Physiologically-based pharmacokinetic modeling to evaluate in vitro-to-in vivo extrapolation for intestinal P-glycoprotein inhibition

Understanding CYP3A4 and P-gp mediated drug-drug interactions through PBPK modeling - Case example of pralsetinib

Pralsetinib, a potent and selective inhibitor of oncogenic RET fusion and RET mutant proteins, is a substrate of the drug metabolizing enzyme CYP3A4 and a substrate of the efflux transporter P-gp based on in vitro data. Therefore, its pharmacokinetics (PKs) may be affected by co-administration of potent CYP3A4 inhibitors and inducers, P-gp inhibitors, and combined CYP3A4 and P-gp inhibitors. With the frequent overlap between CYP3A4 and P-gp substrates/inhibitors, pralsetinib is a challenging and representative example of the need to more quantitatively characterize transporter-enzyme interplay. A physiologically-based PK (PBPK) model for pralsetinib was developed to understand the victim drug-drug interaction (DDI) risk for pralsetinib. The key parameters driving the magnitude of pralsetinib DDIs, the P-gp intrinsic clearance and the fraction metabolized by CYP3A4, were determined from PBPK simulations that best captured observed DDIs from three clinical studies. Sensitivity analyses and scenario simulations were also conducted to ensure these key parameters were determined with sound mechanistic rationale based on current knowledge, including the worst-case scenarios. The verified pralsetinib PBPK model was then applied to predict the effect of other inhibitors and inducers on the PKs of pralsetinib. This work highlights the challenges in understanding DDIs when enzyme-transporter interplay occurs, and demonstrates an important strategy for differentiating enzyme/transporter contributions to enable PBPK predictions for untested scenarios and to inform labeling.

Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

Miniaturized method for the quantification of persistent organic pollutants and their metabolites in HepG2 cells: assessment of their biotransformation

Biotransformation can greatly influence the accumulation and, subsequently, toxicity of substances in living beings. Although traditionally these studies to quantify metabolization of a compound have been carried out with in vivo species, currently, in vitro test methods with very different cell lines are being developed for their evaluation. However, this is still a very limited field due to multiple variables of a very diverse nature. So, an increasing number of analytical chemists are working with cells or other similar biological samples of very small size. This makes it necessary to address the development of analytical methods that allow determining their concentration both inside the cells and in their exposure medium. The aim of this study is to develop a set of analytical methodologies for the quantification of polycyclic aromatic hydrocarbons, PAHs (phenanthrene, PHE), and polybrominated diphenyl ethers, PBDEs (2,2',4,4'-tetrabromodiphenyl ether, BDE-47), and their major metabolites in cells and their exposure medium. Analytical methodologies, based on miniaturized ultrasound probe-assisted extraction, gas chromatography-mass spectrometry-microelectron capture detector (GC-MS-µECD), and liquid chromatography-fluorescence detector (LC-FL) determination techniques, have been optimized and then applied to a biotransformation study in HepG2 at 48 h of exposure. Significant concentrations of the major metabolites of PHE (1-OH, 2-OH, 3-OH, 4-OH-, and 9-OH-PHE) and BDE-47 (5-MeO-, 5-OH-, and 3-OH-BDE-47) were detected and quantified inside the cells and in the exposure medium. These results provide a new method for determination and improve information on the metabolization ratios for a better knowledge of the metabolic pathways and their toxicity.

Potential antiviral activities of chrysin against hepatitis B virus

BACKGROUND

Interferon and nucleos(t)ide analogues are current therapeutic treatments for chronic Hepatitis B virus (HBV) infection with the limitations of a functional cure. Chrysin (5, 7-dihydroxyflavone) is a natural flavonoid, known for its antiviral and hepatoprotective activities. However, its anti-HBV activity is unexplored.

METHODS

In the present study, the anti-hepatitis B activity of chrysin was investigated using the in vitro experimental cell culture model, HepG2 cells. In silico studies were performed where chrysin and lamivudine (used here as a positive control) were docked with high mobility group box 1 protein (HMGB1). For the in vitro studies, wild type HBV genome construct (pHBV 1.3X) was transiently transfected in HepG2. In culture supernatant samples, HBV surface antigen (HBsAg) and Hepatitis B e antigen (HBeAg) were measured by enzyme-linked immunosorbent assay (ELISA). Secreted HBV DNA and intracellular covalently closed circular DNA (cccDNA) were measured by SYBR green real-time PCR. The 3D crystal structure of HMGB1 (1AAB) protein was developed and docked with the chrysin and lamivudine. In silico drug-likeness, Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties of finest ligands were performed by using SwissADME and admetSAR web servers.

