Digital Twins in Healthcare Discovering Complex Biological Rules for Achieving Precision and Personalization in Healthcare

Abstract

The pursuit of precision and personalization in healthcare has led to investigating computational means to model sophisticated patient-related aspects. Extending this line of enquiry, this study investigates the application of Digital Twins (DTs) to model patients—i.e., to create virtual representations of patients that integrate multi-omic and clinical data—to uncover complex biological rules that can inform treatment planning. As a clinical case study, we focus on Triple-Negative Breast Cancer (TNBC). We examine how DTs can be used to enhance the planning of PD-1/PD-L1 inhibitor therapies for TNBS. PD-1/PD-L1 is a class of immune checkpoint inhibitors with variable patient responses for TNBC. Our approach aims to computationally identify patterns of statistical congruence in cellular information that may predict therapeutic outcomes, thereby enabling clinicians to determine in advance which patients are most likely to benefit from specific immunotherapies.