Human Drug Clearance Research Articles

HPB P26 Ex vivo normothermic perfusion of liver, kidney and spleen to predict human pharmacokinetics and assess drug delivery

Abstract Background Many emerging therapeutics fail to provide patient benefit due to unpredictable drug delivery and clearance following clinical translation. Normothermic machine perfusion (NMP) can preserve physiological processes in human-sized organs ex vivo, which may facilitate preclinical characterisation of pharmacokinetics and drug delivery. The performance of ex vivo human and porcine clearance organs (liver, kidney and spleen) to 1) predict human drug pharmacokinetics and 2) assess the delivery of emerging therapeutics was investigated. A small molecule chemotherapy (irinotecan) with well-established pharmacokinetics was delivered to determine the former. Nanotherapeutics and viral vectors, two classes of emerging therapeutics, were delivered to determine the latter. Methods Human liver (n=4), kidney (n=4) and porcine liver (n=11), spleen (n=6) and kidney (n=5) underwent NMP. Organ physiology was measured. Irinotecan (Medac) was delivered to human and porcine liver and kidney. Plasma, tissue, bile and urinary irinotecan and its metabolites were quantified by high performance liquid chromatography. Pharmacokinetic parameters were compared to patient data. Non-PEGylated (ThermoFisher) and PEGylated (in-house), nanoparticles were delivered to liver, spleen, and kidney. Nanoparticles were quantified in biological samples by fluorescence. A lentiviral vector (Oxford Biomedica) was administered to liver and viral kinetics and genomic integration determined using RT-qPCR. Noncompartmental analysis was used for pharmacokinetics. Results Prolonged physiological preservation (up to 38 hours) of human liver, kidney and porcine liver, kidney and spleen was achieved with NMP. Human and porcine organs support the pharmacokinetic processes of drug distribution, metabolism and urinary and biliary excretion of irinotecan. Moreover, porcine organs successfully predicted human hepatic clearance, renal clearance and total body clearance with an absolute fold error of 1.14, 1.71 and 1.04 when compared to human literature values. Finally, predictable kinetics and biodistribution of PEGylated and non-PEGylated nanoparticles were demonstrated in NMP clearance organs, and NMP liver supported the integration of lentiviral genome into the host hepatocyte DNA. Conclusions Ex vivo NMP organs can be used to predict human drug clearance and assess therapeutic delivery; an exciting prospect for preclinical drug development and for the delivery of hepatocyte-targeted therapies, with the potential to reduce the time, cost and risk in developing life-saving treatments for patients.

Quantitative Translation of Substrate Intrinsic Clearance from Recombinant CYP1A1 to Humans.

CYP1A1 is a cytochrome P450 family 1 enzyme that is mostly expressed in the extrahepatic tissues. To understand the CYP1A1 contribution to drug clearance in humans, we examined the in vitro-in vivo extrapolation (IVIVE) of intrinsic clearance (CLint) for a set of drugs that are in vitro CYP1A1 substrates. Despite being strong in vitro CYP1A1 substrates, 82% of drugs gave good IVIVE with predicted CLint within 2-3-fold of the observed values using human liver microsomes and hepatocytes, suggesting they were not in vivo CYP1A1 substrates due to the lack of extrahepatic contribution to CLint. Only three drugs (riluzole, melatonin and ramelteon) that are CYP1A2 substrates yielded significant underprediction of in vivo CLint up to 11-fold. The fold of CLint underprediction was linearly proportional to human recombinant CYP1A1 (rCYP1A1) CLint, indicating they were likely to be in vivo CYP1A1 substrates. Using these three substrates, a calibration curve can be developed to enable direct translation from in vitro rCYP1A1 CLint to in vivo extrahepatic contributions in humans. In vivo CYP1A1 substrates are planar and small, which is consistent with the structure of the active site. This is in contrast to the in vitro substrates, which include large and nonplanar molecules, suggesting rCYP1A1 is more accessible than what is in vivo. The impact of CYP1A1 on first-pass intestinal metabolism was also evaluated and shown to be minimal. This is the first study providing new insights on in vivo translation of CYP1A1 contributions to human clearance using in vitro rCYP1A1 data.

