Drug Metabolizing Enzymes And Transporters Research Articles

Female genital tract host factors and tenofovir and lamivudine active metabolites.

We previously reported the effect of contraception on cervical tenofovir concentrations in Ugandan women living with HIV. Here we explored the role of cervicovaginal cytokines and drug metabolizing enzymes and transporters (DMETs) to elucidate FGT drug disposition in a Ugandan cohort. Cervicovaginal fluid and cervical biopsies were collected from Ugandan women living with HIV receiving tenofovir/lamivudine-based therapy and intramuscular depot medroxyprogesterone acetate (DMPA-IM; n=25), copper IUD (cuIUD; n=12), or condoms (n=13) as contraception. Cytokines were measured in cervicovaginal fluid (CVF). Ectocervical tenofovir diphosphate (TFVdp) and lamivudine triphosphate (3TCtp), dATP/dCTP concentrations, and immune marker/DMETs gene expression were measured in cervical biopsies. Cervical 3TCtp was not correlated with any CVF cytokines. Cervical TFVdp was correlated with IL-10, IL-7, and IL-17 in CVF. CCR5 mRNA expression in cervical biopsies was higher in cuIUD-users versus condoms-users. Using multivariable linear regression, CVF IL-17, tissue dATP, plasma estradiol, and plasma tenofovir were all significant predictors of cervical TFVdp. Tissue dCTP and plasma lamivudine were significant predictors of cervical 3TCtp. TFVdp concentrations in cervix appear to be influenced by local inflammation. In contrast, 3TCtp FGT exposure was not affected by genital inflammation or DMETS. CuIUD users have more immune cells present, which may in turn influence local TFVdp disposition.

#2436 Genetic variability in rosuvastatin metabolism explains rhabdomyolysis and consequent acute renal failure

Abstract Background and Aims Low density lipoprotein cholesterol is an independent risk factor for atherosclerotic cardiovascular disease. Rosuvastatin is one of the most frequently prescribed statins and also one of the most frequent causes of rhabdomyolysis with consequent acute renal failure (ARF). Risk factors for statin induced rhabdomyolysis, i.e. the type of statin and its dose, patient's age, kidney and liver function, gender and concurrent treatment with cytochrome P450 (CYP) inhibitors, should be considered at the time of statin prescription. Single nucleotide polymorphisms (SNPs) in genes coding the transport proteins (SLCO1B1, ABCG2) and CYP2C9, catalysing rosuvastatin oxidation, may lead to decreased protein function leading to accumulation of plasma rosuvastatin which increases the risk of rhabdomyolysis. Herewith we present a case of a female patient admitted to our department due to rhabdomyolysis and ARF with the aim of alerting against uncritical high dose rosuvastatin prescribing and of pointing out the role of genetic variability in drug metabolizing enzymes and transporters (DMET) in rosuvastatin side effects. Method a 79-years old Caucasian woman admitted to our department due to rhabdomyolysis and ARF was genotyped for polymorphisms in DMET genes coding for CYP2C9 (rs1799853, rs1057910, rs28371686, rs28371685), SLCO1B1 (rs4149056, rs2306283) and ABCG2 (rs2231142) using competitive allele specific polymerase chain reaction (KASPar). Results Presented patient suffered from an acute myocardial infarction in October 2023. She was treated with percutaneous coronary intervention, antiaggregation therapy and hypolipidemic therapy (rosuvastatin 40 mg/ezetimibe 10 mg). Her creatinine was 55 μM (eGFR 61 ml/min) at the time of statin prescription. Several months later she was admitted septic with myoglobin >50.000 μg/L and creatinine approximately 1600 μM. Infection was treated with piperacillin/tazobactam. Myoglobin was removed with Theranova dialyzer. The patient was dismissed home two weeks later with creatinine 230 μM. As potential patient's risk factors for rhabdomyolysis we considered high dose of rosuvastatin, patient's higher age and slower rosuvastatin disposition due to SNPs in DMET. Sepsis may have been an additional contributory factor. Genotyping revealed polymorphisms in all three genes involved in rosuvastatin metabolism and transport: CYP2C9, SLCO1B1 and ABCG2 (Table 1). Two genotypes resulted in the phenotypes with slower rosuvastatin metabolism, probably leading to gradual accumulation of plasma rosuvastatin, which may have manifested in rhabdomyolysis that contributed to ARF. Heterozygous CYP2C9*2 and *3 carriers have, in comparison to the wild type carriers, approximately 30% and 80% slower rosuvastatin metabolism, respectively. Heterozygous ABCG2 c.421C>A carrier state may also contribute slightly to rosuvastatin accumulation. Furthermore, the potential interaction with inflammation should not be overlooked, as inflammation may have a major effect on drug metabolism and transport through downregulation of CYP enzymes as well as drug transporters. If genotypes of the respective DMET genes were known in advance, high dose rosuvastatin could have been avoided despite relatively good kidney function at the time of rosuvastatin prescription. Conclusion Statins should be prescribed with caution and their dose adapted to the risk factors that should always be checked for at the time of statin prescription. We recommend periodical surveillance of serum creatinine, eGFR, urine protein to creatinine ratio, liver tests, creatine kinase and myoglobin in patients treated with high dose statins. Furthermore, SLCO1B1, ABCG2 and CYP2C9 genotyping before rosuvastatin prescription would enable insight into its metabolism. Genotype information could lead to more cautious statin prescribing, especially in the presence of other factors, such as infection or concomitant drug treatment that may modify the activity of DMET. In the case of chronic kidney disease drug dosing should be appropriately adjusted in accordance with drug's Summary of product characteristics and KDIGO guidelines.

