Recent Pat Anticancer Drug Discov. 2022 Oct 18. doi: 10.2174/1574892818666221018091415. Online ahead of print.

ABSTRACT

Many cancer patients die due to their treatment failing because of their disease's resistance to chemotherapy and other forms of radiation therapy. Resistance may develop at any stage of therapy, even at the beginning. Several factors influence current therapy, including the type of cancer and the existence of genetic abnormalities. The response to treatment is not always predicted by the existence of a genetic mutation and might vary for various cancer subtypes. It is clear that cancer patients must be assigned a particular treatment or combination of drugs based on prediction models. Preliminary studies utilizing artificial intelligence-based prediction models have shown promising results. Building therapeutically useful models is still difficult despite enormous increases in computer capacity due to the lack of adequate clinically important pharmacogenomics data. Machine learning is the most widely used branch of artificial intelligence. Here, we review the current state in the area of using machine learning to predict treatment response. In addition, examples of machine learning algorithms being employed in clinical practice are offered.

PMID:36263487 | DOI:10.2174/1574892818666221018091415

Front Pharmacol. 2022 Oct 3;13:984383. doi: 10.3389/fphar.2022.984383. eCollection 2022.

ABSTRACT

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

PMID:36263124 | PMC:PMC9573988 | DOI:10.3389/fphar.2022.984383

Nucleic Acids Res. 2022 Oct 19:gkac911. doi: 10.1093/nar/gkac911. Online ahead of print.

ABSTRACT

Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicting drug sensitivity. While most advancements have focused on omics profiles, cancer drug sensitivity scores precalculated by the original sources are often used as-is, without consideration for variabilities between studies. It is well-known that significant inconsistencies exist between the drug sensitivity scores across datasets due to differences in experimental setups and preprocessing methods used to obtain the sensitivity scores. As a result, many studies opt to focus only on a single dataset, leading to underutilization of available data and a limited interpretation of cancer pharmacogenomics analysis. To overcome these caveats, we have developed CREAMMIST (https://creammist.mtms.dev), an integrative database that enables users to obtain an integrative dose-response curve, to capture uncertainty (or high certainty when multiple datasets well align) across five widely used cancer cell-line drug-response datasets. We utilized the Bayesian framework to systematically integrate all available dose-response values across datasets (>14 millions dose-response data points). CREAMMIST provides easy-to-use statistics derived from the integrative dose-response curves for various downstream analyses such as identifying biomarkers, selecting drug concentrations for experiments, and training robust machine learning models.

PMID:36259664 | DOI:10.1093/nar/gkac911

Med J Aust. 2022 Oct 18. doi: 10.5694/mja2.51759. Online ahead of print.

NO ABSTRACT

PMID:36259142 | DOI:10.5694/mja2.51759

Drug Metab Pers Ther. 2022 Oct 19. doi: 10.1515/dmpt-2022-0123. Online ahead of print.

ABSTRACT

OBJECTIVES: Clinical Pharmacogenetics Implementation Consortium (CPIC) is a platform that advances the pharmacogenomics (PGx) practice by developing evidence-based guidelines. The purpose of this study was to analyze the CPIC database for ADME related genes and their corresponding drugs, and evidence level for drug-gene pairs; and to determine the presence of these drug-gene pairs in the highest mortality diseases in the United States.

METHODS: CPIC database was evaluated for drug-gene pairs related to absorption, distribution, metabolism, and excretion (ADME) properties. National Vital Statistics from Centers for Disease Control and Prevention was used to identify the diseases with the highest mortality. CPIC levels are assigned to different drug-gene pairs based on varying levels of evidence as either A, B, C, or D. All drug-gene pairs assigned with A/B, B/C, or C/D mixed levels were excluded from this study. A stepwise exclusion process was followed to determine the prevalence of various ADME drug-gene pairs among phase I/II enzymes or transporters and stratify the drug-gene pairs relevant to different disease conditions most commonly responsible for death in the United States.