Cloning and Functional Characterization of Dog OCT1 and OCT2: Another Step in Exploring Species Differences in Organic Cation Transporters.

OCT1 and OCT2 are polyspecific membrane transporters that are involved in hepatic and renal drug clearance in humans and mice. In this study, we cloned dog OCT1 and OCT2 and compared their function to the human and mouse orthologs. We used liver and kidney RNA to clone dog OCT1 and OCT2. The cloned and the publicly available RNA-Seq sequences differed from the annotated exon-intron structure of OCT1 in the dog genome CanFam3.1. An additional exon between exons 2 and 3 was identified and confirmed by sequencing in six additional dog breeds. Next, dog OCT1 and OCT2 were stably overexpressed in HEK293 cells and the transport kinetics of five drugs were analyzed. We observed strong differences in the transport kinetics between dog and human orthologs. Dog OCT1 transported fenoterol with 12.9-fold higher capacity but 14.3-fold lower affinity (higher KM) than human OCT1. Human OCT1 transported ipratropium with 5.2-fold higher capacity but 8.4-fold lower affinity than dog OCT1. Compared to human OCT2, dog OCT2 showed 10-fold lower transport of fenoterol and butylscopolamine. In conclusion, the functional characterization of dog OCT1 and OCT2 reported here may have implications when using dogs as pre-clinical models as well as for drug therapy in dogs.

Predicting the Drug Clearance Pathway with Structural Descriptors.

The clearance, by renal elimination or hepatic metabolism, is one of the most important pharmacokinetic parameters of a drug. It allows the half-life, bioavailability, and drug-drug interactions to be predicted, and it can also affect the dose regimen of a drug. Predicting the clearance pathways of new chemical candidates during drug development is vital in order to minimize the risks of possible side effects and drug interactions. Many in vivo methods have been established to predict drug clearance in humans, and these mainly rely on data from in vivo studies in preclinical species-mainly rats, dogs, and monkeys. They are also time consuming and expensive. The aim of this study was to find the relationship between structural parameters of drugs and their clearance pathways. The clearance pathway of each drug was obtained from the literature. Various structural descriptors [Abraham solvation parameters, topological polar surface area, numbers of hydrogen-bond donors and acceptors, number of rotatable bonds, molecular weight, logarithm of the partition coefficient (logP), and logarithm of the distribution coefficient at pH7.4 (logD7.4)] were applied to develop a mechanistic model for predicting clearance pathways. The results of this study indicate that compounds with logD7.4>1 or with zero or one hydrogen-bond donor undergo hepatic metabolism, whereas the clearance pathway for chemicals with logD7.4 < - 2 is renal elimination. Furthermore, models established using logistic regression based on five structural parameters for compounds with - 2 < logD7.4 < 1 could be used in a clearance pathway prediction tool. The overall prediction accuracies of the first and second models were 84.8% and 84.4%, respectively. The developed model can be used to find the clearance pathways of new drug candidates with acceptable accuracy. The main descriptors that are used to evaluate this parameter are the hydrophobicity and the number of hydrogen-bonding functional groups of the compound.

Amplifying the impact of kidney microphysiological systems: predicting renal drug clearance using mechanistic modelling based on reconstructed drug secretion.

Accurate prediction of pharmacokinetic parameters, such as renal clearance, is fundamental to the development of effective and safe new treatments for patients. However, conventional renal models have a limited ability to predict renal drug secretion, a process that is dependent on transporters in the proximal tubule. Improvements in microphysiological systems (MPS) have extended our in vitro capabilities to predict pharmacokinetic parameters. In this study a kidney-MPS model was developed that successfully recreated renal drug secretion. Human proximal tubule cells grown in the kidney-MPS, resem­bling an in vivo phenotype, actively secreted the organic cation drug metformin and organic anion drug cidofovir, in contrast to cells cultured in conventional culture formats. Metformin and cidofovir renal secretory clearance were predicted from kid­ney-MPS data within 3.3- and 1.3-fold, respectively, of clinically reported values by employing a semi-mechanistic drug distribution model using kidney-MPS drug transport parameters together with in vitro to in vivo extrapolation. This approach introduces an effective application of a kidney-MPS model coupled with pharmacokinetic modelling tools to evaluate and predict renal drug clearance in humans. Kidney-MPS renal clearance predictions can potentially complement pharma-cokinetic animal studies and contribute to the reduction of pre-clinical species use during drug development.

