Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks.

Dry powder inhalers are increasingly popular for delivering drugs to the lungs for the treatment of respiratory diseases, but are complex products with multivariate performance determinants. Heuristic product development guided by in vitro aerosol performance testing is a costly and time-consuming process. This study investigated the feasibility of using artificial neural networks (ANNs) to predict fine particle fraction (FPF) based on formulation device variables.

In silico prediction of aqueous solubility using simple QSPR models: the importance of phenol and phenol-like moieties.

Recently the authors published a robust QSPR model of aqueous solubility which exploited the computationally derived molecular descriptor topographical polar surface area (TPSA) alongside experimentally determined melting point and logP. This model (the "TPSA model") is able to accurately predict to within ± one log unit the aqueous solubility of 87% of the compounds in a chemically diverse data set of 1265 molecules. This is comparable to results achieved for established models of aqueous solubility e.

Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area.

The General Solubility Equation (GSE) is a QSPR model based on the melting point and log P of a chemical substance. It is used to predict the aqueous solubility of nonionizable chemical compounds. However, its reliance on experimentally derived descriptors, particularly melting point, limits its applicability to virtual compounds.