RESULTS: From a total of 442 drug-gene pairs in the CPIC database, after exclusion of 86 drug-gene pairs with levels A/B, B/C, or C/D, and 211 non-ADME related genes, 145 ADME related drug-gene pairs resulted. From the 145 ADME related drug-genes pairs, the following were the distribution of levels: Level A: 43 (30%), Level B: 22 (15%), Level C: 59 (41%), Level D: 21 (14%). The most prevalent ADME gene with CPIC level A classification was cytochrome P450 2C9 (CYP2C9) (26%) and overall, the most prevalent ADME gene in the CPIC database was CYP2D6 (30%). The most prevalent diseases related to the CPIC evidence related drugs were cancer and depression.

CONCLUSIONS: We found that there is an abundance of ADME related genes in the CPIC database, including in the high mortality disease states of cancer and depression. There is a differential level of pharmacogenomic evidence in drug-gene pairs enlisted in CPIC where levels A and D having the greatest number of drug-gene pairs. CYP2D6 was the most common ADME gene with CPIC evidence for drug-gene pairs. Pharmacogenomic applications of CPIC evidence can be leveraged to individualize patient therapy and lower adverse effect events.

PMID:36257916 | DOI:10.1515/dmpt-2022-0123

Psychiatry Res. 2022 Oct 8;317:114891. doi: 10.1016/j.psychres.2022.114891. Online ahead of print.

ABSTRACT

QT prolongation is a heart rhythm condition that impacts the lives of many people and when severe can be life-threatening. QT prolongation has been linked to variations in several genes, but it can also arise in the course of treatments with medications such as certain antipsychotics and antidepressants. However, it is unclear whether the risk of medication-induced QT prolongation (MIQTP) depends on specific genetic vulnerability. Here, we review the available literature on the interplay between genetic risk and medication exposure in the context of psychiatric treatment. A review was conducted on the genetic contribution to MIQTP in psychiatric patients. A literature search was conducted on the PubMed platform with 8 papers meeting criteria for review. A total of 3,838 patients from 8 studies meeting criteria for a psychotic or mood disorder were included in this review. All studies found evidence for the genetic contribution to MIQTP. The specific genes identified in these studies included the NOS1AP, ABCB1, KCNH2, SLC22A23, EPB41L4A, LEP, CACNA1C, CERKL, SLCO3A1, BRUNOL4, NRG3, NUBPL, PALLD, NDRG4 and PLN genes. The findings highlight both the importance of monitoring heart parameters in psychiatry and the possible role for genetic profiling to increase the treatment safety.

PMID:36257205 | DOI:10.1016/j.psychres.2022.114891

Pharmacogenet Genomics. 2022 Oct 17. doi: 10.1097/FPC.0000000000000483. Online ahead of print.

ABSTRACT

OBJECTIVE: The study of ABCB1 and CYP3A4/3A5 gene polymorphism genes is promising in terms of their influence on prothrombin time variability, the residual equilibrium concentration of direct oral anticoagulants (DOACs) in patients with atrial fibrillation and the development of new personalized approaches to anticoagulation therapy in these patients. The aim of the study is to evaluate the effect of ABCB1 (rs1045642) C>T; ABCB1 (rs4148738) C>T and CYP3A5 (rs776746) A>G, CYP3A4*22(rs35599367) C>T gene polymorphisms on prothrombin time level and residual equilibrium concentration of rivaroxaban in patients with atrial fibrillation.

METHODS: In total 86 patients (42 men and 44 female), aged 67.24 ± 1.01 years with atrial fibrillation were enrolled in the study. HPLC mass spectrometry analysis was used to determine rivaroxaban residual equilibrium concentration. Prothrombin time data were obtained from patient records.

RESULTS: The residual equilibrium concentration of rivaroxaban in patients with ABCB1 rs4148738 CT genotype is significantly higher than in patients with ABCB1 rs4148738 CC (P = 0.039). The analysis of the combination of genotypes did not find a statistically significant role of combinations of alleles of several polymorphic markers in increasing the risk of hemorrhagic complications when taking rivaroxaban.

