Evaluation and Characterization of Trk Kinase Inhibitors for the Treatment of Pain: Reliable Binding Affinity Predictions from Theory and Computation.
Optimization of ligand binding affinity to the target protein of interest is a primary objective in small-molecule drug discovery. Until now, the prediction of binding affinities by computational methods has not been widely applied in the drug discovery process, mainly because of its lack of accuracy and reproducibility as well as the long turnaround times required to obtain results. Herein we report on a collaborative study that compares tropomyosin receptor kinase A (TrkA) binding affinity predictions using two recently formulated fast computational approaches, namely, Enhanced Sampling of Molecular dynamics with Approximation of Continuum Solvent (ESMACS) and Thermodynamic Integration with Enhanced Sampling (TIES), to experimentally derived TrkA binding affinities for a set of Pfizer pan-Trk compounds. ESMACS gives precise and reproducible results and is applicable to highly diverse sets of compounds. It also provides detailed chemical insight into the nature of ligand-protein binding. TIES can predict and thus optimize more subtle changes in binding affinities between compounds of similar structure. Individual binding affinities were calculated in a few hours, exhibiting good correlations with the experimental data of 0.79 and 0.88 from the ESMACS and TIES approaches, respectively. The speed, level of accuracy, and precision of the calculations are such that the affinity predictions can be used to rapidly explain the effects of compound modifications on TrkA binding affinity. The methods could therefore be used as tools to guide lead optimization efforts across multiple prospective structurally enabled programs in the drug discovery setting for a wide range of compounds and targets.
Centre for Computational Science, Department of Chemistry, University College London , London WC1H 0AJ, United Kingdom.
Shunzhou
S
Wan
Centre for Computational Science, Department of Chemistry, University College London , London WC1H 0AJ, United Kingdom.
Agastya P
AP
Bhati
Pfizer Global R&D , The Portway Building, Granta Park, Cambridge CB21 6GS, United Kingdom.
Sarah
S
Skerratt
Pfizer Worldwide Research and Development , 610 Main Street, Cambridge, Massachusetts 02139, United States.
Kiyoyuki
K
Omoto
Pfizer Worldwide Research and Development , Groton Laboratories, Groton, Connecticut 06340, United States.
Veerabahu
V
Shanmugasundaram
Pfizer Global R&D , The Portway Building, Granta Park, Cambridge CB21 6GS, United Kingdom.
Sharan K
SK
Bagal
Centre for Computational Science, Department of Chemistry, University College London , London WC1H 0AJ, United Kingdom.
Peter V
0000-0002-8787-7256
PV
Coveney
D015195:Drug Design; D062105:Molecular Docking Simulation; D056004:Molecular Dynamics Simulation; D010146:Pain / Q000188:drug therapy* / Q000201:enzymology; D011485:Protein Binding; D047428:Protein Kinase Inhibitors / Q000378:metabolism* / Q000494:pharmacology* / Q000627:therapeutic use; D020917:Receptor, trkA / Q000037:antagonists & inhibitors* / Q000737:chemistry / Q000378:metabolism*; D013816:Thermodynamics
D047428:Protein Kinase Inhibitors; D020917:Receptor, trkA