Can In Vitro-In Vivo Extrapolation Be Successful? Recognizing the Incorrect Clearance Assumptions.

For a number of years, our laboratory has been investigating the underlying reasons for the published poor in vitro-in vivo extrapolation (IVIVE) predictability of human clearance both from a theoretical and from an experimental perspective. Here, we critically examine clearance concepts and commonly employed IVIVE approaches, concluding that there is no theoretical reason that IVIVE should work, just as it does not. Our analysis, however, has identified 10 misconceptions and/or poorly understood aspects of clearance that are listed in the Conclusion section of this manuscript. Chief among these are that all published human drug clearance values are arterial clearances-clearance calculated as organ blood flow multiplied by the extraction ratio is the arterial clearance of the organ of elimination (and not the published drug clearance value)-and that the well-stirred model equation taught in all pharmacokinetic courses that relates organ blood flow, fraction unbound in blood, and intrinsic clearance has no validity. We further list 10 conclusions relating to the IVIVE process. The primary IVIVE-related conclusions are that the intrinsic clearance value determined from an in vitro incubation is an arterial intrinsic clearance, there is no theoretical basis upon which an arterial intrinsic clearance can be related to a whole-body arterial clearance to accomplish IVIVE, there are no published data demonstrating that in vitro intrinsic metabolic clearance can predict in vivo organ clearance as IVIVE assumes, and the scientific basis for the hypothesized albumin-mediated hepatic uptake phenomenon is invalid. We further propose three IVIVE process recommendations.

Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists.

Site-Directed Mutagenesis at the Molybdenum Pterin Cofactor Site of the Human Aldehyde Oxidase: Interrogating the Kinetic Differences Between Human and Cynomolgus Monkey.

The estimation of the drug clearance by aldehyde oxidase (AO) has been complicated because of this enzyme's atypical kinetics and species and substrate specificity. Since human AO (hAO) and cynomolgus monkey AO (mAO) have a 95.1% sequence identity, cynomolgus monkeys may be the best species for estimating AO clearance in humans. Here, O6-benzylguanine (O6BG) and dantrolene were used under anaerobic conditions, as oxidative and reductive substrates of AO, respectively, to compare and contrast the kinetics of these two species through numerical modeling. Whereas dantrolene reduction followed the same linear kinetics in both species, the oxidation rate of O6BG was also linear in mAO and did not follow the already established biphasic kinetics of hAO. In an attempt to determine why hAO and mAO are kinetically distinct, we have altered the hAO V811 and F885 amino acids at the oxidation site adjacent to the molybdenum pterin cofactor to the corresponding alanine and leucine in mAO, respectively. Although some shift to a more monkey-like kinetics was observed for the V811A mutant, five more mutations around the AO cofactors still need to be investigated for this purpose. In comparing the oxidative and reductive rates of metabolism under anaerobic conditions, we have come to the conclusion that despite having similar rates of reduction (4-fold difference), the oxidation rate in mAO is more than 50-fold slower than hAO. This finding implies that the presence of nonlinearity in AO kinetics is dependent upon the degree of imbalance between the rates of oxidation and reduction in this enzyme. SIGNIFICANCE STATEMENT: Although they have as much as 95.1% sequence identity, human and cynomolgus monkey aldehyde oxidase are kinetically distinct. Therefore, monkeys may not be good estimators of drug clearance in humans.

