The research behind DeepC.

Human oral bioavailability is usually estimated from animal studies, which are costly, ethically disputed and often fail to match human values. A machine learning pipeline trained on 192 FDA-approved drugs, combining molecular descriptors, structural fingerprints, published animal bioavailability and features that account for cytochrome P450 metabolism, predicted human oral bioavailability with R² = 0.766 across 100 repeats of five-fold cross-validation. It outperformed every earlier model and every single species, primates included (R² = 0.691), and rat and dog data alone still reached R² = 0.723. Lipophilicity, surface area and CYP features were the most informative predictors.

Missing or incomplete critical quality attribute (CQA) data is a persistent problem in pharmaceutical research and manufacturing. Five machine learning methods were benchmarked against conventional imputation across five datasets covering 14 CQAs. Machine learning exceeded R² = 0.80 for several datasets and attributes with up to 30% of values missing and outperformed traditional methods in most scenarios, while complex or weakly informative CQAs stayed harder to recover. A web-based platform lets scientists apply and evaluate these imputation workflows on their own data.

Conductive polymers (CPs) are a class of functional polymers that can respond to a voltage stimulus. As a drug delivery system (DDS), the voltage applied to CPs can modulate drug release, offering unprecedented spatial and temporal control as we strive for precision and personalised medicines. However, this requires the drug to be charged in order to respond to a voltage stimulus, where unfortunately there are numerous drugs that are neutrally-charged. Herein, we propose an innovative solution that leverages charged polymeric particles as an intermediary vehicle. In this investigation, we electrospray paracetamol, a neutrally-charged drug, with poly(lactic-co-glycolic acid) (PLGA) to obtain drug-loaded microparticles with an overall negative charge of −53.01 ± 2.71 mV. The drug-loaded microparticles were then mixed with a CP and fabricated into films using a three dimensional (3D) printer. The films were subjected to voltages of 0, +1 and −1 V, where multivariate analysis of variance (MANOVA) revealed statistical significance in their drug release profiles. Applying −1 V (i.e., the same charge as the PLGA particle) resulted in a three-fold increase in drug release compared to 0 V (i.e., passive release), increasing from 9.82 ± 1.32 % to 29.03 ± 2.34 %. Furthermore, the platform was confirmed to respond and exhibit pulsatile release when switching the voltage “on-and-off”. Another benefit of leveraging PLGA discovered included sustained drug release, which is discussed. The findings suggest that charged polymeric particles can enable voltage-responsive release of neutrally-charged drugs from CPs, and signals a promising strategy for widening the clinical impact of CPs as DDSs.