Human Enteroid Monolayers: A Novel, Functionally-Stable Model for Investigating Oral Drug Disposition.

To further the development of an in vitro model which faithfully recapitulates drug disposition of orally administered drugs, we investigated the utility of human enteroid monolayers to simultaneously assess intestinal drug absorption and first-pass metabolism processes. We cultured human enteroid monolayers from three donors, derived via biopsies containing duodenal stem cells that were propagated and then differentiated atop permeable Transwell® inserts, and confirmed transformation into a largely enterocyte population via RNA-seq analysis and immunocytochemical (ICC) assays. Proper cell morphology was assessed and confirmed via bright field microscopy and ICC imaging of tight junction proteins and other apically and basolaterally localized proteins. Enteroid monolayer barrier integrity was demonstrated by elevated transepithelial electrical resistance (TEER) that stabilized after 10 days in culture and persisted for 42 days. These results were corroborated by low paracellular transport probe permeability at 7 and 21 days in culture. The activity of a prominent drug metabolizing enzyme, CYP3A, was confirmed at 7, 21, and 42 days culture under basal, 1α,25(OH)2 vitamin D3-induced, and 6',7'-dihydroxybergamottin-inhibited conditions. The duration of these experiments is particularly noteworthy, as this is the first study assessing drug metabolizing enzymes and transporters (DMET) expression/function for enteroids cultured for greater than 12 days. The sum of these results suggests enteroid monolayers are a promising ex vivo model to investigate and quantitatively predict an orally administered drug's intestinal absorption and/or metabolism. Significance Statement This study presents a novel ex vivo model of the human intestine, human intestinal organoid (enteroid) monolayers, that maintain barrier function and metabolic functionality for up to 42-days in culture. The incorporation of both barrier integrity and metabolic function over an extended period within the same model is an advancement over historically used in vitro systems, which either lack one or both of these attributes or have limited viability.

Sex and the Kidney Drug-Metabolizing Enzymes and Transporters: Are Preclinical Drug Disposition Data Translatable to Humans?

Cross-species differences in drug transport and metabolism are linked to poor translation of preclinical pharmacokinetic and toxicology data to humans, often resulting in the failure of new chemical entities (NCEs) during clinical drug development. Specifically, inaccurate prediction of renal clearance and renal accumulation of NCEs due to differential abundance of enzymes and transporters in kidneys can lead to differences in pharmacokinetics and toxicity between experimental animals and humans. We carried out liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based protein quantification of 78 membrane drug-metabolizing enzymes and transporters (DMETs) in the kidney membrane fractions of humans, rats, and mice for characterization of cross-species and sex-dependent differences. In general, majority of DMET proteins were higher in rodents than in humans. Significant cross-species differences were observed in 30 out of 33membrane DMET proteins quantified in all three species. Although no significant sex-dependent differences were observed in humans, the abundance of 28 and 46 membrane proteins showed significant sex dependence in rats and mice, respectively. These cross-species and sex-dependent quantitative abundance data are valuable for gaining a mechanistic understanding of drug renal disposition and accumulation. Further, these data can also be integrated into systems pharmacology tools, such as physiologically based pharmacokinetic models, to enhance the interpretation of preclinical pharmacokinetic and toxicological data.

