Variation In Drug Metabolizing Enzymes Research Articles

Harnessing Pharmacogenomics in Clinical Research on Psychedelic-Assisted Therapy.

Psychedelics have recently re-emerged as potential treatments for various psychiatric conditions that impose major public health costs and for which current treatment options have limited efficacy. At the same time, personalized medicine is increasingly being implemented in psychiatry to provide individualized drug dosing recommendations based on genetics. This review brings together these topics to explore the utility of pharmacogenomics (a key component of personalized medicine) in psychedelic-assisted therapies. We summarized the literature and explored the potential implications of genetic variability on the pharmacodynamics and pharmacokinetics of psychedelic drugs including lysergic acid diethylamide (LSD), psilocybin, N,N-dimethyltryptamine (DMT), 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT), ibogaine and 3,4-methylenedioxymethamphetamine (MDMA). Although existing evidence is limited, particularly concerning pharmacodynamics, studies investigating pharmacokinetics indicate that genetic variants in drug-metabolizing enzymes, such as cytochrome P450, impact the intensity of acute psychedelic effects for LSD and ibogaine, and that a dose reduction for CYP2D6 poor metabolizers may be appropriate. Furthermore, based on the preclinical evidence, it can be hypothesized that CYP2D6 metabolizer status might contribute to altered acute psychedelic experiences with 5-MeO-DMT and psilocybin when combined with monoamine oxidase inhibitors. In conclusion, considering early evidence that genetic factors can influence the effects of certain psychedelics, we suggest that pharmacogenomic testing should be further investigated in clinical research. This is necessary to evaluate its utility in improving the safety and therapeutic profile of psychedelic therapies and a potential future role in personalizing psychedelic-assisted therapies, should these treatments become available.

Drug metabolizing enzymes pharmacogenetic variation-informed antidepressant therapy approach for common mental disorders: A systematic review and meta-analysis

ImportanceCurrently, 30–50 % of individuals with depression and 40 % with anxiety—collectively referred to as common mental disorders (CMDs), exhibit inadequate responses to antidepressant treatments. ObjectiveTo assess the effectiveness and safety of drug-metabolizing enzyme pharmacogenetic variation informed treatment (PGxIT) versus usual antidepressant treatment (UT) in patients with CMDs. Data sourcesA literature search was conducted in the MEDLINE, Scopus, and Cochrane Library databases from inception until January 30, 2024. Study selectionStudies were selected based on CMD diagnoses, reporting on the genetic variations of drug-metabolizing enzyme (DME) genes in relation to antidepressants, involving PGxIT and UT groups with human subjects, and published in English. Data extraction and synthesisData extraction and quality assessment were performed independently by two authors. A pooled risk ratio (RR) with 95 % CI was estimated using both random and fixed-effect models, and heterogeneity was assessed using Cochran's Q test and the I2 statistic. The publication bias of eligible studies was assessed using post hoc Doi plots and the LFK index. ResultsThis systematic review included 18 studies (n = 7021). The PGxIT demonstrated greater efficacy in the remission of symptoms of depressive disorder at 8 weeks (RR 1.523 [95 % CI: 1.255–1.843]; I2 = 48 %) and 12 weeks (RR 1.631 [95 % CI: 1.001–2.657]; I2 = 86 %; p < 0.01), and symptoms of anxiety disorder compared to UT. Additionally, the risk of adverse drug events (ADEs) was significantly lower in the PGxIT group (RR = 0.65 [95 % CI: 0.52–0.82]; I2 = 0 %) than in the UT group. The certainty of evidence for both outcomes was moderate. Conclusions and relevanceThis systematic review and meta-analysis suggest that pharmacogenetically guided antidepressant treatment, based on genetic variation in drug-metabolizing enzymes, is associated with superior efficacy in the remission of symptoms for patients with depressive disorders and a reduction in ADEs compared to usual treatment and the findings of the systematic review for remission in anxiety disorders indicate that, PGx guided treatment is also associated with increased remission of symptoms in anxiety disorders compared to usual treatment.

