Reported predictions of human in vivo hepatic clearance from in vitro data have used a variety of values for the scaling factors human microsomal protein (MPPGL) and hepatocellularity (HPGL) per gram of liver, generally with no consideration of the extent of their inter-individual variability. We have collated and analysed data from a number of sources, to provide weighted meangeo values of human MPPGL and HPGL of 32 mg g-1 (95% Confidence Interval (CI); 29-34 mg.g-1) and 99x10(6) cells.g-1 (95% CI; 74-131 mg.g-1), respectively. Although inter-individual variability in values of MPPGL and HPGL was statistically significant, gender, smoking or alcohol consumption could not be detected as significant covariates by multiple linear regression. However, there was a weak but statistically significant inverse relationship between age and both MPPGL and HPGL. These findings indicate the importance of considering differences between study populations when forecasting in vivo pharmacokinetic behaviour. Typical clinical pharmacology studies, particularly in early drug development, use young, fit, healthy male subjects of around 30 years of age. In contrast, the average age of patients for many diseases is about 60 years of age. The relationship between age and MPPGL observed in this study estimates values of 40 mg.g-1 for a 30 year old individual and 31 mg.g-1 for a 60 year old individual. Investigators may wish to consider the reported covariates in the selection of scaling factors appropriate for the population in which estimates of clearance are being predicted. Further studies are required to clarify the influence of age (especially in paediatric subjects), donor source and ethnicity on values of MPPGL and HPGL. In the meantime, we recommend that the estimates (and their variances) from the current meta-analysis be used when predicting in vivo kinetic parameters from in vitro data.

Background and ObjectivesThe interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug–drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug–drug interactions becomes very challenging. To address this, an in vitro–in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator®.MethodsIn this article an IVIVE technique for liver transporters is described and a full-body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDIs involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic-anion transporting polypeptides OATP1B1, OAT1B3 and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide [NTCP] and breast cancer resistance protein [BCRP]) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies that had not been used for development of the rosuvastatin model.ResultsThe simulated area under the plasma concentration–time curve (AUC), maximum concentration (Cmax) and the time to reach Cmax (tmax) values of rosuvastatin over the dose range of 10–80 mg, were within 2-fold of the observed data. Subsequently, the validated model was used to investigate the impact of coadministration of cyclosporine (ciclosporin), an inhibitor of OATPs, BCRP and NTCP, on the exposure of rosuvastatin in healthy volunteers.ConclusionThe results show the utility of the model to integrate a wide range of in vitro and in vivo data and simulate the outcome of clinical studies, with implications for their design.Electronic supplementary materialThe online version of this article (doi:10.1007/s40262-013-0097-y) contains supplementary material, which is available to authorized users.

