PKPD modeling of acquired resistance to anti-cancer drug treatment.

Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials.

Simulation-Based Evaluation of PK/PD Indices for Meropenem Across Patient Groups and Experimental Designs.

PURPOSE: Antibiotic dose predictions based on PK/PD indices rely on that the index type and magnitude is insensitive to the pharmacokinetics (PK), the dosing regimen, and bacterial susceptibility. In this work we perform simulations to challenge these assumptions for meropenem and Pseudomonas aeruginosa. METHODS: A published murine dose fractionation study was replicated in silico. The sensitivity of the PK/PD index towards experimental design, drug susceptibility, uncertainty in MIC and different PK profiles was evaluated. RESULTS: The previous murine study data were well replicated with fT > MIC selected as the best predictor. However, for increased dosing frequencies fAUC/MIC was found to be more predictive and the magnitude of the index was sensitive to drug susceptibility. With human PK fT > MIC and fAUC/MIC had similar predictive capacities with preference for fT > MIC when short t1/2 and fAUC/MIC when long t1/2. CONCLUSIONS: A longitudinal PKPD model based on in vitro data successfully predicted a previous in vivo study of meropenem. The type and magnitude of the PK/PD index were sensitive to the experimental design, the MIC and the PK. Therefore, it may be preferable to perform simulations for dose selection based on an integrated PK-PKPD model rather than using a fixed PK/PD index target.

Translational PK/PD modeling to increase probability of success in drug discovery and early development.

In this review we present ways in which translational PK/PD modeling can address opportunities to enhance probability of success in drug discovery and early development. This is achieved by impacting efficacy and safety-driven attrition rates, through increased focus on the quantitative understanding and modeling of translational PK/PD. Application of the proposed principles early in the discovery and development phases is anticipated to bolster confidence of successfully evaluating proof of mechanism in humans and ultimately improve Phase II success. The present review is centered on the application of predictive modeling and simulation approaches during drug discovery and early development, and more specifically of mechanism-based PK/PD modeling. Case studies are presented, focused on the relevance of M&S contributions to real-world questions and the impact on decision making.

Model-based assessment of erlotinib effect in vitro measured by real-time cell analysis.

Real time cell analysis (RTCA) is an impedance-based technology which tracks various living cell characteristics over time, such as their number, morphology or adhesion to the extra cellular matrix. However, there is no consensus about how RTCA data should be used to quantitatively evaluate pharmacodynamic parameters which describe drug efficacy or toxicity. The purpose of this work was to determine how RTCA data can be analyzed with mathematical modeling to explore and quantify drug effect in vitro. The pharmacokinetic-pharmacodynamic erlotinib concentration profile predicted by the model and its effect on the human epidermoïd carcinoma cell line A431 in vitro was measured through RTCA output, designated as cell index. A population approach was used to estimate model parameter values, considering a plate well as the statistical unit. The model related the cell index to the number of cells by means of a proportionality factor. Cell growth was described by an exponential model. A delay between erlotinib pharmacokinetics and cell killing was described by a transit compartment model, and the effect potency, by an E max function of erlotinib concentration. The modeling analysis performed on RTCA data distinguished drug effects in vitro on cell number from other effects likely to modify the relationship between cell index and cell number. It also revealed a time-dependent decrease of erlotinib concentration over time, described by a mono-exponential pharmacokinetic model with nonspecific binding.

Pharmacokinetic/Pharmacodynamic predictors of clinical potency for hepatitis C virus nonnucleoside polymerase and protease inhibitors.

This analysis was conducted to determine whether the hepatitis C virus (HCV) viral kinetics (VK) model can predict viral load (VL) decreases for nonnucleoside polymerase inhibitors (NNPolIs) and protease inhibitors (PIs) after 3-day monotherapy studies of patients infected with genotype 1 chronic HCV. This analysis includes data for 8 NNPolIs and 14 PIs, including VL decreases from 3-day monotherapy, total plasma trough concentrations on day 3 (C(min)), replicon data (50% effective concentration [EC(50)] and protein-shifted EC(50) [EC(50,PS)]), and for PIs, liver-to-plasma ratios (LPRs) measured in vivo in preclinical species. VK model simulations suggested that achieving additional log(10) VL decreases greater than one required 10-fold increases in the C(min). NNPolI and PI data further supported this result. The VK model was successfully used to predict VL decreases in 3-day monotherapy for NNPolIs based on the EC(50,PS) and the day 3 C(min). For PIs, however, predicting VL decreases using the same model and the EC(50,PS) and day 3 C(min) was not successful; a model including LPR values and the EC(50) instead of the EC(50,PS) provided a better prediction of VL decrease. These results are useful for designing phase 1 monotherapy studies for NNPolIs and PIs by clarifying factors driving VL decreases, such as the day 3 C(min) and the EC(50,PS) for NNPolIs or the EC(50) and LPR for PIs. This work provides a framework for understanding the pharmacokinetic/pharmacodynamic relationship for other HCV drug classes. The availability of mechanistic data on processes driving the target concentration, such as liver uptake transporters, should help to improve the predictive power of the approach.