RESULTS

Data showed that chrysin significantly decreases HBeAg, HBsAg secretion, supernatant HBV DNA and cccDNA, in a dose dependent manner. The docking studies demonstrated HMGB1 as an important target for chrysin as compared to lamivudine. Chrysin revealed high binding affinity and formed a firm kissing complex with HMGB1 (∆G = - 5.7 kcal/mol), as compared to lamivudine (∆G = - 4.3 kcal/mol), which might be responsible for its antiviral activity.

CONCLUSIONS

The outcome of our study establishes chrysin as a new antiviral against HBV infection. However, using chrysin to treat chronic HBV disease needs further endorsement and optimization by in vivo studies in animal models.

Integrating Forward and Reverse Translation in PBPK Modeling to Predict Food Effect on Oral Absorption of Weakly Basic Drugs

INTRODUCTION

Ketoconazole and posaconazole are two weakly basic broad-spectrum antifungals classified as Biopharmaceutics Classification System class II drugs, indicating that they are highly permeable, but exhibit poor solubility. As a result, oral bioavailability and clinical efficacy can be impacted by the formulation performance in the gastrointestinal system. In this work, we have leveraged in vitro biopharmaceutics and clinical data available in the literature to build physiologically based pharmacokinetic (PBPK) models for ketoconazole and posaconazole, to determine the suitability of forward in vitro-in vivo translation for characterization of in vivo drug precipitation, and to predict food effect.

METHODS

A stepwise modeling approach was utilized to derive key parameters related to absorption, such as drug solubility, dissolution, and precipitation kinetics from in vitro data. These parameters were then integrated into PBPK models for the simulation of ketoconazole and posaconazole plasma concentrations in the fasted and fed states.

RESULTS

Forward in vitro-in vivo translation of intestinal precipitation kinetics for both model drugs resulted in poor predictions of PK profiles. Therefore, a reverse translation approach was applied, based on limited fitting of precipitation-related parameters to clinical data. Subsequent simulations for ketoconazole and posaconazole demonstrated that fasted and fed state PK profiles for both drugs were adequately recapitulated.

CONCLUSION

The two examples presented in this paper show how middle-out modeling approaches can be used to predict the magnitude and direction of food effects provided the model is verified on fasted state PK data.

Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations

The physiologically based pharmacokinetic (PBPK) approach can be used to develop mathematical models for predicting the absorption, distribution, metabolism, and elimination (ADME) of administered drugs in virtual human populations. Haloperidol is a typical antipsychotic drug with a narrow therapeutic index and is commonly used in the management of several medical conditions, including psychotic disorders. Due to the large interindividual variability among patients taking haloperidol, it is very likely for them to experience either toxic or subtherapeutic effects. We intend to develop a haloperidol PBPK model for identifying the potential sources of pharmacokinetic (PK) variability after intravenous and oral administration by using the population-based simulator, PK-Sim. The model was initially developed and evaluated to predict the PK of haloperidol and its reduced metabolite in adult healthy population after intravenous and oral administration. After evaluating the developed PBPK model in healthy adults, it was used to predict haloperidol–rifampicin drug–drug interaction and was extended to tuberculosis patients. The model evaluation was performed using visual assessments, prediction error, and mean fold error of the ratio of the observed-to-predicted values of the PK parameters. The predicted PK values were in good agreement with the corresponding reported values. The effects of the pathophysiological changes and enzyme induction associated with tuberculosis and its treatment, respectively, on haloperidol PK, have been predicted precisely. For all clinical scenarios that were evaluated, the predicted values were within the acceptable two-fold error range.

Predicting drug-drug interactions with physiologically based pharmacokinetic/pharmacodynamic modelling and optimal dosing of apixaban and rivaroxaban with dronedarone co-administration

BACKGROUND

The concurrent administration of dronedarone and oral anti-coagulants is common because both are used in managing atrial fibrillation (AF). Dronedarone is a moderate inhibitor of the cytochrome P450 3A4 (CYP3A4) enzyme and P-glycoprotein (P-gp). Apixaban and rivaroxaban are P-gp and CYP3A4 substrates. This study aims to investigate the impact of exposure and bleeding risk of apixaban or rivaroxaban when co-administered with dronedarone using physiologically based pharmacokinetic/pharmacodynamic analysis.

METHODS

Modeling and simulation were conducted using Simcyp® Simulator. The parameters required for dronedarone modeling were collected from the literature. The developed dronedarone physiologically based pharmacokinetic (PBPK) model was verified using reported drug-drug interactions (DDIs) between dronedarone and CYP3A4 and P-gp substrates. The model was applied to evaluate the DDI potential of dronedarone on the exposure of apixaban 5 mg every 12 h or rivaroxaban 20 mg every 24 h in geriatric and renally impaired populations. DDIs precipitating major bleeding risks were assessed using exposure-response analyses derived from literature.

