Industry Perspective on Standardizing Food-Effect Studies for New Drug Development

Safety, pharmacokinetics, and pharmacodynamics in healthy Chinese volunteers treated with SC0062, a highly selective endothelin-A receptor antagonist

This study evaluated the safety, tolerability, pharmacokinetics (PK), pharmacodynamics (PD), and food effects (FE) of SC0062, a highly active endothelin-A (ETA ) receptor antagonist, in healthy subjects. The primary objectives of this first-in-human phase I study, comprised of single-ascending-dose, multiple-ascending-dose, and FE parts, were to characterize the safety and tolerability of SC0062, and FE. The secondary objectives were to determine the PK behavior of SC0062 and its major active metabolite M18, whereas exploratory objectives focused on PD effects, principally effects on endothelin-1 (ET-1) and total bile acids (TBA). Single doses of 10 to 100 mg and multiple daily doses of 20 and 50 mg for 6 days were well tolerated. SC0062 was rapidly absorbed and plasma exposure of SC0062 and M18 increased disproportionately with dose, achieving steady state by day 3, with accumulation ratios of 1.22 and 1.89 on day 6 for SC0062 and M18, respectively. The geometric mean (geometric standard deviation) terminal elimination half-life (t1/2 ) values of SC0062 and M18 were 7.25 (1.70) h and 13.73 (1.32) h, respectively. Plasma ET-1 concentrations were dose-proportional, whereas plasma TBA concentrations behaved erratically. Following a single 50 mg dose of SC0062 after a high-fat meal, Cmax values for SC0062 and M18 increased by 41% and 32%, respectively, and median Tmax values for SC0062 were 3 h longer than fasting values; exposure was unaffected. These favorable safety, PK, and PD results provide a foundation for further studies of SC0062 in pulmonary arterial hypertension, chronic kidney disease, and other relevant indications.

Comparison of Predictions by BCS, rDCS and Machine Learning for the Effect of Food on Oral Drug Absorption Based on Features Calculated In silico

In this study, observed food effects of 473 drugs were categorized into positive, negative, or no effects and compared with the predictions made by machine learning (ML), the Biopharmaceutics Classification System (BCS) and refined Developability Classification System (rDCS). All methods used primarily in silico estimates for prediction, and for ML, four algorithms were evaluated using nested cross-validation to select important information from 371 features calculated based on the chemical structure. Approximately 18 features, including estimated solubility in biorelevant media, were selected as important, and the random forest classifier was the best among four algorithms with 36.6% error rate (ER) and 10.8% opposite prediction rate (OPR). The prediction by rDCS utilizing solubility in a biorelevant medium was somewhat inferior, but not by much; 41.0% ER and 11.4% OPR. Compared with these two methods, the prediction by BCS was inferior; 54.5% ER and 21.4% OPR. ER was improved modestly by using measured features instead of in silico estimates when BCS was applied to a subset of 151 drugs (46.4% from 55.0%). ML and rDCS predicted the food effects of the same subset using in silico estimates with ERs of 37.7% and 42.4%, respectively, suggesting that the predictions by ML and rDCS using in silico features are similar or more accurate than those by BCS using measured features. These results suggest that ML was useful in revealing essential features from complex information and, together with rDCS, is effective in predicting food effects during drug development, including early drug discovery.