Residence Time Prediction of Type 1 and 2 Kinase Inhibitors from Unbinding Simulations

• Published in: Journal of chemical information and modeling Vol. 60; no. 1; pp. 342 - 348
• Main Authors: Braka, Abdennour, Garnier, Norbert, Bonnet, Pascal, Aci-Sèche, Samia
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 27-01-2020
• Subjects: Binding sites; Chemical Sciences; Cheminformatics; Computer Science; Computer simulation; Free energy; In vivo methods and tests; Kinases; Life Sciences; Ligands; Modeling and Simulation; Molecular dynamics; Molecular Dynamics Simulation; Optimization; Parameters; Predictions; Protein Binding; Protein Kinase Inhibitors - chemistry; Protein Kinase Inhibitors - pharmacology; Quantitative Methods; Residence time distribution; Statistical distributions; Thermodynamic equilibrium; Thermodynamics
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.9b00497

In the early stage of a drug discovery process, the selection and optimization of a ligand is mainly based on equilibrium thermodynamic constants such as K D or IC50 values, which are representatives of the affinity of the compound for its target. However, these criteria are not able to correctly evaluate the efficacy of compounds in vivo and result in many failures of compound development during phase II of clinical trials. Residence time (RT) is an important parameter associated to an in vivo drug’s safety and efficacy. The determination or modulation of kinetic rates correlated to RT may be performed to identify the best drug candidates in the early stages of a drug design project. For this purpose, a number of experimental methodologies were developed but remain costly in both time and money. Herein, we developed a novel protocol based on biased molecular dynamics simulations and transition-state theory in order to predict relative ligand kinetic rates and relative RTs of a series of compounds. First, we have repeatedly simulated the unbinding process of the ligand from its binding site to the outside of the target. Next, we sample the conformational space along the determined unbinding paths to allow relevant statistical distributions of the system. The free energy profiles associated to these distributions are then computed and used to predict the kinetics parameters. The studied set was composed of eight ligands with fast, intermediate, and slow dissociation rates and binding to the active and inactive states of p38α protein kinase. The proposed method provides an excellent correlation between the predicted values and the experimentally measured kinetic rates, in addition to a detailed characterization of the kinetic paths at the atomic level.