Clin Transl Sci. 2025 Apr;18(4):e70173. doi: 10.1111/cts.70173.

ABSTRACT

Lurbinectedin is an oncogenic transcription inhibitor indicated for the treatment of small cell lung cancer (SCLC), which has also shown activity against other malignancies. In this work, two independent cohorts of 180 (discovery cohort) and 719 (validation cohort) cancer patients receiving lurbinectedin in Phases I, II, or III clinical trials were enrolled. Using a population pharmacokinetic (popPK) model of the discovery cohort, patients with extremely high (n = 10, cohort 1) and low (n = 10, cohort 2) etaCL values (i.e., a variable used as a surrogate of unexplained CL interindividual variability) were identified. They were sequenced for 42 candidate genes involved in lurbinectedin pharmacokinetics. A total of 34 variants located in 20 genes were significantly associated with lurbinectedin etaCL; the best nine hits (located in CYP3A5, CYP3A4, ABCB1, ARNT, NR5A2, NR1H4, and FOXA3) were subsequently genotyped in the validation cohort. A strong additive association between CYP3A4 and CYP3A5 genotypes (informed as a CYP3A activity score [AS] variable) and lurbinectedin clearance (CL) and exposure was confirmed, for example, patients with an AS of 3, 2, or 1 showed a 2.3-, 1.6-, and 1.5-fold higher total lurbinectedin CL compared to those with an AS of 0 and 2.3-, 1.8-, and 1.6-fold higher unbound lurbinectedin CL. In conclusion, preemptive CYP3A genotyping may offer a valuable approach for personalizing treatment with lurbinectedin in cancer patients.

PMID:40146606 | DOI:10.1111/cts.70173

Curr Psychiatry Rep. 2025 Mar 27. doi: 10.1007/s11920-025-01606-8. Online ahead of print.

ABSTRACT

Bipolar Disorder (BD) is a chronic mental health condition characterized by significant mood swings, including periods of mania or hypomania and depression. Affecting approximately 1-2% of the global population, BD is associated with impaired social functioning, decreased quality of life, and an increased risk of suicide. The disorder presents a substantial burden on healthcare systems and imposes significant economic costs due to lost productivity and the need for extensive treatment and support services. This comprehensive review synthesizes global trends in BD pharmacotherapy over the past 1 to 3 years, focusing on emerging medications, novel treatment protocols, and ongoing debates within the field. Additionally, the review explores differences in prescribing patterns across developed and developing countries, introduces the impact of pharmacogenomics and personalized medicine on treatment outcomes. PURPOSE OF REVIEW: The primary purpose of this review is to provide a comprehensive and up-to-date synthesis of the global trends in the use of medications for the treatment of BD over the past 1 to 3 years. This review aims to outline the latest studies, clinical trials, and meta-analyses relevant to BD pharmacotherapy, highlighting new discoveries and advancements. Furthermore, this review will address ongoing debates and controversies in the field, such as the role of antidepressants in BD treatment and the long-term use of antipsychotics, aiming to bridge knowledge gaps and guide future research directions. RECENT FINDINGS: Studies continue to reinforce the efficacy of lithium in mood stabilization and reduction of suicidal behavior, despite its declining use due to safety concerns. Mood stabilizing anticonvulsants like valproate and carbamazepine continue to be vital alternatives, each with distinct side effect profiles necessitating careful patient monitoring. The approval and increasing use of novel atypical antipsychotics, such as lurasidone (2013) and cariprazine (2015), has expanded treatment options, offering efficacy in different phases of BD with relatively favorable side effect profiles. Antidepressants remain contentious, with evidence suggesting their benefits primarily when used in combination with mood stabilizers. Emerging agents like lumateperone (Dec 2021) and esketamine show promise, while pharmacogenomic research is paving the way for more personalized treatments. The landscape of BD pharmacotherapy is marked by significant advancements and ongoing challenges. Lithium and mood stabilizing anticonvulsants remain foundational treatments, albeit with adherence challenges and side effect concerns. The advent of new atypical antipsychotics and novel agents offers promising therapeutic options, while antidepressants continue to be debated. Personalized medicine and pharmacogenomics could emerge as transformative approaches, allowing for more tailored and effective treatments. However, disparities in medication accessibility between developed and developing countries highlight the need for global collaboration to optimize BD management. Continued research and innovation are essential to addressing the complexities of BD and improving patient outcomes worldwide.