Mass spectrometry-based quantification of CYP enzymes to establish in vitro/in vivo scaling factors for intestinal and hepatic metabolism in beagle dog.

PURPOSE: Physiologically based models, when verified in pre-clinical species, optimally predict human pharmacokinetics. However, modeling of intestinal metabolism has been a gap. To establish in vitro/in vivo scaling factors for metabolism, the expression and activity of CYP enzymes were characterized in the intestine and liver of beagle dog. METHODS: Microsomal protein abundance in dog tissues was determined using testosterone-6ß-hydroxylation and 7-hydroxycoumarin-glucuronidation as markers for microsomal protein recovery. Expressions of 7 CYP enzymes were estimated based on quantification of proteotypic tryptic peptides using multiple reaction monitoring mass spectrometry. CYP3A12 and CYP2B11 activity was evaluated using selective marker reactions. RESULTS: The geometric mean of total microsomal protein was 51 mg/g in liver and 13 mg/cm in intestine, without significant differences between intestinal segments. CYP3A12, followed by CYP2B11, were the most abundant CYP enzymes in intestine. Abundance and activity were higher in liver than intestine and declined from small intestine to colon. CONCLUSIONS: CYP expression in dog liver and intestine was characterized, providing a basis for in vitro/in vivo scaling of intestinal and hepatic metabolism.

Model-Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing.

The availability of multidimensional data together with the development of modern techniques for data analysis represent an exceptional opportunity for clinical pharmacology. Data science-defined in this special issue as the novel approaches to the collection, aggregation, and analysis of data-can significantly contribute to characterize drug-response variability at the individual level, thus enabling clinical pharmacology to become a critical contributor to personalized healthcare through precision dosing. We propose a minireview of methodologies for achieving precision dosing with a focus on an artificial intelligence technique called reinforcement learning, which is currently used for individualizing dosing regimen in patients with life-threatening diseases. We highlight the interplay of such techniques with conventional pharmacokinetic/pharmacodynamic approaches and discuss applicability in drug research and early development.

Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk.

There is an increasing expectation that computational approaches may supplement existing human decision-making. Frontloading of models for cardiac safety prediction is no exception to this trend, and ongoing regulatory initiatives propose use of high-throughput in vitro data combined with computational models for calculating proarrhythmic risk. Evaluation of these models requires robust assessment of the outcomes. Using FDA Adverse Event Reporting System reports and electronic healthcare claims data from the Truven-MarketScan US claims database, we quantify the incidence rate of arrhythmia in patients and how this changes depending on patient characteristics. First, we propose that such datasets are a complementary resource for determining relative drug risk and assessing the performance of cardiac safety models for regulatory use. Second, the results suggest important determinants for appropriate stratification of patients and evaluation of additional drug risk in prescribing and clinical support algorithms and for precision health.

Mechanistic modeling of hepatic transport from cells to whole body: application to napsagatran and fexofenadine.

