The Importance of Villous Physiology and Morphology in Mechanistic Physiologically-Based Pharmacokinetic Models

Purpose

Existing PBPK models incorporating intestinal first-pass metabolism account for effect of drug permeability on accessible absorption surface area by use of “effective” permeability, P _eff , without adjusting number of enterocytes involved in absorption or proportion of intestinal CYP3A involved in metabolism. The current model expands on existing models by accounting for these factors.

Methods

The PBPK model was developed using SAAM II. Midazolam clinical data was generated at GlaxoSmithKline.

Results

The model simultaneously captures human midazolam blood concentration profile and previously reported intestinal availability, using values for CYP3A CLu _int , permeability and accessible surface area comparable to literature data. Simulations show: (1) failure to distinguish absorbing from non-absorbing enterocytes results in overestimation of intestinal metabolism of highly permeable drugs absorbed across the top portion of the villous surface only; (2) first-pass extraction of poorly permeable drugs occurs primarily in enterocytes, drugs with higher permeability are extracted by enterocytes and hepatocytes; (3) CYP3A distribution along crypt-villous axes does not significantly impact intestinal metabolism; (4) differences in permeability of perpetrator and victim drugs results in their spatial separation along the villous axis and intestinal length, diminishing drug-drug interaction magnitude.

Conclusions

The model provides a useful tool to interrogate intestinal absorption/metabolism of candidate drugs.     

The (R)-omeprazole hydroxylation index reflects CYP2C19 activity in healthy Japanese volunteers

Purpose

Omeprazole has (R)- and (S)-enantiomers, which exhibit different pharmacokinetics (PK) among patients with cytochrome P450 (CYP) 2C19 genotype groups. The aim of this study was to investigate whether the 1-point, 4-h postdose (R)-omeprazole hydroxylation index (HI) of racemic omeprazole reflects the three CYP2C19 genotype groups in Japanese individuals.

Methods

Ninety healthy Japanese individuals were enrolled and classified into the three different CYP2C19 genotype groups: homozygous extensive metabolizers (hmEMs; n?=?34), heterozygous EMs (htEMs; n?=?44), and poor metabolizers (PMs; n?=?12). Blood samples were drawn 4 h after the intake of an oral dose of omeprazole 40 mg, and plasma levels of omeprazole and its metabolites were analyzed by high-performance liquid chromatography (HPLC) using a chiral column.

Results

Mean plasma concentrations of (R)- and (S)-omeprazole in PMs were significantly higher than those in hmEMs and htEMs, and similar results were obtained in the case of omeprazole sulfone. Additionally, there was a significant difference in plasma concentrations of (R)-5-hydroxyomeprazole among CYP2C19 genotype groups, whereas no significant differences were observed in that of (S)-5-hydroxyomeprazole. Similarly, (R)-omeprazole HI in hmEMs, htEMs, and PMs were 5.6, 3.1, and 0.3, respectively, which were significantly different, but no significant difference was present in the (S)-omeprazole HI.

Conclusion

Our findings demonstrate that (R)-omeprazole HI correlated better with CYP2C19 genotype groups than racemic-omeprazole HI, and these results may be useful for classification among patients in CYP2C19 genotype groups prior to omeprazole treatment.     

Physiological Modeling to Understand the Impact of Enzymes and Transporters on Drug and Metabolite Data and Bioavailability Estimates

Purpose

To obtain mathematical solutions that correlate drug and metabolite exposure and systemic bioavailability (F _sys) with physiological determinants, transporters and enzymes.

Methods

A series of physiologically-based pharmacokinetic (PBPK) models that included renal excretion and sequential metabolism within the intestine and/or liver as metabolite formation organs were developed. The area under the curve for drug (AUC) and formed metabolite (AUC{mi,P}) were solved by matrix inversion.

