J Oral Pathol Med. 2021 Mar 19. doi: 10.1111/jop.13174. Online ahead of print.

ABSTRACT

BACKGROUND: Head and neck cancer (HNSCC) is one of the most lethal cancers characterized by high relapse and poor prognosis. Several miRNAs have been implicated in HNSCC, including the tumor suppressor miR-137. A large CpG island (CpG73) spans most of the miR-137 gene sequence and stretches 659-bp downstream, ending just upstream of miR-2682 in the same host gene. Here, we assessed the role of the MIR137/MIR2682 locus in HNSCC.

METHODS: MiRNA expression was analyzed in paired cancerous and normal tissues from 77 HNSCC patients by Quantitative Reverse-Transcription PCR. CpG73 methylation in paired tissues from 48 patients was determined by Combined Bisulfite Restriction Analysis. Associations between expression and methylation levels and patient clinicopathological parameters were investigated.

RESULTS: Decreased expression of miR-137 (p<0.01) and miR-2682 (p<0.01) precursors was observed in cancerous tissues, most significantly in oropharyngeal tumors. Lower miR-137 levels correlated with increased perineural invasiveness (p=0.04). Predicted common miRNA targets MTDH and Notch1 were upregulated in tumor tissues. The CpG73 region between miR-137 and miR-2682 was hypermethylated in tumors. Methylation was observed in 60.4% of cancerous compared to 31.6% of normal tissues, and methylation levels were significantly higher (p<0.01) in tumors. Increased methylation correlated with decreased disease-free patient survival (P=0.024).

CONCLUSION: The MIR137/MIR2682 locus correlated with HNSCC perineural invasiveness. This is the first report showing miR-2682 downregulation in head and neck cancer. Our results support the tumor suppressive role of miR-137 and miR-2682. The inverse correlation between CpG73 hypermethylation and disease-free survival suggests this epigenetic mark may have prognostic value in HNSCC.

PMID:33740841 | DOI:10.1111/jop.13174

Pediatr Int. 2021 Mar 19. doi: 10.1111/ped.14703. Online ahead of print.

ABSTRACT

BACKGROUND: A prospective audit and feedback is a method that allows the antimicrobial stewardship program (ASP) team to interact with the attending physicians to tailor antibiotic therapy, including de-escalation, as appropriate. This study aimed to evaluate the acceptance and outcomes of an ASP de-escalation recommendation in children who received meropenem.

METHODS: A prospective cohort study was conducted in children aged 1 month to 18 years who received meropenem in a tertiary care teaching hospital. The ASP team gave the recommendation between 72 and 120 hours after initiating meropenem therapy. Acceptance of de-escalation recommendation among primary physicians was evaluated within 24 hours of recommendation. Outcomes included clinical success rate at the 7th day and incidence rate of acquisition of carbapenem-resistant Gram-negative bacteria (CR-GNB) within 30 days.

RESULTS: From March to December 2019, 217 children with the median (IQR) age of 2.1 (0.6,9.5) years received meropenem. The ASP team gave the recommendation as 127 (58.5%) cases of continuation and 90 (41.5%) cases of de-escalation. The overall acceptance of ASP de-escalation recommendation was 57.8% (95%CI 46.9-68.1%). The clinical success rates were 85.2% in the accepted group compared to 77.5% in the rejected group (p=0.06). The incidence rate of acquisition CR-GNB within 30 days after treatment was 5.8% in the accepted group and 15.8% in the rejected group (p=0.03).

CONCLUSIONS: About half of the recommendations to de-escalate meropenem prescriptions were accepted through ASP intervention. CR-GNB acquisitions were less likely in the de-escalation group. For carbapenem use to combat multidrug-resistant organisms, a proper de-escalation strategy after the initial 72-hours should be encouraged.

PMID:33740838 | DOI:10.1111/ped.14703

Reprod Biol. 2021 Mar 15;21(2):100499. doi: 10.1016/j.repbio.2021.100499. Online ahead of print.

