Revolutionising Pharmacological Predictions with Synthetic Model Combination 

Clinical pharmacologists understand that patients are unique biological systems that respond differently to drugs and that adjusting dosing to specific circumstances is often necessary. In everyday bedside work as in clinical trials, trial and error can be time-consuming, and waiting weeks to learn how a patient responds is not always feasible. That is when mathematical models come in to help make predictions about patient outcomes.

However, what model should be used for the situation at hand?

Synthetic Model Combination (SMC) is our newest answer to that question, a game-changing machine learning method for constructing new model ensembles. SMC builds a new ensemble weighing existing models according to their likelihood to accurately represent a novel case. Based on our results, SMC is more robust and gives more accurate predictions than existing models, especially when there is no new data to judge which existing model is best. SMC can be reverse engineered to the needs of the situation and find applicable data for individuals who do not fit any of the existing models – this is ground-breaking, especially for under-represented groups and special cases.

Read the paper here.

For a deeper understanding of how SMC works and how it can impact pharmacology, we strongly suggest watching this short conversation between Prof Richard Peck (CCAIM) and Alex Chan (PhD student in the van der Schaar lab), leading contributors to the article: