Cocrystal Synthesis through Crystal Structure Prediction.

Crystal structure prediction (CSP) is an invaluable tool in the pharmaceutical industry because it allows to predict all the possible crystalline solid forms of small-molecule active pharmaceutical ingredients. We have used a CSP-based cocrystal prediction method to rank ten potential cocrystal coformers by the energy of the cocrystallization reaction with an antiviral drug candidate, MK-8876, and a triol process intermediate, 2-ethynylglyclerol. For MK-8876, the CSP-based cocrystal prediction was performed retrospectively and successfully predicted the maleic acid cocrystal as the most likely cocrystal to be observed. The triol is known to form two different cocrystals with 1,4-diazabicyclo[2.2.2]octane (DABCO), but a larger solid form landscape was desired. CSP-based cocrystal screening predicted the triol-DABCO cocrystal as rank one, while a triol-l-proline cocrystal was predicted as rank two. Computational finite-temperature corrections enabled determination of relative crystallization propensities of the triol-DABCO cocrystals with different stoichiometries and prediction of the triol-l-proline polymorphs in the free-energy landscape. The triol-l-proline cocrystal was obtained during subsequent targeted cocrystallization experiments and was found to exhibit an improved melting point and deliquescence behavior over the triol-free acid, which could be considered as an alternative solid form in the synthesis of islatravir.

Computational and Experimental Screening Approaches to Aripiprazole Salt Crystallization.

INTRODUCTION: The screening of multicomponent crystal system (MCC) is a key method for improving physicochemical properties of active pharmaceutical ingredients (APIs). The challenges associated with experimental salt screening include a large number of potential counterions and solvent systems and tendency to undergo disproportionation to produce free form during crystallization. These challenges may be mitigated by a combination of experimental and computational approaches to salt screening. The goal of this study is to evaluate performance of the counterion screening methods and propose and validate novel approaches to virtual solvent screening for MCC crystallization. METHODS: The actual performance of the ΔpKa > 3 rule for counterion selection was validated using multiple screenings reports. Novel computational models for virtual solvent screening to avoid MCC incongruent crystallization were proposed. Using the ΔpKa rule, 10 acid counterions were selected for experimental aripiprazole (APZ) salt screening using 10 organic solvents. The experimental results were used to validate the proposed novel virtual solvent screen models. RESULTS: Experimental APZ salt screening resulted in a total of eight MCCs which included glucuronate, mesylate, oxalate, tartrate, salicylate and mandelate. The new model to virtually screen solvents provided a general agreement with APZ experimental findings in terms of selecting the optimal solvent for MCC crystallization. CONCLUSION: The rational selection of counterions and organic solvents for MCC crystallization was presented using combined novel computational model as well as experimental studies. The current virtual solvent screen model was successfully implemented and validated which can be easily applied to newly discovered APIs.

Emerging Landscape of Computational Modeling in Pharmaceutical Development.

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

Guiding Lead Optimization for Solubility Improvement with Physics-Based Modeling.

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, which does not rely on any prior knowledge and explicitly describes the solid-state contribution, in order to guide the improvement of poor solubility during the lead optimization. A superior performance of a quantum mechanical (QM)-based thermodynamic cycle approach relative to a molecular mechanical (MM)-based one in application to the optimization of two pharmaceutical series was demonstrated. The QM-based model also provided insights into the source of poor solubility of the lead compounds, allowing the selection of the optimal strategies for chemical modification and formulation. It is concluded that the application of that approach to guide solubility improvement at the late discovery and/or early development stages of the drug design proves to be highly attractive.

A Fast Chromatographic Method for Estimating Lipophilicity and Ionization in Nonpolar Membrane-Like Environment.

This study describes the design and implementation of a new chromatographic descriptor called log k'80 PLRP-S that provides information about the lipophilicity of drug molecules in the nonpolar environment, both in their neutral and ionized form. The log k'80 PLRP-S obtained on a polymeric column with acetonitrile/water mobile phase is shown to closely relate to log Ptoluene (toluene dielectric constant ε ∼ 2). The main intermolecular interactions governing log k'80 PLRP-S were deconvoluted using the Block Relevance (BR) analysis. The information provided by this descriptor was compared to ElogD and calclog Ptol, and the differences are highlighted. The "charge-flush" concept is introduced to describe the sensitivity of log k'80 PLRP-S to the ionization state of compounds in the pH range 2 to 12. The ability of log k'80 PLRP-S to indicate the propensity of neutral molecules and monoanions to form Intramolecular Hydrogen Bonds (IMHBs) is proven through a number of examples.

