Personalized medicine has emerged as a multidisciplinary approach aimed at optimizing healthcare based on individuals’ genetic, biochemical, and molecular profiles. The integration of biochemistry with omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, allows for the consideration of individual variability in disease diagnosis, treatment, and monitoring, thereby enhancing therapeutic efficacy while minimizing adverse drug reactions. Genomic and functional genomic approaches provide fundamental tools to understand genetic variations and gene functions, whereas transcriptomic, proteomic, and metabolomic analyses enable the evaluation of dynamic cellular and molecular processes.

Biomarkers constitute essential components in the clinical implementation of personalized medicine. Diagnostic, prognostic, predictive, pharmacodynamic, and monitoring biomarkers provide critical information for disease detection, prediction of therapeutic response, and prognosis assessment. Molecular biomarkers discovered through genomic and proteomic approaches, such as single nucleotide polymorphisms (SNPs), DNA methylation, and microsatellite instability, serve as guides in managing cancer and other complex diseases.

Pharmacogenomics plays a pivotal role in predicting drug responses based on an individual’s genetic makeup, thereby guiding optimal drug selection and dosing. Variations in genes coding for drug-metabolizing enzymes, particularly the cytochrome P450 family, directly influence drug efficacy and safety. In oncology, genetic indicators such as HER2 and KRAS mutations are used to tailor targeted therapies. Bioinformatics and data analytics facilitate the processing of large-scale datasets, modeling of molecular interactions, and integration of these data into clinical decision-making processes.

Clinically, biochemical and molecular biomarkers have proven effective in personalizing treatment and monitoring disease progression across various conditions, including cancer, cardiovascular diseases, diabetes, neurological disorders, and autoimmune diseases. At the same time, the implementation of personalized medicine raises ethical, legal, and social considerations, including data privacy, informed consent, genetic discrimination, and healthcare access disparities.

Finally, artificial intelligence–driven analyses, CRISPR-based gene editing technologies, and multi-omics integration are emerging as key determinants in the future of personalized medicine. This biochemistry-based, multidisciplinary approach is transforming healthcare by improving treatment efficacy and safety while enhancing individual quality of life, representing a revolutionary shift in clinical practice.