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: closed (30 September 2021).

Special Issue Editors

Dr. Alessio Squassina
E-Mail Website
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
E-Mail 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, TX, USA
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

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Keywords

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

Published Papers (5 papers)

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Research

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Article
Influence of Pathogenic and Metabolic Genes on the Pharmacogenetics of Mood Disorders in Alzheimer’s Disease
Pharmaceuticals 2021, 14(4), 366; https://doi.org/10.3390/ph14040366 - 15 Apr 2021
Viewed by 648
Abstract
Background: Mood disorders represent a risk factor for dementia and are present in over 60% of cases with Alzheimer’s disease (AD). More than 80% variability in drug pharmacokinetics and pharmacodynamics is associated with pharmacogenetics. Methods: Anxiety and depression symptoms were assessed in 1006 [...] Read more.
Background: Mood disorders represent a risk factor for dementia and are present in over 60% of cases with Alzheimer’s disease (AD). More than 80% variability in drug pharmacokinetics and pharmacodynamics is associated with pharmacogenetics. Methods: Anxiety and depression symptoms were assessed in 1006 patients with dementia (591 females, 415 males) and the influence of pathogenic (APOE) and metabolic (CYP2D6, CYP2C19, and CYP2C9) gene variants on the therapeutic outcome were analyzed after treatment with a multifactorial regime in a natural setting. Results and Conclusions: (i) Biochemical, hematological, and metabolic differences may contribute to changes in drug efficacy and safety; (ii) anxiety and depression are more frequent and severe in females than males; (iii) both females and males respond similarly to treatment, showing significant improvements in anxiety and depression; (iv) APOE-3 carriers are the best responders and APOE-4 carriers tend to be the worst responders to conventional treatments; and (v) among CYP2D6, CYP2C19, and CYP2C9 genophenotypes, normal metabolizers (NMs) and intermediate metabolizers (IMs) are significantly better responders than poor metabolizers (PMs) and ultra-rapid metabolizers (UMs) to therapeutic interventions that modify anxiety and depression phenotypes in dementia. APOE-4 carriers and CYP-related PMs and UMs deserve special attention for their vulnerability and poor response to current treatments. Full article
(This article belongs to the Special Issue The Pharmacogenomics of Mood Stabilizers)
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Article
Integrative Genomic–Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis
Pharmaceuticals 2021, 14(2), 118; https://doi.org/10.3390/ph14020118 - 04 Feb 2021
Cited by 1 | Viewed by 791
Abstract
Clozapine (CLZ) is the only antipsychotic drug that has been proven to be effective in patients with refractory psychosis, but it has also been proposed as an effective mood stabilizer; however, the complex mechanisms of action of CLZ are not yet fully known. [...] Read more.
Clozapine (CLZ) is the only antipsychotic drug that has been proven to be effective in patients with refractory psychosis, but it has also been proposed as an effective mood stabilizer; however, the complex mechanisms of action of CLZ are not yet fully known. To find predictors of CLZ-associated phenotypes (i.e., the metabolic ratio, dosage, and response), we explore the genomic and epigenomic characteristics of 44 patients with refractory psychosis who receive CLZ treatment based on the integration of polygenic risk score (PRS) analyses in simultaneous methylome profiles. Surprisingly, the PRS for bipolar disorder (BD-PRS) was associated with the CLZ metabolic ratio (pseudo-R2 = 0.2080, adjusted p-value = 0.0189). To better explain our findings in a biological context, we assess the protein–protein interactions between gene products with high impact variants in the top enriched pathways and those exhibiting differentially methylated sites. The GABAergic synapse pathway was found to be enriched in BD-PRS and was associated with the CLZ metabolic ratio. Such interplay supports the use of CLZ as a mood stabilizer and not just as an antipsychotic. Future studies with larger sample sizes should be pursued to confirm the findings of this study. Full article
(This article belongs to the Special Issue The Pharmacogenomics of Mood Stabilizers)
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Article
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
Cited by 7 | Viewed by 993
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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Review

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Review
Pharmacogenomics of Lithium Response in Bipolar Disorder
Pharmaceuticals 2021, 14(4), 287; https://doi.org/10.3390/ph14040287 - 24 Mar 2021
Viewed by 926
Abstract
Despite being the most widely studied mood stabilizer, researchers have not confirmed a mechanism for lithium’s therapeutic efficacy in Bipolar Disorder (BD). Pharmacogenomic applications may be clinically useful in the future for identifying lithium-responsive patients and facilitating personalized treatment. Six genome-wide association studies [...] Read more.
Despite being the most widely studied mood stabilizer, researchers have not confirmed a mechanism for lithium’s therapeutic efficacy in Bipolar Disorder (BD). Pharmacogenomic applications may be clinically useful in the future for identifying lithium-responsive patients and facilitating personalized treatment. Six genome-wide association studies (GWAS) reviewed here present evidence of genetic variations related to lithium responsivity and side effect expression. Variants were found on genes regulating the glutamate system, including GAD-like gene 1 (GADL1) and GRIA2 gene, a mutually-regulated target of lithium. In addition, single nucleotide polymorphisms (SNPs) discovered on SESTD1 may account for lithium’s exceptional ability to permeate cell membranes and mediate autoimmune and renal effects. Studies also corroborated the importance of epigenetics and stress regulation on lithium response, finding variants on long, non-coding RNA genes and associations between response and genetic loading for psychiatric comorbidities. Overall, the precision medicine model of stratifying patients based on phenotype seems to derive genotypic support of a separate clinical subtype of lithium-responsive BD. Results have yet to be expounded upon and should therefore be interpreted with caution. Full article
(This article belongs to the Special Issue The Pharmacogenomics of Mood Stabilizers)
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Review
Pharmacogenetics of Carbamazepine and Valproate: Focus on Polymorphisms of Drug Metabolizing Enzymes and Transporters
Pharmaceuticals 2021, 14(3), 204; https://doi.org/10.3390/ph14030204 - 01 Mar 2021
Cited by 3 | Viewed by 775
Abstract
Pharmacogenomics can identify polymorphisms in genes involved in drug pharmacokinetics and pharmacodynamics determining differences in efficacy and safety and causing inter-individual variability in drug response. Therefore, pharmacogenomics can help clinicians in optimizing therapy based on patient’s genotype, also in psychiatric and neurological settings. [...] Read more.
Pharmacogenomics can identify polymorphisms in genes involved in drug pharmacokinetics and pharmacodynamics determining differences in efficacy and safety and causing inter-individual variability in drug response. Therefore, pharmacogenomics can help clinicians in optimizing therapy based on patient’s genotype, also in psychiatric and neurological settings. However, pharmacogenetic screenings for psychotropic drugs are not routinely employed in diagnosis and monitoring of patients treated with mood stabilizers, such as carbamazepine and valproate, because their benefit in clinical practice is still controversial. In this review, we summarize the current knowledge on pharmacogenetic biomarkers of these anticonvulsant drugs. Full article
(This article belongs to the Special Issue The Pharmacogenomics of Mood Stabilizers)
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