The human breast cancer resistance protein (BCRP, gene symbol
ABCG2
) is an ATP-binding cassette (ABC) efflux transporter. It was so named because it was initially cloned from a multidrug-resistant ...breast cancer cell line where it was found to confer resistance to chemotherapeutic agents such as mitoxantrone and topotecan. Since its discovery in 1998, the substrates of BCRP have been rapidly expanding to include not only therapeutic agents but also physiological substances such as estrone-3-sulfate, 17β-estradiol 17-(β-
d
-glucuronide) and uric acid. Likewise, at least hundreds of BCRP inhibitors have been identified. Among normal human tissues, BCRP is highly expressed on the apical membranes of the placental syncytiotrophoblasts, the intestinal epithelium, the liver hepatocytes, the endothelial cells of brain microvessels, and the renal proximal tubular cells, contributing to the absorption, distribution, and elimination of drugs and endogenous compounds as well as tissue protection against xenobiotic exposure. As a result, BCRP has now been recognized by the FDA to be one of the key drug transporters involved in clinically relevant drug disposition. We published a highly-accessed review article on BCRP in 2005, and much progress has been made since then. In this review, we provide an update of current knowledge on basic biochemistry and pharmacological functions of BCRP as well as its relevance to drug resistance and drug disposition.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Fetal exposure to drugs cannot be readily estimated from single time point cord blood sampling at the time of delivery. Therefore, we developed a physiologically based pharmacokinetic (PBPK) model to ...estimate fetal drug exposure throughout pregnancy. In this study, we report verification of this novel maternal-fetal PBPK (m-f-PBPK) model for drugs that passively diffuse across the placenta and are not metabolized/transported there. Our recently built m-f-PBPK model was populated with gestational age-dependent changes in maternal drug disposition and maternal-fetal physiology. Using midazolam as an in vivo calibrator, the transplacental passive diffusion clearance of theophylline and zidovudine was first estimated. Then, for verification, the predicted maternal plasma (MP) and umbilical venous (UV) plasma drug concentrations by our m-f-PBPK were compared against those observed at term. Overall, our m-f-PBPK model well predicted the maternal and fetal exposure to the two verification drugs, theophylline and zidovudine, at term, across a range of dosing regimens, with nearly all observed MP and UV plasma drug concentrations falling within the 90% prediction interval i.e., 5th-95th percentile range of a virtual pregnant population (
= 100). Prediction precision and bias of theophylline MP and UV were 14.5% and 12.4%, and 9.4% and 7.5%, respectively. Furthermore, for zidovudine, after the exclusion of one unexpectedly low MP concentration, prediction precision and bias for MP and UV were 50.3% and 30.2, and 28.3% and 15.0%, respectively. This m-f-PBPK should be useful to predict fetal exposure to drugs, throughout pregnancy, for drugs that passively diffuse across the placenta.
Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are ...routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a “base” PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal–fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Many women take drugs during their pregnancy to treat a variety of clinical conditions. To optimize drug efficacy and reduce fetal toxicity, it is important to determine or predict fetal drug ...exposure throughout pregnancy. Previously, we developed and verified a maternal-fetal physiologically based pharmacokinetic (m-f PBPK) model to predict fetal K
(unbound fetal plasma AUC/unbound maternal plasma AUC) of drugs that passively cross the placenta. Here, we used in vitro transport studies in Transwell, in combination with our m-f PBPK model, to predict fetal K
of drugs that are effluxed by placental P-glycoprotein (P-gp)-namely, dexamethasone, betamethasone, darunavir, and lopinavir. Using Transwell, we determined the efflux ratio of these drugs in hMDR1-MDCK
cells, in which human P-gp was overexpressed and the endogenous P-gp was knocked out. Then, using the proteomics-informed efflux ratio-relative expressive factor approach, we predicted the fetal K
of these drugs at term. Finally, to verify our predictions, we compared them with the observed in vivo fetal K
at term. The latter was estimated using our m-f PBPK model and published fetal umbilical vein (UV)/maternal plasma drug concentrations obtained at term (UV/maternal plasma). Fetal K
predictions for dexamethasone (0.63), betamethasone (0.59), darunavir (0.17), and lopinavir (0.08) were successful, as they fell within the 90% confidence interval of the corresponding in vivo fetal K
(0.30-0.66, 0.29-0.71, 0.11-0.22, 0.04-0.19, respectively). This is the first demonstration of successful prediction of fetal K
of P-gp drug substrates from in vitro studies. SIGNIFICANCE STATEMENT: For the first time, using in vitro studies in cells, this study successfully predicted human fetal K
of P-gp substrate drugs. This success confirms that the m-f PBPK model, combined with the ER-REF approach, can successfully predict fetal drug exposure to P-gp substrates. This success provides increased confidence in the use of the ER-REF approach, combined with the m-f PBPK model, to predict fetal K
of drugs (transported by P-gp or other transporters), both at term and at earlier gestational ages.
