PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

Predict the Drug-Drug Interaction of a Novel PI3Kα/δ Inhibitor, TQ-B3525, and Its Two Metabolites Using Physiologically Based Pharmacokinetic Modeling

A novel dual PI3K α/δ inhibitor, TQ-B3525, has been developed for the targeted treatment of lymphoma and solid tumors. TQ-B3525 is primarily metabolized by CYP3A4 and FOM3, while also serving as a substrate for the P-glycoprotein transporter. The aim of this study was to anticipate the drug-drug interaction (DDI) of TQ-B3525 and its two metabolites with CYP3A4 enzyme potent inducer (rifampicin) and CYP3A4/P-gp inhibitor (itraconazole) utilizing a physiologically based pharmacokinetic (PBPK) modeling approach. Clinical data from healthy and cancer patient adults were employed to construct and evaluate the PBPK model for TQ-B3525, M3, and M8-3. Models involving rifampicin combined with midazolam, itraconazole combined with midazolam or digoxin were utilized to showcase the robustness of evaluating DDI effects. The simulated drug exposure of TQ-B3525, M3, and M8-3 in healthy and patient adults were consistent with clinical data, and the mean fold error values were within the acceptable ranges. The simulated results of positive substrates correspond to those reported in the literature. Co-administration with rifampicin reduces Cmax and AUC of TQ-B3525 to 76.1% and 46.0%, while increasing the levels of M3 and M8-3. With itraconazole, Cmax and AUC of TQ-B3525 rise to 131% and 204%, but decrease substantially for M3 and M8-3. PBPK model simulation results showed that the systemic exposure of TQ-B3525 was significantly affected when co-administered with CYP3A4/P-gp inducers and inhibitors. This indicates that the combination with strong inducers and inhibitors should be carefully avoided or adjust the dosage of TQ-B3525 in clinic.

An open-label study to explore the optimal design of CYP3A drug-drug interaction clinical trials in healthy Chinese people

A drug-drug interaction (DDI) trial of cytochrome P450 3A (CYP3A) is a necessary part of early-phase trials of drugs mainly metabolized by this enzyme, but CYP3A DDI clinical trials do not have a standard design, especially for Chinese people. We aimed to offer specific recommendations for CYP3A DDI clinical trial design. This was an open, three-cycle, self-controlled study. Healthy subjects were given different administration strategies of CYP3A4 perpetrators. In each cycle, blood samples were collected before and within 24 h after the administration of midazolam, the CYP3A indicator substrate. The plasma concentrations of midazolam and 1-hydroxymidazolam was obtained using liquid chromatography tandem mass spectrometry assay. For CYP3A inhibition, itraconazole exposure with a loading dose could increase the exposure of midazolam by 3.21-fold based on maximum plasma concentration (Cmax), 8.37-fold based on area under the curve Pharmacology Research & Perspectives for review only from zero to the time point (AUC0-t), and 11.22-fold based on area under the curve from zero to infinity (AUC0-∞). The data were similar for itraconazole pretreatment without a loading dose. For CYP3A induction, the exposure of rifampin for 7 days decreased the plasma concentration of midazolam ~0.27-fold based on Cmax, ~0.18-fold based on AUC0-t, and ~0.18-fold based on AUC0-∞. Midazolam exposure did not significantly change when the pretreatment of rifampin increased to 14 days. This study showed that itraconazole pretreatment for 3 days without a loading dose was enough for CYP3A inhibition, and pretreatment with rifampin for 7 days could induce near-maximal CYP3A levels.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

The effect of itraconazole, a strong CYP3A4 inhibitor, on the pharmacokinetics of the first-in-class ACKR3/CXCR7 antagonist, ACT-1004-1239

Cytochrome P450 (CYP) 3A4 is an enzyme involved in the metabolism of many drugs that are currently on the market and is therefore a key player in drug-drug interactions (DDIs). ACT-1004-1239 is a potent and selective, first-in-class ACKR3/CXRC7 antagonist being developed as a treatment for demyelinating diseases including multiple sclerosis. Based on the human absorption, distribution, metabolism, and excretion (ADME) study results, ACT-1004-1239 is predominantly metabolized by CYP3A4. This study investigated the effect of the strong CYP3A4 inhibitor, itraconazole, on the pharmacokinetics of single-dose ACT-1004-1239 in healthy male subjects. In the open-label, fixed-sequence DDI study, a total of 16 subjects were treated. Each subject received a single dose of 10 mg ACT-1004-1239 (Treatment A) in the first period followed by concomitant administration of multiple doses of 200 mg itraconazole and a single dose of 10 mg ACT-1004-1239 in the second period. We report a median of difference in tmax (90% confidence interval, CI) of 0.5 h (0.0, 1.0) comparing both treatments. The geometric mean ratio (GMR) (90% CI) of Cmax and AUC0-∞ was 2.16 (1.89, 2.47) and 2.77 (2.55, 3.00), respectively. The GMR (90% CI) of t1/2 was 1.46 (1.26, 1.70). Both treatments were well-tolerated with an identical incidence in subjects reporting treatment-emergent adverse events (TEAE). The most frequently reported TEAEs were headache and nausea. In conclusion, ACT-1004-1239 is classified as a moderately sensitive CYP3A4 substrate (i.e., increase of AUC ≥2- to <5-fold), and this should be considered in further clinical studies if CYP3A4 inhibitors are concomitantly administered.

