In-silico methods of cocrystal screening: A review on tools for rational design of pharmaceutical cocrystals

COSMOPharm: Drug-Polymer Compatibility of Pharmaceutical Amorphous Solid Dispersions from COSMO-SAC

The quantum mechanics-aided COSMO-SAC activity coefficient model is applied and systematically examined for predicting the thermodynamic compatibility of drugs and polymers. The drug-polymer compatibility is a key aspect in the rational selection of optimal polymeric carriers for pharmaceutical amorphous solid dispersions (ASD) that enhance drug bioavailability. The drug-polymer compatibility is evaluated in terms of both solubility and miscibility, calculated using standard thermodynamic equilibrium relations based on the activity coefficients predicted by COSMO-SAC. As inherent to COSMO-SAC, our approach relies only on quantum-mechanically derived σ-profiles of the considered molecular species and involves no parameter fitting to experimental data. All σ-profiles used were determined in this work, with those of the polymers being derived from their shorter oligomers by replicating the properties of their central monomer unit(s). Quantitatively, COSMO-SAC achieved an overall average absolute deviation of 13% in weight fraction drug solubility predictions compared to experimental data. Qualitatively, COSMO-SAC correctly categorized different polymer types in terms of their compatibility with drugs and provided meaningful estimations of the amorphous-amorphous phase separation. Furthermore, we analyzed the sensitivity of the COSMO-SAC results for ASD to different model configurations and σ-profiles of polymers. In general, while the free volume and dispersion terms exerted a limited effect on predictions, the structures of oligomers used to produce σ-profiles of polymers appeared to be more important, especially in the case of strongly interacting polymers. Explanations for these observations are provided. COSMO-SAC proved to be an efficient method for compatibility prediction and polymer screening in ASD, particularly in terms of its performance-cost ratio, as it relies only on first-principles calculations for the considered molecular species. The open-source nature of both COSMO-SAC and the Python-based tool COSMOPharm, developed in this work for predicting the API-polymer thermodynamic compatibility, invites interested readers to explore and utilize this method for further research or assistance in the design of pharmaceutical formulations.

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.

Graph Neural Networks with Multi-features for Predicting Cocrystals using APIs and Coformers Interactions

INTRODUCTION

Active pharmaceutical ingredients (APIs) have gained direct pharmaceutical interest, along with their in vitro properties, and thus utilized as auxiliary solid dosage forms upon FDA guidance and approval on pharmaceutical cocrystals when reacting with coformers, as a potential and attractive route for drug substance development.

METHODS

However, screening and selecting suitable and appropriate coformers that may potentially react with APIs to successfully form cocrystals is a time-consuming, inefficient, economically expensive, and labour-intensive task. In this study, we implemented GNNs to predict the formation of cocrystals using our introduced API-coformers relational graph data. We further compared our work with previous studies that implemented descriptor-based models (e.g., random forest, support vector machine, extreme gradient boosting, and artificial neural networks).

RESULTS

All built graph-based models show compelling performance accuracies (i.e., 91.36, 94.60 and 95. 95% for GCN, GraphSAGE, and RGCN respectively). RGCN demonstrated effectiveness and prevailed among the built graph-based models due to its capability to capture intricate and learn nuanced relationships between entities such as non-ionic and non-covalent interactions or link information between APIs and coformers which are crucial for accurate predictions and representations.

CONCLUSION

These capabilities allows the model to adeptly learn the topological structure inherent in the graph data.

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.

Novel co-crystal of 3-methylcinnamic acid with berberine (1:1): synthesis, characterization, and intestinal absorption property

OBJECTIVE

To synthesis a novel 'Pharmaceutical Cocrystal' of berberine (BBR) with coformer 3-methylcinnamic acid (3MCA) for increasing its solubility and intestinal absorption property.

SIGNIFICANCE

BBR-HCl has poor liposolubility, difficulty in penetrating the cell membrane and absorption in the gastrointestinal tract, low bioavailability, and limited clinical application. A new cocrystal is formed by the interaction between 3-MCA and BBR through molecular interaction, which improves the physicochemical properties, intestinal absorption property, and hygroscopicity.

