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IQ 3Rs Reproducibility Webinar Series

Join the IQ 3Rs Reproducibility Working Group for a 6-webinar series starting on 14 July. Register for the entire series or for individual webinars. Each webinar is one-hour long and attendees will be able to engage with speakers and moderators via Q&A after each webinar presentation. Download all abstracts.

13 Jul

A Change Management Reflection on Improving Experimental Design  (On Demand)

Natasha Karp, AstraZeneca |  Susan Portugal, Pfizer

Abstract:

Meta Research [1] exploring the barriers and enablers to rigours preclinical research finds that whilst uptake of best practice and recommendations are low, basic scientists are highly motivated to apply methods of rigorous design and reporting and are aware of the benefits. In this seminar, we will discuss the barriers to engagement and use change management theory to reflect on how we as a community can drive change and support researchers to engage with best practice recommendations. A case study implementation from an international pharma company will be presented, highlighting both the successes and challenges faced in supporting R&D scientists to improve experimental design quality.

[1] Lalu, Manoj M., et al. "Identifying barriers and enablers to rigorous conduct and reporting of preclinical laboratory studies." PLoS biology 21.1 (2023): e3001932.

20 Jul

Replicability and Reproducibility in Animal Studies  (On Demand)

Steve Novick, Eli Lilly  |  Xin Huang, AbbVie

In the pharmaceutical industry, animal experiments are often performed as the final go/no-go step prior to a clinical trial. Animal-to-human translations may be deduced from a well-planned in-vivo study that follows good statistical practices (GSP); the converse implies uncertainty in the go/no-go review points. Traditionally, GSP involves sample size and statistical power analysis, animal randomization, blinding, and blocking. To this, we add the concepts of replicability and reproducibility. A drug discovery experiment is replicable if a repeat study results in the same conclusion. Experimental results are reproducible if, given the same data set and possibly the same computer code, the data analysis is repeatable. In vivo study results that are both replicable and reproducible provide highly-confident go/no-go decisions and, as such, arguably should be part of the standard review process prior to a clinical trial. The concepts and issues around replicability and reproducibility in drug discovery are explored in this presentation.

3 Aug

Opportunities for Adaptive Design in the Preclinical Space  (On Demand)

Yi-Lin Chiu, AbbVie  |  Sun Yan,  AbbVie

Background: For an experiment to identify a target dose of interest including to find the maximum tolerated dose (MTD), the goal is to estimate the dose with accuracy and precision while minimizing wastes in animals. The conventional fixed design and an adaptive design have been considered. We evaluate the pros and cons including operating characteristics of the designs. In this example, the target dose is defined as the dose at which 30% mortality rate is induced but can be adjusted under different settings without loss of generosity. This methodology has been implemented in real studies.

Methods: A typical fixed design would require testing multiple animals for each dose level in an escalation manner until reaching the dose that corresponds to 30% mortality rate with reasonable statistical confidence. For the proposed continuous reassessment method (CRM), a drug dose will be escalated/de-escalated according to the model estimated MTD with adaptation.

Results and Conclusions: Compared with a conventional fixed design, the CRM design estimates the MTD more accurately while utilizing fewer animals overall.

14 Sep

Opportunities for Bayesian Study Design: consideration of sequential Bayes factors for sample size reduction in preclinical experiments  (On Demand)

Tony Pourmohamad, Genentech  |  John Rigney, GSK

Abstract: Preclinical studies are an essential part of pharmaceutical development, yet traditional methods for designing and analyzing these types of studies can be inefficient and wasteful. Even worse, when the units of study are animals, ethical concerns can arise. The 3Rs initiative was established for the ethical treatment of animals through the replacement, reduction, and refinement of animal experiments. In this presentation, we focus on the reduction aspect of the 3Rs initiative through the use of sequential Bayes factors. The use of sequential Bayes factors has the potential to help design more efficient experiments, that can be analyzed sequentially, in order to reduce the average number of animals needed in preclinical studies. An added bonus, sequential Bayes factors provide a means of quantifying evidence both for and against the null hypothesis, a characteristic not common to traditional preclinical trial analysis methods. Illustrations highlighting the success of sequential Bayes factors are provided for two real seven-day preclinical experiments in rats, as well as extensive simulation studies.

5 October 

Improving in vivo research rigor to impact reproducibility (On Demand)

Joe Garner, Stanford Univ.  |  Brianna Gaskill, Novartis

Goal: To educate on opportunities to improve research rigor, providing robust data supporting effective decision-making in drug development.

Objective: Partnering statisticians and in vivo scientists presenting strategies to improve in vivo research rigor and reproducibility to support better-informed decision-making. Promoting the value of active collaboration and integration of statistical expertise and 3Rs considerations to provide continuous improvement in research design and experimental methods, data analysis, and interpretation.

Key takeaway: The value of instituting collaborations early in the research planning process and maintaining engagement throughout the experimental process.

16 November

Importance of systematic assessment of scientific validity in in vivo study design

Alan Olzinski, GSK  |  Noel Dybdal, Genentech

Goal: To educate on opportunities to improve research rigor, providing robust data supporting effective decision-making in drug development.

Objective: Partnering statisticians and in vivo scientists presenting strategies to improve in vivo research rigor and reproducibility to support better-informed decision-making. Promote the value of active collaboration and integration of statistical expertise and 3Rs considerations to provide continuous improvement in research design and experimental methods, data analysis, and interpretation.

Key takeaway: The value of instituting collaborations early in the research planning process and maintaining engagement throughout the experimental process.