ABSTRACT

The unpredictable variability in patients' responses to gonadotropins represents one of the most intractable IVF treatment problems. Identifying the genetic variants associated with ovarian responses to gonadotropins is an important step towards developing individualised pharmacogenetics protocols for ovarian stimulation. The purpose of the current study was to evaluate correlations between FSHR rs6165, FSHR rs616, and ESR1 rs2234693 gene variants and the degree of ovarian response to gonadotropin in Egyptian women undergoing ICSI treatment. Two hundred and eighty Egyptian women (mean age of 20-35) undergoing ICSI treatment were enrolled in a cross-sectional study conducted between January 2017 and May 2019. The women were classified into three groups based on ovarian response: normal responders (retrieved oocytes = 4-15) (n = 80), poor responders (retrieved oocytes < 4) (n = 92), and high responders (retrieved oocytes> 15) (n = 108). Genomic DNA was extracted from blood samples, and PCR and DNA sequencing were performed to identify genetic variations in the different study groups. FSHR and ESR1 genetic variants were then compared in normal, poor, and high responders. DNA sequencing results showed significant differences in the frequencies of FSHR rs6166 and ESR1 rs2234693 genotypes in poor responders compared with normal responders (P ≤ 0.001 and P ≤ 0.001, respectively). In contrast, no significant differences in the frequencies of FSHR rs6166, FSHR rs6165, or ESR1 rs2234693 genotypes were observed in high responders compared with normal responders (P ≤ 0.074, P ≤ 0.353, and P ≤ 0.060, respectively). These results suggest that FSHR and ESR1 gene variants could predict the degree of ovarian response to Controlled ovarian hyperstimulation in Egyptian women.

PMID:33740738 | DOI:10.1016/j.repbio.2021.100499

Epilepsy Res. 2021 Mar 11;173:106614. doi: 10.1016/j.eplepsyres.2021.106614. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of this study was to explore the prescription pattern of antiepileptics and the relationship between antiepileptics and adverse drug reactions (ADRs) in a Japanese population.

METHODS: A retrospective observational cohort study was conducted by reviewing the medical records of patients who visited or were admitted to a single tertiary care center between January 2011 and June 2019, were treated with antiepileptics, and developed allergic ADRs associated with these drugs.

RESULTS: In total, 14,230 unique patients received antiepileptics during the study period. Diazepam was the most frequently used antiepileptic drug (74.8 %), followed by phenobarbital (14.3 %), valproic acid (11.4 %), fosphenytoin (10.0 %), and carbamazepine (7.3 %). Although a trend of increasing prevalence of newer generation antiepileptics was noted, most patients are still treated with older generation antiepileptics. Thirty-two (0.22 %) unique patients experienced ADRs associated with antiepileptics, and the antiepileptic drug most frequently associated with ADRs was carbamazepine, at a rate of 1.4 %. Three patients developed Stevens-Johnson syndrome/toxic epidermal necrolysis, in two of which carbamazepine was implicated. Most patients experienced ADRs associated with aromatic antiepileptics (84.4 %) or older generation antiepileptics (81.3 %).

SIGNIFICANCE: This is the first study to assess the relationship between ADRs and antiepileptics at a tertiary care center in Japan. Based on our results, most patients were prescribed older generation antiepileptics, and most ADR events were linked to the administration of drugs in this category; thus, identification of patients at risk of developing ADRs is critical in order to prevent such events.

PMID:33740697 | DOI:10.1016/j.eplepsyres.2021.106614

Biomed Pharmacother. 2021 Mar 16;138:111485. doi: 10.1016/j.biopha.2021.111485. Online ahead of print.

ABSTRACT

Aberrant alteration of epigenetic information disturbs chromatin structure and gene function, thereby facilitating cancer development. Several drugs targeting histone deacetylases (HDACs), a group of epigenetic enzymes, have been approved for treating hematologic malignancies in the clinic. However, patients who suffer from solid tumors often respond poorly to these drugs. In this study, we report a selective entinostat derivative, MPT0L184, with potent cancer-killing activity in both cell-based and mouse xenograft models. A time-course analysis of cell-cycle progression revealed that MPT0L184 treatment elicited an early onset of mitosis but prevented the division of cells with duplicated chromosomes. We show that MPT0L184 possessed potent inhibitory activity toward HDAC1 and 2, and its HDAC-inhibitory activity was required for initiating premature mitotic signaling. HDAC inhibition by MPT0L184 reduced WEE1 expression at the transcription level. In addition, MPT0L184 treatment also downregulated ATR-mediated CHK1 phosphorylation independent of HDAC inhibition. Furthermore, gastric cancer cells resistant to HDAC inhibitors were vulnerable to MPT0L184. Taken together, our study discovers MPT0L184 as a novel HDAC inhibitor that can trigger premature mitosis and potentially counteract drug resistance of cancers.

PMID:33740521 | DOI:10.1016/j.biopha.2021.111485

Cell Chem Biol. 2021 Mar 18;28(3):271-282. doi: 10.1016/j.chembiol.2021.02.016.

