Cocrystal virtual screening based on the XGBoost machine learning model

Novel Drug-Drug Cocrystalline Forms of Carbamazepine with Sulfacetamide: Preparation, Characterization, and In Vitro/In Vivo Performance Evaluation

Objectives: Drug-drug cocrystallization represents a promising approach for the development of novel combination drugs with improved physicochemical and biopharmaceutical properties. The aim of the present research is to prepare novel drug-drug cocrystalline forms of antiepileptic drug carbamazepine (CBZ) with sulfacetamide (SCTM). Methods: The novel CBZ cocrystal methanol solvate and cocrystal hydrate were prepared via solvent evaporation technique and characterized by single crystal X-ray diffraction, differential scanning calorimetry and thermogravimetric analysis. Results: Single-crystal X-ray diffraction and thermal analysis revealed that the multicomponent solids are isostructural, wherein the solvent molecule does not play a structure-forming role. To optimize the synthesis of [CBZ+SCTM+H2O] (1:1:0.7), the binary and ternary phase diagrams were constructed in acetonitrile at 25 °C. A thorough investigation of the cocrystal hydrate behavior in aqueous solution showed that the pH of the dissolution medium exerted a significant effect on the stability and solubility of [CBZ+SCTM+H2O] (1:1:0.7). According to the dissolution and diffusion experiments in a buffer solution pH 6.5, the cocrystal hydrate characterized an enhanced dissolution rate and flux of CBZ. Pharmacokinetic studies in rabbits showed that the novel cocrystal hydrate exhibited a comparable bioavailability to the parent CBZ. Conclusions: Overall, this work reports the preparation of a novel CBZ drug-drug cocrystal hydrate, which can be considered as an alternative CBZ solid form for oral usage, possessing additive pharmacological effect.

Unlocking the Potential of Machine Learning in Co-crystal Prediction by a Novel Approach Integrating Molecular Thermodynamics

Co-crystal engineering is of interest for many applications in pharmaceutical, chemical, and materials fields, but rational design of co-crystals is still challenging. Although artificial intelligence has revolutionized decision-making processes in material design, limitations in generalization and mechanistic understanding remain. Herein, we sought to improve prediction of co-crystals by combining mechanistic thermodynamic modeling with machine learning. We constructed a brand-new co-crystal database, integrating drug, coformer, and reaction solvent information. By incorporating various thermodynamic models, the predictive performance was significantly enhanced. Benefiting from the complementarity of thermodynamic mechanisms and structural descriptors, the model coupling three thermodynamic models achieved optimal predictive performance in coformer and solvent screening. The model was rigorously validated against benchmark models using challenging independent test sets, showcasing superior performance in both coformer and solvent predicting with accuracy over 90%. Further, we employed SHAP analysis for model interpretation, suggesting that thermodynamic mechanisms are prominent in the model's decision-making. Proof-of-concept studies on ketoconazole validated the model's efficacy in identifying coformers/solvents, demonstrating its potential in practical application. Overall, our work enhanced the understanding of co-crystallization and highlighted the strategy that integrates mechanistic insights with data-driven models to accelerate the rational design and synthesis of co-crystals, as well as various other functional materials.

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.

Recent advances in pharmaceutical cocrystals of theophylline

Currently, cocrystallization is a promising strategy for tailoring the physicochemical properties of active pharmaceutical ingredients. Theophylline, an alkaloid and the most primary metabolite of caffeine, is a readily available compound found in tea and coffee. It functions primarily as a bronchodilator and respiratory stimulant, making it a mainstay treatment for lung diseases like asthma. Theophylline's additional potential benefits, including anti-inflammatory and anticancer properties, and its possible role in neurological disorders, have garnered significant research interest. Cocrystal formation presents a viable approach to improve the physicochemical properties of theophylline and potentially mitigate its toxic effects. This review comprehensively explores several successful studies that utilized cocrystallization to favorably alter the physicochemical properties of theophylline or its CCF. Notably, cocrystals can not only enhance the solubility and bioavailability of theophylline but also exhibit synergistic effects with other APIs. The review further delves into the hydrogen bonding sites within the theophylline structure and the hydrogen bonding networks observed in cocrystal structures.

Efficient cocrystal coformer screening based on a Machine learning Strategy: A case study for the preparation of imatinib cocrystal with enhanced physicochemical properties

Cocrystal engineering, which involves the self-assembly of two or more components into a solid-state supramolecular structure through non-covalent interactions, has emerged as a promising approach to tailor the physicochemical properties of active pharmaceutical ingredient (API). Efficient coformer screening for cocrystal remains a challenge. Herein, a prediction strategy based on machine learning algorithms was employed to predict cocrystal formation and seven reliable models with accuracy over 0.890 were successfully constructed. Imatinib was selected as the model drug and the models established were applied to screen 31 potential coformers. Experimental verification results indicated RF-8 is the optimal model among seven models with an accuracy of 0.839. When the seven models were combined for coformer screening of Imatinib, the combinational model achieved an accuracy of 0.903, and eight new solid forms were observed and characterized. Benefiting from intermolecular interactions, the obtained multicomponent crystals displayed enhanced physicochemical properties. Dissolution and solubility experiments showed the prepared multicomponent crystals had higher cumulative dissolution rate and remarkably improved the solubility of imatinib, and IM-MC exhibited comparable solubility to Imatinib mesylate α form. Stability test and cytotoxicity results showed that multicomponent crystals exhibited excellent stability and the drug-drug cocrystal IM-5F exhibited higher cytotoxicity than pure API.

