Pharmacogenomics and AI: Minimizing Adverse Reactions and Optimizing Dose Personalization in Personalized Medicine

Pharmacogenomics and artificial intelligence (AI) are revolutionizing the way we approach personalized medicine. These emerging technologies promise to minimize adverse reactions to medications and optimize dosing personalization, thereby enhancing the safety and efficacy of treatments. In this context, clinical algorithms based on AI are playing a crucial role by integrating genetic and clinical data to guide therapeutic decisions.

Integration of Pharmacogenomics and AI in Clinical Practice

The application of AI in pharmacogenomics has proven particularly effective in optimizing the dosing of complex medications such as warfarin. A recent study developed a neural network-based algorithm that utilizes genetic and clinical variables to predict the safe and effective dose of warfarin, significantly reducing adverse reactions and improving the time to reach therapeutic INR [1].

Moreover, pharmacogenetics is emerging as an essential tool for identifying genetic predispositions to adverse drug reactions. This approach not only helps prevent unwanted side effects but also allows for the identification of underlying genetic diseases that may not have been previously diagnosed [2].

In the field of tuberculosis, the combination of therapeutic drug monitoring (TDM) and pharmacometabolomics has enabled precise adjustments of anti-TB medication doses, reducing drug resistance and improving treatment outcomes [3].

Conclusions

The integration of pharmacogenomics and AI in clinical practice is transforming the way we approach personalized medicine. By minimizing adverse reactions and optimizing dosing personalization, these technologies are enhancing the safety and efficacy of treatments. As we continue to advance in this direction, it is crucial for healthcare professionals to stay informed about developments in clinical algorithms and their application in daily practice.

References

• [1] Artificial neural network-based pharmacogenomic algorithm for warfarin dose optimization.
• [2] Role of Pharmacogenetics in Adverse Drug Reactions: An Update towards Personalized Medicine.
• [3] Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management.

Created 20/1/2025