The use of in vitro-in vivo extrapolation (IVIVE) of metabolic data in the prediction of population clearance has become an important tool in both discovery and preclinical phases of the drug development process (Rostami-Hodjegan and Tucker, 2007). Scaling of metabolic rate constants derived using microsomal protein (MSP) isolated from human livers or those from recombinantly expressed cytochrome P450 enzymes requires knowledge of the amount of microsomal protein per gram of liver (MPPGL) among other scaling factors (Barter et al., 2007). The most commonly used value of MPPGL in human IVIVE is 45 mg g Ϫ1 (Obach, 1997;Soars et al., 2002;Andersson et al., 2004;Uchaipichat et al., 2006) reported in a review by Houston (1994). However, this value is not obtained from human livers; instead it is a combination of data generated using rat tissue from a number of sources (Joly et al., 1975;Lin et al., 1978; Baarnhielm et al., 1986;Chiba et al., 1990). Several values of MPPGL determined using human tissue have been reported in the literature, and a detailed review of these studies has been the focus of a recent consensus article on the most appropriate values of MPPGL for use in IVIVE alongside other scaling factors such as human hepatocellularity (Barter et al., 2007). Collation of values of MPPGL from five studies (114 observations; age range 11-80 years, median 48 years; 47 females) has indicated a weak but statistically significant inverse relationship between MPPGL and donor age (Barter et al., 2007). Barter et al. (2007) assumed that different experimental procedures and technical staff carrying out the exercise ("operators") would not introduce bias into their analysis. However, these effects have not been assessed systematically. Assessing the operator effect also has implications for within-laboratory pooling of data. The preparation and analysis of replicate samples, which are required to differentiate between methodological and true biological variability in values of MPPGL, is time-consuming (2 days/liver). To increase output, processing of samples may be performed by more than one individual (operator); however, this will require an indication of consistency between estimates of MPPGL by these operators.In addition, determination of values of MPPGL from fresh tissue from large numbers of donors is hampered by the infrequent supply of human tissue and logistical problems associated with immediate analysis of the samples when they arrive at the laboratory. In theory, the use of tissue stored at Ϫ80°C solves this problem. However, the use of frozen samples assumes maintenance of protein structure and function through the freeze-thaw process. Thus, to ensure adequate compatibility between the results obtained within and between differ-Z.E.B. was supported by Simcyp Ltd., (Sheffield, UK) and European Union Framework 6 (BIOSIM).Article, publication date, and citation information can be found at http://dmd.aspetjournals.org. doi:10.1124/dmd.108.021311. ABBREVIATIONS:IVIVE, in vitro-in vivo extrapolation; MSP, micr...

The results of this study indicate the value of simulation based on mechanistic physiologically based pharmacokinetic modelling (PBPK) in anticipating the likely extent of any differences in the kinetics of CYP substrates in Chinese and Caucasian populations arising from demographic, physiological and genetic differences.

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In this study, we present efavirenz physiologically based pharmacokinetic (PBPK) model development as an example of our best practice approach that uses a stepwise approach to verify the different components of the model. First, a PBPK model for efavirenz incorporating in vitro and clinical pharmacokinetic (PK) data was developed to predict exposure following multiple dosing (600 mg q.d.). Alfentanil i.v. and p.o. drug‐drug interaction (DDI) studies were utilized to evaluate and refine the CYP3A4 induction component in the liver and gut. Next, independent DDI studies with substrates of CYP3A4 (maraviroc, atazanavir, and clarithromycin) and CYP2B6 (bupropion) verified the induction components of the model (area under the curve [AUC] ratios within 1.0–1.7‐fold of observed). Finally, the model was refined to incorporate the fractional contribution of enzymes, including CYP2B6, propagating autoinduction into the model (Racc 1.7 vs. 1.7 observed). This validated mechanistic model can now be applied in clinical pharmacology studies to prospectively assess both the victim and perpetrator DDI potential of efavirenz.

Prediction of metabolic clearance in extreme individuals rather than the 'average human' is becoming an attractive tool within the pharmaceutical industry. The current study involved prediction of variability in metabolic clearance for alprazolam, triazolam and midazolam with emphasis on the following factors: first, evaluation of clearance prediction accuracy using intrinsic clearance (CL(int)) data from in vitro metabolic data and back-calculation from in vivo clearance data. Second, the sensitivity of predicted in vivo variability to changes in variability for physiological parameters (e.g. liver weight, haematocrit, CYP3A abundance). Finally, reported estimates of variability in hepatic CYP3A4 abundance (coefficient of variation (CV) 95%) were refined by separating experimental from interindividual variability using a repeat measurement protocol in 52 human liver samples. Using in vitro metabolic data, predicted clearances were within 2-fold of observed for triazolam and midazolam. Clearance of alprazolam was overpredicted by 2.0- to 3.7-fold. Use of in vivo CL(int) values improved prediction of intravenous clearance to within 2-fold of observed for all drugs. Initially, the variability in clearance was overestimated for all drugs (by 1.8- to 3.6-fold). Use of a reduced hepatic CYP3A4 CV of 41%, representative of interindividual variability alone improved predictions of variability in clearance for all drugs to within 2-fold of observed.