Population factors such as age, gender, ethnicity, genotype and disease state can cause inter-individual variability in pharmacokinetic (PK) profile of drugs. Primarily, this variability arises from differences in abundance of drug metabolizing enzymes and transporters (DMET) among individuals and/or groups. Hence, availability of compiled data on abundance of DMET proteins in different populations can be useful for developing physiologically based pharmacokinetic (PBPK) models. The latter are routinely employed for prediction of PK profiles and drug interactions during drug development and in case of special populations, where clinical studies either are not feasible or have ethical concerns. Therefore, the main aim of this work was to develop a repository of literature-reported DMET abundance data in various human tissues, which included compilation of information on sample size, technique(s) involved, and the demographic factors. The collation of literature reported data revealed high inter-laboratory variability in abundance of DMET proteins. We carried out unbiased meta-analysis to obtain weighted mean and percent coefficient of variation (%CV) values. The obtained %CV values were then integrated into a PBPK model to highlight the variability in drug PK in healthy adults, taking lamotrigine as a model drug. The validated PBPK model was extrapolated to predict PK of lamotrigine in paediatric and hepatic impaired populations. This study thus exemplifies importance of the DMET protein abundance database, and use of determined values of weighted mean and %CV after meta-analysis in PBPK modelling for the prediction of PK of drugs in healthy and special populations.
Medication (drug) use in human pregnancy is prevalent. Determining fetal safety and efficacy of drugs is logistically challenging. However, predicting (not measuring) fetal drug exposure (systemic and tissue) throughout pregnancy is possible through maternal‐fetal physiologically based pharmacokinetic (PBPK) modeling and simulation. Such prediction can inform fetal drug safety and efficacy. Fetal drug exposure can be quantified in 2 complementary ways. First, the ratio of the steady‐state unbound plasma concentration in the fetal plasma (or area under the plasma concentration–time curve) to the corresponding maternal plasma concentration (ie, Kp,uu). Second, the maximum unbound peak (Cu,max,ss,f) and trough (Cu,min,ss,f) fetal steady‐state plasma concentrations. We (and others) have developed a maternal‐fetal PBPK model that can successfully predict maternal drug exposure. To predict fetal drug exposure, the model needs to be populated with drug specific parameters, of which transplacental clearances (active and/or passive) and placental/fetal metabolism of the drug are critical. Herein, we describe in vitro studies in cells/tissue fractions or the perfused human placenta that can be used to determine these drug‐specific parameters. In addition, we provide examples whereby this approach has successfully predicted systemic fetal exposure to drugs that passively or actively cross the placenta. Apart from maternal‐fetal PBPK models, animal studies also have the potential to estimate fetal drug exposure by allometric scaling. Whether such scaling will be successful is yet to be determined. Here, we review the above approaches to predict fetal drug exposure, outline gaps in our knowledge to make such predictions and map out future research directions that could fill these gaps.
Black pepper, though commonly employed as a spice, has many medicinal properties.It consists of volatile oils, alkaloids, pungent resins, etc., of which piperine is a major constituent. Though safe at low doses, piperine causes alteration in the activity of drug metabolising enzymes and transporters at high dose and is known to precipitate liver toxicity. It has a potential to form reactive metabolite(s) (RM) owing to the presence of structural alerts, such as methylenedioxyphenyl (MDP), α, β-unsaturated carbonyl group (Michael acceptor), and piperidine. The present study was designed to detect and characterize stable and RM(s) of piperine formed on in vitro incubation with human liver microsomes. The investigation of RMs was done with the aid of trapping agents, viz, glutathione (GSH) and N-acetylcysteine (NAC). The samples were analysed by ultra-high performance liquid chromatography coupled with high resolution mass spectrometry (UHPLC-HRMS) using Thermo Scientific Q Exactive Plus Orbitrap. Full scan MS followed by data-dependent MS 2 (Full MS-ddMS 2 ) mode was used to establish mass spectrometric fragmentation pathways of protonated piperine and its metabolites. In total, four stable metabolites and their isomers (M1a-c, M2a-b, M3a-c, and M4a-b) were detected. Their formation involved removal of carbon (3, M1a-c), hydroxylation (2, M2a-b), hydroxylation with hydrogenation (3, M3a-c), and dehydrogenation (2, M4a-b). Out of these metabolites, M1, M2, and M3 are reported earlier in the literature, but their isomers and two M4 variants are novel. In addition, six novel conjugates of RMs, including three GSH conjugates of m/z 579 and three NAC conjugates of m/z 435, were also observed.
The increasing incidence of ocular diseases has accelerated research into therapeutic interventions needed for the eye. Ocular enzymes play important roles in the metabolism of drugs and endobiotics. Various ocular drugs are designed as prodrugs that are activated by ocular enzymes. Moreover, ocular enzymes have been implicated in the bioactivation of drugs to their toxic metabolites.The key purpose of this study was to compare global proteomes of the pooled samples of the eye (n 5 11) and the liver (n 5 50) with a detailed analysis of the abundance of enzymes involved in the metabolism of xenobiotics and endobiotics. We used the postmitochondrial supernatant fraction (S9 fraction) of the lens-free whole eye homogenate as a model to allow accurate comparison with the liver S9 fraction. A total of 269 proteins (including 23 metabolic enzymes) were detected exclusively in the pooled eye S9 against 648 proteins in the liver S9 (including 174 metabolic enzymes), whereas 424 proteins (including 94 metabolic enzymes) were detected in both the organs. The major hepatic cytochrome P450 and UDP-glucuronosyltransferases enzymes were not detected, but aldehyde dehydrogenases and glutathione transferases were the predominant proteins in the eye. The comparative qualitative and quantitative proteomics data in the eye versus liver is expected to help in explaining differential metabolic and physiologic activities in the eye. SIGNIFICANCE STATEMENTInformation on the enzymes involved in xenobiotic and endobiotic metabolism in the human eye in relation to the liver is scarcely available. The study employed global proteomic analysis to compare the proteomes of the lens-free whole eye and the liver with a detailed analysis of the enzymes involved in xenobiotic and endobiotic metabolism. These data will help in better understanding of the ocular metabolism and activation of drugs and endobiotics.
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