A mechanistic model was applied to quantitatively derive the kinetic parameters from in vitro hepatic uptake transport data. These parameters were used as input to simulate in vivo elimination using a fully mechanistic physiologically based pharmacokinetic (PBPK) model. Fexofenadine and napsagatran, both BDDCS class 3 drugs, were chosen as model compounds. In rat, both compounds are hardly metabolized and are eliminated unchanged mostly through biliary excretion. Uptake was estimated in this study based on plated rat hepatocytes, and a mechanistic model was used to derive the active and passive transport parameters, namely Michaelis-Menten uptake parameters (V(maxI) and K(mI,u)) together with passive diffusion (P(dif)) and nonspecific binding. Maximum transport velocity and passive diffusion were scaled to in vivo parameters (J(maxI) and PS(TC)) using hepatocellularity. Biliary excretion, through passive and active transport, was assessed from in vivo studies. These transport parameters were then used as input in a whole body physiologically based model in which the liver compartment was parametrized for the different passive and active transport processes. Each of the processes was linked to the free concentration in the relevant compartment. For napsagatran hepatic uptake, no passive diffusion and no binding were detected in vitro besides the active transport (K(mI,u) = 88.4 +/- 8.1 microM, V(maxI) = 384 +/- 19 pmol/mg/min). Fexofenadine was rapidly taken up into rat hepatocytes (K(mI,u) = 271 +/- 35 microM, V(maxI) = 3162 +/- 274 pmol/mg/min), and some contribution of passive diffusion to the uptake (P(dif) = 2.08 +/- 0.67 microL/mg/min) was observed. For fexofenadine, the biliary export rate was found to be slower than the uptake, leading to drug accumulation in liver. No accumulation was observed for napsagatran where excretion was faster than hepatic uptake. Observed plasma, liver and bile concentration time profiles were compared to PBPK simulations based on scaled in vitro transport kinetic parameters. An uncertainty analysis indicated that for both compounds the scaled in vitro uptake clearance had to be adjusted with an additional empirical scaling factor of 10 to match the plasma and liver concentrations and biliary excretion profiles. Applying this model, plasma clearance (CL(P)) and half-life (t(1/2)), maximum liver concentration (C(maxL)) and fraction excreted in bile (f(bile)) were predicted within 2-fold. In vitro uptake data had most impact on the simulated plasma and biliary excretion profiles, while accurate simulations of liver concentrations required also quantitative estimates of biliary excretion transport. This study indicated that the mechanistic model allowed for accurate evaluation of in vitro experiments; and the scaled kinetic parameters of hepatic uptake transport enabled the prediction of in vivo PK profiles and plasma clearances, using PBPK modeling.

Prediction of pharmacokinetic profile of valsartan in human based on in vitro uptake transport data.

The aim of this study was to evaluate a strategy based on a physiologically based pharmacokinetic (PBPK) model for the prediction of PK profiles in human using in vitro data when elimination of compounds relies on active transport processes. The strategy was first applied to rat in vivo and in vitro data in order to refine the PBPK model. The model could then be applied to human in vitro uptake transport data using valsartan as a probe substrate. Plated rat and human hepatocytes, and cell lines overexpressing human OATP1B1 and OATP1B3 were used for in vitro uptake experiments. The uptake rate of valsartan was higher for rat hepatocytes (K (m,u) = 28.4 +/- 3.7 muM, V (max) = 1318 +/- 176 pmol/mg/min and P (dif) = 1.21 +/- 0.42 microl/mg/min) compared to human hepatocytes (K (m,u) = 44.4 +/- 14.6 microM, V (max) = 304 +/- 85 pmol/mg/min and P (dif) = 0.724 +/- 0.271 microl/mg/min). OATP1B1 and 1B3 parameters were correlated to human hepatocyte data using experimentally established relative activity factors (RAF). Resulting PBPK simulations using those in vitro data were compared for plasma (human and rat) and bile (rat) concentration-time profiles following i.v. bolus administration of valsartan. An uncertainty analysis indicated that the scaled in vitro uptake clearance had to be adjusted with an additional empirical scaling factor of 5 to match the plasma concentrations and biliary excretion profiles. Applying this model, plasma clearances (CL(P)) for rat and human were predicted within two-fold relative to predictions based on respective in vitro data. The corrected hepatic uptake transport kinetic parameters enabled the prediction of valsartan in vivo PK profiles and plasma clearances, using PBPK modeling. Moreover, the interspecies difference in elimination rate observed in vivo was correctly reflected in the transport parameters determined in vitro. More data are needed to support more general applications of the proposed approach including its use for metabolized compounds.

Prediction of pharmacokinetic profile of valsartan in humans based on in vitro uptake-transport data.

