In 2011, AstraZeneca embarked on a major revision of its research and development (R&D) strategy with the aim of improving R&D productivity, which was below industry averages in 2005-2010. A cornerstone of the revised strategy was to focus decision-making on five technical determinants (the right target, right tissue, right safety, right patient and right commercial potential). In this article, we describe the progress made using this '5R framework' in the hope that our experience could be useful to other companies tackling R&D productivity issues. We focus on the evolution of our approach to target validation, hit and lead optimization, pharmacokinetic/pharmacodynamic modelling and drug safety testing, which have helped improve the quality of candidate drug nomination, as well as the development of the right culture, where 'truth seeking' is encouraged by more rigorous and quantitative decision-making. We also discuss where the approach has failed and the lessons learned. Overall, the continued evolution and application of the 5R framework are beginning to have an impact, with success rates from candidate drug nomination to phase III completion improving from 4% in 2005-2010 to 19% in 2012-2016.

This document was developed to enable greater consistency in the practice, application, and documentation of Model‐Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of “good practice” recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

Summary-level longitudinal data on the clinical efficacy of drugs for rheumatoid arthritis (RA) are available in the literature. This information can be used to optimize the clinical development of new drugs for RA. The aim of this study was twofold: first, to quantify the time course of the ACR20 score across approved drugs and patient populations, and second, to apply this knowledge in the decision-making process for a specific compound, canakinumab. The integrated analysis included data from 37 phase II-III studies describing 13,474 patients. It showed that, with the tested doses/regimens of canakinumab, there was only a low probability that this drug would be better than the most effective current treatments. This finding supported the decision not to continue with clinical development of canakinumab in RA. This paper presents the first longitudinal model-based meta-analysis of ACR20. The framework can be applied to any other compound targeting RA, thereby supporting internal and external decision making at all clinical development stages.

Pharmacokinetic (PK) pharmacodynamic (PD) modeling was applied to understand and quantitate the interplay between tesaglitazar (a peroxisome proliferator-activated receptor alpha/gamma agonist) exposure, fasting plasma glucose (FPG), hemoglobin (Hb), and glycosylated hemoglobin (HbA1c) in type 2 diabetic patients. Data originated from a 12-week dose-ranging study with tesaglitazar. The primary objective was to develop a mechanism-based PD model for the FPG-HbA1c relationship. The secondary objective was to investigate possible mechanisms for the tesaglitazar effect on Hb. Following initiation of tesaglitazar therapy, time to new FPG steady state was approximately 9 weeks, and tesaglitazar potency in females was twice that in males. The model included aging of red blood cells (RBCs) using a transit compartment approach. The RBC life span was estimated to 135 days. The transformation from RBC to HbA1c was modeled as an FPG-dependent process. The model indicated that the tesaglitazar effect on Hb was caused by hemodilution of RBCs.