CONCLUSION: Patients with ABCB1 rs4148738 CT genotype have a statistically significantly higher residual equilibrium concentration of rivaroxaban in blood than patients with ABCB1 rs4148738 CC genotype, which should be considered when assessing the risk of hemorrhagic complications and risk of drug-drug interactions. Further studies of the effect of rivaroxaban pharmacogenetics on the safety profile and efficacy of therapy are needed.

PMID:36256705 | DOI:10.1097/FPC.0000000000000483

Database (Oxford). 2022 Oct 18;2022:baac092. doi: 10.1093/database/baac092.

ABSTRACT

Pharmacogenomics (PGx) provides information about routine precision medicine, based on the patient's genotype. However, many of the available information about human allele frequencies, and about clinical drug-gene interactions, is based on American and European populations. PharmaKoVariome database was constructed to support genetic testing for safe prescription and drug development. It consolidated and stored 2507 diseases, 11 459 drugs and 61 627 drug-target or druggable genes from public databases. PharmaKoVariome precomputed ethnic-specific abundant variants for approximately 120 M single-nucleotide variants of drug-target or druggable genes. A user can search by gene symbol, drug name, disease and reference SNP ID number (rsID) to statistically analyse the frequency of ethnical variations, such as odds ratio and P-values for related genes. In an example study, we observed five Korean-enriched variants in the CYP2B6 and CYP2D6 genes, one of which (rs1065852) is known to be incapable of metabolizing drug. It is also shown that 4-6% of North and East Asians have risk factors for drugs metabolized by the CYP2D6 gene. Therefore, PharmaKoVariome is a useful database for pharmaceutical or diagnostic companies for developing diagnostic technologies that can be applied in the Asian PGx industry. Database URL: http://www.pharmakovariome.com/.

PMID:36255213 | DOI:10.1093/database/baac092

Technol Cancer Res Treat. 2022 Jan-Dec;21:15330338221123109. doi: 10.1177/15330338221123109.

ABSTRACT

Objectives: The TNFAIP8 gene family and TNFAIP2 gene are inextricably linked to an elevated risk of cancer development. This systemic review and meta-analysis seeks to establish the relationship between TNFAIP8 (rs11064, rs1045241, rs1045242, and rs3813308), TNFAIP8L1 (rs1060555), and TNFAIP2 (rs710100 and rs8126) polymorphisms with the risk of cancer. Methods and Materials: A systematic search of multiple databases from January 2022 to April 2022 was used to identify relevant studies. Odds ratios (ORs) with corresponding 95% CI and p-value were calculated to assess the association. Bonferroni correction was performed to correct p-values. Trial sequential analysis (TSA) and in-silico messenger RNA expression were also performed. Review Manager 5.4 software was used for performing this meta-analysis. Results: This study comprised 6909 cancer patients and 7087 healthy participants from 14 studies. Four genetic models of rs11064 (codominant 2 [COD2]: OR = 2.30, p = 7.83 × 10-5; codominant 3 [COD3]: OR = 2.10, p = .0006; recessive model [RM]: OR = 2.24, p = .0001; AC: OR = 1.47, p = .037), two genetic models of rs1045241 (codominant 1 [COD1]: OR = 1.27, p = .009; overdominant model [ODM]: OR = 1.24, p = .018), four genetic models of rs1045242 (COD1: OR = 1.52, p = .005; dominant model (DM): OR = 1.56, p = .002; OD: OR = 1.48, p = .008; AC: OR = 1.48, p = .002), and three genetic models of rs8126 (COD2: OR = 1.41, p = .0005; COD3: OR = 1.44, p = .0002; RM: OR = 1.43, p = .0001) were statistically linked to cancer risk. Only one genetic model of rs1060555 polymorphism showed a significant protective association with cancer (COD2: OR = 0.80, p = .048). The outcomes of TSA also validated the findings of the meta-analysis. Conclusion: This study summarizes that rs11064, rs1045241, and rs1045242 polymorphisms of TNFAIP8 gene and rs8126 polymorphism of TNFAIP2 gene are significantly linked with the risk of cancer development. This meta-analysis was registered at INPLASY (registration number: INPLASY202270073).

