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.

Type 2 diabetes mellitus (T2DM) is a progressive, metabolic disorder characterized by reduced insulin sensitivity and loss of beta-cell mass (BCM), resulting in hyperglycemia. Population pharmacokinetic-pharmacodynamic (PKPD) modeling is a valuable method to gain insight into disease and drug action. A semi-mechanistic PKPD model incorporating fasting plasma glucose (FPG), fasting insulin, insulin sensitivity, and BCM in patients at various disease stages was developed. Data from 3 clinical trials (phase II/III) with a peroxisome proliferator-activated receptor agonist, tesaglitazar, were used to develop the model. In this, a modeling framework proposed by Topp et al was expanded to incorporate the effects of treatment and impact of disease, as well as variability between subjects. The model accurately described FPG and fasting insulin data over time. The model included a strong relation between insulin clearance and insulin sensitivity, predicted 40% to 60% lower BCM in T2DM patients, and realistic improvements of BCM and insulin sensitivity with treatment. The treatment response on insulin sensitivity occurs within the first weeks, whereas the positive effects on BCM arise over several months. The semi-mechanistic PKPD model well described the heterogeneous populations, ranging from nondiabetic, insulin-resistant subjects to long-term treated T2DM patients. This model also allows incorporation of clinical-experimental studies and actual observations of BCM.

Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose–response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.Electronic supplementary materialThe online version of this article (doi:10.1007/s10928-017-9550-0) contains supplementary material, which is available to authorized users.

The relatively low impact of individualised dosage on the pharmacokinetic and pharmacodynamic variability of melagatran supported the use of a fixed-dose regimen in the studied population of orthopaedic surgery patients, including those with mild to moderate renal impairment.

AimsTo characterize ticagrelor exposure‐response relationship for platelet inhibition in patients with stable coronary artery disease (CAD) and a history of myocardial infarction (MI), using nonlinear mixed effects modelling and simulation.MethodsPlatelet function data were integrated with plasma concentration data of ticagrelor and its active metabolite AR‐C1249010XX in a population pharmacokinetic (PK) and pharmacodynamic (PD) model, based on two clinical studies. In the ONSET/OFFSET study, PK and platelet function were assessed in 123 CAD patients receiving placebo, ticagrelor (180 mg followed by 90 mg twice daily) or clopidogrel (600 mg followed by 75 mg once daily). In the PEGASUS‐TIMI 54 platelet function substudy, PK and platelet function were assessed during maintenance dosing in 180 prior MI patients receiving placebo, ticagrelor 60 mg or ticagrelor 90 mg twice daily.ResultsPlatelet inhibition by ticagrelor was described by a sigmoidal Emax model. On average, half maximal inhibition was reached at ticagrelor concentrations of 116 (RSE: 5.3%) nmol l–1. Simulations showed that near maximal platelet inhibition is achieved with both ticagrelor 60 and 90 mg twice daily. At simulated lower doses, platelet inhibition is overall reduced, more variable between patients, and show greater peak‐to‐trough variability. Ticagrelor antiplatelet response was similar between the studied patient populations.ConclusionsIn patients with stable CAD or a history of MI, near maximal platelet inhibition is achieved with both ticagrelor 60 and 90 mg twice daily. At modelled doses <60 mg, the response is reduced overall, more variable between patients, and patients will display greater peak‐to‐trough variability.