The human blood‐brain barrier (BBB) transporter P‐gp can efflux amyloid‐β (Aβ) out of the central nervous system (CNS). Aβ is thought to be the causative agent for Alzheimer’s disease (AD). Using ...positron emission tomography imaging, we have shown that BBB P‐gp activity is reduced in AD, as quantified by the in vivo brain distribution of the P‐gp probe 11C‐verapamil. Therefore, the aim of this study was to determine whether this reduced BBB P‐gp activity in AD was due to decreased P‐gp abundance at the BBB. Using targeted proteomics, we quantified the abundance of P‐gp and other drug transporters in gray matter brain microvessels isolated from 43 subjects with AD and 38 age‐matched controls (AMCs) from regions affected by AD (hippocampus and the parietal lobe of the brain cortex) and not affected by AD (cerebellum). First, P‐gp abundance was decreased in the BBB of the hippocampus vs. the cerebellum in both subjects with AD and AMCs, and therefore was not AD‐related. In addition, gray matter BBB abundance of P‐gp (and of other transporters) in the hippocampus and the parietal lobe was not different between AD and AMC. The gray matter BBB abundance of all drug transporters decreased with age, likely due to age‐dependent decrease in the density of brain microvessels. Collectively, the observed reduced in vivo cerebral BBB P‐gp activity in AD cannot be explained by reduced abundance of P‐gp at the BBB. Nevertheless, the drug transporter abundance at the human gray matter BBB data provided here can be used to predict brain distribution of drugs targeted to treat CNS diseases, including AD.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
To inform fetal drug safety, it is important to determine or predict fetal drug exposure throughout pregnancy. The former is not possible in the first or second trimester. In contrast, at the time of ...birth, fetal drug exposure, relative to maternal exposure, can be estimated as Kp,uu (unbound fetal umbilical venous (UV) plasma area under the curve (AUC)/unbound maternal plasma (MP) AUC), provided the observed UV/MP values, spanning the dosing interval, are available from multiple maternal‐fetal dyads. However, this fetal Kp,uu cannot be extrapolated to other drugs. To overcome the above limitations, we have used an efflux ratio‐relative expression factor (ER‐REF) approach to successfully predict the fetal Kp,uu of P‐gp substrates. Because many drugs taken by pregnant people are also BCRP substrates, here, we extend this approach to drugs that are effluxed by both placental BCRP and P‐gp or P‐gp alone. To verify our predictions, we chose drugs for which UV/MP data were available at term: glyburide and imatinib (both BCRP and P‐gp substrates) and nelfinavir (only P‐gp substrate). First, the ER of the drugs was determined using Transwells and MDCKII cells expressing either BCRP or P‐gp. Then, the ER was scaled using the proteomics‐informed REF value to predict the fetal Kp,uu of the drug at term. The ER‐REF predicted fetal Kp,uu of glyburide (0.43), imatinib (0.42), and nelfinavir (0.40) fell within two‐fold of the corresponding in vivo fetal Kp,uu (0.44, 0.37, and 0.46, respectively). These data confirm that the ER‐REF approach can successfully predict fetal drug exposure to BCRP/P‐gp and P‐gp substrates, at term.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
In order to optimize central nervous system (CNS) drug development, accurate prediction of the drug’s human steady‐state unbound brain interstitial fluid‐to‐plasma concentration ratio (Kp,uu,brain) ...is critical, especially for drugs that are effluxed by the multiple drug resistance transporters (e.g., P‐glycoprotein, P‐gp). Due to lack of good in vitro human blood‐brain barrier models, we and others have advocated the use of a proteomics‐informed relative expressive factor (REF) approach to predict Kp,uu,brain. Therefore, we tested the success of this approach in humans, with a focus on P‐gp substrates, using brain positron emission tomography imaging data for verification. To do so, the efflux ratio (ER) of verapamil, N‐desmethyl loperamide, and metoclopramide was determined in human P‐gp‐transfected MDCKII cells using the Transwell assay. Then, using the ER estimate, Kp,uu,brain of the drug was predicted using REF (ER approach). Alternatively, in vitro passive and P‐gp–mediated intrinsic clearances (CLs) of these drugs, estimated using a five‐compartmental model, were extrapolated to in vivo using REF (active CL) and brain microvascular endothelial cells protein content (passive CL). The ER approach successfully predicted Kp,uu,brain of all three drugs within twofold of observed data and within 95% confidence interval of the observed data for verapamil and N‐desmethyl loperamide. Using the in vitro–to–in vivo extrapolated clearance approach, Kp,uu,brain was reasonably well predicted but not the brain unbound interstitial fluid drug concentration‐time profile. Therefore, we propose that the ER approach be used to predict Kp,uu,brain of CNS candidate drugs to enhance their success in development.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Cannabis is used for both recreational and medicinal purposes. The most abundant constituents are the cannabinoids - cannabidiol (CBD, nonpsychoactive) and (-)-