PMID:36254562 | DOI:10.1177/15330338221123109

Ther Drug Monit. 2022 Oct 11. doi: 10.1097/FTD.0000000000001043. Online ahead of print.

ABSTRACT

BACKGROUND: Though atorvastatin (ATV) is well-tolerated, patients may report muscle complaints. These are difficult to predict owing to high inter-individual variability. Such side effects are linked to intramuscular accumulation of ATV. This study aimed to investigate the relative role of transporters expressed in muscle tissue in promoting or limiting drug access to cells. The impact of common single nucleotide polymorphisms (SNPs) in SLCO2B1 coding for OATP2B1 and ABCC1 coding for MRP1 on ATV transport was also evaluated.

METHODS: HEK293 cells were stably transfected with plasmids containing cDNA encoding wild-type or variant SLCO2B1 and/or ABCC1 to generate single and double stable transfectant HEK293 recombinant models overexpressing variant or wild-type OATP2B1 (influx) and/or MRP1 (efflux) proteins. Variant plasmids were generated by site-directed mutagenesis. Expression analyses were performed to validate recombinant models. Accumulation and efflux experiments were performed at different concentrations. ATV was quantified by LC-MS/MS, and kinetic parameters were compared between single and double HEK-transfectants expressing wild-type and variant proteins.

RESULTS: The results confirm the involvement of OATP2B1 and MRP1 in ATV cellular transport as it was demonstrated that intracellular accumulation of ATV was boosted by OATP2B1 overexpression whereas ATV accumulation was decreased by MRP1 overexpression. In double-transfectants, it was observed that increased ATV intracellular accumulation driven by OATP2B1 influx was partially counteracted by MRP1 efflux. The c.935G>A SNP in SLCO2B1 was associated with decreased ATV OATP2B1-mediated influx, whereas the c.2012G>T SNP in ABCC1 appeared to increase MRP1 efflux activity against ATV.

CONCLUSIONS: Intracellular ATV accumulation is regulated by OATP2B1 and MRP1 transporters, whose functionality is modulated by natural genetic variants. This is significant, as it may play a role in ATV muscle side-effect susceptibility.

PMID:36253893 | DOI:10.1097/FTD.0000000000001043

Hum Genomics. 2022 Oct 17;16(1):45. doi: 10.1186/s40246-022-00418-8.

ABSTRACT

BACKGROUND: It has been suggested that bipolar disorder (BD) is associated with clinical and biological features of accelerated aging. In our previous studies, we showed that long-term lithium treatment was correlated with longer leukocyte telomere length (LTL) in BD patients. A recent study explored the role of TL in BD using patients-derived lymphoblastoid cell lines (LCLs), showing that baseline TL was shorter in BD compared to controls and that lithium in vitro increased TL but only in BD. Here, we used the same cell system (LCLs) to explore if a 7-day treatment protocol with lithium chloride (LiCl) 1 mM was able to highlight differences in TL between BD patients clinically responders (Li-R; n = 15) or non-responders (Li-NR; n = 15) to lithium, and if BD differed from non-psychiatric controls (HC; n = 15).

RESULTS: There was no difference in TL between BD patients and HC. Moreover, LiCl did not influence TL in the overall sample, and there was no difference between diagnostic or clinical response groups. Likewise, LiCl did not affect TL in neural precursor cells from healthy donors.

CONCLUSIONS: Our findings suggest that a 7-day lithium treatment protocol and the use of LCLs might not represent a suitable approach to deepen our understanding on the role of altered telomere dynamics in BD as previously suggested by studies in vivo.