Results

The PBPK models revealed that AUC{mi,P} was dependent on dispositional parameters (transport and elimination) for the drug and metabolite. The solution was unique for each metabolite formation organ and was dependent on the type of drug and metabolite elimination organs. The AUC ratio of the formed metabolite after oral and intravenous drug dosing was useful for determination of the fraction absorbed (F _abs) and not the systemic bioavailability (F _sys) when either intestine or liver was the only drug elimination organ.

Conclusions

The AUC ratio of the formed metabolite after oral and intravenous drug dosing differed from that for drug and would not provide F _sys. However, the AUC ratio of the formed metabolite for oral and intravenous drug dosing furnished the estimate of F _abs when intestine or liver was the only drug metabolic organ.     

Assessment of interaction potential of AZD2066 using in vitro metabolism tools, physiologically based pharmacokinetic modelling and in vivo cocktail data

Purpose

Static and dynamic (PBPK) prediction models were applied to estimate the drug–drug interaction (DDI) risk of AZD2066. The predictions were compared to the results of an in vivo cocktail study. Various in vivo measures for tolbutamide as a probe agent for cytochrome P450 2C9 (CYP2C9) were also compared.

Methods

In vitro inhibition data for AZD2066 were obtained using human liver microsomes and CYP-specific probe substrates. DDI prediction was performed using PBPK modelling with the SimCYP simulator? or static model. The cocktail study was an open label, baseline, controlled interaction study with 15 healthy volunteers receiving multiple doses of AD2066 for 12 days. A cocktail of single doses of 100 mg caffeine (CYP1A2 probe), 500 mg tolbutamide (CYP2C9 probe), 20 mg omeprazole (CYP2C19 probe) and 7.5 mg midazolam (CYP3A probe) was simultaneously applied at baseline and during the administration of AZD2066. Bupropion as a CYP2B6 probe (150 mg) and 100 mg metoprolol (CYP2D6 probe) were administered on separate days. The pharmacokinetic parameters for the probe drugs and their metabolites in plasma and urinary recovery were determined.

Results

In vitro AZD2066 inhibited CYP1A2, CYP2B6, CYP2C9, CYP2C19 and CYP2D6. The static model predicted in vivo interaction with predicted AUC ratio values of >1.1 for all CYP (except CYP3A4). The PBPK simulations predicted no risk for clinical relevant interactions. The cocktail study showed no interaction for the CYP2B6 and CYP2C19 enzymes, a possible weak inhibition of CYP1A2, CYP2C9 and CYP3A4 activities and a slight inhibition (29 %) of CYP2D6 activity. The tolbutamide phenotyping metrics indicated that there were significant correlations between CL_form and AUC_TOL, CL, Ae_met and LnTOL_24h. The MR_Ae in urine showed no correlation to CL_form.

Conclusions

DDI prediction using the static approach based on total concentration indicated that AZD20066 has a potential risk for inhibition. However, no DDI risk could be predicted when a more in vivo-like dynamic prediction method with the PBPK with SimCYP? software based on early human PK data was used and more parameters (i.e. free fraction in plasma, no DDI risk) were taken into account. The clinical cocktail study showed no or low risks for clinical relevant DDI interactions. Our findings are in line with the hypothesis that the dynamic prediction method predicts DDI in vivo in humans better than the static model based on total plasma concentrations.     

Mechanistic Modeling to Predict the Transporter- and Enzyme-Mediated Drug-Drug Interactions of Repaglinide

Purpose

Quantitative prediction of complex drug-drug interactions (DDIs) is challenging. Repaglinide is mainly metabolized by cytochrome-P-450 (CYP)2C8 and CYP3A4, and is also a substrate of organic anion transporting polypeptide (OATP)1B1. The purpose is to develop a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics and DDIs of repaglinide.

Methods

In vitro hepatic transport of repaglinide, gemfibrozil and gemfibrozil 1-O-β-glucuronide was characterized using sandwich-culture human hepatocytes. A PBPK model, implemented in Simcyp (Sheffield, UK), was developed utilizing in vitro transport and metabolic clearance data.

