Novel Cocrystals of Vonoprazan: Machine Learning-Assisted Discovery

Vonoprazan (VPZ) is the first-in-class potassium-competitive acid blocker (P-CAB), and has many advantages over proton pump inhibitors (PPIs). It is administered as a fumarate salt for the treatment of acid-related diseases, including reflux esophagitis, gastric ulcer, and duodenal ulcer, and for eradication of Helicobacter pylori. To discover novel cocrystals of VPZ, we adopted an artificial neural network (ANN)-based machine learning model as a virtual screening tool that can guide selection of the most promising coformers for VPZ cocrystals. Experimental screening by liquid-assisted grinding (LAG) confirmed that 8 of 19 coformers selected by the ANN model were likely to create new solid forms with VPZ. Structurally similar benzenediols and benzenetriols, i.e., catechol (CAT), resorcinol (RES), hydroquinone (HYQ), and pyrogallol (GAL), were used as coformers to obtain phase pure cocrystals with VPZ by reaction crystallization. We successfully prepared and characterized three novel cocrystals: VPZ-RES, VPZ-CAT, and VPZ-GAL. VPZ-RES had the highest solubility among the novel cocrystals studied here, and was even more soluble than the commercially available fumarate salt of VPZ in solution at pH 6.8. In addition, novel VPZ cocrystals had superior stability in aqueous media than VPZ fumarates, demonstrating their potential for improved pharmaceutical performance.

Automated summary

In this study, artificial neural network (ANN)-based machine learning model was used to screen potential coformers for Vonoprazan (VPZ) cocrystals. The selected coformers were confirmed to create new solid forms with VPZ through experimental screening. Three novel cocrystals, VPZ-RES, VPZ-CAT, and VPZ-GAL, were successfully prepared and characterized. These cocrystals demonstrated improved solubility and stability, indicating their potential for enhanced pharmaceutical performance.