Major Source of Error in QSPR Prediction of Intrinsic Thermodynamic Solubility of Drugs: Solid vs Nonsolid State Contributions?

The main purpose of this study is to define the major limiting factor in the accuracy of the quantitative structure-property relationship (QSPR) models of the thermodynamic intrinsic aqueous solubility of the drug-like compounds. For doing this, the thermodynamic intrinsic aqueous solubility property was suggested to be indirectly "measured" from the contributions of solid state, ΔGfus, and nonsolid state, ΔGmix, properties, which are estimated by the corresponding QSPR models. The QSPR models of ΔGfus and ΔGmix properties were built based on a set of drug-like compounds with available accurate measurements of fusion and thermodynamic solubility properties. For consistency ΔGfus and ΔGmix models were developed using similar algorithms and descriptor sets, and validated against the similar test compounds. Analysis of the relative performances of these two QSPR models clearly demonstrates that it is the solid state contribution which is the limiting factor in the accuracy and predictive power of the QSPR models of the thermodynamic intrinsic solubility. The performed analysis outlines a necessity of development of new descriptor sets for an accurate description of the long-range order (periodicity) phenomenon in the crystalline state. The proposed approach to the analysis of limitations and suggestions for improvement of QSPR-type models may be generalized to other applications in the pharmaceutical industry.

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.

Close intramolecular sulfur-oxygen contacts: modified force field parameters for improved conformation generation.

The Cambridge Structural Database (CSD) offers an excellent data source to study small molecule conformations and molecular interactions. We have analyzed 130 small molecules from the CSD containing an intramolecular sulfur-oxygen distance less than the sum of their van der Waals (vdW) radii. Close S···O distances are observed in several important medicinal chemistry motifs (e.g. a carbonyl oxygen connected by a carbon or nitrogen linker to a sulfur) and are not treated well with existing parameters in the MMFFs or OPLS_2005 force fields, resulting in suboptimal geometries and energetics. In this work, we develop modified parameters for the OPLS_2005 force field to better treat this specific interaction in order to generate conformations close to those found in the CSD structures. We use a combination of refitting a force field torsional parameter, adding a specific atom pair vdW term, and attenuating the electrostatic interactions to obtain an improvement in the accuracy of geometry minimizations and conformational searches for these molecules. Specifically, in a conformational search 58 % of the cases produced a conformation less than 0.25 Å from the CSD crystal conformation with the modified OPLS force field parameters developed in this work. In contrast, 25 and 37 % produced a conformation less than 0.25 Å with the MMFFs and OPLS_2005 force fields, respectively. As an application of the new parameters, we generated conformations for the tyrosine kinase inhibitor axitinib (trade name Inlyta) that could be correctly repacked into three observed polymorphic structures, which was not possible with conformations generated using MMFFs or OPLS_2005. The improved parameters can be mapped directly onto physical characteristics of the systems that are treated inadequately with the molecular mechanics force fields used in this study and potentially other force fields as well.

QTAIM application in drug development: prediction of relative stability of drug polymorphs from experimental crystal structures.

Prediction of the most stable crystal form based on the strongest intermolecular hydrogen bonds (HBs) only, was successfully applied to ten polymorphic drug systems, using the Quantum Theory of Atoms in Molecules (QTAIM). The results of the predictions were demonstrated to be superior to the thermodynamic stability ranking based on molecular mechanical (COMPASS forcefield), DFT and DFT-D calculations, as well as on the QTAIM predictions based on the total intermolecular HBing interactions strength. The obtained results support the validity of the best donor/best acceptor hierarchical approach for polymorph stability analysis of drug-like molecules: weak interactions are not as important for stability ranking as the strongest HBs. In addition, the proposed QTAIM approach allowed a reasonable ranking of the relative stability of multiple polymorphic crystalline forms of two test systems, axitinib and sulfathiazole.

Virtual Coformer Screening by Crystal Structure Predictions: Crucial Role of Crystallinity in Pharmaceutical Cocrystallization.

