Computational medicine: Grand challenges and opportunities for revolutionizing personalized healthcare

Abstract

Medical advances in treatment strategies against age-related global killers such as cardiovascular disease, cancer, and stroke have been responsible for the significant gains in the average global life expectancy observed since the second half of the 20th century (Hunter and Reddy, 2013). Still, medical research has been less successful at prolonging healthy life. Globally, one in three adults live with multiple chronic conditions (Hajat and Stein, 2018). In the US, 80% of the population older than 65 live with at least one chronic condition, and 50% live with two (Hunter and Reddy, 2013). As the aging population is growing rapidly, the incidence of age-related, costly, chronic conditions such as heart disease, cancer, diabetes, and Alzheimer’s is reaching epidemic proportions. In the US alone, healthcare spending is already composing far more of the national gross domestic product than any other sector including defense, education, energy, and transportation (US Government Spending, 2017). With annual total costs of age-related diseases expected to skyrocket, all nations are in pressing need to reduce the economic burden of population aging. Prolonging lifespan without prolonging health span is financially unsustainable for all nations. Computational medicine emerged in the past decade as an interdisciplinary field dedicated to integrating advanced computational modeling, data-driven technologies, and supercomputing to derive new knowledge about the biological mechanisms of disease and deeper understanding of factors driving inter-patient variability (Bukowski et al., 2021). Such knowledge enables development of precision strategies to diagnose and treat disease, sustain wellbeing, and optimize utilization of healthcare resources (Winslow et al., 2012). Computational medicine has the potential to drive transformative advances in healthcare, extend health span, and reign in healthcare costs by i) enabling a more holistic understanding of the broad spectrum of all factors, processes, and their interplay impacting wellbeing at the individual and the population level, and by ii) translating such understanding into dynamically adaptive, personalized medical decisions to drive effective and sustainable health management practices. There are already several efforts demonstrating the potential of computational medicine across various diseases and conditions (e.g., (Louis et al., 2014; Mulder et al., 2018; Athanasiou et al., 2019; Bukowski et al., 2021; Yu and Kibbe, 2021; Hiram Guzzi et al., 2022; Toma et al., 2022). To achieve its full potential, computational medicine should be able to build a digital twin of the human by mapping the human genome (i.e., genomic profile), phenome (i.e., physiologic status), and exposome (i.e., physical and social environment) in real-time and across the human lifetime. Understanding the human genome-phenome-exposome interplay is an ambitious endeavor which demands a multi-disciplinary team of biologists, physicists, chemists, OPEN ACCESS