In Vitro-in Vivo Extrapolation Method Research Articles

Concentration dependence of in vitro biotransformation rates of hydrophobic organic sunscreen agents in rainbow trout S9 fractions: Implications for bioaccumulation assessment.

In vitro biotransformation studies were performed to support the bioaccumulation assessment of 3 hydrophobic organic ultraviolet filters (UVFs), 4-methylbenzylidene camphor (4-MBC), 2-ethylhexyl-4-methoxycinnamate (EHMC), and octocrylene. In vitro depletion rate constants (kdep ) were determined for each UVF using rainbow trout liver S9 fractions. Incubations performed with and without added cofactors showed complete (4-MBC) or partial (EHMC and octocrylene) dependence of kdep on addition of the reduced form of nicotinamide adenine dinucleotide phosphate (NADPH), suggesting that hydrolysis of EHMC and octocrylene by NADPH-independent enzymes (e.g., carboxylesterases) is an important metabolic route. The concentration dependence of kdep was then evaluated to estimate Michaelis-Menten parameters (KM and Vmax ) for each UVF. Measured kdep values were then extrapolated to apparent whole-body biotransformation rate constants using an in vitro-in vivo extrapolation (IVIVE) model. Bioconcentration factors (BCFs) calculated from kdep values measured at concentrations well below KM were closer to empirical BCFs than those calculated from kdep measured at higher test concentrations. Modeled BCFs were sensitive to in vitro binding assumptions employed in the IVIVE model, highlighting the need for further characterization of chemical binding effects on hepatic clearance. The results suggest that the tested UVFs are unlikely to accumulate to levels exceeding the European Union Registration, Evaluation, Authorisation, and Restriction regulation criterion for bioaccumulative substances (BCF > 2000 L kg-1 ). However, consideration of appropriate in vitro test concentrations and binding correction factors are important when IVIVE methods are used to refine modeled BCFs. Environ Toxicol Chem 2019;38:548-560. © 2018 SETAC.

Strategies to improve the prediction accuracy of hepatic intrinsic clearance of three antidiabetic drugs: Application of the extended clearance concept and consideration of the effect of albumin on CYP2C metabolism and OATP1B-mediated hepatic uptake

The antidiabetic drugs glibenclamide, repaglinide, and nateglinide are well-known substrates for hepatic uptake transporters of the organic anion transporting polypeptide (OATP) family and metabolizing enzymes of the cytochrome P450 (CYP) 2C subfamily. The systemic exposure of these drugs varies substantially among individuals, impacted by genetic polymorphisms of transporters and metabolizing enzymes as well as drug-drug interactions. The use of the conventional in vitro-in vivo extrapolation (IVIVE) method was found to underestimate their hepatic intrinsic clearance (CLint,all); the clinically observed CLint,all values were ≥10-fold higher than the predicted values from in vitro data. In order to improve the accuracy in predicting CLint,all of these drugs, the following modifications were implemented; i) the extended clearance concept was applied during IVIVE processes, ii) albumin was added to metabolic assays using human liver microsomes (to minimize the impact of intrinsic inhibitors on kinetic parameters for CYP2C-mediated metabolism) and to hepatic uptake assays (to accommodate the enhanced hepatic uptake observed with albumin-bound drugs), and iii) differing rates of efflux and influx via diffusion were used. The IVIVE method with these modifications yielded the predicted CLint,all values from in vitro data in closer agreement with the CLint,all values observed in vivo; the fold differences between the predicted and observed CLint,all values reduced from 13–15 to 5.9–6.7. Our current approach offers an improvement in the prediction of CLint,all and further investigations are warranted to enhance the prediction accuracy of IVIVE.

In Vitro–In Vivo Extrapolation Method to Predict Human Renal Clearance of Drugs

Renal clearance is a key determinant of the elimination of drugs. To date, only few in vitro-in vivo extrapolation (IVIVE) approaches have been described to predict the renal organ clearance as the net result of glomerular filtration, tubular secretion, and tubular reabsorption. In this study, we measured in LLC-PK1 cells the transport of 20 compounds that cover all four classes of the Biopharmaceutical Drug Disposition System. These data were incorporated into a novel kidney model to predict all renal clearance processes in human. We showed that filtration and secretion were main contributors to the renal organ clearance for all compounds, whereas reabsorption was predominant for compounds assigned to classes 1 and 2. Our results suggest that anionic drugs were not significantly secreted in LLC-PK1 cells, resulting in under-predicted clearances. When all study compounds were included a high overall correlation between the reported and predicted renal organ clearances was obtained (R² = 0.83). The prediction accuracy in terms of percentage within twofold and threefold error was 70% and 95%, respectively. In conclusion, our novel IVIVE method allowed to predict the human renal organ clearance and the contribution of each underlying process.

Comparative Assessment of In Vitro–In Vivo Extrapolation Methods used for Predicting Hepatic Metabolic Clearance of Drugs

The purpose of this study was to perform a comparative analysis of various in vitro--in vivo extrapolation (IVIVE) methods used for predicting hepatic metabolic clearance (CL) of drugs on the basis of intrinsic CL data determined in microsomes. Five IVIVE methods were evaluated: the "conventional and conventional bias-corrected methods" using the unbound fraction in plasma (fu(p) ), the "Berezhkovskiy method" in which the fu(p) is adjusted for drug ionization, the "Poulin et al. method" using the unbound fraction in liver (fu(liver) ), and the "direct scaling method," which does not consider any binding corrections. We investigated the effects of the following scenarios on the prediction of CL: the use of preclinical or human datasets, the extent of plasma protein binding, the magnitude of CL in vivo, and the extent of drug disposition based on biopharmaceutics drug disposition classification system (BDDCS) categorization. A large and diverse dataset of 139 compounds was collected, including those from the literature and in house from Genentech. The results of this study confirm that the Poulin et al. method is robust and showed the greatest accuracy as compared with the other IVIVE methods in the majority of prediction scenarios studied here. The difference across the prediction methods is most pronounced for (a) albumin-bound drugs, (b) highly bound drugs, and (c) low CL drugs. Predictions of CL showed relevant interspecies differences for BDDCS class 2 compounds; the direct scaling method showed the greatest predictivity for these compounds, particularly for a reduced dataset in rat that have unexpectedly high CL in vivo. This result is a reflection of the direct scaling method's natural tendency to overpredict the true metabolic CL. Overall, this study should facilitate the use of IVIVE correlation methods in physiologically based pharmacokinetics (PBPK) model.

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. © 2011 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4090–4110, 2011