One of the most popular strategies of the optimization of drug properties in the pharmaceutical industry appears to be a solid form changing into a cocrystalline form. A number of virtual screening approaches have been previously developed to allow a selection of the most promising cocrystal formers (coformers) for an experimental follow-up. A significant drawback of those methods is related to the lack of accounting for the crystallinity contribution to cocrystal formation. To address this issue, we propose in this study two virtual coformer screening approaches based on a modern cloud-computing crystal structure prediction (CSP) technology at a dispersion-corrected density functional theory (DFT-D) level. The CSP-based methods were for the first time validated on challenging cases of indomethacin and paracetamol cocrystallization, for which the previously developed approaches provided poor predictions. The calculations demonstrated a dramatic improvement of the virtual coformer screening performance relative to the other methods. It is demonstrated that the crystallinity contribution to the formation of paracetamol and indomethacin cocrystals is a dominant one and, therefore, should not be ignored in the virtual screening calculations. Our results encourage a broad utilization of the proposed CSP-based technology in the pharmaceutical industry as the only virtual coformer screening method that directly accounts for the crystallinity contribution.

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.

New Tricks of the Trade for Crystal Structure Refinement.

Accurate crystal structures and their experimental uncertainties, determined by X-ray diffraction/neutron diffraction techniques, are vital for crystal engineering studies, such as polymorph stability and crystal morphology calculations. Because of differences in crystal growth and data measurement conditions, crystallographic databases often contain multiple entries of varying quality of the same compound. The choice of the most reliable and best quality crystal structure from many very similar structures remains an unresolved problem, especially for nonexperts. In addition, while crystallographers can make use of some professional software (i.e., Materials Studio) for structure refinement, noncrystallographers may not have access to it. In the present paper, we propose a simple method to study the sensitivity of the crystal lattice energy to changes in the structural parameters, which creates a diagnostic tool to test the quality of crystal structure files and to improve the low-quality structures based on lattice energy distribution. Thus, noncrystallographers could take the proposed idea and program/optimize crystal structure by themselves. They can have their in-house program to determine the reliability of the selected crystal data and then use the best quality data or carry out structural optimization for low-quality data. The proposed method will benefit a broad cross-section of scientific researchers, especially those in solid-state and physical chemistry.

Trans-inactivation: Repression in a wrong place.

Trans-inactivation is the repression of genes on a normal chromosome under the influence of a rearranged homologous chromosome demonstrating the position effect variegation (PEV). This phenomenon was studied in detail on the example of brownDominant allele causing the repression of wild-type brown gene on the opposite chromosome. We have investigated another trans-inactivation-inducing chromosome rearrangement, In(2)A4 inversion. In both cases, brownDominant and In(2)A4, the repression seems to be the result of dragging of the euchromatic region of the normal chromosome into the heterochromatic environment. It was found that cis-inactivation (classical PEV) and trans-inactivation show different patterns of distribution along the chromosome and respond differently to PEV modifying genes. It appears that the causative mechanism of trans-inactivation is de novo heterochromatin assembly on euchromatic sequences dragged into the heterochromatic nuclear compartment. Trans-inactivation turns out to be the result of a combination of heterochromatin-induced position effect and the somatic interphase chromosome pairing that is widespread in Diptera.

The Differences Between Cis- and Trans-Gene Inactivation Caused by Heterochromatin in Drosophila.

Position-effect variegation (PEV) is the epigenetic disruption of gene expression near the de novo-formed euchromatin-heterochromatin border. Heterochromatic cis-inactivation may be accompanied by the trans-inactivation of genes on a normal homologous chromosome in trans-heterozygous combination with a PEV-inducing rearrangement. We characterize a new genetic system, inversion In(2)A4, demonstrating cis-acting PEV as well as trans-inactivation of the reporter transgenes on the homologous nonrearranged chromosome. The cis-effect of heterochromatin in the inversion results not only in repression but also in activation of genes, and it varies at different developmental stages. While cis-actions affect only a few juxtaposed genes, trans-inactivation is observed in a 500-kb region and demonstrates а nonuniform pattern of repression with intermingled regions where no transgene repression occurs. There is no repression around the histone gene cluster and in some other euchromatic sites. trans-Inactivation is accompanied by dragging of euchromatic regions into the heterochromatic compartment, but the histone gene cluster, located in the middle of the trans-inactivated region, was shown to be evicted from the heterochromatin. We demonstrate that trans-inactivation is followed by de novo HP1a accumulation in the affected transgene; trans-inactivation is specifically favored by the chromatin remodeler SAYP and prevented by Argonaute AGO2.