PMID:36253798 | DOI:10.1186/s40246-022-00418-8

J Sci Med Sport. 2022 Oct 12:S1440-2440(22)00438-8. doi: 10.1016/j.jsams.2022.10.004. Online ahead of print.

ABSTRACT

OBJECTIVES: To determine the number of training days lost due to COVID-19 and vaccination against COVID-19 in elite athletes.

DESIGN: Retrospective cohort study.

METHODS: The questionnaire on the impact of vaccination and COVID-19 on training plans was filled out by 1073 elite Polish athletes who underwent routine medical screening between September and December 2021.

RESULTS: COVID-19 was diagnosed in 421 subjects (39 %), of whom 26 % were asymptomatic. On the 10-point scale, <1 % of athletes had perceived severity of the disease above 8, whereas for 64 % it was 4 or below. Vaccination against COVID-19 was administered in 820 athletes (76 %), and adverse events were observed more frequently after the first dose than the second (69 % vs. 47 %). Influence on training (modified or lost) was declared by 369 of 421 (88 %) COVID-19 athletes, and by 226 of 820 vaccinated athletes (28 %). During the observation period, the average number of lost training days was 8.1 for COVID-19 and 2.6 for vaccination (p < 0.001). The cumulative number of person-days lost due to COVID-19 was 1041 versus 295 after vaccination thus, the average loss ratio was 1041/1073 = 0.97 vs. 295/820 = 0.36, respectively, p < 0.01.

CONCLUSIONS: Athletes have a considerable loss of training days due to COVID-19. Vaccination against COVID-19 causes significantly smaller and predictable loss. This supports the inclusion of vaccination into prevention policies for athletes whenever they are available.

PMID:36253224 | DOI:10.1016/j.jsams.2022.10.004

Brief Bioinform. 2022 Oct 17:bbac433. doi: 10.1093/bib/bbac433. Online ahead of print.

ABSTRACT

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

PMID:36252807 | DOI:10.1093/bib/bbac433

Front Pharmacol. 2022 Sep 30;13:981874. doi: 10.3389/fphar.2022.981874. eCollection 2022.

ABSTRACT

Preparations from Hippophaë rhamnoides L. (sea buckthorn) have been traditionally used in the treatment of skin and digestive disorders, such as gastritis, gastric and duodenal ulcers, uterine erosions, as well as oral, rectal, and vaginal mucositis, in particular in the Himalayan and Eurasian regions. An influence of an aqueous extract from the fruits of H. rhamnoides (HR) on leakage of lipopolysaccharide (LPS) from Escherichia coli through gut epithelium developed from the human colorectal adenocarcinoma (Caco-2) monolayer in vitro and glucose transporter 2 (GLUT2) translocation were the principal objectives of the study. Additionally, the effect of HR on the production of pro- and anti-inflammatory cytokines (interleukins: IL-8, IL-1β, IL-10, IL-6; tumor necrosis factor: TNF-α) by the Caco-2 cell line, human neutrophils (PMN), and peripheral blood mononuclear cells (PBMC) was evaluated. The concentration of LPS on the apical and basolateral sides of the Caco-2 monolayer was evaluated with a Limulus Amebocyte Lysate (LAL) assay. GLUT2 translocation was evaluated using an immunostaining assay, whereas secretion of cytokines by cell cultures was established with an enzyme-linked immunosorbent (ELISA) assay. HR (500 μg/ml) significantly inhibited LPS leakage through epithelial monolayer in vitro in comparison with non-treated control. The treatment of Caco-2 cells with HR (50-100 μg/ml) showed GLUT2 expression similar to the non-treated control. HR decreased the secretion of most pro-inflammatory cytokines in all tested models. HR might prevent low-grade chronic inflammation caused by metabolic endotoxemia through the prevention of the absorption of LPS and decrease of chemotactic factors released by immune and epithelial cells, which support its use in metabolic disorders in traditional medicine.

