The evolution of the OATP hepatic uptake transport protein family in DMPK sciences: from obscure liver transporters to key determinants of hepatobiliary clearance

Over the last two decades the impact on drug pharmacokinetics of the organic anion transporting polypeptides (OATPs: OATP-1B1, 1B3 and 2B1), expressed on the sinusoidal membrane of the hepatocyte, has been increasingly recognized. OATP-mediated uptake into the hepatocyte coupled with subsequent excretion into bile via efflux proteins, such as MRP2, is often referred to as hepatobiliary excretion. OATP transporter proteins can impact some drugs in several ways including pharmacokinetic variability, pharmacodynamic response and drug-drug interactions (DDIs). The impact of transporter mediated hepatic clearance is illustrated with case examples, from the literature and also from the Pfizer portfolio. The currently available in vitro techniques to study the hepatic transporter proteins involved in the hepatobiliary clearance of drugs are reviewed herein along with recent advances in using these in vitro data to predict the human clearance of compounds recognized by hepatic uptake transporters.

Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling

BACKGROUND

The importance of predicting human pharmacokinetics during compound selection has been recognized in the pharmaceutical industry. To this end there are many different approaches that are applied.

METHODS

In this study we compared the accuracy of physiologically based pharmacokinetic (PBPK) methodologies implemented in GastroPlus™ with the one-compartment approach routinely used at Pfizer for human pharmacokinetic plasma concentration-time profile prediction. Twenty-one Pfizer compounds were selected based on the availability of relevant preclinical and clinical data. Intravenous and oral human simulations were performed for each compound. To understand any mispredictions, simulations were also performed using the observed clearance (CL) value as input into the model.

RESULTS

The simulation results using PBPK were shown to be superior to those obtained via traditional one-compartment analyses. In many cases, this difference was statistically significant. Specifically, the results showed that the PBPK approach was able to accurately predict passive distribution and absorption processes. Some issues and limitations remain with respect to the prediction of CL and active transport processes and these need to be improved to further increase the utility of PBPK modelling. A particular advantage of the PBPK approach is its ability to accurately predict the multiphasic shape of the pharmacokinetic profiles for many of the compounds tested.

CONCLUSION

The results from this evaluation demonstrate the utility of PBPK methodology for the prediction of human pharmacokinetics. This methodology can be applied at different stages to enhance the understanding of the compounds in a particular chemical series, guide experiments, aid candidate selection and inform clinical trial design.

Measurement of binding of basic drugs to acidic phospholipids using surface plasmon resonance and incorporation of the data into mechanistic tissue composition equations to predict steady-state volume of distribution

Acidic phospholipid binding plays an important role in determining the tissue distribution of basic drugs. This article describes the use of surface plasmon resonance to measure binding affinity (K(D)) of three basic drugs to phosphatidylserine, a major tissue acidic phospholipid. The data are incorporated into mechanistic tissue composition equations to allow prediction of the steady-state volume of distribution (V(ss)). The prediction accuracy of V(ss) using this approach is compared with the original methodology described by Rodgers et al. (J Pharm Sci 94:1259-1276), in which the binding to acidic phospholipids is calculated from the blood/plasma concentration ratio (BPR). The compounds used in this study [amlodipine, propranolol, and 3-dimethylaminomethyl-4-(4-methylsulfanyl-phenoxy)-benzenesulfonamide (UK-390957)] showed higher affinity binding to phosphatidylserine than to phosphatidylcholine. When the binding affinity to phosphatidylserine was incorporated into mechanistic tissue composition equations, the V(ss) was more accurately predicted for all three compounds by using the surface plasmon resonance measurement than by using the BPR to estimate acidic phospholipid binding affinity. The difference was particularly marked for UK-390957, a sulfonamide that has a high BPR due to binding to carbonic anhydrase. The novel approach described in this article allows the binding affinity of drugs to an acidic phospholipid (phosphatidylserine) to be measured directly and demonstrates the utility of the binding data in the prediction of V(ss).

