Jennifer E. Sager

Age- and calorie-independent life span extension from dietary restriction by bacterial deprivation in Caenorhabditis elegans

BackgroundDietary restriction (DR) increases life span and delays age-associated disease in many organisms. The mechanism by which DR enhances longevity is not well understood.ResultsUsing bacterial food deprivation as a means of DR in C. elegans, we show that transient DR confers long-term benefits including stress resistance and increased longevity. Consistent with studies in the fruit fly and in mice, we demonstrate that DR also enhances survival when initiated late in life. DR by bacterial food deprivation significantly increases life span in worms when initiated as late as 24 days of adulthood, an age at which greater than 50% of the cohort have died. These survival benefits are, at least partially, independent of food consumption, as control fed animals are no longer consuming bacterial food at this advanced age. Animals separated from the bacterial lawn by a barrier of solid agar have a life span intermediate between control fed and food restricted animals. Thus, we find that life span extension from bacterial deprivation can be partially suppressed by a diffusible component of the bacterial food source, suggesting a calorie-independent mechanism for life span extension by dietary restriction.ConclusionBased on these findings, we propose that dietary restriction by bacterial deprivation increases longevity in C. elegans by a combination of reduced food consumption and decreased food sensing.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms “PBPK” and “physiologically based pharmacokinetic model” to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Inhibition of CYP2C19 and CYP3A4 by Omeprazole Metabolites and their Contribution to Drug-Drug Interactions

The aim of this study was to evaluate the contribution of metabolites to drug-drug interactions (DDI) using the inhibition of CYP2C19 and CYP3A4 by omeprazole and its metabolites as a model. Of the metabolites identified in vivo, 5-hydroxyomeprazole, 5′-O-desmethylomeprazole, omeprazole sulfone, and carboxyomeprazole had a metabolite to parent area under the plasma concentration–time curve (AUCm/AUCp) ratio ≥ 0.25 when either total or unbound concentrations were measured after a single 20-mg dose of omeprazole in a cocktail. All of the metabolites inhibited CYP2C19 and CYP3A4 reversibly. In addition omeprazole, omeprazole sulfone, and 5′-O-desmethylomeprazole were time dependent inhibitors (TDI) of CYP2C19, whereas omeprazole and 5′-O-desmethylomeprazole were found to be TDIs of CYP3A4. The in vitro inhibition constants and in vivo plasma concentrations were used to evaluate whether characterization of the metabolites affected DDI risk assessment. Identifying omeprazole as a TDI of both CYP2C19 and CYP3A4 was the most important factor in DDI risk assessment. Consideration of reversible inhibition by omeprazole and its metabolites would not identify DDI risk with CYP3A4, and with CYP2C19, reversible inhibition values would only identify DDI risk if the metabolites were included in the assessment. On the basis of inactivation data, CYP2C19 and CYP3A4 inhibition by omeprazole would be sufficient to identify risk, but metabolites were predicted to contribute 30–63% to the in vivo hepatic interactions. Therefore, consideration of metabolites may be important in quantitative predictions of in vivo DDIs. The results of this study show that, although metabolites contribute to in vivo DDIs, their relative abundance in circulation or logP values do not predict their contribution to in vivo DDI risk.

Fluoxetine and norfluoxetine mediated complex drug-drug interactions: in vitro to in vivo correlation of effects on CYP2D6, CYP2C19 and CYP3A4

Fluoxetine and its circulating metabolite norfluoxetine comprise a complex multiple‐inhibitor system that causes reversible or time‐dependent inhibition of the cytochrome P450 (CYP) family members CYP2D6, CYP3A4, and CYP2C19 in vitro. Although significant inhibition of all three enzymes in vivo was predicted, the areas under the concentration–time curve (AUCs) for midazolam and lovastatin were unaffected by 2‐week dosing of fluoxetine, whereas the AUCs of dextromethorphan and omeprazole were increased by 27‐ and 7.1‐fold, respectively. This observed discrepancy between in vitro risk assessment and in vivo drug–drug interaction (DDI) profile was rationalized by time‐varying dynamic pharmacokinetic models that incorporated circulating concentrations of fluoxetine and norfluoxetine enantiomers, mutual inhibitor–inhibitor interactions, and CYP3A4 induction. The dynamic models predicted all DDIs with less than twofold error. This study demonstrates that complex DDIs that involve multiple mechanisms, pathways, and inhibitors with their metabolites can be predicted and rationalized via characterization of all the inhibitory species in vitro.

