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. Our results suggest that about 25% of drugs are predicted to be very bitter, with even higher prevalence (~40%) in COVID19 drug candidates and in microbial natural products. Only ~10% of toxic molecules are predicted to be intensely bitter, and it is also suggested that intense bitterness does not correlate with hepatotoxicity of drugs. However, very bitter compounds may be more cardiotoxic than not very bitter compounds, possessing significantly lower QPlogHERG values. BitterIntense allows quick and easy prediction of strong bitterness of compounds of interest for food, pharma and biotechnology industries. We estimate that implementation of BitterIntense or similar tools early in drug discovery process may lead to reduction in delays, in animal use and in overall financial burden.

Bitter-blockers as a taste masking strategy: A systematic review towards their utility in pharmaceuticals.

Acceptable palatability of an oral dosage form is crucial to patient compliance. Excipients can be utilised within a formulation to mask the bitterness of a drug. One such category is the bitter-blockers. This term is used inconsistently within the literature and has historically been used to describe any additive which alters the taste of an unpleasant compound. This review defines a bitter-blocker as a compound which interacts with the molecular pathway of bitterness at a taste-cell level and compiles data obtained from publication screening of such compounds. Here, a novel scoring system is created to assess their potential utility in a medicinal product using factors such as usability, safety, efficacy and quality of evidence to understand their taste-masking ability. Sodium acetate, sodium gluconate and adenosine 5'monophophate each have a good usability and safety profile and are generally regarded as safe and have shown evidence of bitter-blocking in human sensory panels. These compounds could offer a much needed option to taste-mask particularly aversive medicines where traditional methods alone are insufficient.

Assessment of bitter taste of pharmaceuticals with multisensor system employing 3 way PLS regression.

The application of the potentiometric multisensor system (electronic tongue, ET) for quantification of the bitter taste of structurally diverse active pharmaceutical ingredients (API) is reported. The measurements were performed using a set of bitter substances that had been assessed by a professional human sensory panel and the in vivo rat brief access taste aversion (BATA) model to produce bitterness intensity scores for each substance at different concentrations. The set consisted of eight substances, both inorganic and organic - azelastine, caffeine, chlorhexidine, potassium nitrate, naratriptan, paracetamol, quinine, and sumatriptan. With the aim of enhancing the response of the sensors to the studied APIs, measurements were carried out at different pH levels ranging from 2 to 10, thus promoting ionization of the compounds. This experiment yielded a 3 way data array (samples×sensors×pH levels) from which 3wayPLS regression models were constructed with both human panel and rat model reference data. These models revealed that artificial assessment of bitter taste with ET in the chosen set of API's is possible with average relative errors of 16% in terms of human panel bitterness score and 25% in terms of inhibition values from in vivo rat model data. Furthermore, these 3wayPLS models were applied for prediction of the bitterness in blind test samples of a further set of API's. The results of the prediction were compared with the inhibition values obtained from the in vivo rat model.