Applications of In Silico Solvent Screening and an Interactive Web-Based Portal for Pharmaceutical Crystallization Process Development.

In an effort to reduce development time and costs associated with active pharmaceutical ingredient process solvent selection and crystallization design, a tiered approach to crystallization solvent selection was developed that leverages different solubility modeling tools selected on the basis of available data and the intended use of the prediction. To facilitate easy access to routine solubility modeling functionality with a high level of automation and parallelization, a web-based in silico solvent-screening tool was also developed as well as a user interface to visualize and interpret the large number of predicted results. Examples are presented to illustrate the utility of the workflow and solvent-screening tool at various stages of development for a diverse range of crystallization processes. Implementation of the in silico solvent selection workflow has led to a ∼10× reduction in active pharmaceutical ingredient usage and 20% reduction in full-time employee time per project based on average after the first year.

Molecular transport through membranes: Accurate permeability coefficients from multidimensional potentials of mean force and local diffusion constants.

Estimating the permeability coefficient of small molecules through lipid bilayer membranes plays an important role in the development of effective drug candidates. In silico simulations can produce acceptable relative permeability coefficients for a series of small molecules; however, the absolute permeability coefficients from simulations are usually off by orders of magnitude. In addition to differences between the lipid bilayers used in vitro and in silico, the poor convergence of permeation free energy profiles and over-simplified diffusion models have contributed to these discrepancies. In this paper, we present a multidimensional inhomogeneous solubility-diffusion model to study the permeability of a small molecule drug (trimethoprim) passing through a POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) lipid bilayer. Our approach improves the permeation model in three ways: First, the free energy profile (potential of mean force, PMF) is two-dimensional in two key coordinates rather than simply one-dimensional along the direction normal to the bilayer. Second, the 2-D PMF calculation has improved convergence due to application of the recently developed transition-tempered metadynamics with randomly initialized replicas, while third, the local diffusivity coefficient was calculated along the direction of the minimum free energy path on the two-dimensional PMF. The permeability is then calculated as a line integral along the minimum free energy path of the PMF. With this approach, we report a considerably more accurate permeability coefficient (only 2-5 times larger than the experimental value). We also compare our approach with the common practice of computing permeability coefficients based only on the translation of the center of mass of the drug molecule. Our paper concludes with a discussion of approaches for minimizing the computational cost for the purpose of more rapidly screening a large number of drug candidate molecules.

Transition-Tempered Metadynamics Is a Promising Tool for Studying the Permeation of Drug-like Molecules through Membranes.

Metadynamics is an important enhanced sampling technique in molecular dynamics simulation to efficiently explore potential energy surfaces. The recently developed transition-tempered metadynamics (TTMetaD) has been proven to converge asymptotically without sacrificing exploration of the collective variable space in the early stages of simulations, unlike other convergent metadynamics (MetaD) methods. We have applied TTMetaD to study the permeation of drug-like molecules through a lipid bilayer to further investigate the usefulness of this method as applied to problems of relevance to medicinal chemistry. First, ethanol permeation through a lipid bilayer was studied to compare TTMetaD with nontempered metadynamics and well-tempered metadynamics. The bias energies computed from various metadynamics simulations were compared to the potential of mean force calculated from umbrella sampling. Though all of the MetaD simulations agree with one another asymptotically, TTMetaD is able to predict the most accurate and reliable estimate of the potential of mean force for permeation in the early stages of the simulations and is robust to the choice of required additional parameters. We also show that using multiple randomly initialized replicas allows convergence analysis and also provides an efficient means to converge the simulations in shorter wall times and, more unexpectedly, in shorter CPU times; splitting the CPU time between multiple replicas appears to lead to less overall error. After validating the method, we studied the permeation of a more complicated drug-like molecule, trimethoprim. Three sets of TTMetaD simulations with different choices of collective variables were carried out, and all converged within feasible simulation time. The minimum free energy paths showed that TTMetaD was able to predict almost identical permeation mechanisms in each case despite significantly different definitions of collective variables.

Core-shell potential-derived point charges.

