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Pharmaceuticals
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The Pharmacogenomics of Mood Stabilizers
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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) | Viewed by 25564

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, 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
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2900 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

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Published Papers (7 papers)

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Research

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11 pages, 1528 KiB  
Article
Methylomic Biomarkers of Lithium Response in Bipolar Disorder: A Proof of Transferability Study
by Cynthia Marie-Claire, Cindie Courtin, Frank Bellivier, Jan Scott and Bruno Etain
Pharmaceuticals 2022, 15(2), 133; https://doi.org/10.3390/ph15020133 - 23 Jan 2022
Cited by 6 | Viewed by 2590
Abstract
Response to lithium (Li) is highly variable in bipolar disorders (BD) and no clinical or biological predictors of long-term response have been validated to date. Using a genome-wide methylomic approach (SeqCapEpi), we previously identified seven differentially methylated regions (DMRs) that discriminated good from [...] Read more.
Response to lithium (Li) is highly variable in bipolar disorders (BD) and no clinical or biological predictors of long-term response have been validated to date. Using a genome-wide methylomic approach (SeqCapEpi), we previously identified seven differentially methylated regions (DMRs) that discriminated good from non-responders (prophylactic response phenotype defined using the “Alda” scale). This study is a proof of transferability from bench to bedside of this epigenetic signature. For this purpose, we used Methylation Specific High-Resolution Melting (MS-HRM), a PCR based method that can be implemented in any medical laboratory at low cost and with minimal equipment. In 23 individuals with BD, MS-HRM measures of three out of seven DMRs were technically feasible and consistencies between SeqCapEpi and MS-HRM-measures were moderate to high. In an extended sample of individuals with BD (n = 70), the three MS-HRM-measured DMRs mainly predicted nonresponse, with AUC between 0.70–0.80 according to different definitions of the phenotype (Alda- or machine-learning-based definitions). Classification tree analyses further suggested that the MS-HRM-measured DMRs correctly classified up to 84% of individuals as good or non-responders. This study suggested that epigenetic biomarkers, identified in a retrospective sample, accurately discriminate non-responders from responders to Li and may be transferrable to routine practice. Full article
(This article belongs to the Special Issue The Pharmacogenomics of Mood Stabilizers)
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12 pages, 664 KiB  
Article
A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes
by Jan Scott, Mohamed Lajnef, Romain Icick, Frank Bellivier, Cynthia Marie-Claire and Bruno Etain
Pharmaceuticals 2021, 14(11), 1072; https://doi.org/10.3390/ph14111072 - 23 Oct 2021
Cited by 2 | Viewed by 2093
Abstract
Optimal classification of the response to lithium (Li) is crucial in genetic and biomarker research. This proof of concept study aims at exploring whether different approaches to phenotyping the response to Li may influence the likelihood of detecting associations between the response and [...] Read more.
Optimal classification of the response to lithium (Li) is crucial in genetic and biomarker research. This proof of concept study aims at exploring whether different approaches to phenotyping the response to Li may influence the likelihood of detecting associations between the response and genetic markers. We operationalized Li response phenotypes using the Retrospective Assessment of Response to Lithium Scale (i.e., the Alda scale) in a sample of 164 cases with bipolar disorder (BD). Three phenotypes were defined using the established approaches, whilst two phenotypes were generated by machine learning algorithms. We examined whether these five different Li response phenotypes showed different levels of statistically significant associations with polymorphisms of three candidate circadian genes (RORA, TIMELESS and PPARGC1A), which were selected for this study because they were plausibly linked with the response to Li. The three original and two revised Alda ratings showed low levels of discordance (misclassification rates: 8–12%). However, the significance of associations with circadian genes differed when examining previously recommended categorical and continuous phenotypes versus machine-learning derived phenotypes. Findings using machine learning approaches identified more putative signals of the Li response. Established approaches to Li response phenotyping are easy to use but may lead to a significant loss of data (excluding partial responders) due to recent attempts to improve the reliability of the original rating system. While machine learning approaches require additional modeling to generate Li response phenotypes, they may offer a more nuanced approach, which, in turn, would enhance the probability of identifying significant signals in genetic studies. Full article
(This article belongs to the Special Issue The Pharmacogenomics of Mood Stabilizers)
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19 pages, 43976 KiB  
Article
Influence of Pathogenic and Metabolic Genes on the Pharmacogenetics of Mood Disorders in Alzheimer’s Disease
by Ramón Cacabelos, Juan C. Carril, Lola Corzo, Lucía Fernández-Novoa, Rocío Pego, Natalia Cacabelos, Pablo Cacabelos, Margarita Alcaraz, Iván Tellado and Vinogran Naidoo
Pharmaceuticals 2021, 14(4), 366; https://doi.org/10.3390/ph14040366 - 15 Apr 2021
Cited by 6 | Viewed by 3014
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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16 pages, 1880 KiB  
Article
Integrative Genomic–Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis
by Yerye Gibrán Mayén-Lobo, José Jaime Martínez-Magaña, Blanca Estela Pérez-Aldana, Alberto Ortega-Vázquez, Alma Delia Genis-Mendoza, David José Dávila-Ortiz de Montellano, Ernesto Soto-Reyes, Humberto Nicolini, Marisol López-López and Nancy Monroy-Jaramillo
Pharmaceuticals 2021, 14(2), 118; https://doi.org/10.3390/ph14020118 - 4 Feb 2021
Cited by 9 | Viewed by 3080
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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12 pages, 472 KiB  
Article
Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework
by Eugene Lin, Po-Hsiu Kuo, Yu-Li Liu, Younger W.-Y. Yu, Albert C. Yang and Shih-Jen Tsai
Pharmaceuticals 2020, 13(10), 305; https://doi.org/10.3390/ph13100305 - 13 Oct 2020
Cited by 23 | Viewed by 3721
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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12 pages, 1581 KiB  
Review
Pharmacogenomics of Lithium Response in Bipolar Disorder
by Courtney M. Vecera, Gabriel R. Fries, Lokesh R. Shahani, Jair C. Soares and Rodrigo Machado-Vieira
Pharmaceuticals 2021, 14(4), 287; https://doi.org/10.3390/ph14040287 - 24 Mar 2021
Cited by 9 | Viewed by 4517
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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17 pages, 1152 KiB  
Review
Pharmacogenetics of Carbamazepine and Valproate: Focus on Polymorphisms of Drug Metabolizing Enzymes and Transporters
by Teresa Iannaccone, Carmine Sellitto, Valentina Manzo, Francesca Colucci, Valentina Giudice, Berenice Stefanelli, Antonio Iuliano, Giulio Corrivetti and Amelia Filippelli
Pharmaceuticals 2021, 14(3), 204; https://doi.org/10.3390/ph14030204 - 1 Mar 2021
Cited by 24 | Viewed by 5124
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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