Pharmacogenomics: New Personalized Medicine Approach

Ineffective treatments and the management of adverse drug reactions are responsible for a large proportion of health resources. Drug response and toxicity are significantly influenced by genetic variations in drug-metabolizing enzymes, transporters, and targets. This information may be used to help in selecting the appropriate drug and dosage. One of the newest methods of precision medicine is pharmacogenomics, which adjusts drug selection and dosage based on a patient's genetic characteristics. Pharmacogenomics can reduce the chances of negative effects of the drugs and increase the chances of successful treatment, as the medicines are only for the targeted individuals. It can also revolutionize the healthcare industry to be more specific.&#x0D; The main reason that drugs and their dosages are designed based on individuals’ genetic characteristics is that they have a great influence on drug metabolism and its response. The main evidence is the link between dosage requirements and genetic differences in drug transporters such as p-glycoprotein (ABCB1) and OATP-C (SLC21A6), and drug-metabolizing enzymes such as cytochrome P450 (CYP) 2D6, CYP2C19, and CYP2C9. Polymorphisms in these enzymes can alter the drug metabolism phenotype. As these are all proteins translated from genetic information, any change or point mutation can increase or decrease the effectiveness of the enzymes. This can cause more adverse effects in some individuals. Every individual’s body function is unique, and its right to say that the dosage of drugs depends on that.&#x0D; Many pharmacogenomic tests and their outcomes have confirmed the potential to improve therapeutic activities. In the field of oncology, the use of this technique to establish a connection between drug metabolism and genetic biomarkers is more reliable, effective, and precise. PD-L1 expression analysis is an example of pharmacogenomics potential. Expression analysis helps physicians select which cancer patients are suitable for immunotherapy techniques. Survival rates for lung and other cancers have drastically increased by comparing the genetic factors and immunotherapies that trigger the immune response against tumors. Similarly, pharmacogenomics has promising potential in antiviral drugs. It targets the genes that influence such drugs to predict treatment success. Therefore, as our understanding of the genome becomes clearer, the potential of pharmacogenomics will also grow.&#x0D; Although this is a very promising technique and many scientific organizations are pursuing this approach, there has been very little success in implementing it in clinical trials. Targeted drugs are very costly, as they require the identification of candidate genes and drug responses to these genes. Acceptance is another matter to focus on as most people are not comfortable with these approaches; they prefer conventional approaches. Implementation, cost, acceptance, defining targeted genes, and defining drug responses are all barriers to pharmacogenomics right now. Physicians and patients must adapt to avoid the side effects of drugs by implementing a personalized medicine approach.&#x0D; Despite growing interest, the adoption of pharmacogenomics is still in its early stages of development. Attempts to fully apply pharmacogenomics to enhance health will likely take decades. It is important to find a balance between equality concerns across varied populations and increased healthcare efficiency to successfully implement pharmacogenomics. This can be done more effectively through coordinated studies and worldwide collaboration. This not only makes it possible to better utilize resources and knowledge but also sparks political interest in and support for the personalized medicine approach.&#x0D;

Novel approaches to characterize individual drug metabolism and advance precision medicine.

Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual's drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, non-genetic factors are also known to influence drug metabolism phenotypes. This minireview will discuss approaches beyond pharmacogenetic testing to phenotype DMEs- particularly the cytochrome P450 enzymes- in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating non-coding RNAs (ncRNAs) and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to: 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity; 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies; and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. Significance Statement This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. Herein, we highlight the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic (PBPK) strategy for patient characterization and precision dosing.

Synergistic polymorphic interactions of phase II metabolizing genes and their association toward lung cancer susceptibility in North Indians

ABSTRACT Lung cancer is a multifactorial carcinoma with diverse heterogeneity. Genetic variations in drug-metabolizing enzymes may lead to defective detoxification and clearance of carcinogenic compounds. The high-order gene-gene interaction has been carried out between different genotypes of Phase II detoxification genes (NQO1, SULT1A1, NAT2, and EPHX1). Our results depict the genetic combination of SULT1A1 R 213 H with NAT2 × 5B L 161 L, SULT1A1 R 213 H with NAT2 × 5C K 268 R, EPHX1 H 139 R and NAT2 × 5B L 161 L exhibit a protective effect towards lung cancer risk. Further, the triple combinations of NQO1 P 187 S, EPHX1 Y 113 H, and EPHX1 H 139 R; NQO1 P 187 S, EPHX1 Y 113 H, and NAT2 × 6 R 197 Q; NQO1 P 187 S, EPHX1 Y 113 H, and NAT2 × 7 G 286 E; SULT1A1 R 213 H, EPHX1 H 139 R, and NAT2 × 7 G 286 E suggested a two-fold increased risk of lung cancer for subjects. Genetic polymorphisms of phase II detoxifying genes (NAT2, NQO1, EPHX1, SULT1A1) are prognostic markers for lung cancer.

