Algorithmic benchmark modulation: A novel method to develop success rates for clinical studies.

BACKGROUND: High-quality decision-making in the pharmaceutical industry requires accurate assessments of the Probability of Technical Success of clinical trials. Failure to do so will lead to lost opportunities for both patients and investors. Pharmaceutical companies employ different methodologies to determine Probability of Technical Success values. Some companies use power and assurance calculations; others prefer to use industry benchmarks with or without the overlay of subjective modulations. At AstraZeneca, both assurance calculations and industry benchmarks are used, and both methods are combined with modulations. METHODS: AstraZeneca has recently implemented a simple algorithm that allows for modulation of a Probability of Technical Success value. The algorithm is based on a set of multiple-choice questions. These questions cover a comprehensive set of issues that have historically been considered by AstraZeneca when subjective modulations to Probability of Technical Success values were made but do so in a much more structured way. RESULTS: A set of 57 phase 3 Probability of Technical Success assessments suggests that AstraZeneca's historical estimation of Probability of Technical Success has been reasonably accurate. A good correlation between the subjective modulation and the modulation algorithm was found. This latter observation, combined with the finding that historically AstraZeneca has been reasonably accurate in its estimation of Probability of Technical Success, gives confidence in the validity of the novel method. DISCUSSION: Although it is too early to demonstrate whether the method has improved the accuracy of company's Probability of Technical Success assessments, we present our data and analysis here in the hope that it may assist the pharmaceutical industry in addressing this key challenge. This new methodology, developed for pivotal studies, enables AstraZeneca to develop more consistent Probability of Technical Success assessments with less effort and can be used to adjust benchmarks as well as assurance calculations. CONCLUSION: The Probability of Technical Success modulation algorithm addresses several concerns generally associated with assurance calculations or benchmark without modulation: selection biases, situations where little relevant prior data are available and the difficulty to model many factors affecting study outcomes. As opposed to using industry benchmarks, the Probability of Technical Success modulation algorithm allows to accommodate project-specific considerations.

Impact of a five-dimensional framework on R&D productivity at AstraZeneca.

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.

Human monoclonal antibodies to the insulin-like growth factor 1 receptor inhibit receptor activation and tumor growth in preclinical studies.

INTRODUCTION: The insulin-like growth factor type 1 (IGF-1) receptor contributes importantly to transformation and survival of tumor cells both in vitro and in vivo, and selective antagonists of the IGF-1 receptor (IGF-1R) activity represent an attractive experimental approach for human cancer therapy. METHODS: Using a phage display library, we identified several high-affinity fully human monoclonal antibodies with inhibitory activity against both human and rodent IGF.1Rs. RESULTS: These candidate therapeutic antibodies recognized several distinct epitopes and effectively blocked ligand-mediated receptor signal transduction and cellular proliferation in vitro. They also induced IGF-1R downregulation and catabolism following antibody-mediated endocytosis. These antibodies exhibited activity against human, primate, and rodent IGF-1Rs, and dose-dependently inhibited the growth of established human tumors in nude mice. CONCLUSION: These fully human antibodies therefore have the potential to provide an effective anti-tumor biological therapy in the human clinical setting.

A human monoclonal antibody neutralizes diverse HIV-1 isolates by binding a critical gp41 epitope.

HIV-1 entry into cells is mediated by the envelope glycoprotein receptor-binding (gp120) and membrane fusion-promoting (gp41) subunits. The gp41 heptad repeat 1 (HR1) domain is the molecular target of the fusion-inhibitor drug enfuvirtide (T20). The HR1 sequence is highly conserved and therefore considered an attractive target for vaccine development, but it is unknown whether antibodies can access HR1. Herein, we use gp41-based peptides to select a human antibody, 5H/I1-BMV-D5 (D5), that binds to HR1 and inhibits the assembly of fusion intermediates in vitro. D5 inhibits the replication of diverse HIV-1 clinical isolates and therefore represents a previously unknown example of a crossneutralizing IgG selected by binding to designed antigens. NMR studies and functional analyses map the D5-binding site to a previously identified hydrophobic pocket situated in the HR1 groove. This hydrophobic pocket was proposed as a drug target and subsequently identified as a common binding site for peptide and peptidomimetic fusion inhibitors. The finding that the D5 fusion-inhibitory antibody shares the same binding site suggests that the hydrophobic pocket is a "hot spot" for fusion inhibition and an ideal target on which to focus a vaccine-elicited antibody response. Our data provide a structural framework for the design of new immunogens and therapeutic antibodies with crossneutralizing potential.