Immunosuppressants are foundational in the management of autoimmune diseases and organ transplantation, yet their clinical use is frequently complicated by substantial inter-individual variability in pharmacokinetics, pharmacodynamics, and toxicity profiles. This variability, driven by genetic and disease-related factors, underscores the critical need for personalised dosing strategies to maximize therapeutic efficacy while minimizing adverse effects. Recent advances in model-informed precision dosing, pharmacogenomics, and data-driven predictive analytics have accelerated the evolution of individualized immunosuppressant therapy, moving the field towards a more precise and patient-centered paradigm. In this talk, I will highlight the recent progress in the personalised medicine of mycophenolic acid for pediatric lupus nephritis, focusing on integrating pharmacokinetic modeling, pharmacogenomic profiling, and machine learning models that leverage multi-dimensional patient data to refine therapeutic drug monitoring and support clinical decision-making.