Computational Methods for Modeling Drug Kinetics

药物动力学建模的计算方法

• 批准号: RGPIN-2019-04941
• 负责人: Rowley, Christopher
• 金额: $ 3.04万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737962
• 关键词: Computational; Methods; Modeling; Drug; Kinetics

Small-molecule drugs remain a critical component of modern medicine, although the traditional drug development model has faced declining success rates and escalating costs. Traditional medicinal chemistry has aimed to identify drugs that bind to their protein targets with high affinity. In aid of this, computational chemists have developed methods to estimate drug-protein binding affinities by modeling the drug-protein intermolecular interactions. Over the last 10 years, chemists have found that the rates at which a drug binds and dissociates from its target can be more predictive of a drug's efficacy. In particular, drugs that remain bound for longer periods (a.k.a. prolonged residence time, tD) can have high efficacy. Long-tD drugs are less likely to be displaced, metabolized, or interfere with another cellular component, which can yield improved efficacy with fewer adverse effects. Kinetic effects are most significant for drugs that bind in recessed sites of proteins. Dissociation from these sites requires the drug to pass through a complex set of dynamical paths connecting intermediate binding states. These paths can involve processes that occur on much longer timescales than simple drug diffusion, such as conformational changes of the drug and protein, desolvation, and breaking/making of specific intermolecular contacts. For covalent-modifier drugs, dissociation also involves breaking covalent bonds. Simulation of drug dissociation paths will provide the missing mechanistic data that will enable rational design of long-tD drugs. The long-term objective of this program is to design more effective drugs by predicting their binding kinetics computationally, which will be achieved by our short-term objectives: (A) Markov state models (MSMs) of drug-protein binding, (B) methods to model covalent-modifier drugs, (C) models for drug-protein intermolecular interactions, and (D) validating and applying these methods by calculating the binding kinetics of kinase inhibitors. The drug binding kinetics will be modeled by describing the set of intermediate binding modes as the states of a MSM that are connected by dynamical transition paths. Metadynamics will be used to identify the intermediate states, then path sampling methods will be used to calculate the rates of transitions between states. New models will be developed to describe the repulsive and dispersion components of the drug-protein interaction more realistically. These methods will make it possible to calculate binding kinetics efficiently and accurately. In the validation phase, we will assess our methods for the prediction the kinetics of drugs inhibiting kinase proteins. Many kinases are drug targets, but their conformational flexibility and the high homology within the kinase family has made it difficult to design selective drugs. We will validate our methods using experimental data on kinase-drug interactions then apply these methods to design long-tD kinase-targeting drugs.

小分子药物仍然是现代医学的重要组成部分，尽管传统的药物开发模式面临着成功率下降和成本上升的问题。传统的药物化学旨在鉴定以高亲和力结合其蛋白质靶点的药物。在这种情况下，计算化学家已经开发出通过模拟药物-蛋白质分子间相互作用来估计药物-蛋白质结合亲和力的方法。在过去的10年里，化学家们发现，药物与靶标结合和解离的速率更能预测药物的疗效。特别是，药物保持绑定较长的时间（a.k.a.延长的停留时间tD）可以具有高功效。长tD药物不太可能被取代，代谢或干扰另一种细胞成分，这可以提高疗效，减少不良反应。 动力学效应对于结合在蛋白质凹陷部位的药物最为显著。从这些位点解离需要药物通过一组复杂的连接中间结合状态的动力学路径。这些途径可能涉及比简单药物扩散更长时间尺度上发生的过程，例如药物和蛋白质的构象变化、去溶剂化和特定分子间接触的破坏/形成。对于共价修饰剂药物，解离也涉及破坏共价键。药物解离路径的模拟将提供缺失的机制数据，这将使长tD药物的合理设计成为可能。该计划的长期目标是通过计算预测其结合动力学来设计更有效的药物，这将通过我们的短期目标实现：（A）药物-蛋白质结合的马尔可夫状态模型（MSM），（B）模拟共价修饰剂药物的方法，（C）药物-蛋白质分子间相互作用的模型，和（D）通过计算激酶抑制剂的结合动力学来验证和应用这些方法。药物结合动力学将通过将中间结合模式的集合描述为通过动态过渡路径连接的MSM的状态来建模。将使用元分析来识别中间状态，然后使用路径采样方法来计算状态之间的转换率。将开发新的模型来更真实地描述药物-蛋白质相互作用的排斥和分散成分。这些方法将使其能够有效和准确地计算结合动力学。在验证阶段，我们将评估我们的方法用于预测抑制激酶蛋白的药物的动力学。许多激酶是药物靶点，但它们的构象灵活性和激酶家族内的高度同源性使得难以设计选择性药物。我们将使用激酶-药物相互作用的实验数据验证我们的方法，然后将这些方法应用于设计长tD激酶靶向药物。