NDST3 deacetylates α-tubulin and suppresses V-ATPase assembly and lysosomal acidification.

Lysosomes are key organelles maintaining cellular homeostasis in health and disease. Here, we report the identification of N-deacetylase and N-sulfotransferase 3 (NDST3) as a potent regulator of lysosomal functions through an unbiased genetic screen. NDST3 constitutes a new member of the histone deacetylase (HDAC) family and catalyzes the deacetylation of α-tubulin. Loss of NDST3 promotes assembly of the V-ATPase holoenzyme on the lysosomal membrane and thereby increases the acidification of the organelle. NDST3 is downregulated in tissues and cells from patients carrying the C9orf72 hexanucleotide repeat expansion linked to the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Deficiency in C9orf72 decreases the level of NDST3, and downregulation of NDST3 exacerbates the proteotoxicity of poly-dipeptides generated from the C9orf72 hexanucleotide repeats. These results demonstrate a previously unknown regulatory mechanism through which microtubule acetylation regulates lysosomal activities and suggest that NDST3 could be targeted to modulate microtubule and lysosomal functions in relevant diseases.

A Nanopore Sequencing-based Pharmacogenomic Panel to Personalize Tuberculosis Drug Dosing.

Rationale: Standardized dosing of antitubercular drugs leads to variable plasma drug levels, which are associated with adverse drug reactions, delayed treatment response, and relapse. Mutations in genes affecting drug metabolism explain considerable interindividual pharmacokinetic variability; however, pharmacogenomic assays that predict metabolism of antitubercular drugs have been lacking. Objectives: We sought to develop a Nanopore sequencing panel and validate its performance in patients with active tuberculosis (TB) to personalize treatment dosing. Methods: We developed a Nanopore sequencing panel targeting 15 SNPs in five genes affecting the metabolism of antitubercular drugs. For validation, we sequenced DNA samples (n = 48) from the 1,000 Genomes Project and compared the variant calling accuracy with that of Illumina genome sequencing. We then sequenced DNA samples from patients with active TB (n = 100) from South Africa on a MinION Mk1C and evaluated the relationship between genotypes and pharmacokinetic parameters for isoniazid (INH) and rifampin (RIF). Measurements and Main Results: The pharmacogenomic panel achieved 100% concordance with Illumina sequencing in variant identification for the samples from the 1,000 Genomes Project. In the clinical cohort, coverage was more than 100× for 1,498 of 1,500 (99.8%) amplicons across the 100 samples. Thirty-three percent, 47%, and 20% of participants were identified as slow, intermediate, and rapid INH acetylators, respectively. INH clearance was 2.2 times higher among intermediate acetylators and 3.8 times higher among rapid acetylators, compared with slow acetylators (P < 0.0001). RIF clearance was 17.3% (2.50-29.9) lower in individuals with homozygous AADAC rs1803155 G→A substitutions (P = 0.0015). Conclusions: Targeted sequencing can enable the detection of polymorphisms that influence TB drug metabolism on a low-cost, portable instrument to personalize dosing for TB treatment or prevention.

Integrating rare genetic variants into DPYD pharmacogenetic testing may help preventing fluoropyrimidine-induced toxicity.

Variability in genes involved in drug pharmacokinetics or drug response can be responsible for suboptimal treatment efficacy or predispose to adverse drug reactions. In addition to common genetic variations, large-scale sequencing studies have uncovered multiple rare genetic variants predicted to cause functional alterations in genes encoding proteins implicated in drug metabolism, transport and response. To understand the functional importance of rare genetic variants in DPYD, a pharmacogene whose alterations can cause severe toxicity in patients exposed to fluoropyrimidine-based regimens, massively parallel sequencing of the exonic regions and flanking splice junctions of the DPYD gene was performed in a series of nearly 3000 patients categorized according to pre-emptive DPD enzyme activity using the dihydrouracil/uracil ([UH2]/[U]) plasma ratio as a surrogate marker of DPD activity. Our results underscore the importance of integrating next-generation sequencing-based pharmacogenomic interpretation into clinical decision making to minimize fluoropyrimidine-based chemotherapy toxicity without altering treatment efficacy.

