Major depressive disorder (MDD) implicates a huge burden for patients and society. Although currently available antidepressants are effective treatment options, more than 50% of the patients do not respond to the first administered antidepressant. In addition, in more than 25% with antidepressants-treated patients, adverse effects occur. Currently, the selection of treatment does not reflect objectively measurable data from neurobiological and behavioral systems. However, in the last decades, the understanding of the impact of genetic variants on clinical features such as drug metabolism has grown and can be used to develop tests that enable a patient-tailored individual treatment. In fact, robust evidence was found that genetic variants of CYP450 enzymes such as CYP2D6 and CYP2C19 can be surrogate markers for the metabolism of certain drugs. This article describes a pilot study design aimed to combine clinical variables such as therapeutic drug monitoring, inflammatory and stress markers with static and variable genetic information of depressed patients to develop an algorithm that predicts treatment response, and tolerability using machine learning algorithms. Psychometric evaluation covers the Hamilton Depression Rating Scale, the Childhood Trauma Questionnaire, and adverse drug reactions. An in-depth (epi-)genetic assessment combines genome-wide gene association data with DNA methylation patterns of genes coding CYP enzymes along with a pharmacogenetic battery focusing on CYP enzymes. Using these measures to stratify depressed patients, this approach should contribute to a data-driven assessment and management of MDD, which can be referred to as precision medicine or high-definition medicine.
© Georg Thieme Verlag KG Stuttgart · New York.