The present work details the development of a core-shell model for the purposes of obtaining potential-derived point charges from the ab initio molecular electrostatic potential. In contrast to atomic point charge models, the core-shell model decomposes all atoms into a core with static charge located at a fixed atomic position and a shell with variable charge and position. The optimization of shell charges and positions is discussed. The core-shell model was found to significantly improve description of the ab initio electrostatic potential when compared to potential-derived net atomic point charge models as well as distributed multipoles with contributions up to atomic quadrupole moments. The core-shell model was found to produce similar results as the Weller-Williams lone-pair model and differences in the implementation of the models are discussed.

Effect of gastric pH on the pharmacokinetics of a BCS class II compound in dogs: utilization of an artificial stomach and duodenum dissolution model and GastroPlus,™ simulations to predict absorption.

Dogs are one of the most commonly used non-rodent species in toxicology studies and are known to have basal stomach pH ranging from 2 to 7 in the fasted state. Thus absorption and resulting plasma exposure of weakly basic compounds administered as crystalline suspensions to dogs are often variable. LY2157299 is a potent and selective transforming growth factor (TGF)-beta receptor type 1 kinase (TGF-ßRI) inhibitor that displayed variable absorption in early dog studies. This molecule is a weakly basic Biopharmaceutics Classification System (BCS)Class II compound, and depends on the rate and extent of dissolution to drive oral absorption. An artificial stomach and duodenum (ASD) dissolution model was utilized to evaluate potential effect of gastric pH on the absorption of suspension and buffered solution formulations. GastroPlus™ was also employed to predict the magnitude of gastric pH changes on LY2157299 absorption. The ASD experiments demonstrated that administration of a buffered acidic solution could improve the potential for absorption by normalizing gastric pH and enabling supersaturation in the duodenum. GastroPlus™ modeling suggested that direct modulation of gastric pH could lead to marked changes in bioavailability. Pharmacokinetic experiments were conducted in dogs to evaluate the effect of gastric pH modification on plasma exposure. The data were qualitatively consistent with the predictions.

Significant progress in predicting the crystal structures of small organic molecules--a report on the fourth blind test.

We report on the organization and outcome of the fourth blind test of crystal structure prediction, an international collaborative project organized to evaluate the present state in computational methods of predicting the crystal structures of small organic molecules. There were 14 research groups which took part, using a variety of methods to generate and rank the most likely crystal structures for four target systems: three single-component crystal structures and a 1:1 cocrystal. Participants were challenged to predict the crystal structures of the four systems, given only their molecular diagrams, while the recently determined but as-yet unpublished crystal structures were withheld by an independent referee. Three predictions were allowed for each system. The results demonstrate a dramatic improvement in rates of success over previous blind tests; in total, there were 13 successful predictions and, for each of the four targets, at least two groups correctly predicted the observed crystal structure. The successes include one participating group who correctly predicted all four crystal structures as their first ranked choice, albeit at a considerable computational expense. The results reflect important improvements in modelling methods and suggest that, at least for the small and fairly rigid types of molecules included in this blind test, such calculations can be constructively applied to help understand crystallization and polymorphism of organic molecules.

Application of error-ranked singular value decomposition for the determination of potential-derived atomic-centered point charges.

The present work provides a detailed investigation on the use of singular value decomposition (SVD) to solve the linear least-squares problem (LLS) for the purposes of obtaining potential-derived atom-centered point charges (PD charges) from the ab initio molecular electrostatic potential (V(QM)). Given the SVD of any PD charge calculation LLS problem, it was concluded that (1) all singular vectors are not necessary to obtain the optimal set of PD charges and (2) the most effective set of singular vectors do not necessarily correspond to those with the largest singular values. It is shown that the efficient use of singular vectors can provide statistically well-defined PD charges when compared with conventional PD charge calculation methods without sacrificing the agreement with V(QM). As can be expected, the methodology outlined here is independent of the algorithm for sampling V(QM) as well as the basis set used to calculate V(QM). An algorithm is provided to select the best set of singular vectors used for optimal PD charge calculations. To minimize the subjective comparisons of different PD charge sets, we also provide an objective criterion for determining if two sets of PD charges are significantly different from one another.