CYP3A and CYP2B6 Genotype Predicts Glucose Metabolism Disorder among HIV Patients on Long-Term Efavirenz-Based ART: A Case-Control Study.

Long-term antiretroviral treatment (cART) increases the risk of glucose metabolism disorders (GMDs). Genetic variation in drug-metabolizing enzymes and transporters may influence susceptibility to cART-associated GMDs. We conducted a case-control study to investigate the association of pharmacogenetic variations with cART-induced GMDs. A total of 240 HIV patients on long-term efavirenz-based cART (75 GMD cases and 165 controls without GMDs) were genotyped for CYP3A4*1B, CYP3A5 (*3,*6), CYP2B6*6, UGT2B7*2, ABCB1 (c.3435C>T, c.4036A>G), and SLCO1B1 (*1b, *5). GMD cases were defined as the presence of impaired fasting glucose, insulin resistance, or diabetes mellitus (DM). Case-control genotype/haplotype association and logistic regression analysis were performed by adjusting for age, sex, and BMI. The major CYP3A haplotype were CYP3A5*3 (53.8%), CYP3A4*1B (17.3%), combinations of CYP3A4*1B, and CYP3A5*6 (10.9%), and CYP3A wild type (7%). CYP3A5*6 allele (p = 0.005) and CYP3A5*6 genotype (p = 0.01) were significantly associated with GMD cases. Multivariate analysis indicated CYP3A haplotype as a significant predictor of GMD (p = 0.02) and IFG (p = 0.004). CYP2B6*6 significantly predicted DM (p = 0.03). CYP3A haplotype and CYP2B6*6 genotype are independent significant predictors of GMD and DM, respectively, among HIV patients on long-term EFV-based cART.

Effect of Genetic Variations in Drug-Metabolizing Enzymes and Drug Transporters on the Pharmacokinetics of Rifamycins: A Systematic Review.

BackgroundRifamycins are a novel class of antibiotics clinically approved for tuberculosis chemotherapy. They are characterized by high inter-individual variation in pharmacokinetics. This systematic review aims to present the contribution of genetic variations in drug-metabolizing enzymes and transporter proteins to the inter-individual variation of rifamycin pharmacokinetics.MethodWe followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines. The search for relevant studies was done through PubMed, Embase, Web of Science, and Scopus databases. Studies reporting single nucleotide polymorphism in drug transporters and metabolizing enzymes’ influence on rifamycin pharmacokinetics were solely included. Two reviewers independently performed data extraction.ResultsThe search identified 117 articles of which 15 fulfilled the eligibility criteria and were included in the final data synthesis. The single nucleotides polymorphism in the drug transporters SLCO1B1 rs4149032, rs2306283, rs11045819, and ABCB1 rs1045642 for rifampicin, drug metabolizing enzyme AADAC rs1803155 for rifapentine and CES2 c.-22263A>G (g.738A>G) for rifampicin partly contributes to the variability of pharmacokinetic parameters in tuberculosis patients.ConclusionThe pharmacokinetics of rifamycins is influenced by genetic variation of drug-metabolizing enzymes and transporters. Controlled clinical studies are, however, required to establish these relationships.

Decoding variants in drug-metabolizing enzymes and transporters in solid tumor patients by whole-exome sequencing.

