Peter Gennemark

The paper introduces M4 drug discovery, a method that integrates multi-level, multi-timescale, and multi-species data with mechanistic knowledge to predict clinical outcomes of drugs like GLP-1RAs from preclinical data.

This paper integrates GLP-1RA exenatide studies across heterogeneous cell systems to address variability, providing a unified conclusion and predicting inter-study variabilities.

This paper demonstrates that reversible peptide self-assembly can be used to achieve tuneable pharmacokinetics for therapeutic peptides, as shown with pramlintide in a rat model.

The paper develops a multi-scale digital twin model to simulate insulin resistance progression, validating it with diet and drug intervention data.

The paper introduces a digital twin model that predicts diet response and metabolic changes before and after fasting by integrating clinical data and mechanistic models.

The paper develops a computational model of a liver-islet microphysiological system to predict glucose metabolism and translate experimental results to humans.

The paper presents an in silico mouse model that simulates multi-scale insulin resistance and type 2 diabetes progression across cells, organs, and the whole body.