Abstract
Background
Few new psychiatric drugs have entered the market in recent decades; in contrast, the number of drugs carrying pharmacogenomic labels continues to increase. For the foreseeable future, the advancement of psychiatry and drug therapy may hinge on personalized treatment. Currently, antipsychotic or antidepressant choices rely heavily on the clinical experience of psychiatrists and potentially lengthy iterative trials. During these trials, the clinical response to treatment in acutely depressed patients can be assessed only after several weeks of exposure to the drug. Although pharmacogenomic testing has been used in clinical care for several years, most Chinese clinicians struggle to utilize the information accurately, resulting in expensive tests that provide little real benefit to patients. Here, we demonstrate how to combine the results of pharmacogenomic testing to develop an individualized treatment plan. Our goal is to find the optimal medication regimen and dosage for the patient in the shortest possible time, control symptoms as soon as possible, and predict adverse drug reactions. This approach aims to offer a practical therapeutic idea for clinical practice.
Case presentation
We present the case of a 27-year-old female patient experiencing a relapse of depression. Despite previous attempts with empiric medication, her symptoms remained uncontrolled, leading to exacerbation and drug withdrawal reactions. Utilizing the results of pharmacogenetic testing, we crafted an individualized treatment plan, resulting in rapid remission without any adverse drug reactions.
Conclusion
Recognizing the complexity of antidepressant response, our patients aim to improve their understanding, as well as that of other healthcare providers, by undergoing pharmacogenomics testing. This enhances the credibility of their medication choices. While pharmacogenomics is just one aspect considered in selecting a treatment regimen for depression, it remains a valuable tool for increasing credibility and mitigating potential adverse events.