Public preferences for pharmacogenetic testing in the NHS: Embedding a discrete choice experiment within service design to better meet user needs.

AIMS: Genetic testing can be used to improve the safety and effectiveness of commonly prescribed medicines-a concept known as pharmacogenetics. This study aimed to quantify members of the UK public's preferences for a pharmacogenetic service to be delivered in primary care in the National Health Service. METHODS: Members of the UK population were surveyed via an online panel company. Respondents completed 1 of 2 survey versions, asking respondents to select their preferred pharmacogenetic testing service in the context of a presentation of low mood or pain. A conditional logit model was estimated, before the best functional form for the dataset was identified. Preference heterogeneity was identified via latent class analysis. Coefficients from the final selected models were used to estimate uptake in the context of different hypothetical pharmacogenetic services. RESULTS: Responses from 1993 individuals were included in the analysis. There were no differences observed in preference between the 2 clinical scenarios. Conditional logit analysis, using maximum likelihood estimation, indicated that respondents preferred to have noninvasive tests and wanted their data to be shared between different healthcare organizations to guide future prescribing. There was a preference for regional over national data sharing initiatives, and respondents preferred to have access to their data. Predicted uptake varied considerably, ranging from 51% to >99%, depending on design of the service. CONCLUSION: This study identifies public preferences for a pharmacogenetic testing service and demonstrates how predicted uptake can be impacted by relatively minor adaptations. This highlights areas for prioritization during development of future pharmacogenetic services.

PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis.

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug-response analysis such as tissue distribution of dose-response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug-response phenotypes of cancer models.

Actionable pharmacogenetic variants in Hong Kong Chinese exome sequencing data and projected prescription impact in the Hong Kong population.

Preemptive pharmacogenetic testing has the potential to improve drug dosing by providing point-of-care patient genotype information. Nonetheless, its implementation in the Chinese population is limited by the lack of population-wide data. In this study, secondary analysis of exome sequencing data was conducted to study pharmacogenomics in 1116 Hong Kong Chinese. We aimed to identify the spectrum of actionable pharmacogenetic variants and rare, predicted deleterious variants that are potentially actionable in Hong Kong Chinese, and to estimate the proportion of dispensed drugs that may potentially benefit from genotype-guided prescription. The projected preemptive pharmacogenetic testing prescription impact was evaluated based on the patient prescription data of the public healthcare system in 2019, serving 7.5 million people. Twenty-nine actionable pharmacogenetic variants/ alleles were identified in our cohort. Nearly all (99.6%) subjects carried at least one actionable pharmacogenetic variant, whereas 93.5% of subjects harbored at least one rare deleterious pharmacogenetic variant. Based on the prescription data in 2019, 13.4% of the Hong Kong population was prescribed with drugs with pharmacogenetic clinical practice guideline recommendations. The total expenditure on actionable drugs was 33,520,000 USD, and it was estimated that 8,219,000 USD (24.5%) worth of drugs were prescribed to patients with an implicated actionable phenotype. Secondary use of exome sequencing data for pharmacogenetic analysis is feasible, and preemptive pharmacogenetic testing has the potential to support prescription decisions in the Hong Kong Chinese population.

Clinically actionable genotypes for anticancer prescribing among >1500 patients with pharmacogenomic testing.

BACKGROUND: In recent years, there has been increasing evidence supporting the role of germline pharmacogenomic factors predicting toxicity for anticancer therapies. Although somatic genomic data are used frequently in oncology care planning, germline pharmacogenomic testing is not. This study hypothesizes that comprehensive germline pharmacogenomic profiling could have high relevance for cancer care. METHODS: Between January 2011 and August 2020, patients at the University of Chicago Medical Center were genotyped across custom germline pharmacogenomic panels for reasons unrelated to cancer care. Actionable anticancer pharmacogenomic gene/drug interactions identified by the FDA were defined including: CYP2C9 (erdafitinib), CYP2D6 (gefitinib), DPYD (5-fluorouracil and capecitabine), TPMT (thioguanine and mercaptopurine), and UGT1A1 (belinostat, irinotecan, nilotinib, pazopanib, and sacituzumab-govitecan hziy). The primary objective was to determine the frequency of individuals with actionable or high-risk genotypes across these 5 key pharmacogenes, thus potentially impacting prescribing for at least 1 of these 11 commonly prescribed anticancer therapies. RESULTS: Data from a total of 1586 genotyped individuals were analyzed. The oncology pharmacogene with the highest prevalence of high-risk, actionable genotypes was UGT1A1, impacting 17% of genotyped individuals. Actionable TPMT and DPYD genotypes were found in 9% and 4% of patients, respectively. Overall, nearly one-third of patients genotyped across all 5 genes (161/525, 31%) had at least one actionable genotype. CONCLUSIONS: These data suggest that germline pharmacogenomic testing for 5 key pharmacogenes could identify a substantial proportion of patients at risk with standard dosing, an estimated impact similar to that of somatic genomic profiling. LAY SUMMARY: Differences in our genes may explain why some drugs work safely in certain individuals but can cause side effects in others. Pharmacogenomics is the study of how genetic variations affect an individual's response to medications. In this study, an evaluation was done for important genetic variations that can affect the tolerability of anticancer therapy. By analyzing the genetic results of >1500 patients, it was found that nearly one-third have genetic variations that could alter recommendations of what drug, or how much of, an anticancer therapy they should be given. Performing pharmacogenomic testing before prescribing could help to guide personalized oncology care.

Attitudes on pharmacogenomic results as secondary findings among medical geneticists.

OBJECTIVES: As evidence mounts supporting the utility of pharmacogenomic-guided medication management, incorporating pharmacogenomic genes into secondary finding results from sequencing panels is increasingly under consideration. We studied medical geneticists' attitudes on receiving pharmacogenomic results as secondary finding. METHODS: Four focus groups with 16 medical geneticists total were conducted followed by thematic analysis. RESULTS: All participants ordered genetic sequencing tests; however, the majority had rarely or never ordered pharmacogenomic tests (10/16) or prescribed medications with established response variability (11/16). In total 81.3% expressed low comfort interpreting pharmacogenomic results without appropriate clinical resources (13/16). The positives of receiving pharmacogenomic results as secondary finding included prevention of adverse drug reactions in adults, grateful information-seeking patients, the ability to rapidly prescribe more effective treatments and appreciation of the recent advances in both pharmacogenomic knowledge and available guidelines. Negatives included laboratory reporting issues, exclusivity of pharmacogenomic results to certain populations, lengthy reports concealing pharmacogenomic results in patient charts and laboratories marketing to individuals without prior pharmacogenomic knowledge or targeting inappropriate populations. The most desirable pharmacogenomic resources included a universal electronic health record clinical decision support tool to assist identifying and implementing pharmacogenomic results, a specialized pharmacist as part of the care team, additional pharmacogenomic training during medical/graduate school, and a succinct interpretation of pharmacogenomic results included on laboratory reports. CONCLUSIONS: The majority of participants agreed that adding certain actionable pharmacogenomic genes to the American College of Medical Genetics and Genomics SF list is reasonable; however, this was qualified with a need for additional resources to support implementation.

Clinical Trial in a Dish: Using Patient-Derived Induced Pluripotent Stem Cells to Identify Risks of Drug-Induced Cardiotoxicity.

Drug-induced cardiotoxicity is a significant clinical issue, with many drugs in the market being labeled with warnings on cardiovascular adverse effects. Treatments are often prematurely halted when cardiotoxicity is observed, which limits their therapeutic potential. Moreover, cardiotoxicity is a major reason for abandonment during drug development, reducing available treatment options for diseases and creating a significant financial burden and disincentive for drug developers. Thus, it is important to minimize the cardiotoxic effects of medications that are in use or in development. To this end, identifying patients at a higher risk of developing cardiovascular adverse effects for the drug of interest may be an effective strategy. The discovery of human induced pluripotent stem cells has enabled researchers to generate relevant cell types that retain a patient's own genome and examine patient-specific disease mechanisms, paving the way for precision medicine. Combined with the rapid development of pharmacogenomic analysis, the ability of induced pluripotent stem cell-derivatives to recapitulate patient-specific drug responses provides a powerful platform to identify subsets of patients who are particularly vulnerable to drug-induced cardiotoxicity. In this review, we will discuss the current use of patient-specific induced pluripotent stem cells in identifying populations who are at risk to drug-induced cardiotoxicity and their potential applications in future precision medicine practice. Graphic Abstract: A graphic abstract is available for this article.

Pharmacogenomic testing: perception of clinical utility, enablers and barriers to adoption in Australian hospitals.

BACKGROUND: Despite healthcare professionals (HCP) endorsing the clinical utility of pharmacogenomics testing, use in clinical practice is limited. AIMS: To assess HCP' perceptions of pharmacogenomic testing and identify barriers to implementation. METHODS: HCP involved in prescribing decisions at three hospitals in Sydney, Australia, were invited to participate. The online survey assessed perceptions of pharmacogenomic testing, including: (i) demographic and practice variables; (ii) use, knowledge and confidence; (iii) perceived benefits; (iv) barriers to implementation; and (v) operational and/or system changes and personnel required to implement on site. RESULTS: HCP were predominantly medical practitioners (75/107) and pharmacists (25/107). HCP perceived pharmacogenomic testing was beneficial to identify reasons for drug intolerance (85/95) and risk of side-effects (86/95). Although testing was considered relevant to their practice (79/100), few HCP (23/100) reported past or intended future use (26/100). Few HCP reported confidence in their ability to identify indications for pharmacogenomic testing (14/107), order tests (19/106) and communicate results with patients (16/107). Lack of clinical practice guidelines (62/79) and knowledge (54/77) were identified as major barriers to implementation of pharmacogenomics. Comprehensive reimbursement for testing and clinical practice guidelines, alongside models-of-care involving multidisciplinary teams and local clinical champions were suggested as strategies to facilitate implementation of pharmacogenomic testing into practice. CONCLUSIONS: Pharmacogenomic testing was considered important to guide drug selection and dosing decisions. However, limited knowledge, low confidence and an absence of guidelines impede the use of pharmacogenomic testing. Establishment of local resources including multidisciplinary models-of-care was suggested to facilitate implementation of pharmacogenomics.

On the utilization of polygenic risk scores for therapeutic targeting.

The promise of personalized genomic medicine is that knowledge of a person's gene sequences and activity will facilitate more appropriate medical interventions, particularly drug prescriptions, to reduce the burden of disease. Early successes in oncology and pediatrics have affirmed the power of positive diagnosis and are mostly based on detection of one or a few mutations that drive the specific pathology. However, genetically more complex diseases require the development of polygenic risk scores (PRSs) that have variable accuracy. The rarity of events often means that they have necessarily low precision: many called positives are actually not at risk, and only a fraction of cases are prevented by targeted therapy. In some situations, negative prediction may better define the population at low risk. Here, I review five conditions across a broad spectrum of chronic disease (opioid pain medication, hypertension, type 2 diabetes, major depression, and osteoporotic bone fracture), considering in each case how genetic prediction might be used to target drug prescription. This leads to a call for more research designed to evaluate genetic likelihood of response to therapy and a call for evaluation of PRS, not just in terms of sensitivity and specificity but also with respect to potential clinical efficacy.

Experience with comprehensive pharmacogenomic multi-gene panel in clinical practice: a retrospective single-center study.

AIM: To assess the prevalence of actionable pharmacogenetic interventions in patients who underwent pharmacogenetic testing with a multi-gene panel. METHODS: We retrospectively reviewed single-center electronic health records. A total of 319 patients were enrolled who underwent pharmacogenomic testing with the RightMed test panel using TaqMan quantitative real-time PCR method and copy number variation analysis to determine the SNPs in the 27 target genes. RESULTS: Actionable drug-gene pairs were found in 235 (73.7%) patients. Relevant guidelines on genotype-based prescribing were available for 133 (56.7%) patients at the time of testing. Based on the patients' genotype, 139 (43.6%) patients were using at least one drug with significant pharmacogenetic interactions. CONCLUSION: Two out of three patients had at least one drug-gene pair in their therapy. Further studies should assess the clinical effectiveness of integrating pharmacogenomic data into patients' electronic health records.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey.

Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.

The impact of pharmacogenetic testing in patients exposed to polypharmacy: a scoping review.

Polypharmacy poses a significant risk for adverse reactions. While there are clinical decision support tools to assist clinicians in medication management, pharmacogenetic testing to identify potential drug-gene or drug-drug-gene interactions is not widely implemented in the clinical setting. A PRISMA-compliant scoping review was performed to determine if pharmacogenetic testing for absorption, distribution, metabolism, and excretion (ADME)-related genetic variants is associated with improved clinical outcomes in patients with polypharmacy. Six studies were reviewed. Five reported improved clinical outcomes, reduced side effects, reduction in the number of drugs used, or reduced healthcare utilization. The reviewed studies varied in methodological quality, risk of bias, and outcome measures. Age, diet, disease state, and treatment adherence also influence drug response, and may confound the relationship between genetic polymorphisms and treatment outcomes. Further studies using a randomized control design are needed. We conclude that pharmacogenetic testing represents an opportunity to improve health outcomes in patients exposed to polypharmacy, particularly in patients with psychiatric disorders and the elderly.

Population impact of pharmacogenetic tests in admixed populations across the Americas.

The current population of the Americas (~1 billion people) is highly heterogeneous as a result of five centuries of admixture between three major parental populations, namely Native Amerindians, Europeans, and subSaharan Africans. This heterogeneity has important pharmacogenetic (PGx) implications. The present study explores this issue with respect to the population impact of PGx tests listed in the CPIC and DWPG guidelines for the cancer chemotherapeutic agents fluoropyrimidines, irinotecan, and thiopurines. Population impact was assessed by the number of individuals needed to genotype (NNG) and to treat [NNT] in order to prevent one additional adverse effect. The 1000 Genomes Project database was accessed to obtain genotypes for the relevant PGx markers and to estimate individual proportions of Native, European, and African ancestry for six recently admixed populations across the Americas. The ancestry proportions were then employed to devise three sub-cohorts, denoted NAT, EUR, and AFR, in which one of the three major ancestral roots predominates largely (8 to 30-fold) over the other two. Minor allele frequency (MAF) of NUDT15 rs116855232, TPMT rs1800460, and UGT1A1 rs887829 differed significantly across the three sub-cohorts, whereas no difference was observed for TPMT rs1142345 and 1800462, and the four DPYD variants interrogated. The frequency of combined TPMT/NUDT and UGT1A1, but not DPD, metabolic phenotypes differed significantly among the sub-cohorts. The NNGs for the drug/sub-cohorts pairs, ranged from 12 (irinotecan/UGT1A1 in EUR) to 360 (fluoropyrimidines/DPYD in NAT). Differences in MAF of the interrogated variants and consequently in the distribution of metabolic phenotypes, plus the variable individual proportions of biogeographical ancestry concur to the 30-fold range of NNGs for the PGx tests, across the sub-cohorts. This large variation is likely to influence the perceived benefits and the clinical adoption of PGx screening for the chemotherapeutic agents investigated. This is of especial concern for fluoropyrimidines, in view of the large NNGs observed in the study sub-cohorts (NNG = 176-360) and confirmed in cancer patients from Brazil (NNG = 312).