METHODS

The solvent evaporation method was used to synthesize BCR-3MCA cocrystal. The physicochemical properties of the crystals were confirmed by different spectral techniques, i.e. by X-ray diffraction (PXRD, SXRD), thermogravimetry and differential thermal analysis (DSC, TGA), and scanning electron microscopy (SEM). Hygroscopicity of the cocrystal was evaluated by dynamic water vapor sorption (DVS). The intestinal absorption property was evaluated by the Ussing chamber system.

RESULTS

BBR and 3MCA can be directly self-assembled into uniform co-crystal by hydrogen bonds and π-π stacking interactions. Compared with BBR-HCl, the solubility of BBR-3MCA cocrystal in polar solvents of water, methanol, ethanol, and isopropanol increased by 13.9, 1.5, 4.7, and 15.8 times, respectively. The apparent absorption and the absorption rate constants were increased by 7.7 and 5.6 times, respectively. Surprisingly, BBR-3MCA co-crystal almost had no hygroscopicity.

CONCLUSION

The absolute molecular structure of the co-crystal was further confirmed by single crystal X-ray diffraction. The hydrogen bonds drove the formation of X-like one-dimensional unit. Compared to the BBR-HCl, BBR-3MCA cocrystal displayed superior dissolution and solubility performance, improved physical-chemical properties and significantly improved intestinal absorption.

Recent Advances in Co-Former Screening and Formation Prediction of Multicomponent Solid Forms of Low Molecular Weight Drugs

Multicomponent solid forms of low molecular weight drugs, such as co-crystals, salts, and co-amorphous systems, are a result of the combination of an active pharmaceutical ingredient (API) with a pharmaceutically acceptable co-former. These solid forms can enhance the physicochemical and pharmacokinetic properties of APIs, making them increasingly interesting and important in recent decades. Nevertheless, predicting the formation of API multicomponent solid forms in the early stages of formulation development can be challenging, as it often requires significant time and resources. To address this, empirical and computational methods have been developed to help screen for potential co-formers more efficiently and accurately, thus reducing the number of laboratory experiments needed. This review provides a comprehensive overview of current screening and prediction methods for the formation of API multicomponent solid forms, covering both crystalline states (co-crystals and salts) and amorphous forms (co-amorphous). Furthermore, it discusses recent advances and emerging trends in prediction methods, with a particular focus on artificial intelligence.

Exploring the Supramolecular Interactions and Thermal Stability of Dapsone:Bipyridine Cocrystals by Combining Computational Chemistry with Experimentation

The application of computational screening methodologies based on H-bond propensity scores, molecular complementarity, molecular electrostatic potentials, and crystal structure prediction has guided the discovery of novel cocrystals of dapsone and bipyridine (DDS:BIPY). The experimental screen, which included mechanochemical and slurry experiments as well as the contact preparation, resulted in four cocrystals, including the previously known DDS:4,4'-BIPY (2:1, CC₄₄-B) cocrystal. To understand the factors governing the formation of the DDS:2,2'-BIPY polymorphs (1:1, CC₂₂-A and CC₂₂-B) and the two DDS:4,4'-BIPY cocrystal stoichiometries (1:1 and 2:1), different experimental conditions (such as the influence of solvent, grinding/stirring time, etc.) were tested and compared with the virtual screening results. The computationally generated (1:1) crystal energy landscapes had the experimental cocrystals as the lowest energy structures, although distinct cocrystal packings were observed for the similar coformers. H-bonding scores and molecular electrostatic potential maps correctly indicated cocrystallization of DDS and the BIPY isomers, with a higher likelihood for 4,4'-BIPY. The molecular conformation influenced the molecular complementarity results, predicting no cocrystallization for 2,2'-BIPY with DDS. The crystal structures of CC₂₂-A and CC₄₄-A were solved from powder X-ray diffraction data. All four cocrystals were fully characterized by a range of analytical techniques, including powder X-ray diffraction, infrared spectroscopy, hot-stage microscopy, thermogravimetric analysis, and differential scanning calorimetry. The two DDS:2,2'-BIPY polymorphs are enantiotropically related, with form B being the stable polymorph at room temperature (RT) and form A being the higher temperature form. Form B is metastable but kinetically stable at RT. The two DDS:4,4'-BIPY cocrystals are stable at room conditions; however, at higher temperatures, CC₄₄-A transforms to CC₄₄-B. The cocrystal formation enthalpy order, derived from the lattice energies, was calculated as follows: CC₄₄-B > CC₄₄-A > CC₂₂-A.

