Better Design Leads to Better Medicines
By: Rick Sax, MD | February 10, 2015
At the heart of a quality clinical design process is the availability of good information. Without the ability to access multiple sources of data, as well as the proper tools for integrating, objectively analyzing and modeling such data, then applying it to design, biopharmaceutical companies will remain challenged to improve the success rate of drug candidates.
One of the underlying tenets of “design thinking” is to integrate information to determine what is known, and more importantly, to determine the unknown (e.g., hypotheses, assumptions, risks). This allows the design effort to focus directly on addressing these unknowns, while minimizing the introduction of inadvertent bias.
For the most part, there are only two levers a company can pull to drive better outcomes: selection of the compounds they choose to take into human development, and better design processes and decisions. The industry struggles with this second lever, and it shows in the high rate of failure.
Poor design can result in research that isn’t focused on answering the right questions for a particular upcoming investment decision. Studies may be underpowered (or over-powered relative to the real risk, resulting in great excesses in cost and time), lack proper dosing information, or not identify the optimal target population appropriately. So when results are obtained, they can be ambiguous and, in the worst cases, not provide the information necessary to inform the company whether it was the drug that didn’t work or the design that didn’t work. When this happens, drug candidates might be “killed” or “progressed” inappropriately (at great expense vs. the probable outcome); companies might also need to repeat studies in order to get clarity as to whether to continue development. At the extreme, a Phase III trial with a marginal outcome could mean that the drug is actually working, but the design chosen was insufficient to enable registration. A failure of this type is painful for companies of any size, and could be especially perilous for smaller companies with more at stake on a single molecule.
The availability of a proper design tool could markedly facilitate a company’s ability to visualize and explore data-driven options, increasing the probability of better design decisions. This can help achieve two broad outcomes: a higher probability of obtaining a clear scientific outcome and better operational outcomes to drive down costs and shorten development time.
Where to start
One of the key elements that distinguish good design practice is “prototyping,” i.e., developing and testing different scenarios and trade-offs before “crystallizing” the final design decision. This step is particularly difficult for biopharma teams in the current paradigm, because constructing even a single program or trial design is complex and fraught with assumptions and contingencies. A more systematic, transparent approach could achieve far greater clarity of options and hence, a better probability for successful outcomes.
Further learning about design can be gleaned from a study of leading global companies across diverse industries to understand what constitutes “best practice in design.” Conducted by Hay Group, an international management consultancy, and commissioned by a large pharma client, the study shows that companies most admired for their design practices share many of the same principles: