Star allele search: a pharmacogenetic annotation database and user-friendly search tool of publicly available 1000 Genomes Project biospecimens.

Here we describe a new public pharmacogenetic (PGx) annotation database of a large (n = 3,202) and diverse biospecimen collection of 1000 Genomes Project cell lines and DNAs. The database is searchable with a user friendly, web-based tool ( www.coriell.org/StarAllele/Search ). This resource leverages existing whole genome sequencing data and PharmVar annotations to characterize *alleles for each biospecimen in the collection. This new tool is designed to facilitate in vitro functional characterization of *allele haplotypes and diplotypes as well as support clinical PGx assay development, validation, and implementation.

Attitudes, knowledge, and risk perceptions of patients who received elective genomic testing as a clinical service.

PURPOSE: Elective genomic testing (EGT) is increasingly available clinically. Limited real-world evidence exists about attitudes and knowledge of EGT recipients. METHODS: After web-based education, patients who enrolled in an EGT program at a rural nonprofit health care system completed a survey that assessed attitudes, knowledge, and risk perceptions. RESULTS: From August 2020 to April 2022, 5920 patients completed the survey and received testing. Patients most frequently cited interest in learning their personal disease risks as their primary motivation. Patients most often expected results to guide medication management (74.0%), prevent future disease (70.4%), and provide information about risks to offspring (65.4%). Patients were "very concerned" most frequently about the privacy of genetic information (19.8%) and how well testing predicted disease risks (18.0%). On average, patients answered 6.7 of 11 knowledge items correctly (61.3%). They more often rated their risks for colon and breast cancers as lower rather than higher than the average person but more often rated their risk for a heart attack as higher rather than lower than the average person (all P < .001). CONCLUSION: Patients pursued EGT because of the utility expectations but often misunderstood the test's capabilities.

Pharmacogenomic predictor of long-term residual chemotherapy-induced peripheral neuropathy in ovarian cancer survivors: A substudy of the GINECO Vivrovaire study.

BACKGROUND: Chemotherapy (CT) remains a backbone treatment of epithelial ovarian cancer (EOC) inducing persistent peripheral neuropathy (CIPN). Using a dedicated patient-reported outcome tool, this study investigated persistent CIPN and its pharmacogenetic predictors in a cohort of long-term EOC survivors. METHODS: Vivrovaire was a French multicenter cohort of patients with EOC free of disease 3 years after CT completion. Persistent CIPN was assessed using the FACT/GOG-Ntx4 self-questionnaire. The association of homozygous (hom) or heterozygous (het) single nucleotide polymorphisms (SNPs) in selected genes was evaluated. RESULTS: 130 patients were included with a median time from CT completion of 63 [35-180] months. The median CIPN score was 37 [18-44], with 35 (26.9%) patients reporting severe CIPN (<33). SNPs were identified as follows: CYP2C8 [hom, n = 32 (24.6%)/het, n = 99, (76.2%)]; CYP3A4 [hom, n = 0 (0%)/het, n = 8 (6.2%)], ERCC1 [hom, n = 21 (16.2%)/het, n = 57 (43.8%)], and XPC [hom, n = 45 (34.6%)/het, n = 66 (50.8%)]. In univariate analysis, the identification of ≥1 hom SNP was associated with a lower CIPN score (continuous variable; p = 0.045). Patients harboring hom or het CYP2C8_rs1934951 SNP reported more likely severe CIPN (threshold <33) score (OR 2.482; 95% CI [1.126-5.47], p = 0.024). In the multivariate analyses, age, interval from CT completion, type and number of CT courses were not significantly associated with CIPN score (OR 5.165, 95% CI [0.478-55.83], p = 0.176). CONCLUSIONS: Persistent CIPN is common among ovarian cancer long-term survivors. CYP2C8_rs1934951 SNP may be associated with severe residual CIPN in EOC survivors. More studies are warranted to identify predictive factors of CIPN.

Pharmacogenomic testing and prescribing patterns for patients with cancer in a large national precision medicine cohort.

Population databases could help patients with cancer and providers better understand current pharmacogenomic prescribing and testing practices. This retrospective observational study analysed patients with cancer, drugs with pharmacogenomic evidence and related genetic testing in the National Institutes of Health All of Us database. Most patients with cancer (19 633 (88.3%) vs 2590 (11.7%)) received ≥1 drug and 36 (0.2%) received genetic testing, with a significant association between receiving ≥1 drug and age group (p<0.001), but not sex (p=0.612), race (p=0.232) or ethnicity (p=0.971). Drugs with pharmacogenomic evidence-but not genetic testing-were common for patients with cancer, reflecting key gaps preventing precision medicine from becoming standard of care.

Crosstalk between 5-methylcytosine and N6-methyladenosine machinery defines disease progression, therapeutic response and pharmacogenomic landscape in hepatocellular carcinoma.

BACKGROUND: Accumulated evidence highlights the significance of the crosstalk between epigenetic and epitranscriptomic mechanisms, notably 5-methylcytosine (5mC) and N6-methyladenosine (m6A). Herein, we conducted a widespread analysis regarding the crosstalk between 5mC and m6A regulators in hepatocellular carcinoma (HCC). METHODS: Pan-cancer genomic analysis of the crosstalk between 5mC and m6A regulators was presented at transcriptomic, genomic, epigenetic, and other multi-omics levels. Hub 5mC and m6A regulators were summarized to define an epigenetic and epitranscriptomic module eigengene (EME), which reflected both the pre- and post-transcriptional modifications. RESULTS: 5mC and m6A regulators interacted with one another at the multi-omic levels across pan-cancer, including HCC. The EME scoring system enabled to greatly optimize risk stratification and accurately predict HCC patients' clinical outcomes and progression. Additionally, the EME accurately predicted the responses to mainstream therapies (TACE and sorafenib) and immunotherapy as well as hyper-progression. In vitro, 5mC and m6A regulators cooperatively weakened apoptosis and facilitated proliferation, DNA damage repair, G2/M arrest, migration, invasion and epithelial-to-mesenchymal transition (EMT) in HCC cells. The EME scoring system was remarkably linked to potential extrinsic and intrinsic immune escape mechanisms, and the high EME might contribute to a reduced copy number gain/loss frequency. Finally, we determined potential therapeutic compounds and druggable targets (TUBB1 and P2RY4) for HCC patients with high EME. CONCLUSIONS: Our findings suggest that HCC may result from a unique synergistic combination of 5mC-epigenetic mechanism mixed with m6A-epitranscriptomic mechanism, and their crosstalk defines therapeutic response and pharmacogenomic landscape.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling.

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

Pharmacogenomics implementation in cardiovascular disease in a highly diverse population: initial findings and lessons learned from a pilot study in United Arab Emirates.

BACKGROUND: Pharmacogenomic (PGx) testing has proved its utility and cost-effectiveness for some commonly prescribed cardiovascular disease (CVD) medications. In addition, PGx-guided dosing guidelines are now available for multiple CVD drugs, including clopidogrel, warfarin, and statins. The United Arab Emirates (UAE) population is diverse and multiethnic, with over 150 nationalities residing in the country. PGx-testing is not part of the standard of care in most global healthcare settings, including the UAE healthcare system. The first pharmacogenomic implementation clinical study in CVD has been approved recently, but multiple considerations needed evaluation before commencing. The current report appraises the PGx-clinical implementation procedure and the potential benefits of pursuing PGx-implementation initiatives in the UAE with global implications. METHODS: Patients prescribed one or more of the following drugs: clopidogrel, atorvastatin, rosuvastatin, and warfarin, were recruited. Genotyping selected genetic variants at genes interacting with the study drugs was performed by real-time PCR. RESULTS: For the current pilot study, 160 patients were recruited. The genotypes and inferred haplotypes, diplotypes, and predicted phenotypes revealed that 11.9% of the participants were poor CYP2C19 metabolizers, 35% intermediate metabolizers, 28.1% normal metabolizers, and 25% rapid or ultrarapid metabolizers. Notably, 46.9% of our cohort should receive a recommendation to avoid using clopidogrel or consider an alternative medication. Regarding warfarin, only 20% of the participants exhibited reference alleles at VKORC1-1639G > A, CYP2C9*2, and CYP2C9*3, leaving 80% with alternative genotypes at any of the two genes that can be integrated into the warfarin dosing algorithms and can be used whenever the patient receives a warfarin prescription. For statins, 31.5% of patients carried at least one allele at the genotyped SLCO1B1 variant (rs4149056), increasing their risk of developing myopathy. 96% of our cohort received at least one PGx-generated clinical recommendation for the studied drugs. CONCLUSION: The current pilot analysis verified the feasibility of PGx-testing and the unforeseen high frequencies of patients currently treated with suboptimal drug regimens, which may potentially benefit from PGx testing.

Effect of pharmacogenomics testing guiding on clinical outcomes in major depressive disorder: a systematic review and meta-analysis of RCT.

BACKGROUND: Pharmacogenomic testing guided treatment have been developed to guide drug selection or conversion in major depressive disorder patients. Whether patients benefit from pharmacogenetic testing remains unclear. We aim to evaluates the effect of pharmacogenomic testing guiding on clinical outcomes of major depressive disorder. METHODS: Pubmed, Embase, and Cochrane Library of Clinical Trials were searched from inception until August 2022. Key terms included pharmacogenomic and antidepressive. Odds ratios (RR) with 95% confidence intervals (95%CIs) were calculated using fixed-effects model for low or moderate heterogeneity or random-effects model for high heterogeneity. RESULTS: Eleven studies (5347 patients) were included. Compared with usual group, pharmacogenomic testing guided group was associated with an increased response rate at week 8 (OR 1.32, 95%CI 1.15-1.53, 8 studies, 4328 participants) and week 12 (OR 1.36, 95%CI 1.15-1.62, 4 studies, 2814 participants). Similarly, guided group was associated with an increased rate of remission at week 8 (OR 1.58, 95%CI 1.31-1.92, 8 studies, 3971 participants) and week 12 (OR 2.23, 95%CI 1.23-4.04, 5 studies, 2664 participants). However, no significant differences were found between the two groups in response rate at week 4 (OR 1.12, 95%CI 0.89-1.41, 2 studies, 2261 participants) and week 24 (OR 1.16, 95%CI 0.96-1.41, 2 studies, 2252 participants), and remission rate at week 4 (OR 1.26, 95%CI 0.93-1.72, 2 studies, 2261 participants) and week 24 (OR 1.06, 95%CI 0.83-1.34, 2 studies, 2252 participants). Medication congruence in 30 days was significantly reduced in the pharmacogenomic guided group compared with the usual care group (OR 2.07, 95%CI 1.69-2.54, 3 studies, 2862 participants). We found significant differences between subgroups of target population in response and remission rate. CONCLUSION: Patients with major depressive disorder may benefit from pharmacogenomic testing guided treatment by achieving target response and remission rates more quickly.

The role of CYP2C9*2, CYP2C9*3 and VKORC1-1639 variants on the susceptibility of upper gastrointestinal bleeding: A full case-control study.

Purpose: To investigate whether interindividual variability in the CYP2C9 (*2 and *3 alleles) and VKORC1 (rs9923231) genes is associated with increased risk of upper gastrointestinal bleeding (UGIB) in users of non-steroidal anti-inflammatory drugs (NSAIDs) or low-dose aspirin (LDA). Methods: A full case-control study including 200 cases of patients diagnosed with UGIB and 706 controls was conducted in a Brazilian hospital complex. To perform an analysis of NSAIDs dose-effect, the defined daily dose (DDD) for NSAIDs was calculated in the 7-day etiologic window preceding the data index. Three categories of DDD, considering the genotypes of the genetic variants, were established: non-users of NSAIDs (DDD = 0), DDD ≤0.5, and DDD >0.5. Genetic variants and LDA or NSAIDs use synergism was estimated through Synergism Index (SI) and Relative Excess Risk Due To Interaction (RERI). Results: For DDDs of NSAIDs upward of 0.50, a risk of UGIB was identified in carriers of the *3 allele (OR: 15,650, 95% CI: 1.41-174.10) and in carriers of the variant homozygous genotype (TT) of rs9923231 (OR: 38,850, 95% CI: 2.70-556.00). In LDA users, the risk of UGIB was observed to be similar between carriers of the wild type homozygous genotype and carriers of the variant alleles for the CYP2C9 and VKORC1 genes. No synergism was identified. Conclusion: Our findings suggest an increased risk of UGIB in carriers of the variant allele of rs9923231 and in carriers of the *3 allele associated with doses of NSAIDs greater than 0.5. Hence, the assessment of these variants might reduce the incidence of NSAIDs-related UGIB and contribute to the safety of the NSAIDs user.

Pharmacogenetics in critical care: association between CYP3A5 rs776746 A/G genotype and acetaminophen response in sepsis and septic shock.

BACKGROUND: Pharmacogenetics could represent a further resource to understand the interindividual heterogeneity of response of the host to sepsis and to provide a personalized approach to the critical care patient. METHODS: Secondary analysis of data from the prospective observational study NCT02750163, in 50 adult septic and septic shock patients treated with Acetaminophen (ACT) for pyrexia. We investigated the presence of two polymorphisms, located respectively in the genes UGT1A1 and CYP3A5, that encode for proteins related to the hepatic metabolism of ACT. The main dependent variables explored were plasmatic concentration of ACT, body temperature and hepatic parameters. RESULTS: 8% of the patients carried CYP3A5 rs776746 A/G genotypes and showed significantly higher plasma levels of ACT than GG wild type patients, and than patients with UGT1A1 rs8330 C/G genotypes. CONCLUSIONS: Identifying specific genotypes of response to ACT may be helpful to guide a more personalized titration of therapy in sepsis and septic shock. CYP3A5 might be a good biomarker for ACT metabolism; however further studies are needed to confirm this result. TRIAL REGISTRATION: NCT02750163.

Personalising Social Ills: An Analysis of Race-based Genomics and Personalised Medicine.

The mapping and sequencing of the human genome at the turn of the new millennium marks a pivotal reassessment of genomic science in its potential to replace traditional "one-size-fits-all" medicine with a personalised approach. The use of racial proxies in the development of pharmacogenomic products risks conflating genetics with race under the guise of alleviating health disparities. This article argues that the current genomic approaches to realising personalised medicine do not deliver on the promise for optimised health for all and may result in irreversible harm, including psychological, social and medical harm, to racial minority groups. In light of recent epigenetic findings, the article provides a reconceptualisation of the genome and race, which is necessary to understand enduring racial disparities and the cumulative effects of racial discrimination. It then addresses the need for regulatory oversight of the approval of race-based pharmacogenomic products.

Temporal offset association between the number of irinotecan-related adverse reactions and pharmacogenomic studies: A cross-correlation analysis.

Objectives: Studies have proved that UGT1A1 (*6, *28 and *93) gene polymorphism was closely related to the side effects of irinotecan. This study intends to perform a correlation analysis on the relationship between pharmacogenomic studies and ADRs based on time series. Methods: The ADRs related to irinotecan were derived through the FAERS; searched all pharmacogenomic studies in PubMed and Web of Science; then analyzed the sequence of correlation coefficients between total ADRs, fatal ADRs and pharmacogenomic studies under different time offset. Results: There is a positive correlation between the number of total ADRs and pharmacogenomic studies, of which the maximum correlation coefficient was 0.78 (95 % CI: 0.58-0.90), with a lag of 1 year. There is also a positive correlation between the number of fatal ADRs and pharmacogenomic studies, with the maximum correlation coefficient of 0.87 (95 % CI: 0.73-0.94) and a offset of - 4 years. Conclusion: It was found that both the total ADRs and fatal ADRs were significantly positively correlated with change trend of published pharmacogenomic literatures, which confirmed the role of pharmacogenomic research in promoting the safe use of irinotecan, and have a faster response time in reducing fatal ADRs during clinical application.

Therapeutic drug monitoring in psychiatry.

In medicine, there are two main methods of improving the healthcare provided: perfecting (optimizing) the existing ones and seeking new treatment procedures. Despite of tremendous development in the central nervous system research, current treatment of severe mental illnesses, such as schizophrenia and depressive disorder, is suboptimal. Nowadays, optimization of treatment in psychiatry includes therapeutic drug monitoring (TDM) and pharmacogenomic testing, which examines genetic variation involved in medication metabolism and drug action. The TDM enables to determine drug concentrations in blood and adjust the dose accordingly if clinical effects correlate better with drug blood levels than drug doses. The first international guidelines for TDM in neuropsychopharmacology were published in 2004 and regularly updated. The recent update provides therapeutic reference ranges for a majority of commonly prescribed psychiatric medications and gives example of patients regularly treated in clinical practice profiting from TDM (using antipsychotics and changing their smoking habits). TDM in psychiatry is an underused tool, given its ability to optimize treatment, as well as to improve treatment effectiveness.

Clinical pharmacogenomic testing and reporting: A technical standard of the American College of Medical Genetics and Genomics (ACMG).

Pharmacogenomic testing interrogates germline sequence variants implicated in interindividual drug response variability to infer a drug response phenotype and to guide medication management for certain drugs. Specifically, discrete aspects of pharmacokinetics, such as drug metabolism, and pharmacodynamics, as well as drug sensitivity, can be predicted by genes that code for proteins involved in these pathways. Pharmacogenomics is unique and differs from inherited disease genetics because the drug response phenotype can be drug-dependent and is often unrecognized until an unexpected drug reaction occurs or a patient fails to respond to a medication. Genes and variants with sufficiently high levels of evidence and consensus may be included in a clinical pharmacogenomic test; however, result interpretation and phenotype prediction can be challenging for some genes and medications. This document provides a resource for laboratories to develop and implement clinical pharmacogenomic testing by summarizing publicly available resources and detailing best practices for pharmacogenomic nomenclature, testing, result interpretation, and reporting.

Improved provider preparedness through an 8-part genetics and genomic education program.

PURPOSE: Large-scale genetics education appropriate for general practice providers is a growing priority. We describe the content and impact of a mandatory system-wide program implemented at Sanford Health. METHODS: The Imagenetics Initiative at Sanford Health developed a 2-year genetics education program with quarterly web-based modules that were mandatory for all physicians and advanced practice providers. Scores of 0 to 5 were calculated for each module on the basis of the number of objectives that the participants reported as fulfilled. In addition, the participants completed surveys before starting and after finishing the education program, which included a 7-item measure scored 7 to 28 on the perceived preparedness to practice genetics. RESULTS: Between 2252 and 2822 Sanford Health employees completed each of the 8 quarterly education modules. The ratings were highest for the module about using genomics to improve patient management (mean score = 4.3) and lowest for the module about different types of genetic tests and specialists. The mean perceived preparedness scores increased from 15.7 at pre-education to 19.1 at post-education (P < .001). CONCLUSION: Web-based genetics education was highly effective in increasing health care providers' confidence about using genetics. Both comfort with personal knowledge and confidence regarding access to the system's genomic medicine experts increased significantly. The results demonstrate how scalable approaches can improve provider preparedness.

Fetal pharmacogenomics: A promising addition to complex neonatal care.

OBJECTIVE: Pharmacogenomics (PGx) characterizes genetic variation in medication response. 85-95% of the population carries actionable PGx variants. No prior studies have demonstrated the application and feasibility of PGx in prenatal testing. We assessed parental desire for PGx findings from fetal exome sequencing (ES), evaluated PGx variants, and reviewed implications for medically complex neonates. METHODS: A prospective cohort undergoing ES for nonimmune hydrops fetalis were offered PGx results as a secondary finding. Seven pharmacogenes with Level A evidence, defined by Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, were tested and reported to patients and referring providers. Medication administration records were reviewed. RESULTS: Most participants (36/40, 90%) desired PGx testing. 32/36 (89%) had potentially actionable PGx diplotypes in six genes: CYP2C19 (20/36, 56%), CYP2C9 (16/36, 44%), CYP2D6 (10/36, 28%), SLCO1B1 (13/36, 36%), TPMT (6/36, 17%), UGT1A1 (4/36, 11%). 12/13 (92%) live births had PGx variants. Neonatal chart review indicated that three medications with CPIC Level A evidence were administered to four neonates. None of the patients received a medication that aligned with an actionable pharmacogenetic variant as defined by Level A CPIC guidance. CONCLUSION: Most participants opted to receive PGx results. 89% had actionable variants, consistent with population estimates. Obtaining fetal PGx data is feasible for medically complex neonates. Further studies are needed for broad clinical application of PGx in fetuses with major congenital abnormalities. Our study demonstrates the potential of PGx as useful preemptive clinical information that could be obtained at the time of fetal exome sequencing for other indications. CLINICALTRIALS: gov Registration: NCT03911531.

A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants.

BACKGROUND: The field of pharmacogenomics focuses on the way a person's genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient's genome, hence rendering its incorporation into clinical routine practice a realistic possibility. METHODS: This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation. RESULTS: All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to "Decreased function," 41 variants as "No" function proteins, and 1 variant in "Increased function." CONCLUSION: Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine.

The Role of Pharmacogenomics in Opioid Prescribing.

