Novel Cocrystals of Vonoprazan: Machine Learning-Assisted Discovery

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.

Gastroduodenal injury and repair: novel targets for therapeutic intervention

PURPOSE OF REVIEW

Although the mucosal barrier serves as a primary interface between the environment and host, little is understood about the repair of acute, superficial lesions or deeper, persistent lesions that if not healed, can be the site of increased permeability to luminal antigens, inflammation and/or neoplasia development.

RECENT FINDINGS

Recent studies have focused on focal adhesion kinase, which regulates controlled matrix adhesion during restitution after superficial injury. Actin polymerization regulates cell migration and the importance of actin-related proteins was also highlighted. Work on SARS-CoV-2 infection lent important new insights on gastroduodenal mucosal injury in patients with Covid-19 infection and work done with organoids and intestine-on-a-chip contributed new understanding about how coronaviruses infect gastrointestinal tissues and its resulting barrier dysfunction. A novel risk stratification paradigm was proposed to assist with decision making about repeat endoscopy for patients with gastric or duodenal ulcers and new therapeutic options were studied for ulcer disease. Lastly, work to support the mechanism of metaplasia development after deep injury and parietal cell loss was provided using novel transgenic mouse models.

SUMMARY

Recent studies highlight novel molecular targets to promote mucosal healing after injury of the gastroduodenal mucosa.