Molecular simulations in drug delivery: Opportunities and challenges

分子动力学 药物输送 计算机科学 背景(考古学) 蒙特卡罗方法 生物信息学 药物发现 纳米技术 生化工程 化学 计算化学 材料科学 工程类 数学 古生物学 统计 基因 生物 生物化学
作者
Ratna S. Katiyar,Prateek K. Jha
出处
期刊:Wiley Interdisciplinary Reviews: Computational Molecular Science [Wiley]
卷期号:8 (4) 被引量:92
标识
DOI:10.1002/wcms.1358
摘要

Molecular simulations are promising tools for in silico design of drug delivery formulations, as they provide a prediction of formulation properties prior to synthesis thus minimizing the need for in vitro and in vivo experimentation. The detailed molecular insight obtained by these simulations is precious and often beyond the reach of sophisticated experimental facilities. Although initially limited to the prediction of single‐molecule behavior (e.g., drug orientation in a bilayer), gradual advances in computing speed and efficient simulation approaches have made it feasible to employ these methods for phenomena occurring at substantially large length and time scales (e.g., carrier‐drug complexation) with modest computational cost and resources. We present a nonmathematical review of molecular simulation methods and their applications in drug delivery, with special emphasis on the use of atomistic and coarse‐grained Monte Carlo (MC) and molecular dynamics (MD) methods and excluding the drug docking studies used in drug discovery. Current capabilities and problems associated with the use of these methods in the context of drug delivery are highlighted, along with a discussion of representative applications of molecular simulations in drug delivery. We conclude that while molecular simulations are expected to play a central role in the future of drug delivery field, we require a concerted effort of computational scientists, experimentalists, and industry personnel working on drug delivery to identify specific areas where these simulations can be especially useful. This article is categorized under: Structure and Mechanism > Computational Materials Science Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Software > Molecular Modeling
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