Physiological Regulation of Drug Metabolism and Transport: Pregnancy, Microbiome, Inflammation, Infection, and Fasting

This article is a report on a symposium entitled "Physiological Regulation of Drug Metabolism and Transport" sponsored by the American Society for Pharmacology and Experimental Therapeutics and held at the Experimental Biology 2017 meeting in Chicago, IL. The contributions of physiologic and pathophysiological regulation of drug-metabolizing enzymes and transporters to interindividual variability in drug metabolism are increasingly recognized but in many cases are not well understood. The presentations herein discuss the phenomenology, consequences, and mechanism of such regulation. CYP2D6 transgenic mice were used to provide insights into the mechanism of regulation of this enzyme in pregnancy, via hepatocyte nuclear factor 4α, small heterodimer partner, and retinoids. Regulation of intestinal and hepatic drug-processing enzymes by the intestinal microbiota via tryptophan and its metabolites was investigated. The potential impact of parasitic infections on human drug metabolism and clearance was assessed in mice infected with Schistosoma mansoni or Plasmodium chabaudi chabaudi AS, both of which produced widespread and profound effects on murine hepatic drug-metabolizing enzymes. Finally, the induction of Abcc drug efflux transporters by fasting was investigated. This was demonstrated to occur via a cAMP, protein kinase A/nuclear factor-E2-related factor 2/Sirtuin 1 pathway via antioxidant response elements on the Abcc genes.

Prediction of metabolic drug clearance in humans

Prediction of metabolic drug clearance in humans

The utility of extended clearance classification in predicting human drug clearance in early drug discovery

The utility of extended clearance classification in predicting human drug clearance in early drug discovery

Emerging Clinical Importance of Hepatic Organic Cation Transporter 1 (OCT1) in Drug Pharmacokinetics, Dynamics, Pharmacogenetic Variability, and Drug Interactions.

Hepatic organic cation transporter 1 (OCT1) can be a determinant of drug clearance and distribution, which can impact drug exposure and response. OCT1 was shown recently to be the rate-determining step in the clearance of several drugs in humans, and thereby a mechanism of pharmacogenetic variability and drug-drug interactions (DDIs). OCT1 mediates metformin distribution to the liver (key biophase). As OCT1 modulation impacts metformin response, but not pharmacokinetics (PK), metformin DDI studies require pharmacodynamic endpoint(s) to inform rational metformin dose adjustment.

Prediction of cytochrome P450-mediated drug clearance in humans based on the measured activities of selected CYPs.

Determining drug-metabolizing enzyme activities on an individual basis is an important component of personalized medicine, and cytochrome P450 enzymes (CYPs) play a principal role in hepatic drug metabolism. Herein, a simple method for predicting the major CYP-mediated drug clearance in vitro and in vivo is presented. Ten CYP-mediated drug metabolic activities in human liver microsomes (HLMs) from 105 normal liver samples were determined. The descriptive models for predicting the activities of these CYPs in HLMs were developed solely on the basis of the measured activities of a smaller number of more readily assayed CYPs. The descriptive models then were combined with the Conventional Bias Corrected in vitro–in vivo extrapolation method to extrapolate drug clearance in vivo. The Vmax, Km, and CLint of six CYPs (CYP2A6, 2C8, 2D6, 2E1, and 3A4/5) could be predicted by measuring the activities of four CYPs (CYP1A2, 2B6, 2C9, and 2C19) in HLMs. Based on the predicted CLint, the values of CYP2A6-, 2C8-, 2D6-, 2E1-, and 3A4/5-mediated drug clearance in vivo were extrapolated and found that the values for all five drugs were close to the observed clearance in vivo. The percentage of extrapolated values of clearance in vivo which fell within 2-fold of the observed clearance ranged from 75.2% to 98.1%. These findings suggest that measuring the activity of CYP1A2, 2B6, 2C9, and 2C19 allowed us to accurately predict CYP2A6-, 2C8-, 2D6-, 2E1-, and 3A4/5-mediated activities in vitro and in vivo and may possibly be helpful for the assessment of an individual’s drug metabolic profile.

From Genotype to Phenotype: Cytochrome P450 2D6-Mediated Drug Clearance in Humans.