-Δ
-tetrahydrocannabinol (THC, ...psychoactive). Both have been reported to reversibly inhibit or inactivate cytochrome P450 (CYPs) enzymes. However, the low aqueous solubility, microsomal protein binding, and nonspecific binding to labware were not considered, potentially leading to an underestimation of CYPs inhibition potency. Therefore, the binding-corrected reversible (IC
) and irreversible (
) inhibition potency of each cannabinoid toward major CYPs were determined. The fraction unbound of CBD and THC in the incubation mixture was 0.12 ± 0.04 and 0.05 ± 0.02, respectively. The IC
for CBD toward CYP1A2, 2C9, 2C19, 2D6, and 3A was 0.45 ± 0.17, 0.17 ± 0.03, 0.30 ± 0.06, 0.95 ± 0.50, and 0.38 ± 0.11 µM, respectively; the IC
for THC was 0.06 ± 0.02, 0.012 ± 0.001, 0.57 ± 0.22, 1.28 ± 0.25, and 1.30 ± 0.34 µM, respectively. Only CBD showed time-dependent inactivation (TDI) of CYP1A2, 2C19, and CYP3A, with inactivation efficiencies (
/
) of 0.70 ± 0.34, 0.11 ± 0.06, and 0.14 ± 0.04 minutes
µM
, respectively. A combined (reversible inhibition and TDI) mechanistic static model populated with these data predicted a moderate to strong pharmacokinetic interaction risk between orally administered CBD and drugs extensively metabolized by CYP1A2/2C9/2C19/2D6/3A and between orally administered THC and drugs extensively metabolized by CYP1A2/2C9/3A. These predictions will be extended to a dynamic model using physiologically based pharmacokinetic modeling and simulation and verified with a well-designed clinical cannabinoid-drug interaction study. SIGNIFICANCE STATEMENT: This study is the first to consider the impact of limited aqueous solubility, nonspecific binding to labware, or extensive binding to incubation protein shown by cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC) on their true cytochrome P450 inhibitory potency. A combined mechanistic static model predicted a moderate to strong pharmacokinetic interaction risk between orally administered CBD and drugs extensively metabolized by CYP1A2, 2C9, 2C19, 2D6, or 3A and between orally administered THC and drugs extensively metabolized by CYP1A2, 2C9, or 3A.
Drug transporter expression in tissues (
in vivo
) usually differs from that in cell lines used to measure transporter activity (
in vitro
). Therefore, quantification of transporter expression in ...tissues and cell lines is important to develop scaling factor for
in vitro
to
in vivo
extrapolation (IVIVE) of transporter-mediated drug disposition. Since traditional immunoquantification methods are semiquantitative, targeted proteomics is now emerging as a superior method to quantify proteins, including membrane transporters. This superiority is derived from the selectivity, precision, accuracy, and speed of analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS) in multiple reaction monitoring (MRM) mode. Moreover, LC-MS/MS proteomics has broader applicability because it does not require selective antibodies for individual proteins. There are a number of recent research and review papers that discuss the use of LC-MS/MS for transporter quantification. Here, we have compiled from the literature various elements of MRM proteomics to provide a comprehensive systematic strategy to quantify drug transporters. This review emphasizes practical aspects and challenges in surrogate peptide selection, peptide qualification, peptide synthesis and characterization, membrane protein isolation, protein digestion, sample preparation, LC-MS/MS parameter optimization, method validation, and sample analysis. In particular, bioinformatic tools used in method development and sample analysis are discussed in detail. Various pre-analytical and analytical sources of variability that should be considered during transporter quantification are highlighted. All these steps are illustrated using P-glycoprotein (P-gp) as a case example. Greater use of quantitative transporter proteomics will lead to a better understanding of the role of drug transporters in drug disposition.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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