BackgroundPharmacogenetics is involved in customizing therapy according to the genetic makeup of an individual, and is applicable for chemotherapy, radiotherapy as well as targeted therapy. Drug metabolizing enzymes (DMEs) involving both phase I, and phase II reactions are widely studied. Our study was involved in whole exome sequencing (WES) of cancer patients, followed by analysis for identifying key variations in DMEs, and associated transporters that have a potential impact on treatment outcome.MethodologyA total of 181 solid tumor patients at stage >/= III were subjected to WES by the SureSelectXT Human All Exon V6 + UTR library preparation kit, and sequencing in the Illumina NextSeq 550 system. Bioinformatics analysis involved use of GATK pipeline, and the variants were further assessed for population frequency, functional impact with annovar insilico algorithms. Further variant information from significant DMEs, and transporters were extracted and analyzed with PharmGKB to assess level of evidence and infer their impact on the pathways involved in drug response.ResultsThe total study cohort of 181 solid tumor patients included 60 males, and 121 females respectively. Among DMEs, deleterious mutation in dihydropyrimidine dehydrogenase (DPYD; rs67376798), solute carrier organic anion transporter family member 1B1 (SLCO1B1*5), and cytochrome P450 2D6 (CYP2D6*10) associated with metabolism of anticancer drugs was detected to be in high frequency of 26%, 21% and 25% respectively.ConclusionOur analysis detected variations in both phase I and phase II DMEs, as well as associated transporter genes which has been documented to reduce drug efficacy, as well as cause grade 3 and 4 toxicity. Our study reiterates the significance of pharmacogenomics in stratifying patients for appropriate therapy regimen focused at better treatment outcome and quality of life.

A Population Pharmacokinetic-Pharmacogenetic Model of Lamotrigine in Chinese Children With Epilepsy.

The pharmacokinetics of lamotrigine (LTG) is complex and varies significantly among individuals, especially among children. Therefore, this study aimed to establish a population pharmacokinetic (PPK) model of LTG in Chinese children with epilepsy and to comprehensively evaluate the effects of genetic variations in drug-metabolizing enzymes, transporters, and a transcriptional regulator on LTG pharmacokinetics. Three hundred eighty-five steady-state plasma concentrations were obtained from 179 children (age 10.72 ± 3.05 years and body weight 46.23 ± 17.77 kg) with epilepsy during therapeutic drug monitoring. These patients were divided into the PPK-model group (n = 121) and the PPK-validation group (n = 58) and were genotyped for UGT1A4, UGT2B7, ABCB1, ABCG2, SLC22A1, and HNF4α. PPK analysis was performed by nonlinear mixed effects modeling. In the final model, apparent clearance (CL/F) of LTG was estimated to be 1.48 L/h; 500 mg valproic acid, oxcarbazepine, and UGT2B7-161TT genotype changed the CL/F by -46.2, +31.1, and -21.8%, respectively. Body weight was also identified as a significant covariate affecting LTG CL/F. A PPK-pharmacogenetic model of LTG in Chinese children with epilepsy was successfully established with nonlinear mixed effects modeling. Genotyping for UGT2B7-161C>T may be useful in titrating the optimal LTG dose.

Abstract P3-12-07: Pharmacogenetic determinants of aromatase inhibitors pharmacokinetics and side effects: 6-month results of the adjuvant breast cancer longitudinal PHACS study (NCT01127295)