ABSTRACT

Human induced pluripotent stem cells (hiPSCs) have emerged as a promising platform for pharmacogenomics and drug development. In cardiology, they make it possible to produce unlimited numbers of patient-specific human cells that reproduce hallmark features of heart disease in the culture dish. Their potential applications include the discovery of mechanism-specific therapeutics, the evaluation of safety and efficacy in a human context before a drug candidate reaches patients, and the stratification of patients for clinical trials. Although this new technology has the potential to revolutionize drug discovery, translational hurdles have hindered its widespread adoption for pharmaceutical development. Here we discuss recent progress in overcoming these hurdles that should facilitate the use of hiPSCs to develop new medicines and individualize therapies for heart disease.

PMID:33740432 | DOI:10.1016/j.chembiol.2021.02.016

Int J Stroke. 2021 Mar 19:17474930211007288. doi: 10.1177/17474930211007288. Online ahead of print.

ABSTRACT

Numerous biological mechanisms contribute to outcome after stroke, including brain injury, inflammation, and repair mechanisms. Clinical genetic studies have the potential to discover biological mechanisms affecting stroke recovery in humans and identify intervention targets. Large sample sizes are needed to detect commonly occurring genetic variations related to stroke brain injury and recovery. However, this usually requires combining data from multiple studies where consistent terminology, methodology, and data collection timelines are essential. Our group of expert stroke and rehabilitation clinicians and researchers with knowledge in genetics of stroke recovery here present recommendations for harmonizing phenotype data with focus on measures suitable for multicenter genetic studies of ischemic stroke brain injury and recovery. Our recommendations have been endorsed by the International Stroke Genetics Consortium.

PMID:33739214 | DOI:10.1177/17474930211007288

Allergy. 2021 Mar 19. doi: 10.1111/all.14819. Online ahead of print.

NO ABSTRACT

PMID:33738833 | DOI:10.1111/all.14819

J Biomed Inform. 2021 Mar 15:103732. doi: 10.1016/j.jbi.2021.103732. Online ahead of print.

ABSTRACT

BACKGROUND: Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the later offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed.

APPROACH: We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing.

RESULTS: GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases.

CONCLUSION: GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.

PMID:33737208 | DOI:10.1016/j.jbi.2021.103732

Cardiooncology. 2021 Mar 18;7(1):10. doi: 10.1186/s40959-021-00097-9.

ABSTRACT

BACKGROUND: Immune checkpoint inhibitors (ICIs) are a novel class of anticancer agents that have demonstrated clinical response for both solid and hematological malignancies. ICIs are associated with development of immune-related adverse events including cardiotoxicity. We estimated the incidence of newly diagnosed cardiovascular disease in patients treated with ICIs at a large, tertiary care center.

METHODS: All patients with a cancer diagnosis who received any ICI treatment in the University of Florida's Integrated Data Repository from 2011 to 2017 were included. Cardiovascular disease was defined as a new ICD diagnosis code for cardiomyopathy, heart failure, arrhythmia, heart block, pericardial disease, or myocarditis after initiation of ICI treatment.

RESULTS: Of 102,701 patients with a diagnosis of malignancy, 424 patients received at least one ICI. Sixty-two (14.6%) patients were diagnosed with at least one new cardiovascular disease after initiation of ICI therapy. Of the 374 patients receiving one ICI, 21 (5.6%) developed heart failure. Of the 49 patients who received two ICIs sequentially, three (6.1%) developed heart failure and/or cardiomyopathy. Incident cardiovascular disease was diagnosed at a median of 63 days after initial ICI exposure. One patient developed myocarditis 28 days after receiving nivolumab. Mortality in ICI treated patients with a concomitant diagnosis of incident cardiovascular disease was higher compared to those who did not (66.1% vs. 41.4%, odds ratio = 2.77, 1.55-4.95, p = 0.0006).

CONCLUSIONS: This study suggests a high incidence of newly diagnosed cardiovascular disease after the initiation of ICI therapy in a real-world clinical setting.

PMID:33736707 | DOI:10.1186/s40959-021-00097-9

Drug Metab Pers Ther. 2021 Mar 18. doi: 10.1515/dmdi-2020-0153. Online ahead of print.

