In-Silico Aided Screening and Characterization Results in Stability Enhanced Novel Roxadustat Co-Crystal

Roxadustat (RXD) is an approved drug substances for the treatment of renal anemia. It has poor aqueous solubility and photochemical stability. This study employs a comprehensive approach to enhance the stability and physicochemical properties RXD through coformer selection and characterization. The investigation integrates delta pKa analysis, molecular complementary assessment, molecular electrostatic potential surface analysis, and machine learning techniques to predict potential co-crystal formation and binding interactions between drug molecules and coformers. The co-crystal screening which lead to in a novel RXD-nicotinamide co-crystal (RXD-NA). Experimental characterization underscores the physical and chemical stability of the co-crystals. To elucidate the supramolecular synthons and understand the intermolecular interactions in the RXD-NA co-crystal, Hirshfeld surfaces analysis, quantum theory of atoms in molecules (QTAIM) analysis and non-covalent interaction (NCI) analysis were performed. Computational analysis of photo-isomer formation aligns with experimental observations, further enhancing our understanding of RXD-coformer interactions. RXD-NA co-crystal was found photo-chemically stable as compared to free base API drug substance. This integrated methodology provides a systematic framework for informed co-crystal design, holding promise for optimizing RXD formulations based on molecular interactions and stability considerations. Consequently, this study contributes valuable insights to the field of rational drug design and formulation optimization.

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.

In silico co-crystal design: Assessment of the latest advances

Pharmaceutical co-crystals represent a growing class of crystal forms in the context of pharmaceutical science. They are attractive to pharmaceutical scientists because they significantly expand the number of crystal forms that exist for an active pharmaceutical ingredient and can lead to improvements in physicochemical properties of clinical relevance. At the same time, machine learning is finding its way into all areas of drug discovery and delivers impressive results. In this review, we attempt to provide an overview of machine learning, deep learning and network-based recommendation approaches applied to pharmaceutical co-crystallization. We also present crystal structure prediction as an alternative to machine learning approaches.

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.

Absolute Configuration Determination of Chiral API Molecules by MicroED Analysis of Cocrystal Powders Formed Based on Cocrystal Propensity Prediction Calculations

Establishing the absolute configuration of chiral active pharmaceutical ingredients (APIs) is of great importance. Single crystal X-ray diffraction (scXRD) has traditionally been the method of choice for such analysis, but scXRD requires the growth of large crystals, which can be challenging. Here, we present a method for determining absolute configuration that does not rely on the growth of large crystals. By examining microcrystals formed with chiral probes (small chiral compounds such as amino acids), absolute configuration can be unambiguously determined by microcrystal electron diffraction (MicroED). Our streamlined method employs three steps: (1) virtual screening to identify promising chiral probes, (2) experimental cocrystal screening and (3) structure determination by MicroED and absolute configuration assignment. We successfully applied this method to analyze two chiral API molecules currently on the market for which scXRD was not used to determine absolute configuration.

Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals

In this study, the existing set of carbamazepine (CBZ) cocrystals was extended through the successful combination of the drug with the positional isomers of acetamidobenzoic acid. The structural and energetic features of the CBZ cocrystals with 3- and 4-acetamidobenzoic acids were elucidated via single-crystal X-ray diffraction followed by QTAIMC analysis. The ability of three fundamentally different virtual screening methods to predict the correct cocrystallization outcome for CBZ was assessed based on the new experimental results obtained in this study and data available in the literature. It was found that the hydrogen bond propensity model performed the worst in distinguishing positive and negative results of CBZ cocrystallization experiments with 87 coformers, attaining an accuracy value lower than random guessing. The method that utilizes molecular electrostatic potential maps and the machine learning approach named CCGNet exhibited comparable results in terms of prediction metrics, albeit the latter resulted in superior specificity and overall accuracy while requiring no time-consuming DFT computations. In addition, formation thermodynamic parameters for the newly obtained CBZ cocrystals with 3- and 4-acetamidobenzoic acids were evaluated using temperature dependences of the cocrystallization Gibbs energy. The cocrystallization reactions between CBZ and the selected coformers were found to be enthalpy-driven, with entropy terms being statistically different from zero. The observed difference in dissolution behavior of the cocrystals in aqueous media was thought to be caused by variations in their thermodynamic stability.