Results

In vitro studies suggested significant active hepatic uptake of repaglinide. Mechanistic model adequately described repaglinide pharmacokinetics, and successfully predicted DDIs with several OATP1B1 and CYP3A4 inhibitors (<10% error). Furthermore, repaglinide-gemfibrozil interaction at therapeutic dose was closely predicted using in vitro fraction metabolism for CYP2C8 (0.71), when primarily considering reversible inhibition of OATP1B1 and mechanism-based inactivation of CYP2C8 by gemfibrozil and gemfibrozil 1-O-β-glucuronide.

Conclusions

This study demonstrated that hepatic uptake is rate-determining in the systemic clearance of repaglinide. The model quantitatively predicted several repaglinide DDIs, including the complex interactions with gemfibrozil. Both OATP1B1 and CYP2C8 inhibition contribute significantly to repaglinide-gemfibrozil interaction, and need to be considered for quantitative rationalization of DDIs with either drug.     

Application of in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) modelling to investigate the impact of the CYP2C8 polymorphism on rosiglitazone exposure

Purpose

To predict the impact of the CYP2C8*3 genotype on rosiglitazone exposure in the absence and presence of trimethoprim.

Methods

Prior in vitro and in vivo information for rosiglitazone and trimethoprim were collated from the literature. Specifically, data on the frequency of the different allelic forms of CYP2C8 and their metabolic activity for rosiglitazone were incorporated into a physiologically-based pharmacokinetic (PBPK) model within the Simcyp Simulator (V11.1) to predict differences in the relative exposure of rosiglitazone according to CYP2C8*3 genotype in a virtual population.

Results

Following multiple doses of 8 mg rosiglitazone, the predicted mean AUC_(0–24) was 37 % lower in CYP2C8*3 homozygotes compared with wildtype homozygotes (p?< ?0.001), which was consistent with the 36 % lower value observed in vivo (p?< ?0.001) Kirchheiner et al. (Clin Pharmacol Ther 80:657–667, 2006). Predicted median AUC ratios of rosiglitazone in the presence and absence of trimethoprim ranged from 1.35 to 1.66 for ten virtual trials of subjects with the CYP2C8*1/*1 genotype, which included the observed value of 1.42. In subjects with the CYP2C8*1/*3 genotype, the predicted AUC ratios for all trials were higher than the observed value of 1.18 Kirchheiner et al. (Clin Pharmacol Ther 80:657–667, 2006).

Conclusions

Investigating the drug interactions in individuals with rare allelic forms of drug metabolising enzymes is fraught with many practical problems. Current study demonstrates the utility of prior in vitro metabolism data from such allelic forms to predict the relative exposure of a drug as a function of genotype. However, in vitro inhibition data obtained in one allelic variant (e.g. CYP2C8*1) may not be adequate to predict the in vivo interactions in another allele (e.g. CYP2C8*3), since the inhibitory characteristics of perpetrator might be different in each allelic variant in the same way as that of metabolism of the victim drug by such variants of the enzyme.     

Rationale and conditions for the requirement of chiral bioanalytical methods in bioequivalence studies

Purpose

The aim of the present work was to assess the need for chiral bioanalytical methods in bioequivalence studies.

Methods

The samples from a bioequivalence study of two ibuprofen 2% oral suspensions that had shown bioequivalence for AUC and C_max, but not for t_max (medians of 2.0 and 0.75 h) with a non-chiral method were assayed with a chiral method to investigate whether there was an actual difference in the rate of absorption within the limits of C_max and AUC bioequivalence.

Results

The non-chiral method and the sum of concentrations of both enantiomers obtained with the chiral method gave a similar outcome (90% CI C_max non-chiral: 82.77–96.09, sum of enantiomers: 82.19–98.23; 90% CI AUC_t non-chiral: 107.23–115.49, sum of enantiomers: 105.73–121.35). However, the chiral method showed differences in AUC and C_max that resulted in non-bioequivalence for the individual enantiomers (90% CI C_max S-ibuprofen: 76.05–91.36, R-ibuprofen: 87.84–113.05; 90% CI AUC_t S-ibuprofen: 96.67–105.86, R-ibuprofen: 118.86–142.24). The differences in the pharmacokinetics of each enantiomer, and thus in the enantiomer concentration ratio, were dependent on the rate of absorption.

