IRINI: random group allocation of multiple prognostic factors

The partnership between statisticians and the Institutional Animal Care and Use Committee (IACUC)

In this tutorial we explore the valuable partnership between statisticians and Institutional Animal Care and Use Committees (IACUCs) in the context of animal research, shedding light on the critical role statisticians play in ensuring the ethical and scientifically rigorous use of animals in research. Pharmaceutical statisticians have increasingly become vital members of these committees, contributing expertise in study design, data analysis, and interpretation, and working more generally to facilitate the integration of good statistical practices into experimental procedures. We review the "3Rs" principles (Replacement, Reduction, and Refinement) which are the foundation for the humane use of animals in scientific research, and how statisticians can partner with IACUC to help ensure robust and reproducible research while adhering to the 3Rs principles. We also highlight emerging areas of interest, such as the use of virtual control groups.

A long-acting GDF15 analog causes robust, sustained weight loss and reduction of food intake in an obese nonhuman primate model

Growth Differentiation Factor-15 (GDF15) is a circulating polypeptide linked to cellular stress and metabolic adaptation. GDF15's half-life is ~3 h and activates the glial cell line-derived neurotrophic factor family receptor alpha-like (GFRAL) receptor expressed in the area postrema. To characterize sustained GFRAL agonism on food intake (FI) and body weight (BW), we tested a half-life extended analog of GDF15 (Compound H [CpdH]) suitable for reduced dosing frequency in obese cynomolgus monkeys. Animals were chronically treated once weekly (q.w.) with CpdH or long-acting GLP-1 analog dulaglutide. Mechanism-based longitudinal exposure-response modeling characterized effects of CpdH and dulaglutide on FI and BW. The novel model accounts for both acute, exposure-dependent effects reducing FI and compensatory changes in energy expenditure (EE) and FI occurring over time with weight loss. CpdH had linear, dose-proportional pharmacokinetics (terminal half-life ~8 days) and treatment caused exposure-dependent reductions in FI and BW. The 1.6 mg/kg CpdH reduced mean FI by 57.5% at 1 week and sustained FI reductions of 31.5% from weeks 9-12, resulting in peak reduction in BW of 16 ± 5%. Dulaglutide had more modest effects on FI and peak BW loss was 3.8 ± 4.0%. Longitudinal modeling of both the FI and BW profiles suggested reductions in BW observed with both CpdH and dulaglutide were fully explained by exposure-dependent reductions in FI without increase in EE. Upon verification of the pharmacokinetic/pharmacodynamic relationship established in monkeys and humans for dulaglutide, we predicted that CpdH could reach double digit BW loss in humans. In summary, a long-acting GDF15 analog led to sustained reductions in FI in overweight monkeys and holds potential for effective clinical obesity pharmacotherapy.

Optimized design and analysis of preclinical intervention studies in vivo

Recent reports have called into question the reproducibility, validity and translatability of the preclinical animal studies due to limitations in their experimental design and statistical analysis. To this end, we implemented a matching-based modelling approach for optimal intervention group allocation, randomization and power calculations, which takes full account of the complex animal characteristics at baseline prior to interventions. In prostate cancer xenograft studies, the method effectively normalized the confounding baseline variability, and resulted in animal allocations which were supported by RNA-seq profiling of the individual tumours. The matching information increased the statistical power to detect true treatment effects at smaller sample sizes in two castration-resistant prostate cancer models, thereby leading to saving of both animal lives and research costs. The novel modelling approach and its open-source and web-based software implementations enable the researchers to conduct adequately-powered and fully-blinded preclinical intervention studies, with the aim to accelerate the discovery of new therapeutic interventions.