Exploring the Cocrystal Landscape of Posaconazole by Combining High-Throughput Screening Experimentation with Computational Chemistry

The development of multicomponent crystal forms, such as cocrystals, represents a means to enhance the dissolution and absorption properties of poorly water-soluble drug compounds. However, the successful discovery of new pharmaceutical cocrystals remains a time- and resource-consuming process. This study proposes the use of a combined computational-experimental high-throughput approach as a tool to accelerate and improve the efficiency of cocrystal screening exemplified by posaconazole. First, we employed the COSMOquick software to preselect and rank cocrystal candidates (coformers). Second, high-throughput crystallization experiments (HTCS) were conducted on the selected coformers. The HTCS results were successfully reproduced by liquid-assisted grinding and reaction crystallization, ultimately leading to the synthesis of thirteen new posaconazole cocrystals (7 anhydrous, 5 hydrates, and 1 solvate). The posaconazole cocrystals were characterized by PXRD, ¹H NMR, Fourier transform-Raman, thermogravimetry-Fourier transform infrared spectroscopy, and differential scanning calorimetry. In addition, the prediction performance of COSMOquick was compared to that of two alternative knowledge-based methods: molecular complementarity (MC) and hydrogen bond propensity (HBP). Although HBP does not perform better than random guessing for this case study, both MC and COSMOquick show good discriminatory ability, suggesting their use as a potential virtual tool to improve cocrystal screening.

Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization

Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know a priori which coformers will form cocrystals with a given API, and the current state-of-the-art for cocrystal discovery involves an expensive, time-consuming, and, at the early stages of pharmaceutical development, API material-limited experimental screen. We propose a systematic, high-throughput computational approach primarily aimed at identifying API/coformer pairs that are unlikely to lead to experimentally observable cocrystals and can therefore be eliminated with only a brief experimental check, from any experimental investigation. On the basis of a well-established crystal structure prediction (CSP) methodology, the proposed approach derives its efficiency by not requiring any expensive quantum mechanical calculations beyond those already performed for the CSP investigation of the neat API itself. The approach and assumptions are tested through a computational investigation on 30 potential 1:1 multicomponent systems (cocrystals and solvate) involving 3 active pharmaceutical ingredients and 9 coformers and one solvent. This is complemented with a detailed experimental investigation of all 30 pairs, which led to the discovery of five new cocrystals (three API-coformer combinations, a polymorphic cocrystal example, and one with different stoichiometries) and a cis-aconitic acid polymorph. The computational approach indicates that, for some APIs, a significant proportion of all potential API/coformer pairs could be investigated with only a brief experimental check, thereby saving considerable experimental effort.

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.

Improving the Physicochemical and Biopharmaceutical Properties of Active Pharmaceutical Ingredients Derived from Traditional Chinese Medicine through Cocrystal Engineering

Active pharmaceutical ingredients (APIs) extracted and isolated from traditional Chinese medicines (TCMs) are of interest for drug development due to their wide range of biological activities. However, the overwhelming majority of APIs in TCMs (T-APIs), including flavonoids, terpenoids, alkaloids and phenolic acids, are limited by their poor physicochemical and biopharmaceutical properties, such as solubility, dissolution performance, stability and tabletability for drug development. Cocrystallization of these T-APIs with coformers offers unique advantages to modulate physicochemical properties of these drugs without compromising the therapeutic benefits by non-covalent interactions. This review provides a comprehensive overview of current challenges, applications, and future directions of T-API cocrystals, including cocrystal designs, preparation methods, modifications and corresponding mechanisms of physicochemical and biopharmaceutical properties. Moreover, a variety of studies are presented to elucidate the relationship between the crystal structures of cocrystals and their resulting properties, along with the underlying mechanism for such changes. It is believed that a comprehensive understanding of cocrystal engineering could contribute to the development of more bioactive natural compounds into new drugs.

Accelerating pre-formulation investigations in early drug product life cycles using predictive methodologies and computational algorithms

Precisely developed computational methodologies can allow the drug product lifecycle process to be time-efficient, cost-effective and reliable through a thorough fundamental understanding at the molecular level. Computational methodologies include computational simulations, virtual screening, mathematical modeling and predictive tools. In light of current trends and increased expectations of product discovery in early pharmaceutical development, we have discussed different case studies. These case studies clearly demonstrate the successful application of predictive tools alone or in combination with analytical techniques to predict the physicochemical properties of drug substances and drug products, thereby shortening research and development timelines. The overall goal of this report is to summarize unique predictive methodologies, which can assist pharmaceutical scientists in achieving time-sensitive research goals and avoiding associated risks that can potentially affect the drug product quality.