Conclusions

Due to the fact that in bioequivalence studies the rate of absorption of the new product is unknown, chiral bioanalytical methods should be employed for chiral drugs, such as ibuprofen, whose enantiomers exhibit different pharmacodynamic characteristics and whose enantiomer concentration ratio might be modified by the rate of absorption, irrespective of whether the eutomer is the minor enantiomer or the similarity of the pharmacokinetics of the enantiomers at a given rate of absorption.     

Physiologically-based pharmacokinetic modeling of renally excreted antiretroviral drugs in pregnant women

Aim

Physiological changes during pregnancy can affect drug disposition. Anticipating these changes will help to maximize drug efficacy and safety in pregnant women. Our objective was to determine if physiologically-based pharmacokinetics (PBPK) can accurately predict changes in the disposition of renally excreted antiretroviral drugs during pregnancy.

Methods

Whole body PBPK models were developed for three renally excreted antiretroviral drugs, tenofovir (TFV), emtricitabine (FTC) and lamivudine (3TC). To assess the impact of pregnancy on PK, time-varying pregnancy-related physiological parameters available within the p-PBPK Simcyp® software package were used. Renal clearance during pregnancy followed glomerular filtration changes with or without alterations in secretion. PK profiles were simulated and compared with observed data, i.e. area under the curves (AUC), peak plasma concentrations (C_max) and oral clearances (CL/F).

Results

PBPK models successfully predicted TFV, FTC and 3TC disposition for non-pregnant and pregnant populations. Both renal secretion and filtration changed during pregnancy. Changes in renal clearance secretion were related to changes in renal plasma flow. The maximum clearance increases were approximately 30% (TFV 33%, FTC 31%, 3TC 29%).

Conclusions

Pregnancy PBPK models are useful tools to quantify a priori the drug exposure changes during pregnancy for renally excreted drugs. These models can be applied to evaluate alternative dosing regimens to optimize drug therapy during pregnancy.     

Pharmacokinetic and Pharmacodynamic Modeling of Opioid-Induced Gastrointestinal Side Effects in Patients Receiving Tapentadol IR and Oxycodone IR

Purpose

To understand the relationship between the risk of opioid-related gastrointestinal adverse effects (AEs) and exposure to tapentadol and oxycodone as well as its active metabolite, oxymorphone, using pharmacokinetic/pharmacodynamic models.

Methods

The analysis was based on a study in patients with moderate-to-severe pain following bunionectomy. Population PK modeling was conducted to estimate population PK parameters for tapentadol, oxycodone, and oxymorphone. Time to AEs was analyzed using Cox proportional-hazards models.

Results

Risk of nausea, vomiting, and constipation significantly increased with exposure to tapentadol or oxycodone/oxymorphone. However, elevated risk per drug exposure of AEs for tapentadol was ~3?C4 times lower than that of oxycodone, while elevated AE risk per drug exposure of oxycodone was ~60 times lower than that for oxymorphone, consistent with reported in vitro receptor binding affinities for these compounds. Simulations show that AE incidence following administration of tapentadol IR is lower than that following oxycodone IR intake within the investigated range of analgesic noninferiority dose ratios.

Conclusions

This PK/PD analysis supports the clinical findings of reduced nausea, vomiting and constipation reported by patients treated with tapentadol, compared to patients treated with oxycodone.     

Comparison of acid inhibition with standard dosages of proton pump inhibitors in relation to CYP2C19 genotype in Japanese

Introduction

The aim of therapeutic regimens using proton pump inhibitors (PPIs) in patients with acid-related diseases is to potently inhibit acid secretion for the full 24 h. However, optimum treatment is still unclear because the pharmacodynamics of PPIs differ among CYP2C19 genotypes and most of the previous studies have had loss of sample power.