Preclinical and clinical pharmacokinetics of PF-02413873, a nonsteroidal progesterone receptor antagonist

The recently discovered selective nonsteroidal progesterone receptor (PR) antagonist 4-[3-cyclopropyl-1-(methylsulfonylmethyl)-5-methyl-1H-pyrazol-4-yl]oxy-2,6-dimethylbenzonitrile (PF-02413873) was characterized in metabolism studies in vitro, in preclinical pharmacokinetics in rat and dog, and in an initial pharmacokinetic study in human volunteers. Clearance (CL) of PF-02413873 was found to be high in rat (84 ml · min(-1) · kg(-1)) and low in dog (3.8 ml · min(-1) · kg(-1)), consistent with metabolic stability determined in liver microsomes and hepatocytes in these species. In human, CL was low in relation to hepatic blood flow, consistent with metabolic stability in human in vitro systems, where identified metabolites suggested predominant cytochrome P450 (P450)-catalyzed oxidative metabolism. Prediction of CL using intrinsic CL determined in human liver microsomes (HLM), recombinant human P450 enzymes, and single species scaling (SSS) from pharmacokinetic studies showed that dog SSS and HLM scaling provided the closest estimates of CL of PF-02413873 in human. These CL estimates were combined with a physiologically based pharmacokinetic (PBPK) model to predict pharmacokinetic profiles after oral suspension administration of PF-02413873 in fasted and fed states in human. Predicted plasma concentration versus time profiles were found to be similar to those observed in human over the PF-02413873 dose range 50 to 500 mg and captured the enhanced exposure in fed subjects. This case study of a novel nonsteroidal PR antagonist underlines the utility of PBPK modeling techniques in guiding prediction confidence and design of early clinical trials of novel chemical agents.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance

The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 1: goals, properties of the PhRMA dataset, and comparison with literature datasets

This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration-time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous (n = 19) and oral pharmacokinetic data (n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 4: prediction of plasma concentration-time profiles in human from in vivo preclinical data by using the Wajima approach

The objective of this study was to evaluate the performance of the Wajima allometry (Css -MRT) approach published in the literature, which is used to predict the human plasma concentration-time profiles from a scaling of preclinical species data. A diverse and blinded dataset of 108 compounds from PhRMA member companies was used in this evaluation. The human intravenous (i.v.) and oral (p.o.) pharmacokinetics (PK) data were available for 18 and 107 drugs, respectively. Three different scenarios were adopted for prediction of human PK profiles. In the first scenario, human clearance (CL) and steady-state volume of distribution (Vss ) were predicted by unbound fraction corrected intercept method (FCIM) and Øie-Tozer (OT) approaches, respectively. Quantitative structure activity relationship (QSAR)-based approaches (TSrat-dog ) based on compound descriptors together with rat and dog data were utilized in the second scenario. Finally, in the third scenario, CL and Vss were predicted using the FCIM and Jansson approaches, respectively. For the prediction of oral pharmacokinetics, the human bioavailability and absorption rate constant were assumed as the average of preclinical species. Various statistical techniques were used for assessing the accuracy of the simulation scenarios. The human CL and Vss were predicted within a threefold error range for about 75% of the i.v. drugs. However, the accuracy in predicting key p.o. PK parameters appeared to be lower with only 58% of simulations falling within threefold of observed parameters. The overall ability of the Css -MRT approach to predict the curve shape of the profile was in general poor and ranged between low to medium level of confidence for most of the predictions based on the selected criteria.

Application of physiologically based pharmacokinetic modeling to understanding the clinical pharmacokinetics of UK-369,003