PMID:40146356 | DOI:10.1007/s11920-025-01606-8

Clin Transl Sci. 2025 Apr;18(4):e70210. doi: 10.1111/cts.70210.

ABSTRACT

Monitoring whole blood tacrolimus concentrations is standard in clinical practice; however, it may not fully reflect its therapeutic effects, as tacrolimus primarily acts within lymphocytes. While various intracellular quantification methods have been developed, many involve complex procedures such as evaporation, reconstitution, or specialized tools (e.g., magnetic beads, online solid-phase extraction), limiting their accessibility. This study aimed to develop and validate a streamlined, sensitive method for measuring intracellular tacrolimus concentrations using 5×105 peripheral blood mononuclear cells (PBMCs). Tacrolimus concentrations were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). PBMCs were aliquoted into 50 μL volumes containing 5×105 cells and prepared via acetonitrile-based protein precipitation. Chromatographic separation was performed using a Luna C18 column with a gradient mobile phase consisting of water with 20 mM ammonium acetate, 0.1% formic acid, and methanol at a flow rate of 0.4 mL/min. The method demonstrated excellent linearity between 0.1 and 25 ng/mL, corresponding to intracellular concentrations of 1-250 pg/5×105 cells (r2 = 0.999). Intra- and interday accuracy ranged from 98.1% to 109.8%, with precision between 2.08% and 8.70% across validation runs. Extraction recovery was high (93.0%-97.2%), with minimal matrix effects (100.9% at low QC and 111.6% at high QC). This validated LC-MS/MS method provides a rapid, reliable, and sensitive approach for pharmacokinetic studies and clinical applications, facilitating intracellular tacrolimus monitoring in transplant patients.

PMID:40145774 | DOI:10.1111/cts.70210

JHLT Open. 2024 Aug 6;6:100134. doi: 10.1016/j.jhlto.2024.100134. eCollection 2024 Nov.

ABSTRACT

BACKGROUND: Tacrolimus therapy is standard of care for immunosuppression after lung transplantation. However, tacrolimus exposure variability during the early postoperative period may contribute to poor outcomes in this population. Few studies have examined tacrolimus pharmacokinetics (PK) during this high-risk period.

METHODS: We conducted a retrospective pharmacokinetic study in lung transplant recipients at the University of Pennsylvania who were enrolled in the Lung Transplant Outcomes Group cohort. We used nonlinear mixed-effects regression to derive a population PK model in 270 patients and examined validity in a separate cohort of 114 patients. Covariates were examined with univariate analysis and a multivariable model was developed using forward and backward stepwise selection. The performance of the final model in the validation cohort was examined with calculation of prediction error (PE).

RESULTS: We developed a 1-compartment base model with a fixed rate absorption constant. Covariates improving model fit were postoperative day, hematocrit, transplant type, CYP3A5 genotype, weight, and exposure to cytochrome p450 enzyme (CYP) inhibitor drugs. The strongest predictor of tacrolimus clearance was postoperative day, with median predicted clearance increasing more than 3-fold over the 14-day study period. In the validation cohort, the final model showed a mean PE of 36.4% (95% confidence interval 30.8%-41.9%) and a median PE of 7.2% (interquartile range -29.3% to 70.53%).

CONCLUSIONS: Tacrolimus clearance is highly dynamic during the early postlung transplant period. Population PK models that include lung-transplant-specific covariates may enable precision dosing algorithms that account for this highly dynamic clearance. Future multicenters studies including a broader set of covariates are warranted.

PMID:40145052 | PMC:PMC11935331 | DOI:10.1016/j.jhlto.2024.100134

Pharmaceutics. 2025 Mar 2;17(3):325. doi: 10.3390/pharmaceutics17030325.