Methods

Using pH monitoring, we compared acid inhibition at standard dosage of omeprazole (20 mg, 50 times), lansoprazole (30 mg, 68 times), and rabeprazole (10 mg, 65 times) in Helicobacter pylori-negative healthy young Japanese volunteers.

Results

Median pH with rabeprazole was 5.4 (3.3–7.5), which was significantly greater than with either omeprazole [4.4 (2.1–7.3)] or lansoprazole [4.8 (3.5–6.4)] (both P?Discussion Treatment with the selected PPIs at their standard dosages had difficulty maintaining acid inhibition for a full 24 h, especially in CYP2C19 EM. However, rabeprazole has the merit of less influence of CYP2C19 genotype compared with the other PPIs.     

Pharmacokinetic assessment of a five-probe cocktail for CYPs 1A2, 2C9, 2C19, 2D6 and 3A

AIMS

To assess the pharmacokinetics (PK) of selective substrates of CYP1A2 (caffeine), CYP2C9 (S-warfarin), CYP2C19 (omeprazole), CYP2D6 (metoprolol) and CYP3A (midazolam) when administered orally and concurrently as a cocktail relative to the drugs administered alone.

METHODS

This was an open-label, single-dose, randomized, six-treatment six-period six-sequence William''s design study with a wash-out of 7 or 14 days. Thirty healthy male subjects received 100 mg caffeine, 100 mg metoprolol, 0.03 mg kg⁻¹ midazolam, 20 mg omeprazole and 10 mg warfarin individually and in combination (cocktail). Poor metabolizers of CYP2C9, 2C19 and 2D6 were excluded. Plasma samples were obtained up to 48 h for caffeine, metoprolol and omeprazole, 12 h for midazolam, 312 h for warfarin and the cocktail. Three different validated liquid chromatography tandem mass spectrometry methods were used. Noncompartmental PK parameters were calculated. Log-transformed C_max, AUC_last and AUC for each analyte were analysed with a linear mixed effects model with fixed term for treatment, sequence and period, and random term for subject within sequence. Point estimates (90% CI) for treatment ratios (individual/cocktail) were computed for each analyte C_max, AUC_last and AUC.

RESULTS

There was no PK interaction between the probe drugs when administered in combination as a cocktail, relative to the probes administered alone, as the 90% CI of the PK parameters was within the prespecified bioequivalence limits of 0.80, 1.25.

CONCLUSION

The lack of interaction between probes indicates that this cocktail could be used to evaluate the potential for multiple drug–drug interactions in vivo.     

Expansion of a PBPK model to predict disposition in pregnant women of drugs cleared via multiple CYP enzymes,including CYP2B6, CYP2C9 and CYP2C19

Aim

Conducting PK studies in pregnant women is challenging. Therefore, we asked if a physiologically-based pharmacokinetic (PBPK) model could be used to predict the disposition in pregnant women of drugs cleared by multiple CYP enzymes.

Methods

We expanded and verified our previously published pregnancy PBPK model by incorporating hepatic CYP2B6 induction (based on in vitro data), CYP2C9 induction (based on phenytoin PK) and CYP2C19 suppression (based on proguanil PK), into the model. This model accounted for gestational age-dependent changes in maternal physiology and hepatic CYP3A, CYP1A2 and CYP2D6 activity. For verification, the pregnancy-related changes in the disposition of methadone (cleared by CYP2B6, 3A and 2C19) and glyburide (cleared by CYP3A, 2C9 and 2C19) were predicted.

Results

Predicted mean post-partum to second trimester (PP : T₂) ratios of methadone AUC, C_max and C_min were 1.9, 1.7 and 2.0, vs. observed values 2.0, 2.0 and 2.6, respectively. Predicted mean post-partum to third trimester (PP : T₃) ratios of methadone AUC, C_max and C_min were 2.1, 2.0 and 2.4, vs. observed values 1.7, 1.7 and 1.8, respectively. Predicted PP : T₃ ratios of glyburide AUC, C_max and C_min were 2.6, 2.2 and 7.0 vs. observed values 2.1, 2.2 and 3.2, respectively.

