant progress has been made.
This review illustrates the rapid evolution of qualitative to quantitative molecular dynamics-based methods that have been developed over the last 15 years.
The development of new methods based on molecular dynamics simulations now enables computation of absolute association/dissociation rate constants. Cheaper methods capable of identifying candidates with fast or slow binding kinetics, or rank-ordering rate constants are also available. Together, these methods have generated useful insights into the molecular mechanisms of drug-binding kinetics, and the design of drug candidates with therapeutically favorable kinetics. Although predicting absolute rate constants is still expensive and challenging, rapid improvement is expected in the coming years with the continuing refinement of current technologies, development of new methodologies, and the utilization of machine learning.
药物结合动力学已逐渐被认为是药物发现中需要考虑的一个重要因素。药物的停留时间延长可能会延长某些药物的作用,而对其他药物则会产生毒性。早期评估候选药物的结合动力学可以降低药物发现过程后期的淘汰率。药物结合动力学的计算预测是有用的,因为即使在化合物合成之前也可以对其进行评估。然而,由于涉及的时间尺度较长,药物结合动力学的模拟仍然是一个具有挑战性的问题。尽管如此,还是取得了显著的进展。
本文综述了过去 15 年来,从定性到定量的基于分子动力学的方法的快速发展。
基于分子动力学模拟的新方法的发展现在能够计算绝对的结合/解离速率常数。也有更廉价的方法可以识别结合动力学较快或较慢的候选物,或者对速率常数进行排序。这些方法共同为药物结合动力学的分子机制以及具有治疗上有利动力学的药物候选物的设计提供了有用的见解。虽然预测绝对速率常数仍然昂贵且具有挑战性,但随着现有技术的不断完善、新方法的开发以及机器学习的应用,预计未来几年这一情况将迅速改善。