J Cancer Res Clin Oncol. 2022 Dec 6. doi: 10.1007/s00432-022-04503-2. Online ahead of print.

NO ABSTRACT

PMID:36472769 | DOI:10.1007/s00432-022-04503-2

Biochem Biophys Res Commun. 2022 Nov 28;639:54-61. doi: 10.1016/j.bbrc.2022.11.083. Online ahead of print.

NO ABSTRACT

PMID:36470072 | DOI:10.1016/j.bbrc.2022.11.083

Mol Biol Rep. 2022 Dec 5. doi: 10.1007/s11033-022-08146-x. Online ahead of print.

NO ABSTRACT

PMID:36469259 | DOI:10.1007/s11033-022-08146-x

Age Ageing. 2022 Dec 5;51(12):afac285. doi: 10.1093/ageing/afac285.

NO ABSTRACT

PMID:36469092 | DOI:10.1093/ageing/afac285

Pharmacogenomics. 2022 Dec 5. doi: 10.2217/pgs-2022-0131. Online ahead of print.

NO ABSTRACT

PMID:36468359 | DOI:10.2217/pgs-2022-0131

Front Cell Dev Biol. 2022 Nov 17;10:1003656. doi: 10.3389/fcell.2022.1003656. eCollection 2022.

NO ABSTRACT

PMID:36467413 | PMC:PMC9714307 | DOI:10.3389/fcell.2022.1003656

Saudi Pharm J. 2022 Nov;30(11):1612-1622. doi: 10.1016/j.jsps.2022.09.004. Epub 2022 Sep 16.

NO ABSTRACT

PMID:36465846 | PMC:PMC9715948 | DOI:10.1016/j.jsps.2022.09.004

Infect Drug Resist. 2022 Nov 28;15:6839-6852. doi: 10.2147/IDR.S389442. eCollection 2022.

NO ABSTRACT

PMID:36465811 | PMC:PMC9717595 | DOI:10.2147/IDR.S389442

J Oncol Pharm Pract. 2022 Dec 4:10781552221137302. doi: 10.1177/10781552221137302. Online ahead of print.

NO ABSTRACT

PMID:36464835 | DOI:10.1177/10781552221137302

Neurosci Biobehav Rev. 2022 Dec 1:104965. doi: 10.1016/j.neubiorev.2022.104965. Online ahead of print.

NO ABSTRACT

PMID:36463971 | DOI:10.1016/j.neubiorev.2022.104965

J Psychiatr Res. 2022 Nov 18;157:152-161. doi: 10.1016/j.jpsychires.2022.11.010. Online ahead of print.

NO ABSTRACT

PMID:36463630 | DOI:10.1016/j.jpsychires.2022.11.010

Expert Rev Clin Pharmacol. 2022 Dec 3. doi: 10.1080/17512433.2023.2154652. Online ahead of print.

NO ABSTRACT

PMID:36461813 | DOI:10.1080/17512433.2023.2154652

Chem Res Toxicol. 2022 Dec 2. doi: 10.1021/acs.chemrestox.2c00214. Online ahead of print.

NO ABSTRACT

PMID:36459538 | DOI:10.1021/acs.chemrestox.2c00214

Cancer Control. 2022 Jan-Dec;29:10732748221143879. doi: 10.1177/10732748221143879.

ABSTRACT

OBJECTIVE: Interleukin-17A (IL-17A) genetic polymorphisms are associated with multiple cancer types, including colorectal cancer (CRC). However, no previous study was performed in the Bangladeshi population to evaluate the association. Our study aimed to find the association between two IL-17A variants (rs10484879 C/A and rs3748067 G/A) and susceptibility of CRC.

METHODS AND MATERIALS: This retrospective case-control study comprised 292 CRC patients and 288 age, sex, and BMI matched healthy volunteers. Genotyping of both variants was done by the tetra-primer ARMS-PCR method, and the results were analyzed by the SPSS software package (version-25.0).

RESULTS: Logistic regression analysis indicated that in case of IL-17A rs10484879 polymorphism, AC and AA genotype carriers showed 2.44- and 3.27-times significantly increased risk for CRC development (OR = 2.44, P = .0008 and OR = 3.27, P = .0133, individually). A significant association was also observed for AC + AA genotype (OR = 2.58, P = .0001). Again, over-dominant and allelic model revealed statistically significant link to CRC risk (OR = 2.13, P = .0035 and OR = 2.22, P = .001). For rs3748067 polymorphism, AG and AA genotype carriers showed 2.30- and 2.45-times enhanced risk for CRC (OR = 2.30, P = .005 and OR = 2.45, P = .031). A statistically significant association was also observed for AG + AA genotype (OR = 2.35, P = .001), over-dominant model (OR = 2.05, P = .014), and allelic model (OR = 2.11, P = .0004).

CONCLUSION: This study highlights that IL-17A rs10484879 and rs3748067 polymorphisms may be associated with CRC development. However, further functional research with larger samples may reveal more statistically significant outcomes.

