Special Issue "The Pharmacogenomics of Mood Stabilizers"

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Pharmacology".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Dr. Alessio Squassina
Website SciProfiles
Guest Editor
Laboratory of Pharmacogenomics, Division of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Italy
Interests: pharmacogenomics; psychotropic medications; psychiatric disorders; suicide
Dr. Gabriel R. Fries
Website
Guest Editor
Translational Psychiatry Program, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, Center for Precision Health, School of Biomedical Informatics, Houston, Texas, US
Interests: epigenetics; bipolar disorder; aging; stress; DNA methylation; microRNAs; biomarkers
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Special Issue Information

Dear Colleagues,

The field of pharmacogenomics has experienced unprecedent progress in the last decade, mainly due—but not exclusively—to the implementation of sophisticated laboratory methodologies and the use of bioinformatic approaches, which allowed a better interpretation of omic data. Nevertheless, the translational value of pharmacogenomics of psychotropic medications is still hampered by our limited knowledge on their mechanisms of action and by the phenotypic and biological complexity of psychiatric disorders. The genomic era has made it possible to identify several genes which might significantly contribute to the etiopathogenesis of mood disorders, while pharmacogenomic and pharmacotranscriptomic studies brought us closer to a deeper comprehension of the clinically relevant targets of mood stabilizers—especially in the case of lithium. Overall, data suggest that the efforts put in place so far have paved the path towards a better management of mood stabilizing treatments, and while we are still far from the development of a predictive algorithm for response to these drugs, findings are encouraging and call for more efforts.

For this Special Issue we invite you to contribute original articles or review articles on the different aspects of the pharmacogenomics of mood stabilizers, including studies exploring or reviewing the role of biological systems, either in patients, human-derived cell lines, or animal models, as well as studies implementing statistical approaches to better exploit the large amount of genetic data produced by the pharmacogenomic studies in this field. Manuscripts on how to better disseminate and educate on the pharmacogenomics of mood stabilizers are also welcome.

Dr. Alessio Squassina
Dr. Gabriel R. Fries
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pharmacogenomics
  • mood stabilizers
  • personalized medicine
  • cellular models
  • predictive models
  • education and dissemination

Published Papers (1 paper)

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Research

Open AccessArticle
Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework
Pharmaceuticals 2020, 13(10), 305; https://doi.org/10.3390/ph13100305 - 13 Oct 2020
Abstract
In the wake of recent advances in machine learning research, the study of pharmacogenomics using predictive algorithms serves as a new paradigmatic application. In this work, our goal was to explore an ensemble machine learning approach which aims to predict probable antidepressant treatment [...] Read more.
In the wake of recent advances in machine learning research, the study of pharmacogenomics using predictive algorithms serves as a new paradigmatic application. In this work, our goal was to explore an ensemble machine learning approach which aims to predict probable antidepressant treatment response and remission in major depressive disorder (MDD). To discover the status of antidepressant treatments, we established an ensemble predictive model with a feature selection algorithm resulting from the analysis of genetic variants and clinical variables of 421 patients who were treated with selective serotonin reuptake inhibitors. We also compared our ensemble machine learning framework with other state-of-the-art models including multi-layer feedforward neural networks (MFNNs), logistic regression, support vector machine, C4.5 decision tree, naïve Bayes, and random forests. Our data revealed that the ensemble predictive algorithm with feature selection (using fewer biomarkers) performed comparably to other predictive algorithms (such as MFNNs and logistic regression) to derive the perplexing relationship between biomarkers and the status of antidepressant treatments. Our study demonstrates that the ensemble machine learning framework may present a useful technique to create bioinformatics tools for discriminating non-responders from responders prior to antidepressant treatments. Full article
(This article belongs to the Special Issue The Pharmacogenomics of Mood Stabilizers)
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