Theoretical and experimental design of atypical kinase inhibitors: application to p38 MAP kinase.

Mimics of the benzimidazolone nucleus found in inhibitors of p38 kinase are proposed, and their theoretical potential as bioisosteres is described. A set of calculated descriptors relevant to the anticipated binding interaction for the fragments 1-methyl-1H-benzotriazole 5, 3-methyl-benzo[d]isoxazole 3, and 3-methyl-[1,2,4]triazolo[4,3-a]pyridine 4, pyridine 1, and 1,3-dimethyl-1,3-dihydro-benzoimidazol-2-one 2 are reported. The design considerations and synthesis of p38 inhibitors based on these H-bond acceptor fragments is detailed. Comparative evaluation of the pyridine-, benzimidazolone-, benzotriazole-, and triazolopyridine-based inhibitors shows the triazoles 20 and 25 to be significantly more potent experimentally than the benzimidazolone after which they were modeled. An X-ray crystal structure of 25 bound to the active site shows that the triazole group serves as the H-bond acceptor but unexpectedly as a dual acceptor, inducing movement of the crossover connection of p38alpha. The computed descriptors for the hydrophobic and pi-pi interaction capacities were the most useful in ranking potency.

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

OBJECTIVES: 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. METHODS: The Cambridge Crystallographic Database was the source of structural information. Lattice energies were calculated via force-field based approaches using Materials Studio. The solvation energies were calculated applying quantum chemistry models using Cosmotherm software. KEY FINDINGS: The solubilities of 54 drug-like compounds were mapped onto a solvation energy/crystal packing grid. Four quadrants were identified were different balances of solvation and packing were defining the solubility. A version of the model was developed which allows for the calculation of the two features even in absence of crystal structure. CONCLUSION: Although there are significant number of in-silico models, it has been proven very difficult to predict aqueous solubility accurately. Therefore, we have taken a different approach where the solubility is not predicted directly but is de-convoluted into two constituent features.

2-Aryl-3-methyloctahydrophenanthrene-2,3,7-triols as potent dissociated glucocorticoid receptor agonists.

A significant improvement in agonist activity of the previously described 2-aryloctahydrophenanthrene-2,3,7-triol series of dissociated glucocorticoid receptor agonists (DAGRs) was achieved by modifying the substitution at C3 from (S)-3-hydroxy to (R)-3-hydroxy-3-methyl. The IC50 of the prototype 13 in the efficacy assay measuring repression of IL-1 induced MMP-13 expression was 3.5 nM, exhibiting 87% of the maximal effect of dexamethasone (DEX). It displayed a dissociated profile by exhibiting 42% of the maximal effect of DEX in a mouse mammary tumor virus (MMTV) luciferase reporter transactivation assay. Compound 13 and analogues containing heterocyclic replacements for the C2 phenyl and modified B rings showed high repression of TNFα production in human whole blood, with IC50 values (43-167 nM) approaching the level of DEX (21 nM). On the basis of X-ray structures and force field calculations, the overall potency of this series was attributed to a favorable conformation of the C2α phenyl, induced by the neighboring C3α methyl.

Integrating intramolecular hydrogen bonding (IMHB) considerations in drug discovery using ΔlogP as a tool.

This study demonstrates that ΔlogP(oct-tol) (difference between logP(octanol) and logP(toluene)) describes compounds propensity to form intramolecular hydrogen bonds (IMHB) and may be considered a privileged molecular descriptor for use in drug discovery and for prediction of IMHB in drug candidates. We identified experimental protocols for acquiring reliable ΔlogP(oct-tol) values on a set of compounds representing IMHB motifs most prevalent in medicinal chemistry, mainly molecules capable of forming 6-, 7-member IMHB rings. Furthermore, computational ΔlogP(oct-tol) values obtained with COSMO-RS software provided a good estimate of experimental results and can be used prospectively to assess IMHB. The proposed interpretation method based on ΔlogP(oct-tol) data allowed categorization of the compounds into 2 groups: with high propensity to form IMHB and poor propensity or poor relevance of IMHB. The relative (1)H NMR chemical shift of an exchangeable proton was used to verify presence of IMHB and to validate the IMHB interpretation scheme.

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.

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

A new set of 142 experimentally determined complexation constants between sulfobutylether-ß-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 ß-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 ß-cyclodextrin or sulfobutylether-ß-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.