Conclusions

Our PBPK model integrating prior physiological knowledge, in vitro and in vivo data, allowed successful prediction of methadone and glyburide disposition during pregnancy. We propose this expanded PBPK model can be used to evaluate different dosing scenarios, during pregnancy, of drugs cleared by single or multiple CYP enzymes.     

Extra-Hepatic Cancer Represses Hepatic Drug Metabolism Via Interleukin (IL)-6 Signalling

Purpose

In many cancer patients, the malignancy causes reduced hepatic drug clearance leading to potentially serious complications from the use of anticancer drugs. The mechanisms underlying this phenomenon are poorly understood. We aimed to identify tumor-associated inflammatory pathways that alter drug response and enhance chemotherapy-associated toxicity.

Methods

We studied inflammatory pathways involved in extra-hepatic tumor mediated repression of CYP3A, a major hepatic drug metabolizing cytochrome P450 subfamily, using a murine Engelbreth-Holm-Swarm sarcoma model. Studies in IL-6 knockout mice determined the source of elevated IL-6 in tumor-bearing animals and monoclonal antibodies against IL-6 were used to intervene in this inflammatory pathway.

Results

Our studies confirm elevated plasma IL-6 levels and reveal activation of Jak/Stat and Mapk signalling pathways and acute phase proteins in livers of tumor-bearing mice. Circulating IL-6 was predominantly produced by the tumor xenograft, rather than being host derived. Anti IL-6 antibody intervention partially reversed tumor-mediated inflammation and Cyp3a gene repression.

Conclusions

IL-6 is an important player in cancer-related repression of CYP3A-mediated drug metabolism and activation of the acute phase response. Targeting IL-6 in cancer patients may prove an effective approach to alleviating cancer-related phenomena, such as adverse drug-related outcomes commonly associated with cancer chemotherapy.     

Physiologically Based Pharmacokinetic Modeling of Fimasartan,Amlodipine, and Hydrochlorothiazide for the Investigation of Drug–Drug Interaction Potentials

Purpose

To build a physiologically based pharmacokinetic (PBPK) model for fimasartan, amlodipine, and hydrochlorothiazide, and to investigate the drug–drug interaction (DDI) potentials.

Methods

The PBPK model of each drug was developed using Simcyp software (Version 15.0), based on the information obtained from literature sources and in vitro studies. The predictive performance of the model was assessed by comparing the predicted PK profiles and parameters with the observed data collected from healthy subjects after multiple oral doses of fimasartan, amlodipine, and hydrochlorothiazide. The DDI potentials after co-administration of three drugs were simulated using the final model.

Results

The predicted-to-observed ratios of all the pharmacokinetic parameters met the acceptance criterion. The PBPK model predicted no significant DDI when fimasartan was co-administered with amlodipine or hydrochlorothiazide, which is consistent with the observed clinical data. In the simulation of DDI at steady-state after co-administration of three drugs, the model predicted that fimasartan exposure would be increased by ~24.5%, while no changes were expected for the exposures of amlodipine and hydrochlorothiazide.

Conclusions

The developed PBPK model adequately predicted the pharmacokinetics of fimasartan, amlodipine, and hydrochlorothiazide, suggesting that the model can be used to further investigate the DDI potential of each drug.

     

Application of PBPK modelling in drug discovery and development at Pfizer

1. Early prediction of human pharmacokinetics (PK) and drug–drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties.

2. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding.

3. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.

     

Effects of <Emphasis Type="Italic">CYP2C19</Emphasis> and <Emphasis Type="Italic">CYP2C9</Emphasis> genotypes on pharmacokinetic variability of valproic acid in Chinese epileptic patients: nonlinear mixed-effect modeling

Purpose

To evaluate the effects of CYP2C19 and CYP2C9 genotypes on the pharmacokinetic variability of valproic acid (VPA) in epileptic patients using a population pharmacokinetic (PPK) approach.