ABSTRACT

Background/Objectives: To analyze the genotype that predicts the phenotypic characteristics of a cohort of patients with glaucoma and ocular hypertension (OHT) and explore their influence on the response to ocular hypotensive treatment. Methods: This was a prospective study that included 193 eyes of 109 patients with glaucoma or OHT under monotherapy with beta-blockers, prostaglandin, or prostamide analogues (BBs, PGAs, PDs). Eight single-nucleotide polymorphisms were genotyped using real-time PCR assays: prostaglandin-F2α receptor (PTGFR) (rs3766355, rs3753380); beta-2-adrenergic receptor (ADRB2) (rs1042714); and cytochrome P450 2D6 (CYP2D6) (*2 rs16947; *35 rs769258; *4 rs3892097; *9 rs5030656, and *41 rs28371725). The main variables studied were baseline (bIOP), treated (tIOP), and rate of variation in intraocular pressure (vIOP), and mean deviation of the visual field (MD). The metabolizer phenotype and the CYP2D6 copy number variation were also evaluated. Results: In total, 112 eyes were treated with PGAs (58.0%), 59 with BBs (30.6%), and 22 with PDs (11.4%). For PTGFR (rs3753380), statistically significant differences were observed in vIOP in the PGA group (p = 0.032). Differences were also observed for ADRB2 (rs1042714) in MD (p < 0.001) and vIOP (p = 0.017). For CYP2D6, ultrarapid metabolizers exhibited higher tIOP (p = 0.010) and lower vIOP (p = 0.046) compared to the intermediate and poor metabolizers of the BB group. Additionally, systemic treatment metabolized by CYP2D6 showed a significant influence on vIOP (p = 0.019) in this group. Conclusions: These preliminary findings suggest the future potential of pharmacogenetic-based treatments in glaucoma to achieve personalized treatment for each patient, and thus optimal clinical management.

PMID:40142989 | DOI:10.3390/pharmaceutics17030325

Int J Mol Sci. 2025 Mar 10;26(6):2468. doi: 10.3390/ijms26062468.

ABSTRACT

In recent years, many approved drugs have been discovered using phenotypic screening, which elaborates the exact mechanisms of action or molecular targets of drugs. Drug susceptibility prediction is an important type of phenotypic screening. Large-scale pharmacogenomics studies have provided us with large amounts of drug sensitivity data. By analyzing these data using computational methods, we can effectively build models to predict drug susceptibility. However, due to the differences in data distribution among databases, researchers cannot directly utilize data from multiple sources. In this study, we propose a deep transfer learning model. We integrate the genomic characterization of cancer cell lines with chemical information on compounds, combined with the Encyclopedia of Cancer Cell Lines (CCLE) and the Genomics of Cancer Drug Sensitivity (GDSC) datasets, through a domain-adapted approach and predict the half-maximal inhibitory concentrations (IC50 values). Afterward, the validity of the prediction results of our model is verified. This study effectively addresses the challenge of cross-database distribution discrepancies in drug sensitivity prediction by integrating multi-source heterogeneous data and constructing a deep transfer learning model. This model serves as a reliable computational tool for precision drug development. Its widespread application can facilitate the optimization of therapeutic strategies in personalized medicine while also providing technical support for high-throughput drug screening and the discovery of new drug targets.

PMID:40141112 | DOI:10.3390/ijms26062468

Support Care Cancer. 2025 Mar 26;33(4):323. doi: 10.1007/s00520-025-09385-x.

ABSTRACT

PURPOSE: Psychological distress is highly prevalent among cancer patients. Although several scientific and professional organizations developed guidelines and tools for screening, implementation barriers in cancer care persist. Therefore, it seems to be critical to effectively introduce tools and triage systems that can identify patients' source of distress. The study aims to investigate prevalence and predictors of psychological distress experienced by a mixed sample of adult cancer patients using the Italian version of the Concerns and Help Identifier for Medical Patients (CHIMP_C) Checklist, in order to quickly detect distress.

METHODS: In 2023, 240 adult cancer patients undergoing chemotherapy at the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori "Giovanni Paolo II" in Bari completed the Emotion Thermometers (ET) and the CHIMP_C Checklist. Socio-demographic and clinical factors were collected from medical records. Pearson and Spearman correlations, chi-square tests, and binomial logistic regressions were performed to investigate prevalence and predictors of psychological distress.

RESULTS: Most participants were female (68.3%), with breast cancer being the most common diagnosis (28.7%). Our findings revealed a significant prevalence of distress (49.58%, with DT scores ≥ 5). Notably, emotional and personal concerns emerged as key predictors and risk factors for elevated ET scores.

