Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in‐between?

Abstract

Heart disease continues to be a significant clinical problem in Western society. Predictive models and simulations that integrate physiological understanding with patient information derived from clinical data have huge potential to contribute to improving our understanding of both the progression and treatment of heart disease. In particular they provide the potential to improve patient selection and optimisation of cardiovascular interventions across a range of pathologies. Currently a significant proportion of this potential is still to be realised. In this paper we discuss the opportunities and challenges associated with this realisation. Reviewing the successful elements of model translation for biophysically based models and the emerging supporting technologies, we propose three distinct modes of clinical translation. Finally we outline the challenges ahead that will be fundamental to overcome if the ultimate goal of fully personalised clinical cardiac care is to be achieved.

Imaging-Based Simulations for Predicting Sudden Death and Guiding Ventricular Tachycardia Ablation

“…The immense potential of simulation-driven applications in cardiac patient care has been recognized in a number of recent reviews 54,55 , arguing that clinical translation of physiological models will transform medical practice. However, getting to the point of translation is a long road, and success has been variable depending on the specific applications.…”

The opportunities and challenges for biophysical modelling of beneficial and adverse drug actions on the heart

“…Modeling has therefore increasingly moved from understanding physiology and pathology to interpreting the effects of pharmacological agents 18,19 and medical interventions 17,20 . Here we discuss the recent advances that have led to models being used to interpret drug effects.…”

“…Biophysical modelling approaches, including physiome based 12 , systems biology 13 , quantitative systems pharmacology 14 and physiologically based pharmacokinetic modelling (PKPD) 15,16 , provide a framework that encodes quantitative information about physiology and anatomy in accordance with physical principals to generate outcomes that can be judged in terms of how well they mimic the real situation 12,17 . When components of a model are set sufficiently well that they not only allow M A N U S C R I P T…”

The Cardiac Physiome Project

“…This current one‐size‐fits‐all approach comes at a time when innovation in healthcare is under significant pressure, due to increasingly stringent regulatory requirements and mounting pressure on healthcare budgets due to ageing populations. It is for these reasons that the clinical application of cardiac physiome models explicitly supported by the VPH has for almost a decade been a significant focus for part of the Cardiac Physiome community, as discussed by Niederer & Smith (). They argue that biophysical computational models present three opportunities for translation: (i) the use of a mathematical framework for introducing physical and physiological constraints to interpret diagnostic (particularly image) data; (ii) the development of models that can represent the physiology and pathology of the individual patient; and (iii) the development of representative populations of patient models that can be used for general hypothesis testing, informing clinical guidelines and providing potentially valuable information prior to designing clinical trials.…”