ABSTRACT

Compared to Europe, data on genetic variation in genes transcribing drug metabolizing enzymes among Asian is limited due to ethnic diversity. Here we compare frequencies for clinically relevant single nucleotide polymorphism (SNP) commonly observed in drug metabolizing enzymes between European and Malaysian/Singaporean. Minor allele frequencies (MAF) for the indicated SNPs for European, South Asian and East Asian populations were obtained from the NCBI website (https://www.ncbi.nlm.nih.gov/snp). The SNP prevalence among Malaysian/Singaporean was characterized from gene association studies. Generally, some SNPs in CYP2D6 and CYP2C19 do not show good agreement between the two populations as to the MAF value obtained. CYP2D6*4 tends to be more common among European, whereas CYP2D6*10 is more common in Malays and Chinese among Singaporean. Regardless of different phenotype, MAF of CYP2D6*4 for Indians is similar to that seen by the European. Singaporeans show smaller MAF for CYP2C19*17 but higher CYP2C19*2 frequencies as opposed to European ones. Following growing attention to the contribution of CYP3A4/5, N-acetyltransferases (NAT2), thiopurine methyltransferase (TPMT) and uridine diphosphate glucuronosyltransferases (UGT)2B7 in predicting drug response across Europe, there are limited pharmacogenetics (PGx) studies examining the gene-drug interaction among Malaysian/Singaporean. To better understand the heterogeneity of the drug response, PGx studies for the abovementioned enzymes between ethnics in Malaysian/Singaporean should be identified.

PMID:33735954 | DOI:10.1515/dmdi-2020-0153

Environ Pollut. 2021 Mar 9;278:116863. doi: 10.1016/j.envpol.2021.116863. Online ahead of print.

ABSTRACT

Inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) is a type II acute-phase protein; however, the role of pulmonary ITIH4 after exposure to air pollution remains unclear. In this study, we investigated the role of ITIH4 in the lungs in response to air pollution. ITIH4 expression in bronchoalveolar lavage fluid (BAL) of 47 healthy human subjects and of Sprague-Dawley rats whole-body exposed to air pollution was determined, and the underlying antiapoptotic and matrix-stabilizing pathways in alveolar epithelial A549 cells induced by diesel exhaust particles (DEPs) as well as ITIH4-knockdown were investigated. We found that an interquartile range (IQR) increase in PM2.5 was associated with a decrease of 2.673 ng/mL in ITIH4, an increase of 1.104 pg/mL of 8-isoprostane, and an increase of 6.918 pg/mL of interleukin (IL)-6 in human BAL. In rats, increases in 8-isoprostane, IL-6, and p53 and a decrease in sirtuin-1 (Sirt1) in the lungs and decreases in ITIH4 in the BAL, lungs, and serum were observed after PM2.5 and gaseous exposure. ITIH4 levels in lung lysates were correlated with levels in BAL samples (r = 0.377, p < 0.01), whereas ITIH4 levels in BAL were correlated with IL-6 levels (r = -0.420, p < 0.01). ITIH4 expression was significantly reduced in alveolar epithelial A549 cells by DEP in a dose-dependent manner. A decrease in Sirt1 and increases in phosphorylated extracellular signal-regulated kinase (p-ERK) and caspase-3 were observed after DEP exposure and ITIH4-knockdown. In conclusion, air pollution decreased ITIH4 expression in the lungs, which was associated with alveolar epithelial cell senescence and apoptosis. ITIH4 could be a vital protein in regulating alveolar cell destruction and its inhibition after exposure to air pollution.

PMID:33735794 | DOI:10.1016/j.envpol.2021.116863

J Cell Mol Med. 2021 Mar 18. doi: 10.1111/jcmm.16462. Online ahead of print.

ABSTRACT

The outbreak of the coronavirus disease 2019 (COVID-19) has gathered 1 year of scientific/clinical information. This informational asset should be thoroughly and wisely used in the coming year colliding in a global task force to control this infection. Epidemiology of this infection shows that the available estimates of SARS-CoV-2 infection prevalence largely depended on the availability of molecular testing and the extent of tested population. Within molecular diagnosis, the viability and infectiousness of the virus in the tested samples should be further investigated. Moreover, SARS-CoV-2 has a genetic normal evolution that is a dynamic process. The immune system participates to the counterattack of the viral infection by pathogen elimination, cellular homoeostasis, tissue repair and generation of memory cells that would be reactivated upon a second encounter with the same virus. In all these stages, we still have knowledge to be gathered regarding antibody persistence, protective effects and immunological memory. Moreover, information regarding the intense pro-inflammatory action in severe cases still lacks and this is important in stratifying patients for difficult to treat cases. Without being exhaustive, the review will cover these important issues to be acknowledged to further advance in the battle against the current pandemia.

