| Body: |
Long-term graft survival is still below ideal despite major advancements in kidney
transplantation, and immunosuppression-related problems like infections, cancers, and chronic
allograft injury continue to be a problem. Despite being successful in lowering acute rejection,
current immunosuppressive regimens frequently take a "one-size-fits-all" approach, ignoring
individual differences in immunological risk, pharmacokinetics, and adverse effect profiles.
Key gaps in current practice are highlighted by emerging evidence, such as the underutilisation
of biomarkers, the inconsistent integration of pharmacogenomics, and the lack of tools for
dynamic immune risk stratification.The field is changing as a result of recent and continuing
research into new drugs like costimulation blockers, customised dosage based on donor-derived cell-free DNA or TTV viral load, and machine learning models to forecast drug
toxicity and rejection risk.
By adjusting treatment to the patient's changing immunological profile, personalised immunosuppressive strategies seek to achieve the ideal balance between under- and over-immunosuppression. Clinical risk scoring, non-invasive biomarkers, pharmacogenomic testing, and routine immune monitoring are all components of a multifaceted implementation strategy. By incorporating these tools into standard clinical processes and using digital platforms and decision-support algorithms, treatment decisions can be improved and long-term results can be enhanced.
The practical application of personalised immunosuppression in clinical practice will be discussed in this talk, along with patient stratification, treatment algorithms, and current research that could soon influence standard care. In the end, the move to precision transplant medicine has potential for enhancing patient safety and quality of life in addition to graft longevity. |
