Publications

Publication:Organic Process Research & Development
Authors:Yu-hong Lam*, Yuriy Abramov, Ravi S. Ananthula, Jennifer M. Elward, Lori R. Hilden, Sten O. Nilsson Lill, Per-Ola Norrby, Antonio Ramirez, Edward C. Sherer, Jason Mustakis, and Gerald J. Tanoury*
Date:July 23, 2020

Applications of Quantum Chemistry in Pharmaceutical Process Development: Current State and Opportunities

Org. Process Res. Dev. 2020, 24, 8, 1496–1507

Application of computational methods to understanding and predicting properties of analogues for drug discovery has enjoyed a long history of success. However, the drug development space (post-candidate selection) is currently experiencing a rapid growth in this arena. Due to the revolution in computing hardware development and improved computational techniques, quantum chemical (QC) calculations have become an essential tool in this space, allowing results from complex calculations to inform chemical development efforts. As a result, numerous pharmaceutical companies are employing QC as part of their drug development workflow. Calculations cover the range of transition state calculations, reaction path determination, and potential energy surface scans, among others. The impact of this rapid growth is realized by providing an in-depth understanding of chemical processes and predictive insight into the outcome of potential process routes and conditions. This review surveys the state of the art in these drug development applications in the pharmaceutical industry. Statistics of computational methods, software, and other metrics for publications in the last 14 years (2005–2019) are presented. Predictive applications of quantum chemistry for influencing experiments in reaction optimization and catalyst design are described. Important gaps in hardware and software capabilities that need to be addressed in order for quantum chemistry to become a more practical and impactful tool in pharmaceutical drug development are discussed. Perspectives for the future direction of application of QC to pharmaceutical drug development are proposed.

Publication:Crystal Growth Design
Authors:Dr. Yuriy Abramov, Dr. Peiyu Zhang et al.
Date:Jan. 30, 2020

Computational Insights into Kinetic Hindrance Affecting Crystallization of Stable Forms of Active Pharmaceutical Ingredients

Cryst. Growth Des. 2020, 20, 3, 1512-1525

A computational investigation of the potential source of kinetic hindrance for the late appearance of pharmaceutically relevant stable forms of ritonavir, rotigotine, ranitidine hydrochloride, and pharmaceutical compound A was performed along the crystallization coordinates of the relative rates of conformational interconversion, crystal nucleation, and growth.

Publication:molecular pharmaceutics
Authors:Dr. Yuriy Abramov, Dr. Guangxu Sun et al.
Date:Jan. 13, 2020

Guiding Lead Optimization for Solubility Improvement with Physics-Based Modeling

Mol. Pharmaceutics 2020, 17, 2, 666-673

Although there are a number of computational approaches available for the aqueous solubility prediction, a majority of those models rely on the existence of a training set of thermodynamic solubility measurements or/and fail to accurately account for the lattice packing contribution to the solubility. The main focus of this study is the validation of the application of a physics-based aqueous solubility approach…

Publication:Journal of Computational Chemistry
Authors:Dr. Shuai Liu, Dr. Mingjun Yang et al.
Date:Nov. 13, 2019

Optimal Designs for Pairwise Calculation: an Application to Free Energy Perturbation in Minimizing Prediction Variability

J. Comput. Chem. 2019, 9999, 1–11

Pairwise‐based methods such as the free energy perturbation (FEP) method have been widely deployed to compute the binding free energy differences between two similar host–guest complexes. The calculated pairwise free energy difference is either directly adopted or transformed to absolute…

Publication:Crystal Growth Design
Authors:Dr. Yuriy Abramov, Dr. Guangxu Sun
Date:Oct. 17, 2019

Solid-form Transition Temperature Prediction from a Virtual Polymorph Screening:A Reality Check

Cryst. Growth Des. 2019, 19, 7132−7137

The focus of this study is an estimation of uncertainty of solid-form transition temperature (Ttr) prediction based on modern virtual polymorph screening calculations. That was done through error propagation, utilizing estimated uncertainties of the relative free energy predictions at 0 K as well as of finite-temperature contribution to the polymorphic relative free energy…

Publication:The Journal of Chemical Physics
Authors:Dr. M. A. Bellucci et al.
Date:March 5, 2019

Solubility of Paracetamol in Ethanol by Molecular Dynamics Using the Extended Einstein Crystal Method and Experiments

J. Chem. Phys. 150, 094107 (2019)

Li and co-workers [Li et al., J. Chem. Phys. 146, 214110 (2017)] have recently proposed a methodology to compute the solubility of molecular compounds from first principles, using molecular dynamics simulations. We revise and further explore their methodology that was originally applied to nap…