Advancing rare disease therapeutics through digital twins: Opportunities in drug development and precision dosing

Abstract

Rare disease(s) (RD/RDs) are typically characterized by (i) genetically driven chronic, and life-threatening disease progression, (ii) delayed diagnoses, (iii) limited treatment options, and (iv) substantial economic bur dens due to direct and indirect medical costs. Challenges in RD research include limited patient populations, sparse disease data, poorly understood pathophysiology and reduced trial funding for new exploratory therapies. In recent years, digital twin(s) (DT/DTs) are increasingly used for patient care, disease management, and resource optimization. They serve as virtual replicas of individual patients that enable simulation, prediction, and optimization of outcomes through real-time data integration and can facilitate advancements in treatment outcome and prediction of disease progression leveraging model-based personalized predictions. This review included 16 studies and focuses on how DTs are currently used in RD research by analyzing the underlying modeling techniques, including physiologically based pharmacokinetic (PBPK) modeling, population pharma cokinetic (PopPK) modeling, quantitative systems pharmacology (QSP) modeling, physiome modeling, and combined approaches. It identifies the limitations of these models that currently prevent them from qualifying as true DTs. Furthermore, this review discusses the potential advantages of DTs in drug development for new treatment strategies, disease progression modeling, and clinical decision support for RD research. Finally, it outlines the current state of DT implementation in the RD field, revealing that DT implementation remains in an early stage of development.