Yuriy A. Abramov

Development of a targeted polymorph screening approach for a complex polymorphic and highly solvating API

Elucidation of the most stable form of an active pharmaceutical ingredient (API) is a critical step in the development process. Polymorph screening for an API with a complex polymorphic profile can present a significant challenge. The presented case illustrates an extensively polymorphic compound with an additional propensity for forming stable solvates. In all, 5 anhydrous forms and 66 solvated forms have been discovered. After early polymorph screening using common techniques yielded mostly solvates and failed to uncover several key anhydrous forms, it became necessary to devise new approaches based on an advanced understanding of crystal structure and conformational relationships between forms. With the aid of this analysis, two screening approaches were devised which targeted high-temperature desolvation as a means to increase conformational populations and enhance overall probability of anhydrous form production. Application of these targeted approaches, comprising over 100 experiments, produced only the known anhydrous forms, without appearance of any new forms. The development of these screens was a critical and alternative approach to circumvent solvation issues associated with more conventional screening methods. The results provided confidence that the current development form was the most stable polymorph, with a low likelihood for the existence of a more-stable anhydrous form.

Rational Coformer or Solvent Selection for Pharmaceutical Cocrystallization or Desolvation

It is demonstrated that the fluid-phase thermodynamics theory conductor-like screening model for real solvents (COSMO-RS) as implemented in the COSMOtherm software can be used for accurate and efficient screening of coformers for active pharmaceutical ingredient (API) cocrystallization. The excess enthalpy, H(ex) , between an API-coformer mixture relative to the pure components reflects the tendency of those two compounds to cocrystallize. Thus, predictive calculations may be performed with decent effort on a large set of molecular data in order to identify potentially new cocrystal systems. In addition, it is demonstrated that COSMO-RS theory allows reasonable ranking of coformers for API solubility improvement. As a result, experiments may be focused on those coformers, which have an increased probability of cocrystallization, leading to the largest improvement of the API solubility. In a similar way as potential coformers are identified for cocrystallization, solvents that do not tend to form solvates may be determined based on the highest H(ex) s with the API. The approach was successfully tested on tyrosine kinase inhibitor axitinib, which has a propensity to form relatively stable solvated structures with the majority of common solvents, as well as on thiophanate-methyl and thiophanate-ethyl benzimidazole fungicides, which form channel solvates.

A computational approach to enzyme design: predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring.

Enzyme design is an important area of ongoing research with a broad range of applications in protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, significant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, we develop a computational protocol using an approach that combines molecular dynamics, docking, and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focuses are to understand the molecular basis of substrate recognition and binding in an S-stereoselective ω-aminotransferase (ω-AT), which naturally catalyzes the transamination of pyruvate into alanine, and to predict mutations that enhance the catalytic efficiency of the enzyme. The conversion of (R)-ethyl 5-methyl-3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate in the context of several ω-AT mutants was evaluated using the computational protocol developed in this work. We correctly identify the mutations that yield the greatest improvements in enzyme activity (20-60-fold improvement over wild type) and confirm that the computationally predicted structure of a highly active mutant reproduces key structural aspects of the variant, including side chain conformational changes, as determined by X-ray crystallography. Overall, the protocol developed here yields encouraging results and suggests that computational approaches can aid in the redesign of enzymes with improved catalytic efficiency.

Low solubility in drug development: de‐convoluting the relative importance of solvation and crystal packing

An increasing trend towards low solubility is a major issue for drug development as formulation of low solubility compounds can be problematic. This paper presents a model which de‐convolutes the solubility of pharmaceutical compounds into solvation and packing properties with the intention to understand the solubility limiting features.

Molecular features crucial to the activity of pyrimidine benzamide-based thrombopoietin receptor agonists

The identification of small molecule modulators of biological processes mediated via protein-protein interactions has generally proved to be a challenging endeavor. In the case of the thrombopoietin receptor (TPOr), however, a number of small molecule types have been reported to display biological activity similar to that of the agonist protein TPO. Through a detailed analysis of structure-activity relationships, X-ray crystal structures, NMR coupling constants, nuclear Overhauser effects, and computational data, we have determined the agonism-inducing conformation of one series of small molecule TPOr agonists. The relationship of this agonism-inducing conformation to that of other series of TPO receptor agonists is discussed.

Development of machine learning models of β-cyclodextrin and sulfobutylether-β-cyclodextrin complexation free energies.

A new set of 142 experimentally determined complexation constants between sulfobutylether-β-cyclodextrin and diverse organic guest molecules, and 78 observations reported in literature, were used for the development of the QSPR models by the two machine learning regression methods - Cubist and Random Forest. Similar models were built for β-cyclodextrin using the 233-compound dataset available in the literature. These results demonstrate that the machine learning regression methods can successfully describe the complex formation between organic molecules and β-cyclodextrin or sulfobutylether-β-cyclodextrin. In particular, the root mean square errors for the test sets predictions by the best models are low, 1.9 and 2.7kJ/mol, respectively. The developed QSPR models can be used to predict the solubilizing effect of cyclodextrins and to help prioritizing experimental work in drug discovery.

Quantification of Tribocharging of Pharmaceutical Powders in V-Blenders: Experiments, Multiscale Modeling, and Simulations

Pharmaceutical powders are very prone to electrostatic charging by colliding and sliding contacts. In pharmaceutical formulation processes, particle charging is often a nuisance and can cause problems in the manufacture of products, such as affecting powder flow, fill, and dose uniformity. For a fundamental understanding of the powder triboelectrification, it is essential to study charge transfer under well-defined conditions. Hence, all experiments in the present study were conducted in a V-blender located inside a glove box with a controlled humidity of 20%. To understand tribocharging, different contact surfaces, namely aluminum, Teflon, poly methyl methacrylate, and nylon were used along with 2 pharmaceutical excipients and 2 drug substances. For the pharmaceutical materials, the work function values were estimated using MOPAC, a semiempirical molecular orbital package which has been previously used for the solid-state studies and molecular structure predictions. For a mechanistic understanding of tribocharging, a discrete element model incorporating charge transfer and electrostatic forces was developed. An effort was made to correlate tribocharging of pharmaceutical powders to properties such as cohesive energy density and surface energy. The multiscale model used is restricted as it considers only spherical particles with smooth surfaces. It should be used judiciously for other experimental assemblies because it does not represent a full validation of a tightly integrated model.

Virtual hydrate screening and coformer selection for improved relative humidity stability

Drug formulations of anhydrous solid forms are generally preferred over hydrated forms. This is due to the risks of low exposure and unacceptable physical and chemical stability in comparison with anhydrous formulations. The purpose of the current study was to determine which descriptors can be most efficiently applied to virtual screening in order to provide answers to the following questions: 1) what is the propensity to form a solid state hydrate of a pharmaceutical compound, and 2) in regards to cocrystalline formulation, which coformer would provide for the highest stability with respect to relative humidity (RH) conditions? A number of properties of different complexity were tested to provide answers to these questions, including COSMO-RS excess free energy Gex and enthalpy Hex of hydration of the compound in amorphous state; octanol–water partition coefficient clogP; polar surface area TPSA; different combinations of molecular H-bond donor and acceptor counts; an excess enthalpy of API (active pharmaceutical ingredient)-coformer mixing; and coformer solubilities. It was demonstrated that the Gex property provides the most efficient way of virtual screening of hydration propensity of solid pharmaceutical compounds. It was also demonstrated that a virtual coformer screening based on the API coformer miscibility, as measured by the COSMO-RS Hex property, may be efficiently used to guide the experimental selection of coformers which have an increased probability of cocrystallization and provide the highest RH stability.