How genotypic variation results in phenotypic differences is still a challenge for biology. In the field of drug metabolism, the means by which specific cytochrome P4502D6 (CYP2D6) genotypes yield different phenotypes at various levels (molecular, cellular, and organismal) is an important question, as differences in CYP2D6 activity can contribute to adverse drug reactions. Herein, the genotype of CYP2D6 was determined along with the absolute content of CYP2D6 and microsomal protein per gram of liver in human liver microsomes, the molecular, cellular (microsomal, tissue, organ), and organismal phenotype of CYP2D6 determined; the effect of genotype on each phenotype of CYP2D6-mediated dextromethorphan clearance (CL) was delineated, and the overall genotype-phenotype relationship for CYP2D6 was charted. We demonstrate that changes in the cellular and organismal CL phenotypes are markedly greater than changes seen at the molecular level. With individuals carrying the 1661CC polymorphism, for example, the most noticeable change took place in organ CL phenotype (4.17-fold), followed by tissue (3.75-fold), organism (3.69-fold), microsomal (3.09-fold), and molecular (1.66-fold) phenotypes. In addition, the biggest intragenotype individual coefficient of variation in organismal phenotype was observed in the 1661GG individuals, which reached 104.5%, followed by that of 100TT, 100CT, 1661GC, 100CC, and 1661CC polymorphisms (102.7%, 62.4%, 53.5%, 49.7%, and 44.8%, respectively). Our study has allowed us to chart the genotype-phenotype relationship for CYP2D6 from the molecular to the organismal level as well as allowed us to determine intragenotype individual variation in phenotype with each genotype.

Challenges in preclinical to clinical translation for anticancer carrier-mediated agents.

Major advances in carrier-mediated agents (CMAs), which include nanoparticles and conjugates, have revolutionized drug delivery capabilities over the past decade. While providing numerous advantages over their small-molecule counterparts, there is substantial variability in how individual CMA formulations and patient characteristics affect the pharmacology, pharmacokinetics (PK), and pharmacodynamics (PD) (efficacy and toxicity) of these agents. Development or selection of animal models is used to predict the effects within a particular human disease. A breadth of studies have begun to emphasize the importance of preclinical animal models in understanding and evaluating the interaction between CMAs and the immune system and tumor matrix, which ultimately influences CMA PK (clearance and distribution) and PD (efficacy and toxicity). It is fundamental to study representative preclinical tumor models that recapitulate patients with diseases (e.g., cancer) and evaluate the interplay between CMAs and the immune system, including the mononuclear phagocyte system (MPS), chemokines, hormones, and other immune modulators. Furthermore, standard allometric scaling using body weight does not accurately predict drug clearance in humans. Future studies are warranted to better understand the complex pharmacology and interaction of CMA carriers within individual preclinical models and their biological systems, such as the MPS and tumor microenvironment, and their application to allometric scaling across species. WIREs Nanomed Nanobiotechnol 2016, 8:642-653. doi: 10.1002/wnan.1394 For further resources related to this article, please visit the WIREs website.

Expression of drug transporters in human kidney: impact of sex, age, and ethnicity.

BackgroundDifferences in expression of drug transporters in human kidney contribute to changes in pharmacokinetics and toxicokinetics of a variety of drug compounds. The basal expression levels of genes involved in drug transport processes in the kidney introduces differences in bioavailability, distribution, and clearance of drugs, possibly influencing drug efficacy and adverse reactions. Sex differences in gene expression of transporters are a key cause of differences in sex-dependent pharmacokinetics, which may characterize many drugs and contribute to individual differences in drug efficacy and toxicity. Therefore, evaluating the expression of drug transporters in normal human kidneys is important to better understand differences in drug bioavailability, distribution, and clearance of drugs in humans. Other factors such as age and ethnicity may also contribute to individual differences in gene expression of drug transporters in the human kidney.MethodsQuantitative real-time PCR (QRT-PCR) was performed to determine the gene expression of 30 drug transporters in 95 age-matched normal human kidney tissues. Multiple Student’s t-tests (Sidak-Bonferroni correction) and two-way ANOVA (Bonferroni correction) analyses were used to determine statistically significant differences.ResultsIn the 30 transporter genes examined, sex, ethnicity, and age differences in gene expression were exhibited in normal human kidney tissue. These changes in expression were not found to be differentially significant. However, sex-age and sex-ethnicity interactions were found to be statistically significant. For sex-age interactions, SCL22A12 was found to be significantly higher expressed in females <50 years compared to males <50 years. Expression levels of SLC22A2, SLC22A12, SLC6A16, and ABCB6 were significantly higher in females <50 years compared to females ≥50 years. In sex-ethnicity interactions, expression levels of ATP7B and KCNJ8 were found to be significantly higher in African American females compared to European American females. Also, the expression of SLC31A2 was significantly higher in European American males compared to European American females.ConclusionsSex, age, and ethnic differences impacted the expression of drug transporters in normal human kidneys, which suggests that the analysis of gene expression of drug transporters will aid in improving the usage/dosage of drug therapies influencing personalized medicine and susceptibility to adverse drug reactions.