Differential tissue abundance of membrane-bound drug metabolizing enzymes and transporter proteins by global proteomics.

Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are critical for scaling in vitro and animal data to humans for accurate prediction and interpretation of drug clearance and toxicity. Targeted DMET proteomics which relies on synthetic stable isotope-labeled surrogate peptides as calibrators, is routinely used for the quantification of selected proteins; however, the technique is limited to the quantification of a small number of proteins. Although the global proteomics-based total protein approach (TPA) is emerging as a better alternative for large-scale protein quantification, the conventional TPA doesn't consider differential sequence coverage by identifying unique peptides across proteins. Here, we optimized the TPA approach by correcting protein abundance data by the sequence coverage (SC-TPA), which was applied to quantify 54 DMETs for characterization of i) differential tissue DMET abundance in the human liver, kidney, and intestine, and ii) interindividual variability of DMET proteins in individual intestinal samples (n=13). UGT2B7, MGST1, MGST2, MGST3, CES2, and MRP2 were expressed in all three tissues, whereas, as expected CYP3A4, CYP3A5, CYP2C9, CYP4F2, UGT1A1, UGT2B17, CES1, FMO5, MRP3, and P-gp were present in the liver and intestine. The top three DMET proteins in individual tissues were: CES1>CYP2E1>UGT2B7 (liver), CES2>UGT2B17>CYP3A4 (intestine), and MGST1>UGT1A6>MGST2 (kidney). CYP3A4, CYP3A5, UGT2B17, CES2, and MGST2 showed high interindividual variability in the intestine. These data are relevant for enhancing in vitro to in vivo extrapolation (IVIVE) of drug absorption and disposition and can be used to enhance the accuracy of physiologically based pharmacokinetic (PBPK) prediction of systemic and tissue concentration of drugs. Significance Statement We quantified the abundance and compositions of drug-metabolizing enzymes and transporters (DMETs) in pooled human liver, intestine, and kidney microsomes using an optimized sequence coverage-informed total protein approach. The quantification of DMETs revealed quantitative differences in their levels in the liver, intestine, and kidney. Further, the analysis of individual intestine samples confirmed high variability in the levels of CYP3A4, CYP3A5, UGT2B17, CES2, and MGST2. These data are applicable for the prediction of first-pass metabolism and tissue-specific drug clearance.

Innovations, Opportunities, and Challenges for Predicting Alteration in Drug-Metabolizing Enzyme and Transporter Activity in Specific Populations.

Drug-metabolizing enzymes and transporters (DMETs) are key regulators of the pharmacokinetics, efficacy, and toxicity of therapeutics. Over the past two decades, significant advancements in in vitro methodologies, targeted proteomics, in vitro to in vivo extrapolation methods, and integrated computational approaches such as physiologically based pharmacokinetic modeling have unequivocally contributed to improving our ability to quantitatively predict the role of DMETs in absorption, distribution, metabolism, and excretion and drug-drug interactions. However, the paucity of data regarding alterations in DMET activity in specific populations such as pregnant individuals, lactation, pediatrics, geriatrics, organ impairment, and disease states such as, cancer, kidney, and liver diseases and inflammation has restricted our ability to realize the full potential of these recent advancements. We envision that a series of carefully curated articles in a special supplementary issue of Drug Metabolism and Disposition will summarize the latest progress in in silico, in vitro, and in vivo approaches to characterize alteration in DMET activity and quantitatively predict drug disposition in specific populations. In addition, the supplementary issue will underscore the current scientific knowledge gaps that present formidable barriers to fully understand the clinical implications of altered DMET activity in specific populations and highlight opportunities for multistakeholder collaboration to advance our collective understanding of this rapidly emerging area. SIGNIFICANCE STATEMENT: This commentary highlights current knowledge and identifies gaps and key challenges in understanding the role of drug-metabolizing enzymes and transporters (DMETs) in drug disposition in specific populations. With this commentary for the special issue in Drug Metabolism and Disposition, the authors intend to increase interest and invite potential contributors whose research is focused or has aided in expanding the understanding around the role and impact of DMETs in drug disposition in specific populations.

Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling.

Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.

Deciphering genetic causes for sex differences in human health through drug metabolism and transporter genes

Sex differences have been widely observed in human health. However, little is known about the underlying mechanism behind these observed sex differences. We hypothesize that sex-differentiated genetic effects are contributors of these phenotypic differences. Focusing on a collection of drug metabolism enzymes and transporters (DMET) genes, we discover sex-differentiated genetic regulatory mechanisms between these genes and human complex traits. Here, we show that sex-differentiated genetic effects were present at genome-level and at DMET gene regions for many human complex traits. These sex-differentiated regulatory mechanisms are reflected in the levels of gene expression and endogenous serum biomarkers. Through Mendelian Randomization analysis, we identify putative sex-differentiated causal effects in each sex separately. Furthermore, we identify and validate sex differential gene expression of a subset of DMET genes in human liver samples. We observe higher protein abundance and enzyme activity of CYP1A2 in male-derived liver microsomes, which leads to higher level of an active metabolite formation of clozapine, a commonly prescribed antipsychotic drug. Taken together, our results demonstrate the presence of sex-differentiated genetic effects on DMET gene regulation, which manifest in various phenotypic traits including disease risks and drug responses.

Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes.

Physiologically based pharmacokinetic models, populated with drug-metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7-0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration-time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.

Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity

Introduction Drug efficacy and toxicity are important factors for evaluation in drug development. Drug metabolizing enzymes and transporters (DMETs) play an essential role in drug efficacy and toxicity. Noncoding RNAs (ncRNAs) have been implicated to influence inter-individual variations in drug efficacy and safety by regulating DMETs. An efficient strategy is urgently needed to identify and functionally characterize ncRNAs that mediate drug efficacy and toxicity through regulating DMETs. Areas covered : We outline an integrative strategy to identify ncRNAs that modulate DMETs. We include reliable tools and databases for computational prediction of ncRNA targets with regards to their advantages and limitations. Various biochemical, molecular, and cellular assays are discussed for in vitro experimental verification of the regulatory function of ncRNAs. In vivo approaches for association of ncRNAs with drug treatment and toxicity are also reviewed. Expert opinion : A streamlined integration of computational prediction and wet-lab validation is important to elucidate mechanisms of ncRNAs in the regulation of DMETs related to drug efficacy and safety. Bioinformatic analyses using open-access tools and databases serve as a powerful booster for ncRNA research in toxicology. Further refinement of computational algorithms and experimental technologies is needed to improve accuracy and efficiency in ncRNA target identification and characterization.

Association of Drug-Metabolizing Enzyme and Transporter Gene Polymorphisms and Lipid-Lowering Response to Statins in Thai Patients with Dyslipidemia.

PurposeStatins are increasingly widely used in the primary and secondary prevention of cardiovascular disease. However, there is an inter-individual variation in statin response among patients. The study aims to determine the association between genetic variations in drug-metabolizing enzyme and transporter (DMET) genes and lipid-lowering response to a statin in Thai patients with hyperlipidemia.Patients and MethodsSeventy-nine patients who received statin at steady-state concentrations were recruited. Serum lipid profile was measured at baseline and repeated after 4-month on a statin regimen. The genotype profile of 1936 DMET markers was obtained using Affymetrix DMET Plus genotyping microarrays.ResultsIn this DMET microarray platform, five variants; SLCO1B3 (rs4149117, rs7311358, and rs2053098), QPRT (rs13331798), and SLC10A2 (rs188096) showed a suggestive association with LDL-cholesterol-lowering response. HDL-cholesterol-lowering responses were found to be related to CYP7A1 gene variant (rs12542233). Seven variants, SLCO1B3 (rs4149117, rs7311358, and rs2053098); SULT1E1 (rs3736599 and rs3822172); and ABCB11 (rs4148768 and rs3770603), were associated with the total cholesterol-lowering response. One variant of the ABCB4 gene (rs2109505) was significantly associated with triglyceride-lowering response.ConclusionThis pharmacogenomic study identifies new genetic variants of DMET genes that are associated with the lipid-lowering response to statins. Genetic polymorphisms in DMET genes may impact the pharmacokinetics and lipid-lowering response to statin. The validation studies confirmations are needed in future pharmacogenomic studies.

