Computational Modeling of Solvent Effects on Protein-Ligand Interactions Using Fully Polarizable Continuum Model and Rational Drug Design

This is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model(FPCM)and rational drug design.Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design.To study the binding of a protein with multiple molecular species of a ligand,one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecular species binding with the protein.In this paper,we aim to provide a brief overview of the recent development in computational modeling of the solvent effects on the detailed protein-ligand interactions involving multiple molecular species of a ligand related to rational drug design.In particular,we first briefly discuss the main challenges in computational modeling of the detailed protein-ligand interactions involving the multiple molecular species and then focus on the FPCM model and its applications.The FPCM method allows accurate determination of the solvent effects in the first-principles quantum mechanism(QM)calculations on molecules in solution.The combined use of the FPCM-based QM calculations and other computational modeling and simulations enables us to accurately account for a protein binding with multiple molecular species of a ligand in solution.Based on the computational modeling of the detailed protein-ligand interactions,possible new drugs may be designed rationally as either small-molecule ligands of the protein or engineered proteins that bind/metabolize the ligand.The computational drug design has successfully led to discovery and development of promising drugs.

In Silico Pharmacokinetics Studies for Quinazolines Proposed as EGFR Inhibitors

In silico pharmacokinetics studies can aid the search for molecules with potential ability to be drug candidates. In this paper, a number of quinazoline candidates for epidermal growth factor receptor inhibitors—EGFR, important targets for the treatment of cancer, are computationally analyzed. The literature described that 69 quinazoline molecules were synthesized and the respective half maximum inhibitory concentrations (IC50) were obtained. A bilinear parabolic model was built to investigate the druglikeness by correlating the corresponding lipophilicities, which can be represented by the ideal Log P , with the optimal biological activity in terms of pIC50 values. Structural characteristics leading to improved pharmacokinetics parameters were then analyzed. Compound 56 exhibited the lowest IC50 and, therefore, it had the highest ability to inhibit the EGFR. In the present work, the most potent inhibitor 56 is not calculated to be the most promising drug candidate, since it’s out of the parabolic model obtained due to a Log P above 5, which is not within the expected optimum range. Finally, this work is an example of computational prediction that an experimentally, highly active EGFR inhibitor can be unsuccessful as drug candidate because of pitfalls in pharmacokinetics parameters.