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  • Pharmacogenomics-guided drug personalization in psychiatry: leveraging genetic biomarkers for treatment of depression and ADHD

  • Sahar Masoomi,1,* Kourosh Khayam Abed,2
    1. Researcher in Medical Genetics, Pharmacogenomics, Pharmaceutical Biotechnology, Neuroscience, Nanomedicine, and Applied Artificial Intelligence in Personalized Psychology and Psychiatry
    2. Undergraduate Student in Biotechnology, Naqsh-e Jahan University, Isfahan, Iran


  • Introduction: Mood disorders, including major depressive disorder (MDD), and neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD) represent highly prevalent and heterogeneous psychiatric syndromes, imposing substantial burdens on individuals and society. Despite the availability of numerous pharmacological treatments, therapeutic responses remain highly variable, with up to 40–50% of patients experiencing suboptimal outcomes or adverse effects. This variability arises from complex interactions among genetic, epigenetic, neurobiological, and environmental factors, underscoring the limitations of traditional “one-size-fits-all” pharmacotherapy. Recent advances in pharmacogenomics have provided critical insights into the genetic determinants of drug response, including polymorphisms in cytochrome P450 enzymes (e.g., CYP2D6, CYP2C19), neurotransmitter transporters (e.g., SLC6A4), and neurotrophic and circadian-related genes (e.g., BDNF, NRG1). These discoveries have enabled the identification of predictive biomarkers that can inform personalized medication selection, dosing optimization, and risk mitigation of adverse drug reactions. Furthermore, integrative approaches combining genome-wide association studies (GWAS), transcriptomic profiling, and multi-omic analyses have revealed convergent biological pathways underpinning therapeutic response, highlighting potential targets for individualized interventions. Emerging research employing deep learning models and large-scale genetic datasets further enhances the predictive accuracy for treatment outcomes, particularly in ADHD and MDD populations, by capturing complex polygenic effects and gene-environment interactions. The translation of these findings into clinical practice promises to revolutionize psychiatric care by enabling precision medicine strategies that tailor pharmacotherapy to the molecular and neurobiological profile of each patient. Moreover, coupling pharmacogenomic insights with longitudinal phenotyping, digital health monitoring, and neuroimaging biomarkers may facilitate real-time assessment of drug efficacy and cognitive-behavioral trajectories, ultimately improving patient adherence, functional recovery, and long-term prognosis. In this review, we synthesize current evidence from pharmacogenomic studies, computational modeling, and translational research to elucidate the mechanisms driving inter-individual variability in drug response. We further highlight opportunities and challenges for implementing personalized psychiatry, focusing on depression and ADHD, and propose strategies to integrate genetic, molecular, and clinical data for next-generation precision mental healthcare.
  • Methods: This systematic review covers articles published between 2009 and 2025 on pharmacogenomics in mood disorders and ADHD and the development of personalized therapies. Articles were selected for relevance to drug response, genetic biomarkers, CYP450 polymorphisms, neurotrophic genes, and serotonergic and glutamatergic pathways. Databases searched included PubMed, Scopus, Web of Science, and SpringerLink. Extracted data focused on genes affecting drug metabolism and response, medication types, molecular mechanisms, neurotransmitter pathways, and predictive models using GWAS and deep learning, including studies leveraging transcriptomic and proteomic biomarkers for genotype-based personalized interventions.
  • Results: Data analysis revealed that genetic polymorphisms, particularly in drug-metabolizing genes CYP2D6 and CYP2C19, have a direct impact on drug metabolism rate, effective dosage, and the severity of side effects for antidepressants and ADHD stimulants. Genes such as SLC6A4, BDNF, NRG1, and newly identified genes TMEM117, MYO5B, and NKAIN2 acted as precise predictors of treatment response, reflecting the complex interplay of serotonergic, glutamatergic, and dopaminergic pathways alongside cellular metabolic processes and synaptic neuroplasticity. GWAS and deep learning models demonstrated that multi-layered gene networks can predict drug response with over 83% accuracy and 90% sensitivity in patients with specific genotypes. Mechanisms of drug action include modulation of serotonin and dopamine receptor density at synapses, regulation of BDNF pathways and synaptic neuroplasticity, adjustment of cellular metabolic and neurotrophic pathways, and reduction of neuroinflammation and oxidative stress. Genotype-guided drug selection resulted in faster and more effective therapeutic response, reduced adverse effects, and improved prediction accuracy for long-term treatment trajectories. Moreover, integrated pharmacogenomics models utilizing digital monitoring, longitudinal data, and neurobiomarkers enabled real-time assessment of drug response and precise treatment adjustment, significantly enhancing the path toward personalized and precision medicine in depression and ADHD.
  • Conclusion: Pharmacogenomics, particularly polymorphisms in CYP2D6, CYP2C19, and key neurotrophic and neurotransmitter-related genes, enables precise prediction of treatment response in major depressive disorder and ADHD. Genes such as SLC6A4, BDNF, NRG1, TMEM117, MYO5B, and NKAIN2 guide personalized medication strategies, improving efficacy, accelerating response, and reducing adverse effects. Integration of GWAS and deep learning models allows accurate patient stratification, while molecular mechanisms—including modulation of serotonin and dopamine receptors, synaptic neuroplasticity via BDNF, regulation of metabolic pathways, and reduction of neuroinflammation—underpin therapeutic effects. Combining pharmacogenomic biomarkers with digital monitoring and longitudinal data supports a precision psychiatry approach, optimizing dosing, treatment trajectories, and cognitive outcomes.
  • Keywords: Pharmacogenomics, Personalized Psychiatry, Genetic Biomarkers, Depression, ADHD

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