Stereoselective Metabolism of Bupropion to OH-bupropion, Threohydrobupropion, Erythrohydrobupropion and 4'-OH-bupropion in vitro

Bupropion is a widely used antidepressant, smoking cessation aid, and weight-loss therapy. It is administered as a racemic mixture, but the pharmacokinetics and activity of bupropion are stereoselective. The activity and side effects of bupropion are attributed to bupropion and its metabolites S,S- and R,R-OH-bupropion, threohydrobupropion, and erythrohydrobupropion. Yet the stereoselective metabolism in vitro and the enzymes contributing to the stereoselective disposition of bupropion have not been characterized. In humans, the fraction of bupropion metabolized (fm) to the CYP2B6 probe metabolite OH-bupropion is 5–16%, but ticlopidine increases bupropion exposure by 61%, suggesting a 40% CYP2B6 and/or CYP2C19 fm for bupropion. Yet, the CYP2C19 contribution to bupropion clearance has not been defined, and the enzymes contributing to overall bupropion metabolite formation have not been fully characterized. The aim of this study was to characterize the stereoselective metabolism of bupropion in vitro to explain the stereoselective pharmacokinetics and the effect of drug-drug interactions (DDIs) and CYP2C19 pharmacogenetics on bupropion exposure. The data predict that threohydrobupropion accounts for 50 and 82%, OH-bupropion for 34 and 12%, erythrohydrobupropion for 8 and 4%, and 4′-OH-bupropion for 8 and 2% of overall R- and S-bupropion clearance, respectively. The fm,CYP2B6 was predicted to be 21%, and the fm,CYP2C19, 6% for racemic bupropion. Importantly, ticlopidine was found to inhibit all metabolic pathways of bupropion in vitro, including threohydrobupropion, erythrohydrobupropion, and 4′OH-bupropion formation, explaining the in vivo DDI. The stereoselective pharmacokinetics of bupropion were quantitatively explained by the in vitro metabolic clearances and in vivo interconversion between bupropion stereoisomers.

In vitro to in vivo extrapolation of the complex drug-drug interaction of bupropion and its metabolites with CYP2D6; simultaneous reversible inhibition and CYP2D6 downregulation

Graphical abstract Figure. No Caption available. Abstract Bupropion is a widely used antidepressant and smoking cessation aid and a strong inhibitor of CYP2D6 in vivo. Bupropion is administered as a racemic mixture of R‐ and S‐bupropion and has stereoselective pharmacokinetics. Four primary metabolites of bupropion, threo‐ and erythro‐hydrobupropion and R,R‐ and S,S‐OH‐bupropion, circulate at higher concentrations than the parent drug and are believed to contribute to the efficacy and side effects of bupropion as well as to the CYP2D6 inhibition. However, bupropion and its metabolites are only weak inhibitors of CYP2D6 in vitro, and the magnitude of the in vivo drug‐drug interactions (DDI) caused by bupropion cannot be explained by the in vitro data even when CYP2D6 inhibition by the metabolites is accounted for. The aim of this study was to quantitatively explain the in vivo CYP2D6 DDI magnitude by in vitro DDI data. Bupropion and its metabolites were found to inhibit CYP2D6 stereoselectively with up to 10‐fold difference in inhibition potency between enantiomers. However, the reversible inhibition or active uptake into hepatocytes did not explain the in vivo DDIs. In HepG2 cells and in plated human hepatocytes bupropion and its metabolites were found to significantly downregulate CYP2D6 mRNA in a concentration dependent manner. The in vivo DDI was quantitatively predicted by significant down‐regulation of CYP2D6 mRNA and reversible inhibition of CYP2D6 by bupropion and its metabolites. This study is the first example of a clinical DDI resulting from CYP down‐regulation and first demonstration of a CYP2D6 interaction resulting from transcriptional regulation.

Physiologically Based Pharmacokinetic Model of the CYP2D6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions

Physiologically based pharmacokinetic (PBPK) modeling of drug disposition and drug-drug interactions (DDIs) has become a key component of drug development. PBPK modeling has also been considered as an approach to predict drug disposition in special populations. However, whether models developed and validated in healthy populations can be extrapolated to special populations is not well established. The goal of this study was to determine whether a drug-specific PBPK model validated using healthy populations could be used to predict drug disposition in specific populations and in organ impairment patients. A full PBPK model of atomoxetine was developed using a training set of pharmacokinetic (PK) data from CYP2D6 genotyped individuals. The model was validated using drug-specific acceptance criteria and a test set of 14 healthy subject PK studies. Population PBPK models were then challenged by simulating the effects of ethnicity, DDIs, pediatrics, and renal and hepatic impairment on atomoxetine PK. Atomoxetine disposition was successfully predicted in 100% of healthy subject studies, 88% of studies in Asians, 79% of DDI studies, and 100% of pediatric studies. However, the atomoxetine area under the plasma concentration versus time curve (AUC) was overpredicted by 3- to 4-fold in end stage renal disease and hepatic impairment. The results show that validated PBPK models can be extrapolated to different ethnicities, DDIs, and pediatrics but not to renal and hepatic impairment patients, likely due to incomplete understanding of the physiologic changes in these conditions. These results show that systematic modeling efforts can be used to further refine population models to improve the predictive value in this area.