5-[2-Ethoxy-5-(4-ethyl-piperazine-1-sulfonyl)-pyridin-3-yl]-3-ethyl-2-(2-methoxy-ethyl)-2,6-dihydro-pyrazolo[4,3-d]pyrimidin-7-one (UK-369,003) is a phosphodiesterase-5 inhibitor in clinical development at Pfizer. UK-369,003 is predominantly metabolized by cytochrome P450 3A4 and is also a substrate for the efflux transporter P-glycoprotein. The pharmacokinetics of UK-369,003 has been profiled after oral administration of 1 to 800 mg of an immediate release formulation to healthy volunteers. Nonlinearity was observed in the systemic exposure at doses of 100 mg and greater. In addition, the pharmacokinetics of UK-369,003 has also been investigated after oral administration of the more therapeutically attractive modified release formulation. Systemic exposure was prolonged with the modified release formulation, but bioavailability was reduced in comparison with that of the immediate release formulation. Physiologically based pharmacokinetic modeling strategies are commonly used in drug discovery and development. This work describes application of the physiologically based pharmacokinetic software GastroPlus to understand the pharmacokinetics of UK-369,003. The impact of gut wall and hepatically mediated CYP3A4 metabolism, in addition to the actions of P-glycoprotein, in causing the nonlinear pharmacokinetics of the immediate release formulation and the reduced bioavailability of the modified release form, was investigated. The model accurately described the systemic exposure of UK-369,003 after intravenous and both immediate and modified release oral administration and suggested that CYP3A4 is responsible for the majority of the nonlinearity in systemic exposure observed after administration of the immediate release form. Conversely, the reduced bioavailability of the modified release formulation is believed to be caused by incomplete release from the device, incomplete absorption of released drug, and, to a lesser extent, CYP3A4 metabolism.

Modelling and PBPK simulation in drug discovery

Physiologically based pharmacokinetic (PBPK) models are composed of a series of differential equations and have been implemented in a number of commercial software packages. These models require species-specific and compound-specific input parameters and allow for the prediction of plasma and tissue concentration time profiles after intravenous and oral administration of compounds to animals and humans. PBPK models allow the early integration of a wide variety of preclinical data into a mechanistic quantitative framework. Use of PBPK models allows the experimenter to gain insights into the properties of a compound, helps to guide experimental efforts at the early stages of drug discovery, and enables the prediction of human plasma concentration time profiles with minimal (and in some cases no) animal data. In this review, the application and limitations of PBPK techniques in drug discovery are discussed. Specific reference is made to its utility (1) at the lead development stage for the prioritization of compounds for animal PK studies and (2) at the clinical candidate selection and "first in human" stages for the prediction of human PK.

Impact of end-product inhibition on the determination ofin vitrometabolic clearance

End-product inhibition was explored as a mechanism for the lower clearance determination obtained from microsomes compared with hepatocytes. Triazolam, diazepam and phenytoin microsomal substrate depletion was reduced by 23, 34 and 39%, respectively, when incubated with their primary metabolites. Ki values of 28+/-6 and 11+/-1 microM were obtained when 4'-hydroxydiazepam and p-hydroxyphenytoin where incubated with diazepam and phenytoin, respectively. Alamethicin (a glucuronidation activator) was unsuccessful in alleviating these effects. IC50 values of 17, 32 and 18 microM for phenytoin and 83, 110 and 97 microM for diazepam were observed with salicylamide- (a glucuronidation inhibitor) treated hepatocytes, control hepatocytes and microsomes, respectively, when incubated with their primary metabolites. These differences suggest that metabolite concentrations in the vicinity of the enzyme are lower in hepatocytes compared with microsomes, reducing the likelihood of end-product inhibition in the former system. In conclusion, end-product inhibition may be more prominent in microsomes (in particular for substrate depletion assays where metabolism tends to be more extensive); results suggest that this phenomenon may contribute to the observed variations in metabolism characteristics and intrinsic clearance (CLint) between hepatocytes and microsomes.

Predicting pharmacokinetic food effects using biorelevant solubility media and physiologically based modelling

BACKGROUND

Food-induced changes in gastric emptying time, gastric pH and/or intestinal fluid composition may have an impact on the pharmacokinetics of drugs. The aim of this work was to use mathematical models describing physiology in fed and fasted states together with biorelevant solubility and degradation data to simulate food effects for six compounds from recent Roche projects.

METHODS

The solubility of each compound was measured in different biorelevant media: simulated human gastric fluid for the fasted and fed state, simulated human intestinal fluid for the fasted, fed and high-fat state, and simulated human colonic fluid for the upper and the lower colon. A physiologically based absorption model was developed in GastroPlustrade mark for each compound using permeability, solubility, metabolism and distribution data. By incorporating the appropriate physiological parameters and solubility data into the model, the oral pharmacokinetics of each drug was simulated under fasted, fed and/or high-fat conditions. Predicted and observed plasma concentration-time profiles and food effects were compared for a range of doses to assess the accuracy of the simulations.