Pharmacogenetic information in Swiss drug labels - a systematic analysis.

Implementation of pharmacogenetics (PGx) and individualization of drug therapy is supposed to obviate adverse drug reactions or therapy failure. Health care professionals (HCPs) use drug labels (DLs) as reliable information about drugs. We analyzed the Swiss DLs to give an overview on the currently available PGx instructions. We screened 4306 DLs applying natural language processing focusing on drug metabolism (pharmacokinetics) and we assigned PGx levels following the classification system of PharmGKB. From 5979 hits, 2564 were classified as PGx-relevant affecting 167 substances. 55% (n = 93) were classified as "actionable PGx". Frequently, PGx information appeared in the pharmacokinetics section and in DLs of the anatomic group "nervous system". Unstandardized wording, appearance of PGx information in different sections and unclear instructions challenge HCPs to identify and interpret PGx information and translate it into practice. HCPs need harmonization and standardization of PGx information in DLs to personalize drug therapies and tailor pharmaceutical care.

Comprehensive pharmacogenetic analysis of DPYD, UGT, CDA, and ABCB1 polymorphisms in pancreatic cancer patients receiving mFOLFIRINOX or gemcitabine plus nab-paclitaxel.

Modified FOLFIRINOX (mFOLFIRINOX) and gemcitabine + nab-paclitaxel (GemNab) regimens represent a standard treatment in advanced pancreatic cancer (aPC). DPYD and UGT1A1 variants are relevant predictors of fluoropyrimidine and irinotecan-associated adverse events (AEs). Furthermore, data about the associations between polymorphisms in ABCB and CDA genes and GemNab-related toxicities are still controversial. The present study analyzes the association between DPYD, UGT, ABCB1, CDA variants, and AEs in aPC patients (pts) treated with mFOLFIRINOX or GemNab. Blood samples collected from 104 aPC pts treated with mFOLFIRINOX and 63 with GemNab were tested for DPYD c.1679T>G, IVS14+1G>A, c.2194G>A, c.2846A>T, UGT1A1*28, CDA c.79A>C, and ABCB1 c.1236C>T, c.2677G>T/A, c.3435C>T by real-time PCR and automatic sequencing. In mFOLFIRINOX cohort, DPYD IVS14+1GA genotype was associated with G4 hematological AEs, while the UGT1A1*28 significantly correlated with the risk of thrombocytopenia (p = 0.006). In the GemNab cohort, a significant association between CDA c.79CC and high-grade nausea was observed (p = 0.002). Moreover, the presence of at least a mutant allele in ABCB1 increased the risk of overall hematological AEs (p = 0.01), both further strengthened by the presence of CDA c.79CC (p = 0.0002). DPYD IVS14+1A allele is confirmed to be associated with fluoropyrimidine life-threatening toxicities, and UGT1A1*28 is related with a higher risk of hematologic AEs following irinotecan treatment. CDA c.79C and ABCB1 c.1236T, c.2677T/A, and c.3435T mutant alleles are predictive biomarkers of GemNab-related AEs. All these variants should be considered in aPC pts candidate to mFOLFIRINOX or GemNab treatments.

MASS SPECTROMETRY-BASED PERSONALIZED DRUG THERAPY.