Abstract Supported by a PHRC grant (#09-18-005) Background: Recent literature has suggested that germline genetic variants of drug-metabolizing enzymes or CYP19A1 (coding for aromatase) may be involved in the systemic aromatase inhibitors (AI) concentrations or the occurrence of side effects (Hertz et al. Pharmacogenomics 2017). A prospective multicentre 3-year follow-up study was carried out to investigate the relationships between pharmacogenetics (PG), pharmacokinetics (PK) and toxicity in breast cancer patients treated with adjuvant AI (n=1098) or tamoxifen (n=879). The clinical results and the tamoxifen PG/PK analyses are described elsewhere (abstracts #851544 and #850248). Methods: SNP genotyping of 95 SNPs was performed on the Biomark (Fluidigm) with Taqman assays and was available for 373, 515 and 151 patients treated with anastrozole (ANA), letrozole (LETRO) and exemestane (EXE) respectively. CYP2A6 metaboliser status (MS) (poor, intermediate or normal) was determined based on alleles function (*1, *9, *2) and number of CYP2A6 copies. Trough plasma concentrations of each drug were determined 6 months after the start of the study by UPLC-MS/MS and were available for 342, 463 and 130 patients of the ANA, LETRO and EXE arms. Patients with AI concentrations below the limit of quantification were excluded for non-compliance (9 patients for ANA, 8 patients for LETRO and 7 patients for EXE). Toxicity was measured as a binary outcome (occurrence or worsening of hot flushes, fatigue, pain, arthralgia, vaginal dryness). All genetic associations were adjusted for multiple testing. Results: ANA concentration was significantly higher in patients experiencing pain (p=0.025) and was associated with rs28365063 (UGT2B7 g.372A&amp;gt;G). LETRO concentrations were strongly associated with CYP2A6 metabolizer status (p=0.0001) but did not differ in patients with or without toxicity. In the EXE arm, patients with hot flushes or arthralgia had a significantly lower level of exemestane (p= 0.0002 and p=0.023 respectively) but since the metabolism of EXE leads to active 17-hydroexemestane, we can hypothesize that the lower EXE concentration is an indirect reflection of the metabolite formation. A SNP (rs2307424) in NR1I3 gene (coding for the constitutive androstane receptor CAR) was associated with EXE concentrations. CAR has been shown to regulate CYP2B6, which is involved in the formation of 6-hydroxy-methyl-exemestane (inactive metabolite). Regarding the relationships between PG and toxicity, in the ANA arm, 3 SNPs of CYP19A1 gene tended to be associated with hot flushes worsening (rs934635) and arthralgia (rs10046 and rs2304463) but did not remain significant after multiple tests correction. In the EXE arm, several SNPs in NR1I3 gene were associated with fatigue. In the LETRO arm, patients with a poor CYP2A6 MS had a higher risk of experiencing depression. Conclusions: Our study confirms the predominant role of CYP2A6 in LETRO PK. To our knowledge, this is the first study to report on the role of UGT2B7 rs28365063 in ANA and NR1I3 in EXE PK and side effects. These relationships need to be re-evaluated with the drug concentrations obtained during the 3-year follow-up. Citation Format: Thomas F, Marquet P, Pinguet F, White-Koning M, Robert J, Tafzi N, Solassol I, Despax R, Levasseur N, Ellis S, Massoubre A, Mbatchi L, Le Morvan V, Roché H, Chatelut E, Evrard A. Pharmacogenetic determinants of aromatase inhibitors pharmacokinetics and side effects: 6-month results of the adjuvant breast cancer longitudinal PHACS study (NCT01127295) [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-12-07.

Poster Number: NR 31 - Combinatorial Pharmacogenomics Reduces Polypharmacy and Medication Cost in Elderly Patients with Anxiety and Depression

Introduction: The frequency of adverse drug reactions (ADRs) and therapeutic failures are often increased in elderly patients. Individual genetic variations in drug-metabolizing enzymes and neurotransmitter transporters and receptors add to the heterogeneity of treatment response and safety. Selection of genetically concordant medications is not only important for proper efficacy and avoiding ADRs, but also affects polypharmacy, the number of prescriptions needed to treat psychiatric and associated comorbid conditions, and therefore also impacts healthcare costs.

Significant change of cytochrome P450s activities in patients with hepatocellular carcinoma.

The lack of information concerning individual variation in drug-metabolizing enzymes is one of the most important obstacles for designing personalized medicine approaches for hepatocellular carcinoma (HCC) patients. To assess cytochrome P450 (CYP) in the metabolism of endogenous and exogenous molecules in an HCC setting, the activity changes of 10 major CYPs in microsomes from 105 normal and 102 HCC liver tissue samples were investigated. We found that CYP activity values expressed as intrinsic clearance (CLint) differed between HCC patients and control subjects. HCC patient samples showed increased CLint for CYP2C9, CYP2D6, and CYP2E1 compared to controls. Meanwhile, CYP1A2, CYP2C8, and CYP2C19 CLint values decreased and CYP2A6, CYP2B6, and CYP3A4/5 activity was unchanged relative to controls. For patients with HCC accompanied by fibrosis or cirrhosis, the same activity changes were seen for the CYP isoforms, except for CYP2D6 which had higher values in HCC patients with cirrhosis. Moreover, CYP2D6*10 (100C>T), CYP2C9*3 (42614 A>C), and CYP3A5*3 (6986A>G) polymorphisms had definite effects on enzyme activities. In the HCC group, the CLint of CYP2D6*10 mutant homozygote was decreased by 95% compared to wild-type samples, and the frequency of this homozygote was 2.8-fold lower than the controls.In conclusion, the activities of CYP isoforms were differentially affected in HCC patients. Genetic polymorphisms of some CYP enzymes, especially CYP2D6*10, could affect enzyme activity. CYP2D6*10 allelic frequency was significantly different between HCC patients and control subjects. These findings may be useful for personalizing the clinical treatment of HCC patients as well as predicting the risk of hepatocarcinogenesis.

