Engineering Cocrystals of Naringenin: New Solid Forms and Assessing Predictive Tools for Coformer Selection

Cocrystallization is emerging as a preformulation strategy to tune the performance of pharmaceutical compounds. Development of cocrystals and prediction of cocrystal formation are crucial for efficiently improving performance of pharmaceutical drugs. Cocrystal development is advantaged by a broad pool of potential coformers, but reliably predicting cocrystallization outcomes remains a challenge. This work aims to engineer cocrystals of naringenin (NAR) and critically analyze the performance of contemporary tools for cocrystal prediction. NAR, a flavonoid with anti-inflammatory properties, has attracted interest as a therapy, but its development is challenged by poor solubility. Here, we report 14 NAR cocrystals, including six newly discovered cocrystals with succinimide, aspartame, tetramethylpyrazine, and hexamine. Using our data set, the predictive performance of molecular complementarity (MC), hydrogen bonding propensity (HBP), Hansen solubility parameter (HSP), molecular electrostatic potential surfaces (MEPS), substructure-molecular interaction network-based recommendation (SMINBR), and differential scanning calorimetry (DSC) was rigorously assessed using eight indices. Virtual tools connected by AND or OR logic were further evaluated to determine whether a multitool strategy improved performance. Last, the generalization of tool performance to cocrystals of quercetin and hesperetin was tested. In conclusion, our cocrystal screen using 54 coformers yielded 14 cocrystals of NAR, and predictions based on DSC were far superior to all virtual tools. The need for new virtual tools that can robustly discriminate cocrystallization outcomes is highlighted.