Computational Methods for Modeling Drug Kinetics

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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.