Association Between Combined Presence of Hepatitis C Virus and Polymorphisms in Different Genes With Toxicities of Methotrexate and 6-Mercaptopurine in Children With Acute Lymphoblastic Leukemia.

The aim of the present study is to determine the correlation of hepatitis C virus (HCV) infection and polymorphisms in different genes with toxicity of either methotrexate (MTX) or 6-mercaptopurine (6-MP) administered to children with acute lymphoblastic leukemia (ALL). One hundred children with low-risk ALL, who were treated according to the St. Jude Total therapy XV, were recruited. The recruited children were receiving MTX and 6-MP during maintenance phase. Patients were excluded from the study if they had other types of leukemia. Genotyping analyses for the thiopurine methyltransferase (TPMT), methylenetetrahydrofolate reductase (MTHFR), and glutathione S-transferase (GST) genes were performed using a combination of polymerase chain reaction (PCR) and PCR-RFLP (where RFLP is restriction fragment length polymorphism) protocols. Relevant clinical data on adverse drug reactions were collected objectively (blinded to genotypes) from the patient medical records. There was a significant correlation between the combined presence of HCV and TPMT*3B G460A gene polymorphisms and grades 2-4 hepatotoxicity as aspartate aminotransferase (AST) elevation (P < 0.04). The same observation was seen when comparing either the presence of HCV alone or the presence of the gene polymorphism alone. A significant association between the combined presence of HCV and MTHFR C677T polymorphism and grades 2-4 hepatotoxicity as alanine aminotransferase (ALT), AST, and alkaline phosphatase (ALP) elevation was observed (P values <0.001, 0.02, and 0.001, respectively). The presence of HCV infection had a significant negative effect on hepatic transaminases. The present data support a role for combining analysis of genetic variation in drug-metabolizing enzymes and the presence of HCV in the assessment of specific drugs toxicities in multiagent chemotherapeutic treatment regimens.

Investigating the impact of missense mutations in hCES1 by in silico structure-based approaches.

Genetic variations in drug-metabolizing enzymes have been reported to influence pharmacokinetics, drug dosage, and other aspects that affect therapeutic outcomes. Most particularly, non-synonymous single-nucleotide polymorphisms (nsSNPs) resulting in amino acid changes disrupt potential functional sites responsible for protein activity, structure, or stability, which can account for individual susceptibility to disease and drug response. Investigating the impact of nsSNPs at a protein's structural level is a key step in understanding the relationship between genetic variants and the resulting phenotypic changes. For this purpose, in silico structure-based approaches have proven their relevance in providing an atomic-level description of the underlying mechanisms. The present review focuses on nsSNPs in human carboxylesterase 1 (hCES1), an enzyme involved in drug metabolism. We highlight how prioritization of functional nsSNPs through computational prediction techniques in combination with structure-based approaches, namely molecular docking and molecular dynamics simulations, is a powerful tool in providing insight into the underlying molecular mechanisms of nsSNPs phenotypic effects at microscopic level. Examples of in silico studies of carboxylesterases (CESs) are discussed, ranging from exploring the effect of mutations on enzyme activity to predicting the metabolism of new hCES1 substrates as well as to guiding rational design of CES-selective inhibitors.

Predicting paclitaxel-induced neutropenia using the DMET platform.