CONCLUSION: The combined use of the CHIMP_C Checklist alongside the Emotion Thermometers (ET) could suggest a way for clinicians to identify multifaceted factors contributing to psychological distress in cancer patients during active treatment. This approach not only is focused on facilitating the initiation of timely psychological interventions but also can improve patient access to comprehensive therapeutic programs, thereby enhancing overall quality of care.

PMID:40140163 | DOI:10.1007/s00520-025-09385-x

Ann Med. 2025 Dec;57(1):2478316. doi: 10.1080/07853890.2025.2478316. Epub 2025 Mar 26.

ABSTRACT

BACKGROUND: Isoniazid is primarily metabolized by the arylamine N-acetyltransferase 2 (NAT2) enzyme. Single nucleotide polymorphisms (SNPs) in the NAT2 gene could classify an individual into three distinct phenotypes: rapid, intermediate and slow acetylators. NAT2 SNPs and the slow acetylator phenotype have been implicated as risk factors for the development of antitubercular drug-induced liver injury (AT-DILI) in several tuberculosis (TB) populations.

PATIENTS AND METHODS: We conducted a prospective observational study to characterize and compare the NAT2 SNPs, genotypes and phenotypes among patients with TB and AT-DILI from the Southern and Western regions of India. The NAT2 pharmacogenomic profile of patients from these regions was compared with the reports from several geographically diverse TB populations and participants of different genetic ancestries extracted from literature reviews and the 'All of Us' Research Program database, respectively.

RESULTS: The TB patients of Southern and Western regions of India and several other geographically closer regions exhibited near similar NAT2 MAF characteristics. However significant heterogeneity in NAT2 SNPs was observed within and between countries among AT-DILI populations and the participants of different genetic ancestry from the 'All of Us' Research Program database. The MAF of the NAT2 SNPs rs1041983, rs1801280, rs1799929, rs1799930 and rs1208 of the TB patients from Southern and Western Indian Sites were in near range to that of the South Asian genetic ancestry of 'All of Us' Research Program database. About one-third of the total TB patients from the Southern and Western regions of India were NAT2 slow acetylators, among whom a relatively higher proportion experienced AT-DILI.

CONCLUSION: Further studies exploring the risk of NAT2 SNPs in different AT-DILI patients with larger sample sizes and a population-specific approach are required to establish a policy for NAT2 genotyping as a pre-emptive biomarker for AT-DILI monitoring for personalized isoniazid therapy in clinics.

PMID:40138446 | DOI:10.1080/07853890.2025.2478316

Neurochem Res. 2025 Mar 26;50(2):127. doi: 10.1007/s11064-025-04382-2.

NO ABSTRACT

PMID:40138046 | DOI:10.1007/s11064-025-04382-2

Am J Psychiatry. 2025 Mar 26:appiajp20240295. doi: 10.1176/appi.ajp.20240295. Online ahead of print.

ABSTRACT

OBJECTIVE: The authors provide recommendations on incorporating recent advances in psychiatric genetics into clinical practice for mental health clinicians.

METHOD: The International Society for Psychiatric Genetics Education Committee met monthly to come to a consensus on priority topics in psychiatric genetics. Topics were then assigned to small teams of subspecialty experts to summarize the current knowledge base and create an illustrative clinical case. Topics included, familial aggregation, common and rare genetic variants, epigenetics, gene-environment interactions, pharmacogenomics, genetic counseling, and ethical and social implications. Each section was reviewed and revised by all committee members and then finalized by the Committee Chair.

RESULTS: Key findings highlight the importance of understanding the genetic architecture of psychiatric disorders, the potential applications of genetic information in risk assessment, diagnosis, treatment selection, and patient education, as well as the ethical and social considerations surrounding the use of genetic data. The committee emphasizes the need for a nuanced approach that integrates genetic factors with environmental and experiential factors in a holistic model of care.

CONCLUSION: As psychiatric genetics continues to evolve rapidly, mental health clinicians must stay informed about the latest findings and their clinical implications. Ongoing education, collaboration with genetics professionals, and effective communication strategies are crucial to harness the power of genetics while avoiding potential pitfalls such as genetic determinism and stigma. The committee recommends a balanced perspective that recognizes the complex interplay of genetic and non-genetic factors in shaping mental health outcomes.

