Prioritizing Computational Cocrystal Prediction Methods for Experimental Researchers: A Review to Find Efficient, Cost-Effective, and User-Friendly Approaches

Pharmaceutical cocrystals offer a versatile approach to enhancing the properties of drug compounds, making them an important tool in drug formulation and development by improving the therapeutic performance and patient experience of pharmaceutical products. The prediction of cocrystals involves using computational and theoretical methods to identify potential cocrystal formers and understand the interactions between the active pharmaceutical ingredient and coformers. This process aims to predict whether two or more molecules can form a stable cocrystal structure before performing experimental synthesis, thus saving time and resources. In this review, the commonly used cocrystal prediction methods are first overviewed and then evaluated based on three criteria: efficiency, cost-effectiveness, and user-friendliness. Based on these considerations, we suggest to experimental researchers without strong computational experiences which methods and tools should be tested as a first step in the workflow of rational design of cocrystals. However, the optimal choice depends on specific needs and resources, and combining methods from different categories can be a more powerful approach.

Bifonazole caffeate: The first molecular salt of bifonazole with enhanced biopharmaceutical property based on experiments and quantum chemistry research

In order to make novel breakthroughs in molecular salt studies of BCS class-IV antifungal medication bifonazole (BIF), a salification-driven strategy towards ameliorating attributes and aiding augment efficiency is raised. This strategy fully harnesses structural characters together attributes and benefits of caffeic acid (CAF) to concurrently enhance dissolvability and permeability of BIF by introducing the two ingredients into the identical molecular salt lattice through the salification reaction, which, coupled with the aroused potential activity of CAF significantly amplifies the antifungal efficacy of BIF. Guided by this route, the first BIF-organic molecular salt, BIF-CAF, is directionally designed and synthesized with satisfactorily structural characterizations and integrated theoretical and experimental explorations on the pharmaceutical properties. Single-crystal X-ray diffraction resolving confirms that there is a lipid-water amphiphilic sandwich structure constructed by robust charge-assistant hydrogen bonds in the salt crystal, endowing the molecular salt with the potential to enhance both dissolvability and permeability relative to the parent drug, which is validated by experimental evaluations. Remarkably, the comprehensive DFT-based theoretical investigations covering frontier molecular orbital, molecular electrostatic potential, Hirshfeld surface analysis, reduced density gradient, topology, sphericity and planarity analysis strongly support these observations, thereby allowing some positive relationships between macroscopic properties and microstructures of the molecular salt can be made. Intriguingly, the optimal properties, together with the stimulated activity of CAF markedly augment in vitro antifungal ability of the molecular salt, with magnifying inhibition zones and reducing minimum inhibitory concentrations. These findings fill in the gaps on researches of BIF-organic molecular salt, and adequately exemplify the feasibility and validity by integrating theoretical and experimental approaches to resolve BIF's problems via the salification-driven tactic.

Repurposing of Antifungal Drug Flucytosine/Flucytosine Cocrystals for Anticancer Activity against Prostate Cancer Targeting Apoptosis and Inflammatory Signaling Pathways

This study aimed to repurpose the antifungal drug flucytosine (FCN) for anticancer activity together with cocrystals of nutraceutical coformers sinapic acid (SNP) and syringic acid (SYA). The cocrystal screening experiments with SNP resulted in three cocrystal hydrate forms in which two are polymorphs, namely, FCN-SNP F-I and FCN-SNP F-II, and the third one with different stoichiometry in the asymmetric unit (1:2:1 ratio of FCN:SNP:H2O, FCN-SNP F-III). Cocrystallization with SYA resulted in two hydrated cocrystal polymorphs, namely, FCN-SYA F-I and FCN-SYA F-II. All the cocrystal polymorphs were obtained concomitantly during the slow evaporation method, and one of the polymorphs of each system was produced in bulk by the slurry method. The interaction energy and lattice energies of all cocrystal polymorphs were established using solid-state DFT calculations, and the outcomes correlated with the experimental results. Further, the in vitro cytotoxic activity of the cocrystals was determined against DU145 prostate cancer and the results showed that the FCN-based cocrystals (FCN-SNP F-III and FCN-SYA F-I) have excellent growth inhibitory activity at lower concentrations compared with parent FCN molecules. The prepared cocrystals induce apoptosis by generating oxidative stress and causing nuclear damage in prostate cancer cells. The Western blot analysis also depicted that the cocrystals downregulate the inflammatory markers such as NLRP3 and caspase-1 and upregulate the intrinsic apoptosis signaling pathway marker proteins, such as Bax, p53, and caspase-3. These findings suggest that the antifungal drug FCN can be repurposed for anticancer activity.

Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures

Dapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS: 112-57-2), and diethylene glycol bis(3-aminopropyl) ether (CAS: 4246-51-9). Furthermore, the study proposes the use of intermolecular interactions as molecular descriptors to predict the solubility of dapsone in neat solvents and binary mixtures using machine learning models. An ensemble of regressors was used, including support vector machines, random forests, gradient boosting, and neural networks. Affinities of dapsone to solvent molecules were calculated using COSMO-RS and used as input for model training. Due to the polymorphic nature of dapsone, fusion data are not available, which prohibits the direct use of COSMO-RS for solubility calculations. Therefore, a consonance solvent approach was tested, which allows an indirect estimation of the fusion properties. Unfortunately, the resulting accuracy is unsatisfactory. In contrast, the developed regressors showed high predictive potential. This work documents that intermolecular interactions characterized by solute-solvent contacts can be considered valuable molecular descriptors for solubility modeling and that the wealth of encoded information is sufficient for solubility predictions for new systems, including those for which experimental measurements of thermodynamic properties are unavailable.

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.

Combined virtual and experimental screening of multicomponent crystals of 2,4-dichlorophenoxyacetic acid

Efficient screening of 2,4-D multicomponent crystals by COSMO-RS and molecular complementarity analysis combined with liquid-assisted grinding.