Prospects for productivity

Predictive Human Intestinal Absorption QSAR Models Using Bayesian Regularized Neural Networks

An oral dosage form is generally the most popular with patients. Many drug candidates fail in late development because of unfavourable absorption and pharmacokinetic profiles, or toxicity, among other factors (ADMET properties). This contributes to the fall in the efficiency of the pharmaceutical industry and to the rise in health costs. The ability to predict ADMET properties of drug leads can contribute to overcoming this problem. We have modelled intestinal absorption using several types of molecular descriptors and a non-linear Bayesian regularized neural network. Our models show very good predictive properties and are able to account for essentially all of the variance in the data that is not due to experimental error.

The changing landscape of antiparasitic drug discovery for veterinary medicine

Changes in economic imperatives in the pharmaceutical industry have led to a wave of consolidation, which has had the unintended side effect of shrinking the resource devoted to antiparasitic drug discovery in animal health companies. Scientific changes have altered the way in which drugs could be discovered in the future. New science and business models will need to be implemented to address the demand for innovative antiparasitic drugs in veterinary medicine. Novel drugs are needed to combat drug resistance and for currently non-addressed problems. At the center of the future for this field, however, lies the need for more support into the basic research on the biology of parasites.