The use of paclitaxel in cancer treatment is limited by paclitaxel-induced neutropenia. We investigated the ability of genetic variation in drug-metabolizing enzymes and transporters to predict hematological toxicity. Using a discovery and validation approach, we identified a pharmacogenetic predictive model for neutropenia. For this, a drug-metabolizing enzymes and transporters plus DNA chip was used, which contains 1936 SNPs in 225 metabolic enzyme and drug-transporter genes. Our 10-SNP model in 279 paclitaxel-dosed patients reached 43% sensitivity in the validation cohort. Analysis in 3-weekly treated patients only resulted in improved sensitivity of 79%, with a specificity of 33%. None of our models reached statistical significance. Our drug-metabolizing enzymes and transporters-based SNP-models are currently of limited value for predicting paclitaxel-induced neutropenia in clinical practice. Original submitted 9 March 2015; Revision submitted 20 May 2015.

Gene-environment interactions in determining differences in genetic susceptibility to cancer in subsites of the head and neck.

Genetic differences in susceptibility to cancer in subsites of the head and neck were investigated in a case-control study involving 750 cases of cancers of the oral cavity, larynx, or pharynx, and an equal number of healthy controls. The prevalence of variant genotypes of cytochrome P450 (CYP) 1A1, 1B1, 2E1, or glutathione-S-transferase M1 (null) in cases suggests that polymorphisms in drug metabolizing enzymes (DMEs) modify cancer risk within subsites of the head and neck. Tobacco or alcohol use was found to increase the risk in cases of laryngeal, pharyngeal, or oral cavity cancers. Interaction between genetic variation in DMEs and tobacco smoke (or smoking) exposures conferred significant risk for laryngeal cancer. Likewise, strong associations of the polymorphic genotypes of DMEs with cases of pharyngeal and oral cavity cancer who were tobacco chewers or alcohol users demonstrate that gene-environment interactions may explain differences in genetic susceptibility for cancers of the oral cavity, larynx, and pharynx.

A wide pharmacogenomic panel from a single oncology center in São Paulo: Data from 103 patients since 2005.

e22050 Background: Pharmacogenomics has recently emerged as a major tool in the clinical analysis of cancer treatment efficacy. Understanding how mutations alter drug metabolism is crucial for both prognosis and choosing the most likely beneficial drug for each individual patient. Methods: 103 patients' files were studied and the most common malignancies were breast's (43 patients) and colorectal's (19 pts). The median age was 55 years (IQR 46-65), 32 (31%) were men. We performed a retrospective analysis of the pharmacogenetic-assay results and clinical files, which were recorded between 2005 and 2013. The goal of the study was to correlate the mutagenic profile of cancer patients, by identifying mutations in genes responsible for drug metabolism and DNA repair. Results: All distributions were in Hardy Weinberg, except for UGT1A1. The UGT1A1 analysis revealed that 14/51 patients were at increased risk of neutropenia during treatment with irinotecan due to mutations in that gene. Analysis of mutations affecting fluoropyrimidine metabolism, which upregulate both TS and DPD2A genes, yielded the following results: TS-TYMS mutations in 40/73 pts; MTHFR (55/ 89), and DPD2A (1/74), which were associated with higher toxicity in those patients. As for genes encoding DNA repair proteins such as XRCC1 Arg194, XRCC1 Arg 399 , XRCC3, XRCC7, 52/54 patients had mutations in this gene family. In the study of COMT Val, responsible for the proliferative potential and the carcinogenic effect, mutations were found in 30/45 patients. At least one mutation was found in 48/61 patients in genes encoding DNA-repair and cell detoxification enzymes, such as GSTM1, GSTT1 and GSTP1. As for the CYP2D6 family (variants 3,4,5,6,10), responsible for Tamoxifen metabolism, 12/31 patients presented at least one mutation. Conclusions: Most patients showed genetic variations in drug-metabolizing enzymes. Pharmacogenomics has become a useful tool in the selection of the best drug for each individual patient, thereby decreasing the probability of poor clinical responses and allowing a better control of side effects.