Methods

VPA concentrations were measured in 287 epileptic patients, who were genotyped for CYP2C19*2/*3 and CYP2C9*3. Patients who were on monotherapy with VPA were divided into two groups, a PPK-model group (n?=?177) and a PPK-valid group (n?=?110). The PPK parameter values for VPA were calculated in the PPK-model group by using the NONMEM software. Ultimately, a biological model and a final model were established. Each model was then used to independently predict the concentrations of the PPK-valid group to validate the two models.

Results

There was a significant effect of the CYP2C19 and CYP2C9 genotypes on the pharmacokinetic (PK) variability (P?<?0.01) in the final PPK model of CL/F. The interindividual CL was calculated according to the final model: CL/F?=?0.0951?×?(1?+?e^{0.0267?×?(3???genotype)})?+?0.0071?×?age (L/h). The coefficient of variation (CV) (omega CL/F) of the final model was 29.3%, while that of the biological model was 31.7%. Based on the genotype, the individual PK parameters can be calculated more accurately than before.

Conclusion

The CYP2C19 and CYP2C9 genotypes significantly influenced the PK variability of VPA, as quantified by NONMEM software.

     

Reduced Physiologically-Based Pharmacokinetic Model of Repaglinide: Impact of OATP1B1 and CYP2C8 Genotype and Source of In Vitro Data on the Prediction of Drug-Drug Interaction Risk

Purpose

To investigate the effect of OATP1B1 genotype as a covariate on repaglinide pharmacokinetics and drug-drug interaction (DDIs) risk using a reduced physiologically-based pharmacokinetic (PBPK) model.

Methods

Twenty nine mean plasma concentration-time profiles for SLCO1B1 c.521T>C were used to estimate hepatic uptake clearance (CL_uptake) in different genotype groups applying a population approach in NONMEM v.7.2.

Results

Estimated repaglinide CL_uptake corresponded to 217 and 113 μL/min/10⁶ cells for SLCO1B1 c.521TT/TC and CC, respectively. A significant effect of OATP1B1 genotype was seen on CL_uptake (48% reduction for CC relative to wild type). Sensitivity analysis highlighted the impact of CL_met and CL_diff uncertainty on the CL_uptake optimization using plasma data. Propagation of this uncertainty had a marginal effect on the prediction of repaglinide OATP1B1-mediated DDI with cyclosporine; however, sensitivity of the predicted magnitude of repaglinide metabolic DDI was high. In addition, the reduced PBPK model was used to assess the effect of both CYP2C8*3 and SLCO1B1 c.521T>C on repaglinide exposure by simulations; power calculations were performed to guide prospective DDI and pharmacogenetic studies.

Conclusions

The application of reduced PBPK model for parameter optimization and limitations of this process associated with the use of plasma rather than tissue profiles are illustrated.     

Preclinical Pharmacokinetic/Pharmacodynamic Models to Predict Schedule-Dependent Interaction Between Erlotinib and Gemcitabine

Purpose

To investigate the pharmacological effects of different erlotinib (ER) and gemcitabine (GM) combination schedules by in vitro and in vivo experiments and PK/PD models in non-small cell lung cancer cells.

Methods

H1299 cells were exposed to different ER combined with GM schedules. Cell growth inhibition was analyzed to evaluate these schedules. A preclinical in vivo study was then conducted to compare tumor suppression effects of different schedules in H1299 xenografts. PK/PD models were developed to quantify the anti-tumor interaction of ER and GM.

Results

Synergism was observed when ER preceded GM, but other sequences showed antagonism. The optimal in vitro schedule, or interval schedule, was applied to the animal study, which showed greater anti-tumor effect than simultaneous group. PK/PD models implied that interaction of the two drugs was additive in simultaneous treatment but synergistic in interval schedule. The simulation results showed that interval schedule can delay tumor growth for a longer time, and demonstrated more evident anti-tumor effect compared with simultaneous group if the treatment duration was longer.

Conclusions

Interval schedule of the two drugs can achieve synergistic anti-tumor effect, and is superior to simultaneous treatment.