PMID:40134266 | DOI:10.1176/appi.ajp.20240295

Nat Commun. 2025 Mar 25;16(1):2913. doi: 10.1038/s41467-025-58152-3.

ABSTRACT

Electronic health records (EHRs) coupled with large-scale biobanks offer great promises to unravel the genetic underpinnings of treatment efficacy. However, medication-induced biomarker trajectories stemming from such records remain poorly studied. Here, we extract clinical and medication prescription data from EHRs and conduct GWAS and rare variant burden tests in the UK Biobank (discovery) and the All of Us program (replication) on ten cardiometabolic drug response outcomes including lipid response to statins, HbA1c response to metformin and blood pressure response to antihypertensives (N = 932-28,880). Our discovery analyses in participants of European ancestry recover previously reported pharmacogenetic signals at genome-wide significance level (APOE, LPA and SLCO1B1) and a novel rare variant association in GIMAP5 with HbA1c response to metformin. Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. We also found polygenic risk scores to predict drug response, though they explained less than 2% of the variance. In summary, we present an EHR-based framework to study the genetics of drug response and systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in 41,732 UK Biobank and 14,277 All of Us participants.

PMID:40133288 | DOI:10.1038/s41467-025-58152-3

J Pers Med. 2025 Mar 13;15(3):110. doi: 10.3390/jpm15030110.

ABSTRACT

Background/Objectives: Pharmacogenomics is an emerging field in precision medicine that aims to improve patient outcomes by tailoring drug selection and dosage to an individual's genetic makeup. However, patients in rural communities often cannot take advantage of specialized services such as pharmacogenomics due to various barriers that limit access to healthcare. This article aims to identify the barriers to implementing pharmacogenomic initiatives in rural communities and assess strategies for integrating pharmacogenomics into rural healthcare systems. Methods: This article describes the qualitative research that was conducted using semi-structured interviews with various stakeholders in addition to explaining how strategic frameworks were used to synthesize secondary research. Results: The findings of this article indicated mixed awareness of pharmacogenomics as an option amongst stakeholders, highlighting the need for targeted outreach and education intervention. Solutions such as mail-in testing and telemedicine were determined to be feasible solutions to address various geographical and logistical barriers that exist for rural patients. This article determines that successful strategies will leverage existing infrastructure and prioritize patient care, workflow integration, and adoption. Conclusions: Making pharmacogenomics a viable option for rural patients will take a multi-faceted approach that combines outreach, education, and innovative delivery models to overcome the multiple barriers facing rural communities.

PMID:40137426 | DOI:10.3390/jpm15030110

J Pers Med. 2025 Mar 5;15(3):103. doi: 10.3390/jpm15030103.

ABSTRACT

Background: In the United States, approximately 1 in 5 children experience comorbidities with mental illness, including depression and anxiety, which lead to poor general health outcomes. Adolescents with substance use disorders exhibit high rates of co-occurring mental illness, with over 60% meeting diagnostic criteria for another psychiatric condition in community-based treatment programs. Comorbidities are influenced by both genetic (DNA antecedents) and environmental (epigenetic) factors. Given the significant impact of psychiatric comorbidities on individuals' lives, this study aims to uncover common mechanisms through a Genome-Wide Association Study (GWAS) meta-meta-analysis. Methods: GWAS datasets were obtained for each comorbid phenotype, followed by a GWAS meta-meta-analysis using a significance threshold of p < 5E-8 to validate the rationale behind combining all GWAS phenotypes. The combined and refined dataset was subjected to bioinformatic analyses, including Protein-Protein Interactions and Systems Biology. Pharmacogenomics (PGx) annotations for all potential genes with at least one PGx were tested, and the genes identified were combined with the Genetic Addiction Risk Severity (GARS) test, which included 10 genes and eleven Single Nucleotide Polymorphisms (SNPs). The STRING-MODEL was employed to discover novel networks and Protein-Drug interactions. Results: Autism Spectrum Disorder (ASD) was identified as the top manifestation derived from the known comorbid interaction of anxiety, depression, and attention deficit hyperactivity disorder (ADHD). The STRING-MODEL and Protein-Drug interaction analysis revealed a novel network associated with these psychiatric comorbidities. The findings suggest that these interactions are linked to the need to induce "dopamine homeostasis" as a therapeutic outcome. Conclusions: This study provides a reliable genetic and epigenetic map that could assist healthcare professionals in the therapeutic care of patients presenting with multiple psychiatric manifestations, including anxiety, depression, and ADHD. The results highlight the importance of targeting dopamine homeostasis in managing ASD linked to these comorbidities. These insights may guide future pharmacogenomic interventions to improve clinical outcomes in affected individuals.