PMID:36458977 | DOI:10.1177/10732748221143879

Front Big Data. 2022 Nov 15;5:1059088. doi: 10.3389/fdata.2022.1059088. eCollection 2022.

ABSTRACT

INTRODUCTION: A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data.

METHOD: This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction.

RESULTS: The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration.

DISCUSSION: Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset.

PMID:36458283 | PMC:PMC9705957 | DOI:10.3389/fdata.2022.1059088

Front Pharmacol. 2022 Nov 15;13:752314. doi: 10.3389/fphar.2022.752314. eCollection 2022.

ABSTRACT

Clinical trial and individual patient treatment outcomes have produced accumulating evidence that effective primaquine (PQ) treatment of Plasmodium vivax and P. ovale liver stage hypnozoites is associated with genetic variation in the human cytochrome P450 gene, CYP2D6. Successful PQ treatment of individual and population-wide infections by the Plasmodium species that generate these dormant liver stage forms is likely to be necessary to reach elimination of malaria caused by these parasites globally. Optimizing safe and effective PQ treatment will require coordination of efforts between the malaria and pharmacogenomics research communities.

PMID:36457706 | PMC:PMC9705595 | DOI:10.3389/fphar.2022.752314

J Acoust Soc Am. 2022 Nov;152(5):2828. doi: 10.1121/10.0015092.

ABSTRACT

In 2019, the U.S. Food and Drug Administration issued guidance to increase the efficiency of drug development and support precision medicine, including tailoring treatments to those patients who will benefit based on genetic variation even in the absence of a documented mechanism of action. Although multiple advancements have been made in the field of pharmacogenetics (PGx) for other disease conditions, there are no approved PGx guidelines in the treatment of hearing disorders. In studies of noise-induced hearing loss (NIHL), some progress has been made in the last several years associating genomic loci with susceptibility to noise damage. However, the power of such studies is limited as the underlying physiological responses may vary considerably among the patient populations. Here, we have summarized previous animal studies to argue that NIHL subtyping is a promising strategy to increase the granularity of audiological assessments. By coupling this enhanced phenotyping capability with genetic association studies, we suggest that drug efficacy will be better predicted, increasing the likelihood of success in clinical trials when populations are stratified based on genetic variation or designed with multidrug combinations to reach a broader segment of individuals suffering or at risk from NIHL.

PMID:36456290 | DOI:10.1121/10.0015092

Chin Med J (Engl). 2022 Nov 30. doi: 10.1097/CM9.0000000000002419. Online ahead of print.

NO ABSTRACT

PMID:36455204 | DOI:10.1097/CM9.0000000000002419

Pharmacogenomics. 2022 Dec 1. doi: 10.2217/pgs-2022-0148. Online ahead of print.

ABSTRACT

Pharmacogenomics (PGx) testing is commonly utilized to predict a patient's response to medications based on the presence of genetic variants. However, certain conditions have been associated with potentially inaccurate PGx results. The majority of medications are predominantly metabolized in the liver; therefore, in the case of liver transplantation, PGx results may be misinterpreted in the context of drug-metabolizing enzymes. Other instances of ambiguous PGx results have been reported in the literature in conditions such as allogeneic stem cell or bone marrow transplant, chronic lymphocytic leukemia, acute or chronic myeloid leukemia and blood transfusion. In order to prevent potential inaccuracies in PGx testing, Sanford Imagenetics developed an active, interruptive alert to inform providers of the potential for inaccurate PGx results.

PMID:36454237 | DOI:10.2217/pgs-2022-0148

J Int Med Res. 2022 Nov;50(11):3000605221138492. doi: 10.1177/03000605221138492.

ABSTRACT

OBJECTIVE: Although the prevalence of autism spectrum disorder (ASD) is increasing, appropriate diagnosis and prevention strategies are still lacking. This case-control study was designed to explore the association between ASD and the rs1867503 and rs9951150 polymorphisms of the TF and TCF4 genes, respectively.

METHODS: Ninety-six children with ASD and 118 healthy children were recruited and polymerase chain reaction-restriction fragment length polymorphism technique was applied for genotyping.

RESULTS: The frequencies of the mutant allele G were 48% and 44% for the rs1867503 and rs9951150 polymorphisms, respectively. In our analysis, both TF and TCF4 polymorphisms were associated with an increased risk of developing ASD. AG heterozygotes (OR = 3.18), GG mutant homozygotes (OR = 2.62), AG + GG combined genotypes (OR = 2.98), and G mutant alleles of TF rs1867503 (OR = 1.94) were associated with a significantly elevated risk of ASD. Likewise, AG heterozygotes (OR = 2.92), GG mutant homozygotes (OR = 2.36), AG + GG combined genotypes (OR = 2.72), and G minor alleles of TCF4 rs9951150 (OR = 1.92) were associated with a significantly elevated risk of ASD.

CONCLUSIONS: Our results indicate that TF rs1867503 and TCF4 rs9951150 polymorphisms may be strongly associated with the development of ASD in Bangladeshi children.

PMID:36448207 | DOI:10.1177/03000605221138492