PMID:36249809 | PMC:PMC9561609 | DOI:10.3389/fphar.2022.981874

Front Genet. 2022 Sep 29;13:940661. doi: 10.3389/fgene.2022.940661. eCollection 2022.

ABSTRACT

The use of antiretrovirals (ARVs) as oral, topical, or long-acting pre-exposure prophylaxis (PrEP) has emerged as a promising strategy for HIV prevention. Clinical trials testing Truvada® [tenofovir disoproxil fumarate (TDF)/tenofovir (TFV) and emtricitabine (FTC)] as oral or topical PrEP in African women showed mixed results in preventing HIV infections. Since oral and topical PrEP effectiveness is dependent on adequate drug delivery and availability to sites of HIV infection such as the blood and female genital tract (FGT); host biological factors such as drug transporters have been implicated as key regulators of PrEP. Drug transporter expression levels and function have been identified as critical determinants of PrEP efficacy by regulating PrEP pharmacokinetics across various cells and tissues of the blood, renal tissues, FGT mucosal tissues and other immune cells targeted by HIV. In addition, biological factors such as genetic polymorphisms and genital inflammation also influence drug transporter expression levels and functionality. In this review, drug transporters and biological factors modulating drug transporter disposition are used to explain discrepancies observed in PrEP clinical trials. This review also provides insight at a pharmacological level of how these factors further increase the susceptibility of the FGT to HIV infections, subsequently contributing to ineffective PrEP interventions in African women.

PMID:36246609 | PMC:PMC9557974 | DOI:10.3389/fgene.2022.940661

Biol Psychiatry. 2022 Aug 5:S0006-3223(22)01449-4. doi: 10.1016/j.biopsych.2022.07.014. Online ahead of print.

ABSTRACT

BACKGROUND: Treatment-resistant schizophrenia affects approximately 30% of individuals with the disorder. Clozapine is the medication of choice in treatment-resistant schizophrenia, but optimizing administration and dose titration is complex. The identification of factors influencing clozapine prescription and response, including genetics, is of interest in a precision psychiatry framework.

METHODS: We used linear regression models accounting for demographic, pharmacological, and clinical covariates to determine whether a polygenic risk score (PRS) for schizophrenia would be associated with the highest dose recorded during clozapine treatment. Analyses were performed across 2 independent multiancestry samples of individuals from a UK patient monitoring system, CLOZUK2 (n = 3133) and CLOZUK3 (n = 909), and a European sample from a Norwegian therapeutic drug monitoring service (n = 417). In a secondary analysis merging both UK cohorts, logistic regression models were used to estimate the relationship between schizophrenia PRSs and clozapine doses classified as low, standard, or high.

RESULTS: After controlling for relevant covariates, the schizophrenia PRS was correlated with the highest clozapine dose on record for each individual across all samples: CLOZUK2 (β = 12.22, SE = 3.78, p = .001), CLOZUK3 (β = 12.73, SE = 5.99, p = .034), and the Norwegian cohort (β = 46.45, SE = 18.83, p = .014). In a secondary analysis, the schizophrenia PRS was associated with taking clozapine doses >600 mg/day (odds ratio = 1.279, p = .006).

CONCLUSIONS: The schizophrenia PRS was associated with the highest clozapine dose prescribed for an individual in records from 3 independent samples, suggesting that the genetic liability for schizophrenia might index factors associated with therapeutic decisions in cohorts of patients with treatment-resistant schizophrenia.

PMID:36244804 | DOI:10.1016/j.biopsych.2022.07.014

Metabolism. 2022 Oct 13:155333. doi: 10.1016/j.metabol.2022.155333. Online ahead of print.

NO ABSTRACT

PMID:36244415 | DOI:10.1016/j.metabol.2022.155333

Pharmacogenomics J. 2022 Oct 15. doi: 10.1038/s41397-022-00290-8. Online ahead of print.

