Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades.

During drug discovery and prior to the first human dose of a novel candidate drug, the pharmacokinetic (PK) behavior of the drug in humans is predicted from preclinical data. This helps to inform the likelihood of achieving therapeutic exposures in early clinical development. Once clinical data are available, the observed human PK are compared with predictions, providing an opportunity to assess and refine prediction methods. Application of best practice in experimental data generation and predictive methodologies, and a focus on robust mechanistic understanding of the candidate drug disposition properties before nomination to clinical development, have led to maximizing the probability of successful PK predictions so that 83% of AstraZeneca drug development projects progress in the clinic with no PK issues; and 71% of key PK parameter predictions [64% of area under the curve (AUC) predictions; 78% of maximum concentration (C max) predictions; and 70% of half-life predictions] are accurate to within twofold. Here, we discuss methods to predict human PK used by AstraZeneca, how these predictions are assessed and what can be learned from evaluating the predictions for 116 candidate drugs. It is now universally recognized that an acceptable human PK profile increases the probability of a candidate drug becoming a successful therapy. A variety of tools are available to predict human PK behavior in advance of clinical data, including scaling clearance from in vitro metabolic stability data, and physiologically based scaling of volume of distribution. To improve PK prediction methods, it is crucial to continually assess the performance of predictions with first-time-in-human PK data for new candidate drugs. To date, AstraZeneca have compared observed PK to predictions for 116 candidate drugs, and since the launch of our five-dimensional framework in 2011, we have driven sustained improvements in the quality of PK predictions. [ABSTRACT FROM AUTHOR]