Improvement of the Thermal Stability and Aqueous Solubility of Three Matrine Salts Assembled by the Similar Structure Salt Formers

Matrine (MAT), a natural Chinese herbal medicine, has a unique advantage in the treatment of various chronic diseases. However, its low melting point, low bioavailability, and high dosage restrict its subsequent development into new drugs. In this study, three kinds of MAT salts, namely, MAT-2,5-dihydroxybenzoic acid (MAT-25DHB), MAT-2,6-dihydroxybenzoic acid (MAT-26DHB), and MAT-salicylic acid-hydrate (MAT-SAL-H2O), were designed and synthesized to improve the drugability of MAT. The three salts were characterized by using various analytical techniques, including single-crystal X-ray diffractometry, powder X-ray diffractometry, differential scanning calorimetry, thermogravimetry, and infrared spectroscopy. The results of the thermal stability evaluation showed that the formation of salts improved the stability of MAT; MAT-25DHB is the most stable salt reported at present. The results of aqueous solubility showed that the solubility of MAT-25DHB was higher than that of MAT, while that of MAT-26DHB and MAT-SAL-H2O were less. Given that the MAT-25DHB salt further improved the solubility of MAT, it is expected to be subjected to further research as an optimized salt. Lattice energy and solvation free energy are important factors affecting the solubility of salts; the reasons for the changes of solubility and stability of three kinds of salts are explained by calculating them.

Praziquantel Fifty Years on: A Comprehensive Overview of Its Solid State

This review discusses the entire progress made on the anthelmintic drug praziquantel, focusing on the solid state and, therefore, on anhydrous crystalline polymorphs, amorphous forms, and multicomponent systems (i.e., hydrates, solvates, and cocrystals). Despite having been extensively studied over the last 50 years, new polymorphs and the greater part of their cocrystals have only been identified in the past decade. Progress in crystal engineering science (e.g., the use of mechanochemistry as a solid form screening tool and more strategic structure-based methods), along with the development of analytical techniques, including Synchrotron X-ray analyses, spectroscopy, and microscopy, have furthered the identification of unknown crystal structures of the drug. Also, computational modeling has significantly contributed to the prediction and design of new cocrystals by considering structural conformations and interactions energy. Whilst the insights on praziquantel polymorphs discussed in the present review will give a significant contribution to controlling their formation during manufacturing and drug formulation, the detailed multicomponent forms will help in designing and implementing future praziquantel-based functional materials. The latter will hopefully overcome praziquantel's numerous drawbacks and exploit its potential in the field of neglected tropical diseases.

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.

Property Prediction and Structural Feature Extraction of Polyimide Materials Based on Machine Learning

The construction of material prediction models using machine learning algorithms can aid in the polyimide structural design and screening of materials as well as accelerate the development of new materials. There is a lack of research on predicting the optical properties of polyimide materials and the interpretation of the structural features. Here, we collected 652 polyimide molecular structures and used seven popular machine learning algorithms to predict the glass transition temperature and cut-off wavelength of polyimide materials and extract key feature information of repeating unit structures. The results showed that the root mean square error of the glass transition temperature prediction model was 33.92 °C, and the correlation coefficient was 0.861. The root mean square error of the cut-off wavelength prediction model was 17.18 nm, and the correlation coefficient was 0.837. The elasticity of the molecular structure was also found to be the key factor affecting glass transition temperature, and the presence and location of heterogeneous atoms had a significant effect on the cut-off wavelengths. Finally, eight polyimide materials were synthesized to test the accuracy of the prediction models, and the experimental characterization values agreed with the predicted values. The results would contribute to the development of polyimide structural design and materials preparation for flexible display.

Data-Driven Prediction of the Formation of Co-Amorphous Systems

Co-amorphous systems (COAMS) have raised increasing interest in the pharmaceutical industry, since they combine the increased solubility and/or faster dissolution of amorphous forms with the stability of crystalline forms. However, the choice of the co-former is critical for the formation of a COAMS. While some models exist to predict the potential formation of COAMS, they often focus on a limited group of compounds. Here, four classes of combinations of an active pharmaceutical ingredient (API) with (1) another API, (2) an amino acid, (3) an organic acid, or (4) another substance were considered. A model using gradient boosting methods was developed to predict the successful formation of COAMS for all four classes. The model was tested on data not seen during training and predicted 15 out of 19 examples correctly. In addition, the model was used to screen for new COAMS in binary systems of two APIs for inhalation therapy, as diseases such as tuberculosis, asthma, and COPD usually require complex multidrug-therapy. Three of these new API-API combinations were selected for experimental testing and co-processed via milling. The experiments confirmed the predictions of the model in all three cases. This data-driven model will facilitate and expedite the screening phase for new binary COAMS.

Computational design of quinone electrolytes for redox flow batteries using high-throughput machine learning and theoretical calculations

Molecular design of redox-active materials with higher solubility and greater redox potential windows is instrumental in enhancing the performance of redox flow batteries Here we propose a computational procedure for a systematic evaluation of organic redox-active species by combining machine learning, quantum-mechanical, and classical density functional theory calculations. 1,517 small quinone molecules were generated from the building blocks of benzoquinone, naphthoquinone, and anthraquinone with different substituent groups. The physics-based methods were used to predict HOMO-LUMO gaps and solvation free energies that account for the redox potential differences and aqueous solubility, respectively. The high-throughput calculations were augmented with the quantitative structure-property relationship analyses and machine learning/graph network modeling to evaluate the materials’ overall behavior. The computational procedure was able to reproduce high-performance cathode electrolyte materials consistent with experimental observations and identify new electrolytes for RFBs by screening 100,000 di-substituted quinone molecules, the largest library of redox-active quinone molecules ever investigated. The efficient computational platform may facilitate a better understanding of the structure-function relationship of quinone molecules and advance the design and application of all-organic active materials for RFBs.