Prediction of Human Drug Clearance by Multiple Metabolic Pathways: Integration of Hepatic and Intestinal Microsomal and Cytosolic Data

The current study assesses hepatic and intestinal glucuronidation, sulfation, and cytochrome P450 (P450) metabolism of raloxifene, quercetin, salbutamol, and troglitazone using different in vitro systems. The fraction metabolized by conjugation and P450 metabolism was estimated in liver and intestine, and the importance of multiple metabolic pathways on accuracy of clearance prediction was assessed. In vitro intrinsic sulfation clearance (CL(int, SULT)) was determined in human intestinal and hepatic cytosol and compared with hepatic and intestinal microsomal glucuronidation (CL(int, UGT)) and P450 clearance (CL(int, CYP)) expressed per gram of tissue. Hepatic and intestinal cytosolic scaling factors of 80.7 mg/g liver and 18 mg/g intestine were estimated from published data. Scaled CL(int, SULT) ranged between 0.7 and 11.4 ml · min(-1) · g(-1) liver and 0.1 and 3.3 ml · min(-1) · g(-1) intestine (salbutamol and quercetin were the extremes). Salbutamol was the only compound with a high extent of sulfation (51 and 28% of total CL(int) for liver and intestine, respectively) and also significant renal clearance (26-57% of observed plasma clearance). In contrast, the clearance of quercetin was largely accounted for by glucuronidation. Drugs metabolized by multiple pathways (raloxifene and troglitazone) demonstrated improved prediction of intravenous clearance using data from all hepatic pathways (44-86% of observed clearance) compared with predictions based only on the primary pathway (22-36%). The assumption of no intestinal first pass resulted in underprediction of oral clearance for raloxifene, troglitazone, and quercetin (3-22% of observed, respectively). Accounting for the intestinal contribution to oral clearance via estimated intestinal availability improved prediction accuracy for raloxifene and troglitazone (within 2.5-fold of observed). Current findings emphasize the importance of both hepatic and intestinal conjugation for in vitro-in vivo extrapolation of metabolic clearance.

Controversies in Allometric Scaling for Predicting Human Drug Clearance: An Historical Problem and Reflections on What Works and What Does Not

This review focuses on a discussion of the controversies in allometric scaling (AS) for predicting human clearance from a mathematical and statistical perspective. First, a history of allometric scaling in comparative biology and its use in pharmacokinetics are reviewed. It is shown that the application of AS in predicting human clearance values based on a limited number of animal species (typically, 3 or 4) contains fundamental statistical errors when AS was first introduced from comparative biology. Second, the mathematical nature of various allometrically-based methods is revealed and the soundness of these methods is assessed. It is demonstrated that any of these methods, which incorporate a correction factor in a traditional allometric approach (varying-exponent allometry), not only reduces the statistical power of the allometric analysis, but are also incorrect with regard to aspects of biology. Finally, it is concluded that allometry remains a valuable tool for predicting human clearance, and should be applied in the context of a fixed exponent. However, fixed-exponent allometry does not provide satisfactory accuracy in predicting human clearance, since it is not able to capture the biological differences among species. Therefore, it is recommended that the overall effort in predicting human pharmacokinetics should be directed to the collection and generation of reliable data (both in vitro and in vivo) along with a better understanding of the DMPK properties of the chemical entity.

