Negative Modeling Results: A Dime a Dozen or a Stepping Stone to Scientific Discovery?

Systems pharmacology models, in general, tend to span multiple timescales bridging detailed mechanism with higher level responses or functional outputs. These features serve to put systems pharmacology models in a separate class that brings with it specific challenges, particularly in evaluating such work. When models are constructed on well-understood mechanisms but fail to match experimental data, in what cases should “negative modeling results” be considered scientific findings? I will start with the following premise: systems pharmacology is an approach to facilitate understanding pharmacology (as opposed to an end in itself). Therefore, there must be an interplay between models and experimental results. This can be observed to occur on two timescales (i) inter-publication, where a group publishes experimental results which are then retrospectively analyzed by a computational group or (ii) intra-publication, wherein the same group performs both activities and the outcome of one activity directly feeds into and motivates the other. Systems pharmacology models, in general, tend to span multiple timescales bridging detailed mechanism with higher level responses or functional outputs. Parameter values tend to be justified based on previous work or trained on a small scale data rather identified as part of a large-scale optimization. These features serve to put systems pharmacology models in a separate class that brings with it specific challenges. As an example, the study by Darwich et al. 1 entitled “Evaluation of an in silico PBPK post bariatric surgery model through simulating oral drug bioavailability of atorvastatin and cyclosporine”, presented in this issue, takes a well-established systems pharmacology model and tests its simulations against available clinical data for various types of bariatric surgery. I believe it is fair to say that the overall model performance is mixed, judged by its ability to predict the clinical data, depending on the drug and type of surgery. This has prompted discussion on the value, relevance, and interest of negative modeling results and stimulates one to consider a larger question as well: How should one evaluate systems pharmacology efforts and when are they worthy of publication?