RESULTS

The models were able to distinguish between minor and significant food effects. The simulation captured well the magnitude of the food effects and for the six compounds correctly predicted the observed plasma exposure in fasted, fed and high-fat conditions.

CONCLUSION

Biorelevant solubility tests can be used together with physiologically based absorption models to predict clinical food effects caused by solubility and/or dissolution rate limitations.

A Novel Strategy for Physiologically Based Predictions of Human Pharmacokinetics

BACKGROUND

The major aim of this study was to develop a strategy for predicting human pharmacokinetics using physiologically based pharmacokinetic (PBPK) modelling. This was compared with allometry (of plasma concentration-time profiles using the Dedrick approach), in order to determine the best approaches and strategies for the prediction of human pharmacokinetics.

METHODS

PBPK and Dedrick predictions were made for 19 F. Hoffmann-La Roche compounds. A strategy for the prediction of human pharmacokinetics using PBPK modelling was proposed in this study. Predicted values (pharmacokinetic parameters, plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods.

RESULTS

By following the proposed strategy for PBPK, a prediction would have been made prospectively for approximately 70% of the compounds. The prediction accuracy for these compounds in terms of the percentage of compounds with an average-fold error of <2-fold was 83%, 50%, 75%, 67%, 92% and 100% for apparent oral clearance (CL/F), apparent volume of distribution during terminal phase after oral administration (V(z)/F), terminal elimination half-life (t(1/2)), peak plasma concentration (C(max)), area under the plasma concentration-time curve (AUC) and time to reach C(max) (t(max)), respectively. For the other 30% compounds, unacceptable prediction accuracy was obtained in animals; therefore, a prospective prediction of human pharmacokinetics would not have been made using PBPK. For these compounds, prediction accuracy was also poor using the Dedrick approach. In the majority of cases, PBPK gave more accurate predictions of pharmacokinetic parameters and plasma concentration-time profiles than the Dedrick approach.

CONCLUSIONS

Based on the dataset evaluated in this study, PBPK gave reasonable predictions of human pharmacokinetics using preclinical data and is the recommended approach in the majority of cases. In addition, PBPK modelling is a useful tool to gain insights into the properties of a compound. Thus, PBPK can guide experimental efforts to obtain the relevant information necessary to understand the compound's properties before entry into human, ultimately resulting in a higher level of prediction accuracy.

Substrate depletion approach for determining in vitro metabolic clearance: time dependencies in hepatocyte and microsomal incubations

The substrate depletion method is a popular approach used for the measurement of in vitro intrinsic clearance (CL(int)). However, the incubation conditions used in these studies can vary, the consequences of which have not been systematically explored. Initial substrate depletion incubations using rat microsomes and hepatocytes were performed for eight benzodiazepines: alprazolam, clobazam, clonazepam, chlordiazepoxide, diazepam, flunitrazepam, midazolam, and triazolam. Subsequent predictions of in vivo CL(int) (ranging from 3 to 200 ml/min) and hepatic clearance (CL(H)) (ranging from 0.3 to 15 ml/min) demonstrated that the general predictive ability of this approach was similar to that of the traditional metabolite formation method. A more detailed study of the substrate depletion profiles and CL(int) estimates indicated that the concentration of enzyme used is of particular importance. The metabolism of triazolam, clonazepam, and diazepam was monoexponential at all cell densities using hepatocytes; however, with microsomes, biphasic depletion was apparent, particularly at higher microsomal protein concentrations (2-5 mg/ml). Enzyme activity studies indicated that enzyme loss was more pronounced in the microsomal system (ranged from 8 to 65% activity after a 1-h incubation) compared with the hepatocyte system (approximately 100% activity after a 1-h incubation). For clonazepam (a low clearance substrate), these biphasic profiles could be explained by loss of enzyme activity. To ensure accurate predictions of in vivo CL(int) and CL(H) when using the substrate depletion approach, based on the results obtained for this class of drugs, it is recommended that low enzyme concentrations and short incubation times are used whenever possible.