Interaction and Transport of Methamphetamine and its Primary Metabolites by Organic Cation and Multidrug and Toxin Extrusion Transporters

Methamphetamine is one of the most abused illicit drugs with roughly 1.2 million users in the United States alone. A large portion of methamphetamine and its metabolites is eliminated by the kidney with renal clearance larger than glomerular filtration clearance. Yet the mechanism of active renal secretion is poorly understood. The goals of this study were to characterize the interaction of methamphetamine and its major metabolites with organic cation transporters (OCTs) and multidrug and toxin extrusion (MATE) transporters and to identify the major transporters involved in the disposition of methamphetamine and its major metabolites, amphetamine and para-hydroxymethamphetamine (p-OHMA). We used cell lines stably expressing relevant transporters to show that methamphetamine and its metabolites inhibit human OCTs 1–3 (hOCT1–3) and hMATE1/2-K with the greatest potencies against hOCT1 and hOCT2. Methamphetamine and amphetamine are substrates of hOCT2, hMATE1, and hMATE2-K, but not hOCT1 and hOCT3. p-OHMA is transported by hOCT1–3 and hMATE1, but not hMATE2-K. In contrast, organic anion transporters 1 and 3 do not interact with or transport these compounds. Methamphetamine and its metabolites exhibited complex interactions with hOCT1 and hOCT2, suggesting the existence of multiple binding sites. Our studies suggest the involvement of the renal OCT2/MATE pathway in tubular secretion of methamphetamine and its major metabolites and the potential of drug-drug interactions with substrates or inhibitors of the OCTs. This information may be considered when prescribing medications to suspected or known abusers of methamphetamine to mitigate the risk of increased toxicity or reduced therapeutic efficacy.

Activity of CYP3A4 and CYP2C19 in Young Women Who Use Depot Medroxyprogesterone Acetate (DMPA) [23N]

INTRODUCTION: Cytochrome P450 enzymes (CYPs) affect concentrations of pharmaceutical agents. CYP3A4 metabolizes DMPA, and its activity is regulated by sex steroids. Metabolites of omeprazole (OMP) can be used as biomarkers to characterize activity of CYP3A4 and CYP2C19, another CYP involved in drug metabolism. OMP is metabolized by CYP3A4 to omeprazole-sulphone (SOMP) and by 2C19 to 5-hydroxyomeprazole (HOMP). These biomarkers are noninvasive means to examine effects of DMPA on CYP3A4 and 2C19. METHODS: This was an open-label, nonrandomized, prospective trial with Institutional Review Board approval. 20 female participants aged 18–24 years underwent an overnight fast prior to ingesting 20 mg omeprazole. Blood was collected prior and three hours later. Plasma was analyzed for omeprazole, omeprazole-sulphone, and 5-hydroxyomeprazole by high pressure liquid chromatography. Subjects then received 150 mg DMPA intramuscularly. Omeprazole test was repeated at four and twelve weeks. RESULTS: Nineteen participants completed. For CYP3A4, mean ratio of SOMP/OMP from baseline to four weeks decreased 0.556 to 0.495 (two-tailed P=.352, 95% CI −0.0718 to 0.1818), and baseline to 12 weeks decreased 0.551 to 0.445 (P=.197, 95% CI −0.1447 to 0.3291). These differences are not statistically significant. The HOMP/OMP change from baseline to four weeks increased 0.691 to 0.746 (P=.739, 95% CI −0.3692 to 0.2692), and baseline to 12 weeks increased 0.662 to 0.722 (P=.840, 95% CI −0.6330 to 0.5341). These do not reflect a significant change in CYP2C19 activity. CONCLUSION: Polymorphisms in phenotypic activity of CYPs may affect response to inducers. There was no significant alteration in CYP3A4/2C19 activity induced by DMPA. This is important as these enzymes are involved in metabolism of DMPA and estradiol.