Pharmacokinetics-based Chronotherapy.

Dosing time-dependency of pharmacokinetics (or chronopharmacokinetics) has been long recognized. Studies in recent years have revealed that daily rhythmicity in expression of drug-metabolizing enzymes and transporters (DMETs) are key factors determining chronopharmacokinetics. In this article, we briefly summarize current knowledge with respect to circadian mechanisms of DMETs and discuss how rhythmic DMETs are translated to drug chronoeffects. More importantly, we present our perspectives on pharmacokinetics-based chronotherapy.

The Nuclear Receptor PXR in Chronic Liver Disease.

Pregnane X receptor (PXR), a nuclear receptor known for modulating the transcription of drug metabolizing enzymes and transporters (DMETs), such as cytochrome P450 3A4 and P-glycoprotein, is functionally involved in chronic liver diseases of different etiologies. Furthermore, PXR activity relates to that of other NRs, such as constitutive androstane receptor (CAR), through a crosstalk that in turn orchestrates a complex network of responses. Thus, besides regulating DMETs, PXR signaling is involved in both liver damage progression and repair and in the neoplastic transition to hepatocellular carcinoma. We here summarize the present knowledge about PXR expression and function in chronic liver diseases characterized by different etiologies and clinical outcome, focusing on the molecular pathways involved in PXR activity. Although many molecular details of these finely tuned networks still need to be fully understood, we conclude that PXR and its modulation could represent a promising pharmacological target for the identification of novel therapeutical approaches to chronic liver diseases.

Clinical and Molecular Perspectives on Inflammation-Mediated Regulation of Drug Metabolism and Transport.

Inflammation is a possible cause of variability in drug response and toxicity due to altered regulation in drug-metabolizing enzymes and transporters (DMETs) in humans. Here, we evaluate the clinical and in vitro evidence on inflammation-mediated modulation of DMETs, and the impact on drug metabolism in humans. Furthermore, we identify and discuss the gaps in our current knowledge. A systematic literature search on PubMed, Embase, and grey literature was performed in the period of February to September 2020. A total of 203 papers was included. In vitro studies in primary human hepatocytes revealed strong evidence that CYP3A4 is strongly downregulated by inflammatory cytokines IL-6 and IL-1β. CYP1A2, CYP2C9, CYP2C19, and CYP2D6 were downregulated to a lesser extent. In clinical studies, acute and chronic inflammatory diseases were observed to cause downregulation of CYP enzymes in a similar pattern. However, there is no clear correlation between in vitro studies and clinical studies, mainly because most in vitro studies use supraphysiological cytokine doses. Moreover, clinical studies demonstrate considerable variability in terms of methodology and inconsistencies in evaluation of the inflammatory state. In conclusion, we find inflammation and pro-inflammatory cytokines to be important factors in regulation of drug-metabolizing enzymes and transporters. The observed downregulation is clinically relevant, and we emphasize caution when treating patients in an inflammatory state with narrow therapeutic index drugs. Further research is needed to identify the full extent of inflammation-mediated changes in DMETs and to further support personalized medicine.

Non-uniformity of Changes in Drug-Metabolizing Enzymes and Transporters in Liver Cirrhosis: Implications for Drug Dosage Adjustment.

Liver cirrhosis is a chronic disease that affects the liver structure, protein expression, and overall metabolic function. Abundance data for drug-metabolizing enzymes and transporters (DMET) across all stages of disease severity are scarce. Levels of these proteins are crucial for the accurate prediction of drug clearance in hepatically impaired patients using physiologically based pharmacokinetic (PBPK) models, which can be used to guide the selection of more precise dosing. This study aimed to experimentally quantify these proteins in human liver samples and assess how they can impact the predictive performance of the PBPK models. We determined the absolute abundance of 51 DMET proteins in human liver microsomes across the three degrees of cirrhosis severity (n = 32; 6 mild, 13 moderate, and 13 severe), compared to histologically normal controls (n = 14), using QconCAT-based targeted proteomics. The results revealed a significant but non-uniform reduction in the abundance of enzymes and transporters, from control, by 30–50% in mild, 40–70% in moderate, and 50–90% in severe cirrhosis groups. Cancer and/or non-alcoholic fatty liver disease-related cirrhosis showed larger deterioration in levels of CYP3A4, 2C8, 2E1, 1A6, UGT2B4/7, CES1, FMO3/5, EPHX1, MGST1/3, BSEP, and OATP2B1 than the cholestasis set. Drug-specific pathways together with non-uniform changes of abundance across the enzymes and transporters under various degrees of cirrhosis necessitate the use of PBPK models. As case examples, such models for repaglinide, dabigatran, and zidovudine were successful in recovering disease-related alterations in drug exposure. In conclusion, the current study provides the biological rationale behind the absence of a single dose adjustment formula for all drugs in cirrhosis and demonstrates the utility of proteomics-informed PBPK modeling for drug-specific dose adjustment in liver cirrhosis.

