In Silico Clinical Trials: A Possible Response to Complexity in Pharmacology

Abstract

The complex nature of human diseases, the significant differences between individuals, their own specificity and the inevitable variability in how a treatment is administered make pharmacological research extremely difficult. Due to the inability to completely predict how a product will affect individual patients, it’s not unusual for a drug to show serious problems during clinical tests after having performed well in pre-clinical and laboratory tests. The strategy of in silico Clinical Trials (ISCT) comes as a possible bridge from the insights of molecular understanding to ad personam medical treatments. By refining and complementing traditional clinical trials, ISCTs should provide a proxy for biological conditions. The in silico strategy is typically justified by appealing to the power of big data, that computational strategies involve. Big data seem to imply a different methodology and thus a different way of doing research, thought to be more adequate to address biological complexity. In this chapter we analyze such a view by showing how these new computational strategies enhance and implement traditional approaches rather than changing the nature of clinical investigation. An epistemic look at ISCTs shows how such an issue is addressed in pharmacological studies. The moral of in silico approaches is that complexity cannot be fully addressed per se, only through heuristics. Contra enthusiastic claims arguing for a paradigm shift in scientific research, ISCTs do not change the theoretical account governing current scientific practice, nor the general framework according to which discovery strategies are adopted and justified. On the other hand, ISCTs do provide a change in the methodologies. However, such a novelty should be regarded as a complement and not as a replacement.