Clinical implementation of pharmacogenetics and personalized drug prescription based on e-health: the MedeA initiative

Abstract

There is a growing demand for the clinical implementation of pharmacogenetics (PGx), personalized and precision medicine (PPM) for drug prescription to reduce adverse drug reactions (ADRs), drug failure, and ultimately health care costs. However, it is convenient to clarify the concept of clinical implementation to realize its benefits. Advances on PGx clinical implementation depend on the integration of genetic along with other relevant biomarkers and clinical information influencing variability in drug response for being interpreted and translated into clinical decision-making to optimize drug treatment choice during routine clinical practice. There are several initiatives related to PGx clinical implementation in Europe [1], using either a preventive (pre-emptive) measures approach or a reactive one (point of care) (https://www.icpermed.eu/). Some of them are integrated on different research platforms, that perform the genetic analyses using either single drug-gene combinations, gene-arrays, or next-generation sequencing (NGS) [1]. Nevertheless, many of them just aim to analyze the relevance of genetic information in the absence of other relevant data influencing drug response variation, without considering polypharmacy in the context of multi-morbidity. PGx applied to phar-macokinetic variability is so far the cornerstone of its clinical implementation [2]. Thus, determining actual phenotypes by analyzing metabolic ratios from actual drug treatments to evaluate enzyme activity (dose-dependent phenocopying [3]) as shown during treatment with thioridazine [4], risperidone [5] or fluoxetine [6], including additional influencing factors [7] such as liver or kidney function, and concomitant pharmacological treatments (to prevent drug-drug interactions [8]) becomes essential to develop a precise clinical implementation process. Therefore, despite the need of applying PPM into daily clinical practice, there are still barriers to overcome regarding the inclusion of all factors relevant for variation in drug response. Moreover, another important limitation is information management. Hence, there is a need for developing computational tools that may integrate the relevance of different factors influencing drug response in a context of polypharmacy to simplify the prescribers' decision-making. Currently, drug selection requires manual data entry, therefore a guided drug prescription e-tool integrated into the Electronic Medical Record (EMR) is needed for improving drug choices in the context of drug poly-therapy and poly-pathology. Moreover, the system needs to be evaluated to establish a cost/effectiveness analysis for its implementation in the