We evaluate the clinical validity of the pharmacogenomic test combination and the implementation guidelines for single clinical pharmacogenetics (CPIC) on patient outcomes and blood levels of treatment to assess their ability to tell recipes in the main depression disorder (MDD).
This is a secondary analysis of genomics used to improve depression decisions (guided) randomized controlled trials, which include patients with the diagnosis of MDD, and ≥1 previous drug failure. The ability to predict the increase / decrease in drug metabolism validated on blood levels in screening (adjusted for age, gender, smoking status). Predicted capabilities of genitive drug interactions (pharmacogenomic tests) or therapeutic recommendations (single-genual guidelines) to predict patient outcomes validated on week 8 results (Hamilton Depression rating scale 17-item; Improved symptoms, response, remission.
Analysis is carried out for patients who use Medications that meet the requirements (result n = 1,022, blood levels n = 1,034) and subset take drugs with a single genes (result n = 584, blood levels n = 372). Combinatorial pharmacogenomic test is the only significant predictor of the patient’s results. Both pharmacogenomic test combination and single genual guidelines are significant predictors of blood levels for all drugs when evaluated separately; However, only a political pharmacogenomic test that remains significant when both of them are included in a multivariate model. There is no substantial difference when all drugs are evaluated or for a subset with Single Gen Guidelines. Overall, evaluation of clinical validity occurs Order that the combinatorial pharmacogenomic test is a superior predictor of patient outcomes and medicinal treatment levels when compared to guidelines based on individual genes.
The clinical implementation of Preempractive Pharmacogenomics in Psychiatry: τhe “prepares” learning
Preempreemptive pharmacogenomic testing to prevent detrimental drug reactions (preparing) is the first prospective, pre-emptive pharmacogenomic study conducted in Europe, in the Horizon 2020 program frame. It aims to determine whether implementing pre-emptive pharmacogenomics (PGX) testing from relevant biomarkers Clinically, so the dosage and selection of guided drugs, will result in the overall reduction of events and severity associated with genotypes. Drug reaction (ADR). To achieve that, two groups of patients will be recruited; One that will receive treatment in accordance with standard clinical practices and each other who will receive pharmacogenomic treatment.
Pharmacogenomics Laboratory and individual treatment of the University of Patras, which coordinates and represents Greece in this study, in collaboration with the Psychiatric Department of Patras Public University Hospital, Department of Psychiatry Hospital “Attikon” and the Psychiatric Department of Psychiatric Hospital Athens “Dafni” will recruit 1500 Psychiatric patients who will receive antidepressant care or antipsychotic. Our scientific hypothesis is that patients who receive pharmacogenomic mandu drugs and dose selection will experience 30% less ADR than patients after standard maintenance.
Medications that meet the requirements to be included in the preparation study, are the clinical decisions about the choice of drugs and doses can be guided according to the Netherlands Pharmacogenomic Working Group (DPWG). Overall, 7 antidepressants (citalopram, escitalopram, sertraline, paroxetine, venlafaxine, clomipramine, amitriptyline) and 3 antipsychotics (haloperidol, zuclopenthixol, aripiprazole) related to 17 genetic variations in 2 genes (CYP2D6, CYP2C19) will be checked.
The occurrence, the severity and causality of adverse drug events (Ades) will be assessed during monitoring, in month 1 and 3 after starting the drug-index, and at the end of each arm, using the general toxicity criteria for the scale of side effects (CTCAE) and the causality assessor tool Liverpool (LCAT), respectively. The results of our study are expected to contribute significantly to improving the quality of life of psychiatric patients, by helping to provide the right drugs, with the right dose in terms of efficacy, security and cost effectiveness.
Catalyze pharmacogenomic clinical implementation and personalized drug intervention in Africa
Pharmacogenomics is considered a fruit that hangs low in the genome medicine tree with many examples of the success of its implementation in the clinic. In this perspective, we provide details about pharmacogenomic potential clinical applications in the African population using relevant drug cases and genomic approach throughput; involves many countries and stakeholders; And most importantly exploit the existing knowledge about each large-scale initiative. We emphasize the need to build the right framework for government policies in African countries. We also provide input on different initiatives in the field of implementation of genomic drugs in Africa, not only for their potential to synergy and collaboration between them, but also as a model for replication in other regions throughout the world, aiming for improving health care.
New text mining approach to take the pharmacogenomic association of the literature
Text mining in biomedical literature is a field that has been proven to have various implementations in many fields of research, including genetics, personalized drugs, and pharmacogenomics. In this study, we describe a new text mining approach to extract the pharmacogenomic association. The code used for this purpose is implemented using the R programming language, both through special scripts, if needed, or through utilizing the functions of the existing library. Articles (complete abstracts) that are in accordance with the query specified extracted from PubMed, while concept annotations are lowered by the Central Publator.
Provisions that show mutations or genes and the term chemical complex that are in accordance with normalized drug compounds and sentences that contain the terms mentioned above are filtered and processed to make the appropriate training set. Finally, after training and adequate hyperparameter training, four text classifications are created and evaluated (fasttext, linear kernel SVMS, XGBOOST, LASSO, and elastic-net general linear models) regarding their performance in identifying pharmacogenomic associations. Although further improvements are very important for the exact implementation of this text mining approach in clinical practice, our study stands as a comprehensive, simple, and up-to-date approach to identification and assessment of research articles that are enriched in clinically relevant pharmacogenomyo relationships.
Furthermore, this work highlights a series of challenges regarding the application of effective text mining in biomedical literature, which resolments can be substantially able to contribute to the further development of this field.