Cocrystals by Design: A Rational Coformer Selection Approach for Tackling the API Problems

Active pharmaceutical ingredients (API) with unfavorable physicochemical properties and stability present a significant challenge during their processing into final dosage forms. Cocrystallization of such APIs with suitable coformers is an efficient approach to mitigate the solubility and stability concerns. A considerable number of cocrystal-based products are currently being marketed and show an upward trend. However, to improve the API properties by cocrystallization, coformer selection plays a paramount role. Selection of suitable coformers not only improves the drug's physicochemical properties but also improves the therapeutic effectiveness and reduces side effects. Numerous coformers have been used till date to prepare pharmaceutically acceptable cocrystals. The carboxylic acid-based coformers, such as fumaric acid, oxalic acid, succinic acid, and citric acid, are the most commonly used coformers in the currently marketed cocrystal-based products. Carboxylic acid-based coformers are capable of forming the hydrogen bond and contain smaller carbon chain with the APIs. This review summarizes the role of coformers in improving the physicochemical and pharmaceutical properties of APIs, and deeply explains the utility of afore-mentioned coformers in API cocrystal formation. The review concludes with a brief discussion on the patentability and regulatory issues related to pharmaceutical cocrystals.

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.

Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement

Bexarotene (BEX) was approved by the FDA in 1999 for the treatment of cutaneous T-cell lymphoma (CTCL). The poor aqueous solubility causes the low bioavailability of the drug and thereby limits the clinical application. In this study, we developed a GCN-based deep learning model (CocrystalGCN) for in-silico screening of the cocrystals of BEX. The results show that our model obtained high performance relative to baseline models. The top 30 of 109 coformer candidates were scored by CocrystalGCN and then validated experimentally. Finally, cocrystals of BEX-pyrazine, BEX-2,5-dimethylpyrazine, BEX-methyl isonicotinate, and BEX-ethyl isonicotinate were successfully obtained. The crystal structures were determined by single-crystal X-ray diffraction. Powder X-ray diffraction, differential scanning calorimetry, and thermogravimetric analysis were utilized to characterize these multi-component forms. All cocrystals present superior solubility and dissolution over the parent drug. The pharmacokinetic studies show that the plasma exposures (AUC_0−8h) of BEX-pyrazine and BEX-2,5-dimethylpyrazine are 1.7 and 1.8 times that of the commercially available BEX powder, respectively. This work sets a good example for integrating virtual prediction and experimental screening to discover the new cocrystals of water-insoluble drugs.

Hansen parameter evaluation for the characterization of titania photocatalysts using particle size distributions and combinatorics

Titania photocatalysts have great potential as remediators of air pollution. Although various aspects of photocatalyst synthesis, adsorption and photoactivity have been investigated, a thorough understanding of the particle surface behavior has not yet been fully realized. In order to learn more about the principles behind the surface behavior, we investigate the Hansen solubility/similarity parameters (HSPs) for analyzing and evaluating three photocatalysts synthesized by the gas phase method, solvothermal reaction and sol-gel method, respectively. A particle size distribution-based categorization scheme is introduced for characterizing each material's Hansen parameters based on its interaction with a list of selected probe liquids. The latter was deduced from particle size distributions assessed by analytical centrifugation. Subsequent comparison of the Hansen parameters of the investigated materials shows how HSPs can potentially be used as a model for predicting the pollutant adsorption behavior on the photocatalyst surface. This serves as a first step in heading towards an improved understanding of the particle behavior and translating it into a knowledge-based design, i.e., synthesis and hybridization of novel photocatalysts.

Cocrystals of tuberculosis antibiotics: Challenges and missed opportunities

Cocrystals have been extensively used to improve the physicochemical properties and bioavailability of active pharmaceutical ingredients. Cocrystals of anti-tuberculosis medications are among those commonly reported. This review provides a summary of the tuberculosis antibiotic cocrystals reported in the literature, providing the main results on current tuberculosis medications utilized in cocrystals. Moreover, anti-tuberculosis cocrystals limitations and advantages are described, including evidence for enhanced solubility, stability and effect. Opportunities to enhance anti-tuberculosis medications and fixed dose combinations using cocrystals are given. Several cocrystal pairs are suggested to enhance the effectiveness of anti-tuberculosis drugs.