Going beyond probability of success: Opportunities for statisticians to influence quantitative decision-making at the portfolio level.

The pharmaceutical industry is plagued with long, costly development and high risk. Therefore, a company's effective management and optimisation of a portfolio of projects is critical for success. Project metrics such as the probability of success enable modelling of a company's pipeline accounting for the high uncertainty inherent within the industry. Making portfolio decisions inherently involves managing risk, and statisticians are ideally positioned to champion not only the derivation of metrics for individual projects, but also advocate decision-making at a broader portfolio level. This article aims to examine the existing different portfolio decision-making approaches and to suggest opportunities for statisticians to add value in terms of introducing probabilistic thinking, quantitative decision-making, and increasingly advanced methodologies.

Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs.

Leveraging historical data into the design and analysis of phase 2 randomized controlled trials can improve efficiency of drug development programs. Such approaches can reduce sample size without loss of power. Potential issues arise when the current control arm is inconsistent with historical data, which may lead to biased estimates of treatment efficacy, loss of power, or inflated type 1 error. Consideration as to how to borrow historical information is important, and in particular, adjustment for prognostic factors should be considered. This paper will illustrate two motivating case studies of oncology Bayesian augmented control (BAC) trials. In the first example, a glioblastoma study, an informative prior was used for the control arm hazard rate. Sample size savings were 15% to 20% by using a BAC design. In the second example, a pancreatic cancer study, a hierarchical model borrowing method was used, which enabled the extent of borrowing to be determined by consistency of observed study data with historical studies. Supporting Bayesian analyses also adjusted for prognostic factors. Incorporating historical data via Bayesian trial design can provide sample size savings, reduce study duration, and enable a more scientific approach to development of novel therapies by avoiding excess recruitment to a control arm. Various sensitivity analyses are necessary to interpret results. Current industry efforts for data transparency have meaningful implications for access to patient-level historical data, which, while not critical, is helpful to adjust for potential imbalances in prognostic factors.

Evaluation of the effects of an oral notch inhibitor, crenigacestat (LY3039478), on QT interval, and bioavailability studies conducted in healthy subjects.

PURPOSE: Crenigacestat (LY3039478) is a Notch inhibitor currently being investigated in advanced cancer patients. Conducting clinical pharmacology studies in healthy subjects avoids nonbeneficial drug exposures in cancer patients and mitigates confounding effects of disease state and concomitant medications. METHODS: Three studies were conducted in healthy subjects, assessing safety, pharmacokinetics, effect on QT interval, and relative and absolute bioavailability of crenigacestat. Crenigacestat was administered as single 25, 50, or 75 mg oral doses or as an intravenous dose of 350 µg 13C15N2H-crenigacestat. Electrocardiogram measurements, and plasma and urine samples were collected up to 48 h postdose, and safety assessments were conducted up to 14 days postdose. RESULTS AND CONCLUSIONS: Exposures were dose proportional in the 25 to 75 mg dose range and mean elimination half-life was approximately 5-6 h. The exposure achieved from the new formulated capsule was approximately 30% and 20% higher for area under the plasma concentration time curve from time zero to infinity [AUC(0-∞)] and maximum plasma concentration (Cmax), respectively, compared to the reference drug in capsule formulation. The geometric least-squares mean [90% confidence interval (CI)] absolute bioavailability of crenigacestat was 0.572 (0.532, 0.615). The regression slope (90% CI) of placebo-adjusted QTcF against crenigacestat plasma concentration was - 0.001 (- 0.006, 0.003), suggesting no significant linear association. Thirty-nine subjects completed the studies and the majority of adverse events were mild. Single oral doses of 25 to 75 mg crenigacestat and an IV dose of 350 µg 13C15N2H-crenigacestat were well tolerated in healthy subjects.