PMID:33734600 | DOI:10.1111/jcmm.16462

Brain. 2021 Mar 17:awab108. doi: 10.1093/brain/awab108. Online ahead of print.

ABSTRACT

Accurate and individualized prediction of response to therapies is central to precision medicine. However, due to the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We use the anti-epileptic drug brivaracetam as a case study and combine a hybrid data-/knowledge-driven feature extraction with machine learning to systematically integrate clinical and genetic data from a clinical discovery dataset (n = 235 patients). As such, we construct a model that successfully predicts clinical drug response (AUC = 0.76) and show that even with limited sample size, integrating high-dimensional genetics data with clinical data can inform drug response prediction. After a further validation on data collected from an independently conducted clinical study (AUC = 0.75), we extensively explore our model to gain insights into the determinants of drug response, and identify various clinical and genetic characteristics predisposing to poor response. Finally, we assess the potential impact of our model on clinical trial design and demonstrate that, by enriching for probable responders, significant reductions in clinical study sizes may be achieved. To our knowledge, our model represents the first retrospectively validated machine learning model linking drug mechanism of action and the genetic, clinical and demographic background in epilepsy patients to clinical drug response. Hence, it provides a blueprint for how machine learning-based multimodal data integration can act as a driver in achieving the goals of precision medicine in fields such as neurology.

PMID:33734308 | DOI:10.1093/brain/awab108

Med Decis Making. 2021 Mar 18:272989X21995805. doi: 10.1177/0272989X21995805. Online ahead of print.

ABSTRACT

We discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions, and computation time for an identical decision scenario modeled 4 ways: using 1) coupled-time differential equations (DEQ), 2) a cohort-based discrete-time state transition model (MARKOV), 3) an individual discrete-time state transition microsimulation model (MICROSIM), and 4) discrete event simulation (DES). Relative to DEQ, the net monetary benefit for PGx testing (v. a reference strategy of no testing) based on MARKOV with rate-to-probability conversions using commonly used formulas resulted in different optimal decisions. MARKOV was nearly identical to DEQ when transition probabilities were embedded using a transition intensity matrix. Among stochastic models, DES model outputs converged to DEQ with substantially fewer simulated patients (1 million) v. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and runtime but introduced additional complexity for calculating cost and quality-adjusted life year outcomes due to the inclusion of "jumpover" states after proper embedding of transition probabilities. Among stochastic models, DES offered the most favorable mix of accuracy, reliability, and speed.

PMID:33733932 | DOI:10.1177/0272989X21995805

Pharmacogenomics. 2021 Mar 18. doi: 10.2217/pgs-2020-0159. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) is one of the most significant challenges in the field of cancer pathology. MiRNAs are among the genetic factors associated with the disease. Although many studies have reviewed the expression patterns of various miRNAs in CRC, few studies have focused on different variants of miRNA. In the present review, miRNA variants have been categorized into three subgroups, including miRNA variants that predict susceptibility to CRC, miRNA variants that predict the clinical parameters of CRC and finally, miRNA variants that predict the pharmacological aspects of CRC. Moreover, a comprehensive review of potentially functional miRNA-associated SNPs as well as their importance as candidate cancer biomarkers are discussed.

PMID:33733820 | DOI:10.2217/pgs-2020-0159

Basic Clin Pharmacol Toxicol. 2021 Mar 18. doi: 10.1111/bcpt.13580. Online ahead of print.

ABSTRACT

Few studies have evaluated the influence of valproate on the deterioration of the lipid profile in psychiatric patients. This observational study aimed to compare the evolution of metabolic parameters in a sample of adult patients starting valproate (n=39) with a control group (n=39) of patients starting aripiprazole, a drug associated with a low risk of metabolic deterioration. Data were obtained from a prospective study including psychiatric patients with metabolic parameters monitored during the first year of treatment. During the first month of treatment with valproate (median: 31 days (IQR: 25-36)), mean body mass index increased significantly (from 24.8 kg/m2 at baseline to 25.2 kg/m2 after one month; p=0.03) and mean HDL-C levels decreased significantly (from 1.39 mmol/l to 1.27 mmol/l; p=0.02). In comparison, these metabolic variables remained stable during the first month of treatment with aripiprazole. The proportion of patients with early (i.e. during the first month of treatment) HDL-C decrease of ≥5% was significantly higher under valproate (54%) than aripiprazole (15%) treatment (p<0.001). These findings remind the importance of a prospective metabolic monitoring in patients who initiate valproate treatment. Further research should be conducted on larger samples and should focus on finding effective interventions to prevent such metabolic adverse effects.

