Trp-Trp acts as a multi-functional blocker for human bitter taste receptors, hTAS2R14, hTAS2R16, hTAS2R43, and hTAS2R46

Many functional food ingredients activate human bitter taste receptors (hTAS2Rs). In this study, A novel inhibitor, Trp-Trp, for hTAS2R14 was identified by searching for the agonist peptide's analogs. Trp-Trp also inhibited hTAS2R16, hTAS2R43, and hTAS2R46, which share the same agonists with hTAS2R14. The multi-functional characteristic of Trp-Trp is advantageous for use as bitterness-masking agents in functional foods.

Intense bitterness of molecules: machine learning for expediting drug discovery

Drug development is a long, expensive and multistage process geared to achieving safe drugs with high efficacy. A crucial prerequisite for completing the medication regimen for oral drugs, particularly for pediatric and geriatric populations, is achieving taste that does not hinder compliance. Currently, the aversive taste of drugs is tested in late stages of clinical trials. This can result in the need to reformulate, potentially resulting in the use of more animals for additional toxicity trials, increased financial costs and a delay in release to the market. Here we present BitterIntense, a machine learning tool that classifies molecules into “very bitter” or “not very bitter”, based on their chemical structure. The model, trained on chemically diverse compounds, has above 80% accuracy on several test sets. BitterIntense suggests that intense bitterness does not correlate with toxicity and hepatotoxicity of drugs and that the prevalence of very bitter compounds among drugs is lower than among microbial compounds. BitterIntense allows quick and easy prediction of strong bitterness of compounds of interest for food and pharma industries. We estimate that implementation of BitterIntense or similar tools early in drug discovery and development process may lead to reduction in delays, in animal use and in overall financial burden.Significance StatementDrug development integrates increasingly sophisticated technologies, but extreme bitterness of drugs remains a poorly addressed cause of medicine regimen incompletion. Reformulating the drug can result in delays in the development of a potential medicine, increasing the lead time to the patients. It might also require the use of extra animals in toxicity trials and lead to increased costs for pharma companies. We have developed a computational predictor for intense bitterness, that has above 80% accuracy. Applying the classifier to annotated datasets suggests that intense bitterness does not correlate with toxicity and hepatotoxicity of drugs. BitterIntense can be used in the early stages of drug development to identify drug candidates that require bitterness masking, and thus reduce animal use, time and monetary loss.