OPINION STATEMENT: Pharmacogenomics is increasingly important to guide objective, safe, and effective individualised prescribing. Personalised prescribing has revolutionised treatments in the past decade, allowing clinicians to maximise drug efficacy and minimise adverse effects based on a person's genetic profile. Opioids, the gold standard for cancer pain relief, are among the commonest medications prescribed in palliative care practice. This narrative review examines the literature surrounding opioid pharmacogenomics and its applicability to the palliative care cancer population. There is currently limited intersection between the fields of palliative care and pharmacogenomics, but growing evidence presents a need to build linkages between the two disciplines. Pharmacogenomic evidence guiding opioid prescribing is currently available for codeine and tramadol, which relates to CYP2D6 gene variants. However, these medications are prescribed less commonly for pain in palliative care. Research is accelerating with other opioids, where oxycodone (CYP2D6) and methadone (CYP2B6, ABCB1) already have moderate evidence of an association in terms of drug metabolism and downstream analgesic response and side effects. OPRM1 and COMT are receiving increasing attention and have implications for all opioids, with changes in opioid dosage requirements observed but they have not yet been studied widely enough to be considered clinically actionable. Current evidence indicates that incorporation of pharmacogenomic testing into opioid prescribing practice should focus on the CYP2D6 gene and its actionable variants. Although opioid pharmacogenomic tests are not widely used in clinical practice, the progressively reducing costs and rapid turnover means greater accessibility and affordability to patients, and thus, clinicians will be increasingly asked to provide guidance in this area. The upsurge in pharmacogenomic research will likely discover more actionable gene variants to expand international guidelines to impact opioid prescribing. This rapidly expanding area requires consideration and monitoring by clinicians in order for key findings with clinical implications to be accessible, meaningfully interpretable and communicated.

Pharmacogenomic biomarkers as source of evidence of the effectiveness and safety of antidepressant therapy.

OBJECTIVE: The main goal of this work was to identify, describe, characterize, and classify the scientific evidence regarding the use of pharmacogenomic biomarkers in antidepressant treatment. METHODS: The work was developed in two phases: i) a search for pharmacogenomic biomarkers in summaries of antidepressant drugs with marketing authorization in Portugal; and ii) a systematic literature review based on the data obtained in the first phase, with the main objective of finding international literature that could describe and characterize previously reported biomarkers and identify other relevant biomarkers. Finally, the levels of evidence and recommendation grades were classified. RESULTS: Among the 26 drugs with marketing authorization in Portugal, only 16 had pharmacogenomic information. The most widely studied pharmacogenomic biomarker was CYP2D6. These results were mostly supported by the systematic literature review, which yielded 103 papers, 63 of which were ultimately included in the review. The systematic literature review also revealed the existence of other relevant biomarkers. Most of the included studies show a good level of evidence, which guarantees reliability and good recommendation grades. For the database (built during phase i), the results were informative but resulted in no specific recommendations. CONCLUSIONS: Most pharmacogenomic variants are not studied or acknowledged by genetic tests, and more scientific research is needed to confirm their usefulness. Therefore, only a small number of variants are considered when prescribing antidepressant drugs. In addition, genotyping of patients is not common in clinical practice.

A Rapid Pharmacogenomic Assay to Detect NAT2 Polymorphisms and Guide Isoniazid Dosing for Tuberculosis Treatment.

Rationale: Standardized dosing of antitubercular drugs contributes to a substantial incidence of toxicities, inadequate treatment response, and relapse, in part due to variable drug concentrations achieved. SNPs in the NAT2 (N-acetyltransferase-2) gene explain the majority of interindividual pharmacokinetic variability of isoniazid (INH). However, an obstacle to implementing pharmacogenomic-guided dosing is the lack of a point-of-care assay. Objectives: To develop and test a NAT2 classification algorithm, validate its performance in predicting isoniazid clearance, and develop a prototype pharmacogenomic assay. Methods: We trained random forest models to predict NAT2 acetylation genotype from unphased SNP data using a global collection of 8,561 phased genomes. We enrolled 48 patients with pulmonary tuberculosis, performed sparse pharmacokinetic sampling, and tested the acetylator prediction algorithm accuracy against estimated INH clearance. We then developed a cartridge-based multiplex quantitative PCR assay on the GeneXpert platform and assessed its analytical sensitivity on whole blood samples from healthy individuals. Measurements and Main Results: With a 5-SNP model trained on two-thirds of the data (n = 5,738), out-of-sample acetylation genotype prediction accuracy on the remaining third (n = 2,823) was 100%. Among the 48 patients with tuberculosis, predicted acetylator types were 27 (56.2%) slow, 16 (33.3%) intermediate, and 5 (10.4%) rapid. INH clearance rates were lowest in predicted slow acetylators (median 14.5 L/h), moderate in intermediate acetylators (median 40.3 L/h), and highest in fast acetylators (median 53.0 L/h). The cartridge-based assay accurately detected all allele patterns directly from 25 µl of whole blood. Conclusions: An automated pharmacogenomic assay on a platform widely used globally for tuberculosis diagnosis could enable personalized dosing of INH.