PMID:40137419 | DOI:10.3390/jpm15030103

J Pers Med. 2025 Mar 4;15(3):101. doi: 10.3390/jpm15030101.

ABSTRACT

Background/Objectives: While there is mounting scientific evidence supporting the effectiveness of PGx (pharmacogenomics)-guided medical treatment, its implementation into clinical care is still lagging. Stakeholder buy-in, in particular from prescribers, will be key in the implementation efforts. Previous implementation studies have primarily focused on prescriber attitudes or have used hypothetical scenario methodology in a variety of healthcare settings. Real-world studies provide better insight into prescriber experience and needs. In this prospective observational qualitative research study, we report the perspectives of prescribers working in military medical care after a one-year PGx implementation trial. Methods: At the end of the PGx implementation period, thirteen prescribers participated in a semi-structured interview. The interview was designed based on the Technology Acceptance Model and queried their perceptions of effectiveness and ease of use of the PGx innovation. Results: Three main themes emerged from the qualitative data: (1) the knowledge required for PGx testing, (2) the integration of the testing into the existing workflow and (3) the perceived clinical utility of the PGx results. Prescribers had educational and training opportunities prior to the study but still encountered difficulty with the interpretation of the test results. They generally managed well the workflow changes occasioned by the testing. They reported that the clinical value came primarily from an increased confidence in prescribing safe medications and improving the therapeutic alliance with their patients. There was uncertainty about which patient population would most benefit from the testing. Conclusions: Our results lend support to the general ongoing challenges identified in PGx implementation studies conducted in other clinical settings and using other methodologies. They also revealed specific factors that the prescribers found of value and areas that needed improvement to support future implementation efforts.

PMID:40137417 | DOI:10.3390/jpm15030101

J Pers Med. 2025 Feb 28;15(3):93. doi: 10.3390/jpm15030093.

ABSTRACT

Background: Certain CYP2D6 genotypes are linked to a lower efficacy of tamoxifen therapy. This study aimed to observe CYP2D6 polymorphisms and examine the impact of CYP2D6 genotyping among tamoxifen-treated breast cancer patients in Indonesia. Methods: 150 breast cancer participants were recruited. Buccal swab samples were collected; gDNA was extracted and genotyped using the qPCR method. Blood samples were collected, and measurement of tamoxifen metabolite levels was performed using UPLC-MS/MS. Results: 43.3% (n = 65) of participants were IMs. *10 was the most common haplotype (n = 89, 29.7%), followed by *36 (n = 73, 29.7%), making *10/*36 the most common diplotype (n = 34, 22.7%) in this study. The difference in endoxifen levels between the NM and IM-PM groups at baseline was statistically significant (p ≤ 0.001). A dose increase in tamoxifen to 40 mg daily successfully increased endoxifen levels in IMs to a similar level with NMs at baseline (p > 0.05) without exposing IMs to serious side effects. No statistically significant differences were observed between the 20mg group and the 40 mg group on the adjusted OS (p > 0.05) and the adjusted PFS (p > 0.05). Conclusions: Our study observed a considerably high proportion of CYP2D6 IMs. The dose adjustment of tamoxifen was proven to significantly and safely improve the level of endoxifen and survival.

PMID:40137409 | DOI:10.3390/jpm15030093

Acta Neuropsychiatr. 2025 Mar 25:1-30. doi: 10.1017/neu.2025.11. Online ahead of print.

ABSTRACT

BACKGROUND: Non-adherence and negative attitudes towards medication are major problems in treating psychotic disorders. Cytochrome P450 2D6 (CYP2D6) contributes to the metabolism of aripiprazole and risperidone. Variations in CYP2D6 activity may affect treatment response or adverse effects. The impact of these variations on adherence and medication attitudes is unclear.

