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

Area of Science:

• Biomedical Engineering
• Computational Biology
• Rare Disease Research

Background:

• Rare diseases (RDs) present significant challenges including genetic drivers, delayed diagnoses, limited treatments, and high costs.
• Limited patient populations, sparse data, and underfunded research hinder progress in understanding RD pathophysiology and developing therapies.

Purpose of the Study:

• To review the current applications of digital twins (DTs) in rare disease research.
• To analyze modeling techniques used for DTs in RDs and identify their limitations.
• To discuss the potential of DTs in advancing drug development, disease progression modeling, and clinical decision support for RDs.

Main Methods:

• Systematic review of 16 studies on digital twin applications in rare disease research.
• Analysis of underlying modeling techniques, including PBPK, PopPK, QSP, and physiome modeling.
• Identification of limitations preventing current models from qualifying as true digital twins.

Main Results:

• Digital twins are increasingly utilized for patient care, disease management, and resource optimization in RDs.
• Various modeling approaches are employed, but limitations exist, preventing true digital twin qualification.
• Current implementation of digital twins in rare disease research is in its nascent stages.

Conclusions:

• Digital twins offer significant potential for advancing rare disease research, particularly in drug development and personalized medicine.
• Overcoming current modeling limitations is crucial for realizing the full capabilities of digital twins in RDs.
• Further development and validation are needed to fully integrate digital twins into rare disease clinical practice and research.