The aim of this study was to evaluate a physiologically based pharmacokinetic (PBPK) model for predicting PK profiles in humans based on a model refined in rats and humans in vitro uptake-transport data using valsartan as a probe substrate. Valsartan is eliminated unchanged, mostly through biliary excretion, both in humans and rats. It was, therefore, chosen as model compound to predict in vivo elimination based on in vitro hepatic uptake-transport data using a fully mechanistic PBPK model. Plated rat and human hepatocytes, and cell lines overexpressing human OATP1B1 and OATP1B3 were used for in vitro uptake experiments. A mechanistic two-compartment model was used to derive the active and passive transport parameters, namely uptake Michaelis-Menten parameters (V(max) and K(m,u)) together with passive diffusion (P(dif)). These transport parameters were then used as input in a whole body physiologically based pharmacokinetic (PBPK) model. The uptake rate of valsartan was higher for rat hepatocytes (K(m,u)=28.4+/-3.7 microM, V(max)=1320+/-180 pmol/mg/min, and P(dif) =1.21+/-0.42 microl/mg/min) compared to human hepatocytes (K(m,u)=44.4+/-14.6 microM, V(max)=304+/-85 pmol/mg/min, and P(dif)=0.724+/-0.271 microl/mg/min). OATP1B1 and -1B3 parameters were correlated to human hepatocyte data, using experimentally established relative activity factors (RAF). Resulting PBPK simulations were compared for plasma- (humans and rats) and bile- (rats) concentration-time profiles following iv bolus administration of valsartan. Plasma clearances (CL(P)) for rats and humans were predicted within twofold relative to predictions based on respective in vitro data. The simulations were extended to simulate the impact of either OATP1B1 or -1B3 inhibition on plasma profile. The limited data set indicates that the mechanistic model allowed for accurate evaluation of in vitro transport data; and the resulting hepatic uptake transport kinetic parameters enabled the prediction of in vivo PK profiles and plasma clearances, using PBPK modelling. Moreover, the interspecies difference in elimination rate observed in vivo was correctly reflected in the transport parameters determined in vitro.

Design, data analysis, and simulation of in vitro drug transport kinetic experiments using a mechanistic in vitro model.

The use of in vitro data for quantitative predictions of transporter-mediated elimination in vivo requires an accurate estimation of the transporter Michaelis-Menten parameters, V(max) and K(m), as a first step. Therefore, the experimental conditions of in vitro studies used to assess hepatic uptake transport were optimized regarding active transport processes, nonspecific binding, and passive diffusion (P(dif)). A mechanistic model was developed to analyze and accurately describe these active and passive processes. This two-compartmental model was parameterized to account for nonspecific binding, bidirectional passive diffusion, and active uptake processes based on the physiology of the cells. The model was used to estimate kinetic parameters of in vitro transport data from organic anion-transporting peptide model substrates (e.g., cholecystokinin octapeptide deltorphin II, fexofenadine, and pitavastatin). Data analysis by this mechanistic model significantly improved the accuracy and precision in all derived parameters [mean coefficient of variations (CVs) for V(max) and K(m) were 19 and 23%, respectively] compared with the conventional kinetic method of transport data analysis (mean CVs were 58 and 115%, respectively, using this method). Furthermore, permeability was found to be highly temperature-dependent in Chinese hamster ovary (CHO) control cells and artificial membranes (parallel artificial membrane permeability assay). Whereas for some compounds (taurocholate, estrone-3-sulfate, and propranolol) the effect was moderate (1.5-6-fold higher permeability at 37 degrees C compared with that at 4 degrees C), for fexofenadine a 16-fold higher passive permeability was seen at 37 degrees C. Therefore, P(dif) was better predicted if it was evaluated under the same experimental conditions as V(max) and K(m), i.e., in a single incubation of CHO overexpressed cells or rat hepatocytes at 37 degrees C, instead of a parallel control evaluation at 4 degrees C.

Importance of target-mediated drug disposition for small molecules.

Target concentration is typically not considered in drug discovery. However, if targets are expressed at relatively high concentrations and compounds have high affinity, such that most of the drug is bound to its target, in vitro screens can give unreliable information on compound affinity. In vivo, a similar situation will generate pharmacokinetic (PK) profiles that deviate greatly from those normally expected, owing to target binding affecting drug distribution and clearance. Such target-mediated drug disposition (TMDD) effects on small molecules have received little attention and might only become apparent during clinical trials, with the potential for data misinterpretation. TMDD also confounds human microdosing approaches by providing therapeutically unrepresentative PK profiles. Being aware of these phenomena will improve the likelihood of successful drug discovery and development.

New strategies to address drug-drug interactions involving OATPs.

Pharmacokinetic drug-drug interactions (DDIs) are a major concern in drug development. Drug transport, along with drug metabolism via cytochrome P450s (CYPs), is increasingly being considered as an integral part of the overall pharnmacokinetics profile of a drug. Inhibition of transporters can lead to altered pharmacokinetics, potentially interfering with drug safety and efficacy. There is an increasing number of DDIs observed with statins, which are widely used in combination therapies, and this can be partly attributed to inhibition of individual hepatic transporters. Studies of these inhibitory interactions in vitro has indicated the importance of both hepatic solute carriers of the organic anion transporting polypeptide (OATP) superfamily and CYP inhibition. Mathematical models have been developed to gain more quantitative insights into the interplay between transport and metabolism of drugs. This article reviews new developments in the area of in vitro tools and modeling approaches that are used to study DDIs related to OATP transporters, with a focus on the clinical relevance of the transport-mediated DDIs involving statins.