Overlapping Effects of Genetic Variation and Epigenetics on Drug Response: Challenges of Pharmacoepigenomics

The inf luence of genetic variants on drug response is becoming increasingly better understood. In particular, genetic variants in drugmetabolizing enzymes and drug transporters and also mutations of the tumor genome (e.g., in tyrosine kinase signaling pathways) have been extensively characterized in vitro and in vivo. Whereas the consideration of genomic biomarkers is crucial in modern oncologic approaches, so far for non-oncologic diseases, only a few drugrelated genetic biomarkers can be translated into clinical practice. Clear pharmacokinetic consequences have been derived (e.g., for genetic variants in drug-metabolizing enzymes), leading to complete or severe loss of function as in the cases of phase I enzymes, such as CYP2D6, or the phase II enzyme TPMT [1]. In addition, specific HLA alleles have been convincingly demonstrated to be associated with severe adverse drug reactions (e.g., HLA-B*1502 in abacavir exposure) [2]. The majority of genes influencing pharmacokinetics and pharmacodynamics, however, consist of frequent variants leading to no or only modest changes in drug response, and pharmacogenetic data derived from various different in vitro and in vivo studies have not always been reproducible or are sometimes even contradictory. It is possible that the consequences of genetic traits are partly overexposed by the effects of transcriptional and posttranscriptional gene regulation through induction (e.g., by PXR, GCR or cytokines). Obviously, genetic traits – which are often considered to be static variants – are only one, and sometimes only a small piece in the puzzle of complex pharmacokinetic/pharmacodynamic modeling. In recent years, there has been a rapid increase in knowledge regarding genes that are essentially regulated by epigenetics through activation and silencing by DNA methylation and/or histone acetylation and miRNA-related post-transcriptional processes. These general mechanisms are key players in gene expression. Accordingly, administration of drugs inhibiting DNA methylation, such as 5-azacytidine or the anticonvulsant valproate, may increase ADME gene (related to drug absorption, distribution, metabolism and excretion) expression through activation of its promoters. Moreover, DNA methylation or histone acetylation may change under disease conditions.

Humanized Transgenic Mouse Models for Drug Metabolism and Pharmacokinetic Research

Extrapolation of the metabolic, pharmacokinetic and toxicological data obtained from animals to humans is not always straightforward, given the remarkable species difference in drug metabolism that is due in large part to the differences in drug-metabolizing enzymes between animals and humans. Furthermore, genetic variations in drug-metabolizing enzymes may significantly alter pharmacokinetics, drug efficacy and safety. Thus, humanized transgenic mouse lines, in which the human drug-metabolizing enzymes are expressed in mouse tissues in the presence or absence of mouse orthologues, have been developed to address such challenges. These humanized transgenic mice are valuable animal models in understanding the significance of specific human drug-metabolizing enzymes in drug clearance and pharmacokinetics, as well as in predicting potential drug-drug interactions and chemical toxicity in humans. This review, therefore, aims to summarize the development and application of some humanized transgenic mouse models expressing human drug-metabolizing enzymes. The limitations of these genetically modified mouse models are also discussed.

Opening the Gate for Genomics Data into Clinical Research: A Use Case in Managing Patients‘ DNA Samples from the Bench to Drug Development

The use of human genetic polymorphism data in drug development is not a recent event. Typically, the detection of patients' genetic variations in drug-metabolizing enzymes has become common practice in clinical laboratories. What is new is the scale and diversity of genomics data that has entered into the drug research and development decision-making process. At least three concurrent events contribute to this paradigm shift: first the growing body of evidence that establishes that interindividual variation in both therapeutic response and adverse events are attributable to a genetic component; second the technological progress that enables the consistent and reproducible detection of human genomic quantities; third the expectation that the productivity of new drug development will be increased by identifying which patients would benefit from candidate therapies early in the clinical process. This influx of human genomics data into clinical laboratories requires some logistical adjustment in terms of data management. The major specifications of an information solution system intended for a clinical genomic laboratory are its compliance with regulatory procedures, regarding the handling of human genetic data and its subsequent integration into an existing clinical data management system from the hosting institution. The purpose of this article is to inform the community of the challenges in setting up a center for genomics data that ensures accurate, traceable and integrated data for laboratory management. This is by no means the only way to accomplish the same goal, and is simply presented as one way that Pfizer chose to solve these issues.