PMID:33733594 | DOI:10.1111/bcpt.13580

Pharm Res. 2021 Mar 17. doi: 10.1007/s11095-021-03024-w. Online ahead of print.

ABSTRACT

PURPOSE: Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity.

METHODS: Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50-400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing.

RESULTS: The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·109 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar's test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power.

CONCLUSIONS: The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.

PMID:33733372 | DOI:10.1007/s11095-021-03024-w

Pharmacogenomics J. 2021 Mar 17. doi: 10.1038/s41397-021-00231-x. Online ahead of print.

ABSTRACT

Clozapine (Clz) is an atypical antipsychotic, which its pharmacokinetics can be influenced by several factors. The CYP1A2 and CYP2C19, major enzymes implicated in Clz metabolism, present an interethnic variation on their activity caused by single nucleotide polymorphisms (SNPs). The present study investigated the influence of genetic and nongenetic factors on Clz pharmacokinetics in a southern Mediterranean population. We included adult Tunisian schizophrenic patients having received Clz and undergone a therapeutic drug monitoring (TDM) of Clz by morning C0 monitoring. The genomic DNA was extracted using a salting-out procedure. CYP1A2*1F (rs762551;-163C>A), CYP1A2*1C (rs2069514;-3860 G>A) and CYP 2C19*2 (rs4244285; 681G>A) was analyzed using PCR-RFLP. Fifty-one patients were enrolled in the study. The mutant allele (CYP1A2*1F) was the most frequently detected (58.8%). For CYP1A2*1F, Clz dose-normalized (C0/D ratio) was as high as 1.28 ± 0.37 in CC versus 0.67 ± 0.32 ng mL-1 per mg day-1 in AA group (p < 0.001). The influence of genetic (CYP1A2*1F, CYP1A2*1C and CYP2C19*2) and nongenetic parameters (age, weight, gender, tobacco, coffee, and alcohol consumption) on the variation of the Clz C0/D ratio was investigated. Only the CYP1A2*1 F polymorphism correlates significantly with the Clz C0/D variation and could explain 24% of its variability. Our data support a critical role of the CYP1A2 -163C>A on the variation of Clz exposure in Tunisian schizophrenic patients. Considering its narrow therapeutic range, CYP1A2 genotyping combined with TDM of Clz may improve efficacy and safety of this drug. Further studies are needed to investigate this issue.

PMID:33731885 | DOI:10.1038/s41397-021-00231-x

Pharmacogenomics J. 2021 Mar 17. doi: 10.1038/s41397-021-00228-6. Online ahead of print.

ABSTRACT

The polymorphisms of the 5HTR1A and 5HTR2A receptor genes (rs6295C/G and rs6311G/A) have been evaluated for association with SSRI treatment outcome in various populations with different results. The present study was carried out to determine the association between genotypes of HTR1A-rs6295 and HTR2A-rs6311 with SSRI treatment outcome among the ethnic Malay patients diagnosed with first-episode major depressive disorder (MDD). The patients were recruited from four tertiary hospitals in the Klang Valley region of Malaysia. Predefined efficacy phenotypes based on 25% (partial early response) and 50% (clinical efficacy response) reduction in Montgomery Asberg Depression Rating Scale-self Rated score (MADRS-S) were adopted for assessment of treatment efficacy in this study. Self-reporting for adverse effects (AE) was documented using the Patient Rated Inventory of Side Effect (PRISE) after treatment with SSRI for up to 6 weeks. Adjusted binary logistic regression between genotypes of the polymorphism obtained using sequencing technique with the treatment outcome phenotypes was performed. The 142 patients recruited were made up of 96 females (67.6%) and 46 males (32.4%). Clinical efficacy and Partial early response phenotypes were not significantly associated with genotypes of HTR1A and HTR2A polymorphism. The GG genotype of HTR2A polymorphism has decreased odds for dizziness (CNS) and increased odds for poor concentration. The GA genotype increases the odd for excessive sweating, diarrhoea, constipation and blurred vision. The CC genotype of HTR1A-rs6295 decreases the odd for nausea/vomiting and increases the odd for anxiety. Thus, some genotypes of HTR1A and HTR2A polymorphism were associated with SSRI treatment outcomes in ethnic Malay MDD patients.

PMID:33731884 | DOI:10.1038/s41397-021-00228-6