Metabolic Profiling of Human Long-Term Liver Models and Hepatic Clearance Predictions from In Vitro Data Using Nonlinear Mixed-Effects Modeling.

Early prediction of human clearance is often challenging, in particular for the growing number of low-clearance compounds. Long-term in vitro models have been developed which enable sophisticated hepatic drug disposition studies and improved clearance predictions. Here, the cell line HepG2, iPSC-derived hepatocytes (iCell®), the hepatic stem cell line HepaRG™, and human hepatocyte co-cultures (HµREL™ and HepatoPac®) were compared to primary hepatocyte suspension cultures with respect to their key metabolic activities. Similar metabolic activities were found for the long-term models HepaRG™, HµREL™, and HepatoPac® and the short-term suspension cultures when averaged across all 11 enzyme markers, although differences were seen in the activities of CYP2D6 and non-CYP enzymes. For iCell® and HepG2, the metabolic activity was more than tenfold lower. The micropatterned HepatoPac® model was further evaluated with respect to clearance prediction. To assess the in vitro parameters, pharmacokinetic modeling was applied. The determination of intrinsic clearance by nonlinear mixed-effects modeling in a long-term model significantly increased the confidence in the parameter estimation and extended the sensitive range towards 3% of liver blood flow, i.e., >10-fold lower as compared to suspension cultures. For in vitro to in vivo extrapolation, the well-stirred model was used. The micropatterned model gave rise to clearance prediction in man within a twofold error for the majority of low-clearance compounds. Further research is needed to understand whether transporter activity and drug metabolism by non-CYP enzymes, such as UGTs, SULTs, AO, and FMO, is comparable to the in vivo situation in these long-term culture models.

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.

Cryopreserved human hepatocytes in suspension are a convenient high throughput tool for the prediction of metabolic clearance.

Hepatocyte assays, routinely used to assess the metabolic stability of new chemical entities, were recently improved by using hepatocytes in suspension instead of primary cultures [N. Blanchard, L. Richert, B. Notter, F. Delobel, P. David, P. Coassolo, T. Lavé, Impact of serum on clearance predictions obtained from suspensions and primary cultures of rat hepatocytes, Eur. J. Pharm. Sci. 23 (2004) 189-199]. The aim of the present study was to investigate miniaturising the suspension assay by using cryopreserved human hepatocytes, i.e., 150,000 cells/well in 96-well plates, to predict hepatic clearance (CLH) in order to increase compound throughput and decrease cost and tissue requirements. For this, an evaluation was first carried out with rat hepatocytes. Then, human hepatocytes from various donors were used under these predetermined conditions, either immediately after isolation, either after a 20-h-cold storage period in UW or after cryopreservation. The values of CLint and CLH determined using human hepatocytes in suspension in 96-well plates, immediately after isolation, after cold storage or after cryopreservation, were comparable to those obtained with hepatocytes in primary culture. In particular, the use of cryopreserved human hepatocytes in suspension in a 96-well format appeared to be largely satisfactory as a tool for screening and ranking of compounds in the early phase of the drug discovery process.

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.

Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib.

We quantitatively compare the efficacy of two approved EGFR tyrosine kinase inhibitors, erlotinib and gefitinib, based on in vivo and in vitro data and show how a modeling approach can be used to scale from animal to humans. Gefitinib shows a higher tumor uptake in cancer patients, and we explored the potential impact on pharmacologic and antitumor activity in in vitro and in xenograft mice. Tumor growth inhibition was monitored, and the pharmacokinetics (PK) in plasma and tumor, as well as temporal changes of phospho-Erk (pErk) signals were examined in patient-derived tumor xenograft mice. These data were integrated in a translational PKPD model, allowing us to project an efficacious human dose, which we retrospectively compared with prescribed doses for cancer patients. In vitro experiments showed that cell-cycle arrest was similar for erlotinib and gefitinib. Similar pERK biomarker responses were obtained despite a 6.6-fold higher total tumor exposure for gefitinib. The PKPD model revealed a 3.7-fold higher in vivo potency for gefitinib, which did not translate into a lower anticipated efficacious dose in humans. The model-based dose prediction matched the recommended clinical doses well. These results suggest that despite having lower total tumor-to-plasma ratios, active drug exposure at target site is higher for erlotinib. Considering the PK properties, this translates in a 50% lower recommended daily dose of erlotinib in cancer patients. In summary, total exposure at target site is not suitable to rank compounds, and an integrated modeling and experimental approach can assess efficacy more accurately. Mol Cancer Ther; 15(12); 3110-9. ©2016 AACR.