Predictive Performance of Front-Loaded Experimentation Strategies in Pharmaceutical Discovery: A Bayesian Perspective

Experimentation is a significant innovation process activity and its design is fundamental to the learning and knowledge build-up process. Front-loaded experimentation is known as a strategy seeking to improve innovation process performance; by exploiting early information to spot and solve problems as upstream as possible, costly overruns in subsequent product development are avoided. Front-loaded experimentation has not been studied in the pharmaceutical R&D context, where lots of drug candidates get killed very late in the innovation process if potential problems are insufficiently anticipated upfront. The purpose of this research was to evaluate the predictive performance of front-loaded experimentation strategies in the discovery process. Because predictive performance necessitates conditional probability thinking, a Bayesian methodology is proposed and a rationale is given to develop research propositions using Monte Carlo simulation. An adaptive system paradigm, then, is the basis for designing the simulation model used for top-down theory development. My simulation results indicate that front-loaded strategies in a pharmaceutical discovery context outperform other strategies on positive predictive performance.