AIMS: This study investigates the relationships between CYP2D6 phenotype, self-reported adherence, adverse effects, and attitudes among aripiprazole and risperidone users.

METHODS: This study analyzed data from the SUPER-Finland cohort of 10,474 adults with psychotic episodes, including 1,429 aripiprazole and 828 risperidone users. The Attitudes towards neuroleptic treatment (ANT) questionnaire assessed adherence and adverse effects in all patients, while medication-related attitudes were examined in a subgroup of 1,000 participants. Associations between CYP2D6 phenotypes and outcomes were analyzed using logistic regression and beta regression in aripiprazole and risperidone groups separately.

RESULTS: Among risperidone users, we observed no association between CYP2D6 phenotypes and adherence, adverse effects, or attitudes. Similarly, no link was found between adherence and CYP2D6 phenotypes among aripiprazole users. However, aripiprazole users with the ultrarapid CYP2D6 phenotype had more adverse effects (OR = 1.71, 95 % CI 1.03-2.90, p = 0.041). Among aripiprazole users, CYP2D6 ultrarapid phenotype was associated with less favorable attitudes towards antipsychotic treatment (β = -0.48, p = 0.023).

CONCLUSIONS: We found preliminary evidence that the ultrarapid CYP2D6 phenotype is associated with increased adverse effects and negative attitudes towards antipsychotic medication among aripiprazole users. CYP2D6 phenotype did not influence adherence, adverse effects, or attitudes among risperidone users.

PMID:40130908 | DOI:10.1017/neu.2025.11

IJID Reg. 2025 Feb 16;14:100604. doi: 10.1016/j.ijregi.2025.100604. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Genomic surveillance activity is a useful tool in epidemiologic investigations and monitoring of virus evolution. This study aimed to describe the COVID-19 outbreaks through SARS-CoV-2 virus genomic surveillance by whole genome sequencing.

METHODS: A cross-sectional study was conducted using archived clinical samples of confirmed laboratory-positive COVID-19 from June 2021 to June 2022 from a tertiary center in Malaysia. The samples were subjected to whole genome sequencing. A phylogenetic tree was constructed using the maximum likelihood method in MEGA 11 software. The clinical data were obtained through paper, electronic, and hospital information systems.

RESULTS: A total of 86 clinical samples were successfully sequenced. The phylogenetic tree revealed seven clusters, consisting of 24 cases. Three clusters were associated with health care workers and health care-associated individuals. The SARS-CoV-2 Delta variants were observed in the first three clusters and subsequently replaced with the Omicron variants.

CONCLUSIONS: Whole genome sequencing is robust and reliable, enhancing epidemiologic investigations, leading to the identification of clusters and preventing the spreading of COVID-19 among health care workers. Monitoring of the SARS-CoV-2 variants is necessary to study the viral dynamics and maintain the effectiveness of public health interventions.

PMID:40130259 | PMC:PMC11930704 | DOI:10.1016/j.ijregi.2025.100604

Front Pharmacol. 2025 Mar 10;16:1448106. doi: 10.3389/fphar.2025.1448106. eCollection 2025.

ABSTRACT

INTRODUCTION: Adverse Drug Reactions (ADRs), a widespread phenomenon in clinical drug treatment, are often associated with a high risk of morbidity and even death. Drugs and changes in gene expression are the two important factors that affect whether and how adverse reactions occur. Notably, pharmacogenomics data have recently become more available and could be used to predict ADR occurrence. However, there is a challenge in effectively analyzing the massive data lacking guidance on mutual relationship for ADRs prediction.

METHODS: We constructed separate similarity features for drugs and ADRs using pharmacogenomics data from the Comparative Toxicogenomics Database [CTD, including Chemical-Gene Interactions (CGIs) and Gene-Disease Associations (GDAs)]. We proposed a novel deep learning architecture, DGANet, based on the constructed features for ADR prediction. The algorithm uses Convolutional Neural Networks (CNN) and cross-features to learn the latent drug-gene-ADR associations for ADRs prediction.

