The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases

Abstract: Autoimmune diseases (AIDs) are a group of disorders, in which the immune system attacks the body own tissues leading to chronic inflammation and organ damage. These diseases are difficult to treat due to variability in drug PK among individuals, patient responses to treatment, and the side effects of long-term immunosuppressive therapies. In recent years, pharmacometrics has emerged as a critical tool in Drug Discovery and Development (DDD) and precision medicine. The aim of this review is to explore the diverse roles that pharmacometrics has played in addressing the challenges associated with DDD and personalized therapies in the treatment of AIDs. Methods: The review synthesizes research from past two decades on pharmacometric methodologies, including Physiologically Based Pharmacokinetic (PBPK) modeling, Pharmaco-kinetic/Pharmacodynamic (PK/PD) modeling, Disease Progression (DisP) modeling, population modeling, and Quantitative Systems Pharmacology (QSP). The incorporation of Artificial Intelli-gence (AI) and Machine Learning (ML) into pharmacometrics is also discussed. Results: Pharmacometrics has demonstrated significant potential in optimizing dosing regimens, improving drug safety, and predicting patient-specific responses in AIDs. PBPK and PK/PD models have been instrumental in personalizing treatments, while DisP and QSP models provide insights into disease evolution and pathophysiological mechanisms in AIDs. AI/ML implementation has further enhanced precision of these models. Conclusions: Pharmacometrics plays a crucial role in bridging preclinical findings and clinical applications, driving more personalized and effective treatments for AIDs. Its integration into DDD and translational science in combination with AI and ML algorithms holds promise for advancing therapeutic strategies and improving autoimmune patient outcomes.