Exploring porcine gastric and intestinal fluids using microscopic and solubility estimates: Impact of placebo self-emulsifying drug delivery system administration to inform bio-predictive in vitro tools

Recent Progress on Photocatalytic Synthesis of Ester Derivatives and Reaction Mechanisms

Esters and their derivatives are distributed widely in natural products, pharmaceuticals, fine chemicals and other fields. Esters are important building blocks in pharmaceuticals such as clopidogrel, methylphenidate, fenofibrate, travoprost, prasugrel, oseltamivir, eszopiclone and fluticasone. Therefore, esterification reaction becomes more and more popular in the photochemical field. In this review, we highlight three types of reactions to synthesize esters using photochemical strategies. The reaction mechanisms involve mainly single electron transfer, energy transfer or other radical procedures.

Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks

Despite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependence between FE and drug properties. Accordingly, this study explored the potential for two machine learning (ML) algorithms to predict likely FE. Using a collated database of drugs licensed from 2016-2020, drugs were classified into three groups; positive, negative or no FE. Greater than 250 drug properties were predicted for each drug which were used to train predictive models using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. When compared, ANN outperformed SVM for FE classification upon training (82%, 72%) and testing (72%, 69%). Both models demonstrated higher FE prediction accuracy than the Biopharmaceutics Classification System (BCS) (46%). This exploratory work provided new insights into the connection between FE and drug properties as the Octanol Water Partition Coefficient (S+logP), Number of Hydrogen Bond Donors (HBD), Topological Polar Surface Area (T_PSA) and Dose (mg) were all significant for prediction. Overall, this study demonstrated the utility of ML to facilitate early anticipation of likely FE in pre-clinical development using four well-known drug properties.