ABSTRACT

Psoriatic arthritis (PsA) is a heterogeneous chronic musculoskeletal disease, affecting up to 30% of people with psoriasis. Research into PsA pathogenesis has led to the development of targeted therapies, including Tumor Necrosis Factor inhibitors (TNF-i). Good response is only achieved by ~60% of patients leading to 'trial and error' drug management approaches, adverse reactions and increasing healthcare costs. Robust and well-validated biomarker identification, and subsequent development of sensitive and specific assays, would facilitate the implementation of a stratified approach into clinical care. This review will summarise potential genetic biomarkers for TNF-i (adalimumab, etanercept and infliximab) response that have been reported to date. It will also comment upon the importance of managing clinical confounders when understanding drug response prediction. Variants in multiple gene regions including TNF-A, FCGR2A, TNFAIP3, TNFR1/TNFR1A/TNFRSF1A, TRAIL-R1/TNFRSF10A, FCGR3A have been reported to correlate with TNF-i response at various levels of statistical significance in patients with PsA. However, results were often from heterogenous and underpowered cohorts and none are currently implemented into clinical practice. External validation of genetic biomarkers in large, well-documented cohorts is required, and assessment of the predictive value of combining multiple genetic biomarkers with clinical measures is essential to clinically embed pharmacogenomics into PsA drug management.

PMID:36243888 | DOI:10.1038/s41397-022-00290-8

J Am Pharm Assoc (2003). 2022 Sep 9:S1544-3191(22)00303-X. doi: 10.1016/j.japh.2022.09.002. Online ahead of print.

ABSTRACT

BACKGROUND: Pharmacogenomics (PGx) is an emerging field. Many drug-gene interactions are known but not yet routinely addressed in clinical practice. Therefore, there is a significant gap in care, necessitating development of implementation strategies.

OBJECTIVE: The objective of the study was to assess the impact of implementing a PGx practice model which incorporates comprehensive pharmacogenomic risk evaluation, testing and medication optimization administered by 7 PGx-certified ambulatory care pharmacists embedded across 30 primary care clinic sites.

METHODS: Pharmacogenomic services were implemented in 30 primary care clinics within the Cincinnati, Ohio area. Patients are identified for pharmacogenomic testing using a clinical decision support tool (CDST) that is fully integrated in the electronic medical record (EMR) or by provider designation (e.g., psychotropic drug failure). Pharmacogenomic testing is performed via buccal swab using standardized clinic processes. Discrete data results are returned directly into the EMR/CDST for review by PGx-certified ambulatory care pharmacists. Recommendations and prescriptive changes are then discussed and implemented as a collaborative effort between pharmacist, primary care provider, specialists, and patient.

RESULTS: A total of 422 unique interactions were assessed by the embedded ambulatory care PGx pharmacists (N = 7) during this interim analysis. About half (213) were pharmacogenomic interactions, and of these, 124 were actionable. When an intervention was actionable, 82% of the time a change in medication was recommended. The underlying reasons for recommending therapy alterations were most commonly ineffective therapy (43%), adverse drug reaction prevented (34%), or adverse drug reaction observed (13%).

CONCLUSION: Variations in drug metabolism, response, and tolerability can negatively impact patient outcomes across many disease states and treatment specialties. Incorporation of pharmacogenomic testing with accessible clinical decision support into the team-based care model allows for a truly comprehensive review and optimization of medications. Our initial analysis suggests that comprehensive PGx testing should be considered to enhance medication safety and efficacy in at-risk patients.

PMID:36243653 | DOI:10.1016/j.japh.2022.09.002

Schizophr Res. 2022 Oct 12;250:1-9. doi: 10.1016/j.schres.2022.09.009. Online ahead of print.

ABSTRACT

INTRODUCTION: Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.

METHODS: We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.

RESULTS: Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).

IMPLICATIONS: Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.

PMID:36242784 | DOI:10.1016/j.schres.2022.09.009