Steven Niederer, DPhil: No financial relationships to disclose
The future of quantitative pharmacology lies in harnessing advanced computational methods to transform drug discovery, development, and patient care. By integrating physics-based modelling and machine learning, we are developing multi-scale digital twins that bridge molecular mechanisms to whole-heart function. Through Bayesian inference and mechanistic modelling, these digital twins are personalized to individual patients, providing a structured framework for linking physiology and pathology to clinical outcomes.
These models are being used to analyze preclinical drug assays, identify potential drug targets, and uncover disease mechanisms. The same techniques are applied in human studies to conduct in silico clinical trials, evaluating novel device therapies and drug treatments before human trials—accelerating innovation while reducing costs and risks. This work also underpins our collaboration with the FDA to establish credibility frameworks for cardiac simulations, ensuring regulatory confidence in model-based decision-making.
As we scale these approaches, we are creating virtual cohorts of thousands of hearts, enabling a deeper understanding of how anatomical and physiological variability influences cardiac function and disease presentation. These advancements pave the way for a new era of precision medicine, where patient-specific digital twins can guide therapy optimization, continuous monitoring, and predictive diagnostics.