Prediction of hepatic clearance using cryopreserved human hepatocytes: a comparison of serum and serum-free incubations

Cryopreserved human hepatocytes have been used to predict hepatic in-vivo clearance. Physiologically-based direct scaling methods generally underestimate human in-vivo hepatic clearance. Cryopreserved human hepatocytes were incubated in 100% serum and in serum-free medium to predict the in-vivo hepatic clearance of six compounds (phenazone (antipyrine), bosentan, mibefradil, midazolam, naloxone and oxazepam). Monte Carlo simulations were performed in an attempt to incorporate the variability and uncertainty in the measured parameters to the prediction of hepatic clearance. The intrinsic clearance (CL(int)) and the associated variability of the six compounds decreased in the presence of serum and the values were reproducible across donors. The predicted CL(hep, in-vivo) obtained with hepatocytes from donors incubated in serum was more accurate than the prediction obtained in the absence of serum. For example, the CL(hep, in-vivo) of mibefradil in donor GNG was 4.27 mL min(-1) kg(-1) in the presence of serum and 0.46 mL min(-1) kg(-1) in the absence of serum (4.88 mL min(-1) kg(-1) observed in-vivo). Using the results obtained in this study together with an extended data set (26 compounds), the clearance of 77% of the compounds was predicted within a 2-fold error in the absence of serum. In the presence of serum, 85% of the compounds were successfully predicted within a 2-fold error. In conclusion, cryopreserved human hepatocyte suspensions represented a convenient and predictive model to assess human drug clearance.

A Humanized UGT1 Mouse Model Expressing the UGT1A1*28 Allele for Assessing Drug Clearance by UGT1A1-Dependent Glucuronidation

Humanized mice that express the human UDP-glucuronosyltransferase (UGT) 1 locus have been developed in a Ugt1-null background as a model to improve predictions of human UGT1A-dependent drug clearance. Enzyme kinetic parameters (K(m) and V(max)) and pharmacokinetic properties of three probe drugs were compared using wild-type and humanized UGT1 mice that express the Gilbert's UGT1A1*28 allele [Tg(UGT1(A1*28)) Ugt1(-/-) mice]. The well characterized substrate for UGT1A1, 7-ethyl-10-hydroxy-camptothecin (SN-38), showed the greatest difference in parent drug exposure ( approximately 3-fold increase) and clearance ( approximately 3-fold decrease) in Tg(UGT1(A1*28)) Ugt1(-/-) mice after intravenous administration compared with wild-type and phenobarbital-treated animals. In contrast, the clearance of the UGT2B7 substrate (-)-17-allyl-4, 5alpha-epoxy-3, 14-dihydroxymorphinan-6-one (naloxone) was not altered in Tg(UGT1(A1*28)) Ugt1(-/-) mice. In addition, pharmacokinetic parameters with 1-(4-fluorophenyl)3(R)-[3-(4-fluorophenyl)-3(S)-hydroxypropyl]-4(S)-(4-hydroxyphenyl)-2-azetidinone (ezetimibe, Zetia; Merck & Co., Whitehouse Station, NJ), considered to be a major substrate for UGT1A1, showed small to no dependence on UGT1A1-directed glucuronidation. Enzyme kinetic parameters assessed for SN-38, ezetimibe, and naloxone using liver microsomes prepared from wild-type and Tg(UGT1(A1*28)) Ugt1(-/-) mice showed patterns consistent with the in vivo pharmacokinetic data. For SN-38 glucuronidation, V(max) decreased 5-fold in Tg(UGT1(A1*28)) Ugt1(-/-) mouse liver microsomes compared with microsomes prepared from wild-type mice, and decreased 10-fold compared with phenobarbital-treated Tg(UGT1(A1*28)) Ugt1(-/-) mice. These differences are consistent with SN-38 glucuronidation activities using HLMs isolated from individuals genotyped as UGT1A1*1 or UGT1A1*28. For ezetimibe and naloxone the differences in V(max) were minimal. Thus, Tg(UGT1(A1*28)) Ugt1(-/-) mice can serve as a pharmacokinetic model to further investigate the effects of UGT1A1 expression on drug metabolism.