RESULTS AND DISCUSSION: The performance of DGANet was compared to three state-of-the-art algorithms with different genomic features. According to the results, GDANet outperformed the benchmark algorithms (AUROC = 92.76%, AUPRC = 92.49%), demonstrating a 3.36% AUROC and 4.05% accuracy improvement over the cutting-edge algorithms. We further proposed new genomic features that improved DGANet's predictive capability. Moreover, case studies on top-ranked candidates confirmed DGANet's ability to predict new ADRs.

PMID:40129949 | PMC:PMC11931068 | DOI:10.3389/fphar.2025.1448106

Expert Opin Drug Saf. 2025 Mar 24. doi: 10.1080/14740338.2025.2484474. Online ahead of print.

ABSTRACT

BACKGROUND: Drug-induced sinusitis has been widely reported as an adverse drug reaction in recent years, yet the pharmacogenetic mechanisms and risk factors associated with sinusitis remain elusive.

OBJECTIVE: We aimed to identify the major drugs reported in the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) in relation to sinusitis and to analyze their pharmacogenetic mechanisms through drug target analysis.

METHODS: We conducted a review of the publicly available FAERS database from 2004 to the third quarter of 2023. We extracted genetic tools corresponding to each drug, utilized colocalization analysis, Mendelian randomization (MR) analysis, and cross-tissue drug target analysis to predict the impact of drug targets on sinusitis.

RESULTS: Following the validation of drug-related risks, a total of 13 medications were ultimately identified, including TNF inhibitors: pomalidomide (ROR: 14.77), certolizumab pegol(ROR: 8.21), etanercept (ROR: 7.961), lenalidomide (ROR: 6.998), adalimumab (ROR: 6.677), infliximab (ROR: 3.939); C4B-targeted drugs: human immunoglobulin G (ROR:3.846) and other risk drugs were commonly reported. Co-localization analysis and MR analysis suggests associations between TNF, C4B, and LTA and sinusitis.

CONCLUSION: We demonstrated the risk of sinusitis associated with 13 drugs, including pomalidomide, and the impact of TNF and C4B drugs on sinusitis, which provides guidance for the use of related drugs and the prevention of sinusitis.

PMID:40128146 | DOI:10.1080/14740338.2025.2484474

Postgrad Med J. 2025 Mar 24:qgaf037. doi: 10.1093/postmj/qgaf037. Online ahead of print.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has significantly impacted global health, making it essential to understand its genetic effects on various traits.

METHOD: Leveraging the extensive FinnGen dataset comprising 500 000 individuals, we performed a Mendelian randomization (MR) phenome-wide association study. COVID-19-related phenotypes obtained from the COVID-19 Host Genetics Initiative GWAS (release 7). We employed four distinct approaches, including MR-Egger, weighted median, random-effect inverse variance weighted (IVW), and weighted mode, to conduct the MR analysis.

RESULTS: Two hundred fifty-five potential causal effects of COVID-19 were observed for a diverse range of outcomes using the IVW method, including cardiovascular disorders, respiratory conditions, autoimmune diseases, and metabolic disorders. Apart from a few that can be classified as "other traits," the majority of the traits are disease-related traits. We have also identified 31 traits, wherein all four distinct MR analyses yielded a P-value of less than 0.05. Only one trait remained statistically significant after multiple testing correction using the conservative Bonferroni threshold (P < 2.2E-5).

CONCLUSIONS: This phenome-wide MR study provides valuable insights into the genetically predicted effects of COVID-19 on a comprehensive range of traits. The identified associations contribute to our understanding of the complex interplay between the impact of the post-COVID-19 era on healthcare and may have implications for the development of targeted therapeutic strategies and public health interventions. Key messages What is already known on this topic - COVID-19 has a high mortality rate, and patients often have many sequelae, including myocarditis, acute respiratory distress syndrome, and neurological and hematologic complications. What this study adds Most of the current relevant studies lack large-scale phenotype-group ranging Mendelian randomization (MR) studies on the outcome of COVID-19 due to their small sample sizes. Therefore, this study performed a full phenotypic group MR analysis in the FinnGen dataset to investigate the relationship between COVID-19 and thousands of outcome variables. How this study might affect research, practice or policy- The study identified a set of traits that are strongly associated with genetic susceptibility to the long-term effects of COVID-19.

PMID:40126442 | DOI:10.1093/postmj/qgaf037