Design of Experiments (DOE) is one of the most essential tools scientists can use to accelerate timelines, optimize costs, maximize insights, and minimize risks when making informed decisions. For example, clinical trials employ a variety of experimental designs to determine whether a new medicine effectively improves patients’ lives and to what extent.
If you are developing therapies with the goal of entering human clinical trials, the expertise of statisticians in the field of Experimental Design is indispensable. Regulatory agencies require a thorough understanding of the study’s structure: the number of patients involved, how outcomes are measured, the statistical power necessary to detect a significant effect, and the methods you plan to use for data analysis and reporting. To meet these demands, BioPharma companies must engage a clinical statistics CRO or build an in-house clinical team that includes statisticians, programmers, operations specialists, and data managers. Although these teams may begin small, as trials progress, organizational needs and staffing often scale quickly.
So, why do agencies invest so much time to ensure these plans are robust? As we know, health authorities have a mandate to ensure that medicines are both safe and effective. The public relies on these agencies to minimize risks, guarantee the quality of medicines, and confirm their efficacy for intended uses.
If this level of statistical rigor is required for clinical trials, why don’t more companies prioritize a similar approach with non-clinical statistics? The current economic climate in BioPharma might provide some insight. In 2024 alone, more than 140 layoff announcements have led to a substantial reduction in the workforce, putting pressure on companies to prioritize short-term savings over long-term gains. With a focus on cost-cutting, roles or functions, like non-clinical statisticians, that may be perceived as optional, are often the first to be scaled back or excluded.
However, consider the benefits of applying non-clinical statistical expertise from the early stages of development.
How can we leverage this expertise from the very beginning of the product lifecycle?
How can we design experimental plans that seamlessly guide us through process development, characterization, analytical validation, tech transfer, and, ultimately, commercialization?
By starting with a clear understanding of our desired outcomes, it’s possible to maximize resource efficiency and avoid costly missteps throughout R&D.
Non-clinical statistics can significantly streamline the development process. With a well-executed preclinical statistical plan, companies can craft an IND package that stands up to regulatory scrutiny, reduce the volume of experiments needed for complete process or method qualification for the BLA, and create a robust narrative that supports product development history, specification setting, and process comparability designs. What do all these benefits have in common? They reflect not an ‘extra’ but a strategic investment in efficiency that can smooth and accelerate medicine development.
Engaging non-clinical statisticians, much like clinical statisticians, is crucial to the success of your BioPharma organization. Leveraging tools such as Design of Experiments not only brings rigor to research and development, but also contributes to substantial savings in time, resources, and inefficiency. In today’s competitive and cost-conscious BioPharma landscape, employing non-clinical statistics is a forward-thinking, yet critical approach, that ensures every development dollar is spent effectively, bringing high-quality treatments to patients sooner.
Learn more about how RCH Solutions can support your non-clinical statistical efforts with the expertise of industry veterans, including seasoned non-clinical statisticians like JoAnn Coleman.