Personalized drug therapy aims to provide tailored treatment for individual patient. Mass spectrometry (MS) is revolutionarily involved in this area because MS is a rapid, customizable, cost-effective, and easy to be used high-throughput method with high sensitivity, specificity, and accuracy. It is driving the formation of a new field, MS-based personalized drug therapy, which currently mainly includes five subfields: therapeutic drug monitoring (TDM), pharmacogenomics (PGx), pharmacomicrobiomics, pharmacoepigenomics, and immunopeptidomics. Gas chromatography-MS (GC-MS) and liquid chromatography-MS (LC-MS) are considered as the gold standard for TDM, which can be used to optimize drug dosage. Matrix-assisted laser desorption ionization-time of flight-MS (MALDI-TOF-MS) significantly improves the capability of detecting biomacromolecule, and largely promotes the application of MS in PGx. It is becoming an indispensable tool for genotyping, which is used to discover and validate genetic biomarkers. In addition, MALDI-TOF-MS also plays important roles in identity of human microbiome whose diversity can explain interindividual differences of drug response. Pharmacoepigenetics is to study the role of epigenetic factors in individualized drug treatment. MS can be used to discover and validate pharmacoepigenetic markers (DNA methylation, histone modification, and noncoding RNA). For the emerging cancer immunotherapy, personalized cancer vaccine has effective immunotherapeutic activity in the clinic. MS-based immunopeptidomics can effectively discover and screen neoantigens. This article systematically reviewed MS-based personalized drug therapy in the above mentioned five subfields. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Sex-, Age-, and Race/Ethnicity-Dependent Variations in Drug-Processing and NRF2-Regulated Genes in Human Livers.

Individual variations in xenobiotic metabolism affect the sensitivity to diseases. In this study, the impacts of sex, age, and race/ethnicity on drug-processing genes and nuclear factor erythroid 2-related factor 2 (NRF2) genes in human livers were examined via QuantiGene multiplex suspension array (226 samples) and quantitative polymerase chain reaction (qPCR) (247 samples) to profile the expression of nuclear receptors, cytochrome P450s, conjugation enzymes, transporters, bile acid metabolism, and NRF2-regulated genes. Sex differences were found in expression of about half of the genes, but in general the differences were not large. For example, females had higher transcript levels of catalase, glutamate-cysteine ligase catalytic subunit (GCLC), heme oxygenase 1 (HO-1), Kelch-like ECH-associated protein 1 (KEAP1), superoxide dismutase 1, and thioredoxin reductase-1 compared with males via qPCR. There were no apparent differences due to age, except children had higher glutamate-cysteine ligase modifier subunit (GCLM) and elderly had higher multidrug resistance protein 3. African Americans had lower expression of farnesoid X receptor (FXR) but higher expression of HO-1, Caucasians had higher expression of organic anion transporter 2, and Hispanics had higher expression of FXR, SULT2A1, small heterodimer partner, and bile salt export pump. An examination of 34 diseased and control human liver samples showed that compared with disease-free livers, fibrotic livers had higher NAD(P)H-quinone oxidoreductase 1 (NQO1), GCLC, GCLM, and NRF2; hepatocellular carcinoma had higher transcript levels of NQO1 and KEAP1; and steatotic livers had lower GCLC, GCLM, and HO-1 expression. In summary, in drug-processing gene and NRF2 genes, sex differences were the major findings, and there were no apparent age differences, and race/ethnicity differences occurred for a few genes. These descriptive findings could add to our understanding of the sex-, age-, and race/ethnicity-dependent differences in drug-processing genes as well as NRF2 genes in normal and diseased human livers. SIGNIFICANCE STATEMENT: In human liver drug-processing and nuclear factor erythroid 2-related factor 2 genes, sex differences were the main finding. There were no apparent differences due to age, except children had higher glutamate-cysteine ligase modifier subunit, and elderly had higher multidrug resistance protein 3. African Americans had lower expression of farnesoid X receptor (FXR) but higher expression of heme oxygenase 1, Caucasians had higher expression of organic anion transporter 2, and Hispanics had higher expression of FXR, small heterodimer partner, SULT2A1, and bile salt export pump.