Drug-drug relationship based on target information: application to drug target identification

Development of Nafamostat Mesylate Immediate-Release Tablet by Drug Repositioning Using Quality-by-Design Approach

We aimed to develop nafamostat mesylate immediate-release tablets for the treatment of COVID-19 through drug repositioning studies of nafamostat mesylate injection. Nafamostat mesylate is a serine protease inhibitor known to inhibit the activity of the transmembrane protease, serine 2 enzyme that affects the penetration of the COVID-19 virus, thereby preventing the binding of the angiotensin-converting enzyme 2 receptor in vivo and the spike protein of the COVID-19 virus. The formulation was selected through a stability study after manufacturing by a wet granulation process and a direct tableting process to develop a stable nafamostat mesylate immediate-release tablet. Formulation issues for the selected processes were addressed using the design of experiments and quality-by-design approaches. The dissolution rate of the developed tablet was confirmed to be >90% within 30 min in the four major dissolutions, except in the pH 6.8 dissolution medium. Additionally, an in vivo pharmacokinetic study was performed in monkeys, and the pharmacokinetic profiles of nafamostat injections, oral solutions, and tablets were compared. The half-life during oral administration was confirmed to be significantly longer than the reported literature value of 8 min, and the bioavailability of the tablet was approximately 25% higher than that of the oral solution.

Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening

BACKGROUND

Despite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.

RESULTS

The chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model.

CONCLUSIONS

We report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds.

Machine learning-based chemical binding similarity using evolutionary relationships of target genes

Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information.

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.