QUANTITATIVE PREDICTION OF THE IN VIVO INHIBITION OF DIAZEPAM METABOLISM BY OMEPRAZOLE USING RAT LIVER MICROSOMES AND HEPATOCYTES

The diazepam (DZ)-omeprazole (OMP) interaction has been selected as a prototype for an important drug-drug interaction involving cytochrome P450 inhibition. The availability of an in vivo K(i) value (unbound K(i), 21 microM) obtained from a series of steady-state inhibitor infusion studies allowed assessment of several in vitro-derived predictions of this inhibition interaction. Studies monitoring substrate depletion with time were used to obtain in vitro K(i) values that were evaluated against the more traditional metabolite formation approach using microsomes and hepatocytes. OMP inhibited the metabolism of DZ to its primary metabolites 4'-hydroxydiazepam, 3-hydroxydiazepam, and nordiazepam to different extents over a range of concentrations (0.3-150 microM), and a competitive inhibition model best fitted the data. The K(i) values observed using the substrate depletion approach (16 +/- 3 microM and 7 +/- 2 microM in microsomes and hepatocytes, respectively) were in good agreement with the overall weighted K(i) values obtained using the standard metabolite formation approach (12 +/- 2 microM and 16 +/- 5 microM in microsomes and hepatocytes, respectively). In vitro binding and cell uptake studies as well as human serum albumin studies in hepatocytes confirmed the importance of both intracellular and extracellular unbound concentrations of inhibitor when considering inhibition predictions. Both kinetic approaches and both in vitro systems predicted the in vivo interaction well and provide a good example of the ability of in vitro inhibition studies to quantitatively predict an in vivo drug-drug interaction successfully.

Incorporating measures of variability and uncertainty into the prediction of in vivo hepatic clearance from in vitro data

The existing procedures for quantitative in vitro-in vivo clearance prediction can be significantly biased either by totally neglecting the existing variability and uncertainty by using mean parameter values or by implementing Monte Carlo simulation with statistical distribution of the parameters reconstructed from very small sets of data. The aim of the present study is to develop a methodology for the prediction of in vivo hepatic clearance in the presence of semiquantitative or qualitative data and accounting for the existing uncertainty and variability. The method consists of two steps: 1) transformation of the information available into fuzzy sets (fuzzification); and 2) computation of the in vivo clearance using arithmetic operations with fuzzy sets. To illustrate the approach, rat hepatocyte and microsomal data for eight benzodiazepine compounds are used. A comparison with a standard Monte Carlo procedure is made. The methodology proposed can be used when Monte Carlo simulation may be biased or cannot be implemented. The obtained fuzzy in vivo clearance can be used subsequently in fuzzy simulations of pharmacokinetic models.

In vivo 5-HT2A receptor blockade by quetiapine: an R91150 single photon emission tomography study

BACKGROUND

Atypical antipsychotic drugs are thought to show a high degree of 5-HT2A receptor blockade, which may prevent the emergence of extrapyramidal symptoms.

METHOD

5-HT2A binding was estimated using 123I-5-I-R91150 and single photon emission tomography (SPET) in six schizophrenic subjects treated with quetiapine at a mean (+/-SD) daily dose of 350+/-123 mg for at least 5 weeks and a matched sample of six healthy volunteers. Clinical and side-effect ratings were performed at baseline and at the time of SPET scanning. The reference region approach was used to define a 5-HT2A binding index in the frontal and temporal cortex.

RESULTS

Quetiapine treatment resulted in a significant decline in 5-HT2A receptor availability in the frontal cortex (mean 0.98+/-0.09) relative to healthy volunteers (mean 1.33+/-0.16). All patients showed improvements in clinical symptom or side-effect ratings. The mean frontal cortex:cerebellum ratio after quetiapine treatment was significantly negatively correlated with reduction in the Abnormal Involuntary Rating scale and Simpson-Angus scores (P<0.05 Bonferroni corrected), but not with the reduction in the scores from the scale for the assessment of positive symptoms, the scale for the assessment of negative symptoms, the Montgomery-Asberg depression rating scale or patient age.

CONCLUSION

Quetiapine treatment results in significant in vivo blockade of cortical 5-HT2A, similar to other atypical antipsychotic drugs. This effect may contribute to its placebo level extrapyramidal side-effect profile.