A novel gene associated with small bowel bleeding in patients taking low-dose aspirin

We have previously revealed the clinical factors and genetic polymorphisms associated with gastrointestinal mucosal injury and bleeding, induced by low-dose aspirin (LDA). After performing genome-wide analysis of single nucleotide polymorphisms (SNPs) using the Drug Metabolizing Enzymes and Transporters (DMET) system among drug metabolism and transporter genes, certain SNPs were found to increase the risk for LDA-induced small bowel bleeding. The aim of this study was to identify the SNPs involved in LDA-induced small bowel bleeding. Subjects were patients taking LDA, with small bowel bleeding diagnosed using capsule endoscopy. We investigated the clinical characteristics and the previously identified SNPs, that were examined by the DNA direct sequence method. 56 patients with bleeding and 410 controls taking LDA were enrolled. The risk factors associated with small bowel bleeding included smoking, cerebrovascular diseases, chronic renal failure, non-steroidal anti-inflammatory drug (NSAID) or anticoagulants combination, and two SNPs (CYP4F11 20043G>A (D446N) rs1060463, GSTP1 313A>G rs1695). After propensity score matching, GSTP1 rs1695 was significantly associated with small bowel bleeding. The GSTP1 SNP may be a predictive marker for small bowel bleeding among patients taking LDA.

A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels

The primary goal of precision medicine is to maximize the benefit‐risk relationships for individual patients by delivering the right drug to the right patients at the right dose. To achieve this goal, it has become increasingly important to assess gene‐drug interactions (GDIs) in clinical settings. The US Food and Drug Administration (FDA) periodically updates the table of pharmacogenetic/genomic (PGx) biomarkers in drug labeling on their website. As described herein, an effort was made to categorize various PGx biomarkers covered by the FDA‐PGx table into certain groups. There were 2 major groups, oncology molecular targets (OMT) and drug‐metabolizing enzymes and transporters (DMETs), which constitute ~70% of all biomarkers (~33% and ~35%, respectively). These biomarkers were further classified whether their labeling languages could be actionable in clinical practice. For OMT biomarkers, ~70% of biomarkers are considered actionable in clinical practice as they are critical for the selection of appropriate drugs to individual patients. In contrast, ~30% of DMET biomarkers are considered actionable for the dose adjustments or alternative therapies in specific populations, such as CYP2C19 and CYP2D6 poor metabolizers. In addition, the GDI results related to some of the other OMT and DMET biomarkers are considered to provide valuable information to clinicians. However, clinical GDI results on the other DMET biomarkers can possibly be used more effectively for dose recommendation. As the labels of some drugs already recommend the precise doses in specific populations, it will be desirable to have clear language for dose recommendation of other (or new) drugs if appropriate.

Ultrasensitive Quantification of Drug-metabolizing Enzymes and Transporters in Small Sample Volume by Microflow LC-MS/MS.

Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are broadly applicable to the characterization of in vitro and in vivo models, in vitro to in vivo extrapolation (IVIVE), and interindividual variability prediction. However, the emerging need of DMET quantification in small sample volumes such as organ-on a chip effluent, organoids, and biopsies requires ultrasensitive protein quantification methods. We present an ultrasensitive method that relies on an optimized sample preparation approach involving acetone precipitation coupled with a microflow-based liquid chromatography-tandem mass spectrometry (µLC-MS/MS) for the DMET quantification using limited sample volume or protein concentration, i.e., liver tissues (1-100 mg), hepatocyte counts (~4000 to 1 million cells), and microsomal protein concentration (0.01-1 mg/ml). The method was applied to quantify DMETs in differential tissue S9 fractions (liver, intestine, kidney, lung, and heart) and cryopreserved human intestinal mucosa (i.e., CHIM). The method successfully quantified >75% of the target DMETs in the trypsin digests of 1 mg tissue homogenate, 15,000 hepatocytes, and 0.06 mg/ml microsomal protein concentration. The precision of DMET quantification measured as the coefficient of variation across different tissue weights, cell counts, or microsomal protein concentration was within 30%. The method confirmed significant extrahepatic abundance of non-cytochrome P450 enzymes such as dihydropyridine dehydrogenase (DPYD), epoxide hydrolases (EPXs), arylacetamide deacetylase(AADAC), paraoxonases (PONs), and glutathione S-transferases (GSTs). The ultrasensitive method developed here is applicable to characterize emerging miniaturized in vitro models and small volume biopsies. In addition, the differential tissue abundance data of the understudied DMETs will be important for physiologically-based pharmacokinetic (PBPK) modeling of drugs.

Multiple microRNAs regulate tacrolimus metabolism through CYP3A5

The CYP3A5 gene polymorphism accounts for the majority of inter-individual variability in tacrolimus pharmacokinetics. We found that the basal expression of CYP3A5 in donor grafts also played a significant role in tacrolimus metabolism under the same genetic conditions after pediatric liver transplantation. Thus, we hypothesized that some potential epigenetic factors could affect CYP3A5 expression and contributed to the variability. We used a high-throughput functional screening for miRNAs to identify miRNAs that had the most abundant expression in normal human liver and could regulate tacrolimus metabolism in HepaRG cells and HepLPCs. Four of these miRNAs (miR-29a-3p, miR-99a-5p, miR-532-5p, and miR-26-5p) were selected for testing. We found that these miRNAs inhibited tacrolimus metabolism that was dependent on CYP3A5. Putative miRNAs targeting key drug-metabolizing enzymes and transporters (DMETs) were selected using an in silico prediction algorithm. Luciferase reporter assays and functional studies showed that miR-26b-5p inhibited tacrolimus metabolism by directly regulating CYP3A5, while miR-29a-5p, miR-99a-5p, and miR-532-5p targeted HNF4α, NR1I3, and NR1I2, respectively, in turn regulating the downstream expression of CYP3A5; the corresponding target gene siRNAs markedly abolished the effects caused by miRNA inhibitors. Also, the expression of miR-29a-3p, miR-99a-5p, miR-532-5p, and miR-26b-5p in donor grafts were negatively correlated with tacrolimus C/D following pediatric liver transplantation. Taken together, our findings identify these miRNAs as novel regulators of tacrolimus metabolism.

Utility of Quantitative Proteomics for Enhancing the Predictive Ability of Physiologically Based Pharmacokinetic Models Across Disease States.

Disease states such as liver cirrhosis and chronic kidney disease can lead to altered pharmacokinetics (PK) of drugs by influencing drug absorption, blood flow to organs, plasma protein binding, apparent volume of distribution, and drug-metabolizing enzyme and transporter (DMET) abundance. Narrow therapeutic index drugs are particularly vulnerable to undesired pharmacodynamics (PD) because of the changes in drug PK in disease states. However, systematic clinical evaluation of disease effect on drug PK and PD is not always possible because of the complexity or the cost of clinical studies. Physiologically based PK (PBPK) modeling is emerging as an alternate method to extrapolate drug PK from the healthy population to disease states. These models require information on the effect of disease condition on the activity or tissue abundance of DMET proteins. Although immunoquantification-based abundance data were available in the literature for a limited number of DMET proteins, the emergence of mass spectrometry-based quantitative proteomics as a sensitive, robust, and high-throughput tool has allowed a rapid increase in data availability on tissue DMET abundance in healthy versus disease states, especially in liver tissue. Here, we summarize these data including the available immunoquantification or mRNA levels of DMET proteins (healthy vs disease states) in extrahepatic tissue and discuss the potential applications of DMET abundance data in enhancing the capability of PBPK modeling in predicting drug disposition across disease states. Successful examples of PBPK modeling that integrate differences in DMET proteins between healthy and disease states are discussed.