RESULTS

The model accurately described the pharmacokinetics of orally administered dronedarone in healthy subjects and accurately predicted DDIs between dronedarone and four CYP3A4 and P-gp substrates with fold errors <1.5. Dronedarone co-administration led to a 1.29 (90 % confidence interval (CI): 1.14-1.50) to 1.31 (90 % CI: 1.12-1.46)-fold increase in the area under concentration-time curve for rivaroxaban and 1.33 (90 % CI: 1.15-1.68) to 1.46 (90 % CI: 1.24-1.92)-fold increase for apixaban. The PD model indicated that dronedarone co-administration might potentiate the mean major bleeding risk of apixaban with a 1.45 to 1.95-fold increase. However, the mean major bleeding risk of rivaroxaban was increased by <1.5-fold in patients with normal or impaired renal function.

CONCLUSIONS

Dronedarone co-administration increased the exposure of rivaroxaban and apixaban and might potentiate major bleeding risks. Reduced apixaban and rivaroxaban dosing regimens are recommended when dronedarone is co-administered to patients with AF.

Association between CES1 rs2244613 and the pharmacokinetics and safety of dabigatran: Meta-analysis and quantitative trait loci analysis

Objective

To date, the influence of the carboxylesterase 1 (CES1) rs2244613 genotype on the pharmacokinetics (PKs) and safety of dabigatran remains controversial. Hence, a systematic review was performed to study the association between CES1 rs2244613 genotype and the PKs and safety of dabigatran and CES1 relative expression.

Methods

In addition to the three English databases (Web of Science, PubMed, and Embase), two Chinese databases (CNKI and Wanfang) were thoroughly revised. The mean differences (MD) and corresponding 95% confidence intervals (CI) were applied to evaluate the differences in PKs between the CES1 rs2244613 genotype. Odds ratio (OR) was used to study the risk for bleeding events between the CES1 rs2244613 genotypes. Subsequent expression quantitative trait loci (eQTL) analyses were performed to evaluate genotype-specific expressions in human tissues.

Results

Ten studies (n = 2,777) were included. CES1 rs2244613 G allele carriers exhibited significantly lower dabigatran trough concentrations compared to T allele carriers (MD: −8.00 ng/mL; 95% CI: −15.08 to −0.92; p = 0.03). The risk for bleeding events was significantly lower in carriers of the G allele compared to T allele carriers (OR: 0.65; 95% CI: 0.44–0.96; p = 0.03). Subsequent eQTL analysis showed significant genome-wide expressions in two human tissues, whole blood (p = 5.1 × 10^–10) and liver (p = 6.2 × 10^–43).

Conclusion

Our meta-analysis indicated a definite relation between the CES1 rs2244613 genotype and tolerability variations or pharmacokinetic fluctuations. The carriers of T allele showed higher dabigatran concentrations; therefore, they would benefit from a dose reduction.

Systematic review registration

[https://inplasy.com/inplasy-2022-6-0027/], identifier [NPLASY202260027].

Competitive Metabolism of Polycyclic Aromatic Hydrocarbons (PAHs): An Assessment Using In Vitro Metabolism and Physiologically Based Pharmacokinetic (PBPK) Modeling

Humans are routinely exposed to complex mixtures such as polycyclic aromatic hydrocarbons (PAHs) rather than to single compounds, as are often assessed for hazards. Cytochrome P450 enzymes (CYPs) metabolize PAHs, and multiple PAHs found in mixtures can compete as substrates for individual CYPs (e.g., CYP1A1, CYP1B1, etc.). The objective of this study was to assess competitive inhibition of metabolism of PAH mixtures in humans and evaluate a key assumption of the Relative Potency Factor approach that common human exposures will not cause interactions among mixture components. To test this objective, we co-incubated binary mixtures of benzo[a]pyrene (BaP) and dibenzo[def,p]chrysene (DBC) in human hepatic microsomes and measured rates of enzymatic BaP and DBC disappearance. We observed competitive inhibition of BaP and DBC metabolism and measured inhibition coefficients (Ki), observing that BaP inhibited DBC metabolism more potently than DBC inhibited BaP metabolism (0.061 vs. 0.44 µM Ki, respectively). We developed a physiologically based pharmacokinetic (PBPK) interaction model by integrating PBPK models of DBC and BaP and incorporating measured metabolism inhibition coefficients. The PBPK model predicts significant increases in BaP and DBC concentrations in blood AUCs following high oral doses of PAHs (≥100 mg), five orders of magnitude higher than typical human exposures. We also measured inhibition coefficients of Supermix-10, a mixture of the most abundant PAHs measured at the Portland Harbor Superfund Site, on BaP and DBC metabolism. We observed similar potencies of inhibition coefficients of Supermix-10 compared to BaP and DBC. Overall, results of this study demonstrate that these PAHs compete for the same enzymes and, at high doses, inhibit metabolism and alter internal dosimetry of exposed PAHs. This approach predicts that BaP and DBC exposures required to observe metabolic interaction are much higher than typical human exposures, consistent with assumptions used when applying the Relative Potency Factor approach for PAH mixture risk assessment.