Roadmap for Routine Pharmacogenetic Testing in a Psychiatric University Hospital

Abstract

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.

Frequencies of Genetic Polymorphisms of Clinically Relevant Gene-Drug Pairs in a German Psychiatric Inpatient Population

“…Our recommendation to advocate for preemptive genotyping for clinically actionable gene-drug pairs is also consistent with our previous analyses of the same data set (published elsewhere [20]) where we reported that genotyped patients responded earlier and better to antidepressant drugs and therefore had a shorter duration of stay in the hospital and lower rehospitalization rates [3,4,20].…”

“…If admitted, the risk for genetic polymorphisms to change the response and side effect rates are high, so if not performed in ambulatory setting, the genotyping should be conducted in the hospital setting before starting an antidepressant therapy. Genetic testing can reduce the length of stay, prevent rehospitalizations, and improve patient outcomes (GAF score and CGI score) [3,20].…”

“…Some genetic polymorphisms are known to affect drug metabolism. In particular, the activity of certain CYP enzymes, such as CYP2C19 and CYP2D6, are highly associated with common genetic variants [2][3][4][5][6]. CYP2D6, a member of the cytochrome P450 superfamily, is responsible for the metabolism of about 25 % of commonly prescribed medications, including many antidepressants [7].…”

Four Actionable Bottlenecks and Potential Solutions to Translating Psychiatric Genetics Research: An Expert Review

“…Notably, several researchers have taken care to acknowledge that it will take more time to translate psychiatric genetic information to patient care due to the non-Mendelian nature and heterogeneity of such disorders [102-112]. There has also been a recent increase in the discussion of pharmacogenetic treatment for psychiatric disorders, specifically, examining metabolism rates of currently approved psychiatric medications, response to treatment, gene-drug interactions, or other genetic biomarker differences among individuals [94, 113, 114], or reviewing the literature on such treatments [82, 110, 115-118].…”

Pharmacogenetics in Psychiatry

“…The article by Menke et al [4] describes a new study protocol developed at the University Clinic of Würzburg (Northern Bavaria; Germany). This novel personalized medicine approach aims to include genetic data from CYP2C19 and CYP2D6 as well as additional information such as clinical characteristics, history of childhood trauma, biomarkers, therapeutic drug monitoring (TDM), epigenetics, and polygenic risk scores to develop a machine learning based, combinatorial algorithm.…”