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

PBTK model-based analysis of CYP3A4 induction and the toxicokinetics of the pyrrolizidine alkaloid retrorsine in man

Cytochrome P450 (CYP)3A4 induction by drugs and pesticides plays a critical role in the enhancement of pyrrolizidine alkaloid (PA) toxicity as it leads to increased formation of hepatotoxic dehydro-PA metabolites. Addressing the need for a quantitative analysis of this interaction, we developed a physiologically-based toxicokinetic (PBTK) model. Specifically, the model describes the impact of the well-characterized CYP3A4 inducer rifampicin on the kinetics of retrorsine, which is a prototypic PA and contaminant in herbal teas. Based on consumption data, the kinetics after daily intake of retrorsine were simulated with concomitant rifampicin treatment. Strongest impact on retrorsine kinetics (plasma AUC24 and C max reduced to 67% and 74% compared to the rifampicin-free reference) was predicted directly after withdrawal of rifampicin. At this time point, the competitive inhibitory effect of rifampicin stopped, while CYP3A4 induction was still near its maximum. Due to the impacted metabolism kinetics, the cumulative formation of intestinal retrorsine CYP3A4 metabolites increased to 254% (from 10 to 25 nmol), while the cumulative formation of hepatic CYP3A4 metabolites was not affected (57 nmol). Return to baseline PA toxicokinetics was predicted 14 days after stop of a 14-day rifampicin treatment. In conclusion, the PBTK model showed to be a promising tool to assess the dynamic interplay of enzyme induction and toxification pathways.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Evaluating Drug Interactions between Ritonavir and Opioid Analgesics: Implications from Physiologically Based Pharmacokinetic Simulation

Several commonly used opioid analgesics, such as fentanyl, sufentanil, alfentanil, and hydrocodone, are by report primarily metabolized by the CYP3A4 enzyme. The concurrent use of ritonavir, a potent CYP3A4 inhibitor, can lead to significant drug interactions. Using physiologically based pharmacokinetic (PBPK) modeling and simulation, this study examines the effects of different dosing regimens of ritonavir on the pharmacokinetics of these opioids. The findings reveal that co-administration of ritonavir significantly increases the exposure of fentanyl analogs, with over a 10-fold increase in the exposure of alfentanil and sufentanil when given with ritonavir. Conversely, the effect of ritonavir on fentanyl exposure is modest, likely due to additional metabolism pathways. Additionally, the study demonstrates that the steady-state exposure of hydrocodone and its active metabolite hydromorphone can be increased by up to 87% and 95%, respectively, with concurrent use of ritonavir. The extended-release formulation of hydrocodone is particularly affected. These insights from PBPK modeling provide valuable guidance for optimizing opioid dosing and minimizing the risk of toxicity when used in combination with ritonavir-containing prescriptions.

PBPK modeling to predict the pharmacokinetics of venlafaxine and its active metabolite in different CYP2D6 genotypes and drug-drug interactions with clarithromycin and paroxetine

Venlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI), is indicated for the treatment of major depressive disorder, social anxiety disorder, generalized anxiety disorder, and panic disorder. Venlafaxine is metabolized to the active metabolite desvenlafaxine mainly by CYP2D6. Genetic polymorphism of CYP2D6 and coadministration with other medications can significantly affect the pharmacokinetics and/or pharmacodynamics of venlafaxine and its active metabolite. This study aimed to establish the PBPK models of venlafaxine and its active metabolite related to CYP2D6 genetic polymorphism and to predict drug-drug interactions (DDIs) with clarithromycin and paroxetine in different CYP2D6 genotypes. Clinical pharmacogenomic data for venlafaxine and desvenlafaxine were collected to build the PBPK model. Physicochemical and absorption, distribution, metabolism, and excretion (ADME) characteristics of respective compounds were obtained from previously reported data, predicted by the PK-Sim® software, or optimized to capture the plasma concentration-time profiles. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and plasma concentration-time profiles to the observed data. Predicted plasma concentration-time profiles of venlafaxine and its active metabolite were visually similar to the observed profiles and all predicted AUC and Cmax values for respective compounds were included in the twofold error range of observed values in non-genotyped populations and different CYP2D6 genotypes. When clarithromycin or clarithromycin plus paroxetine was concomitantly administered, predicted plasma concentration-time profiles of venlafaxine properly captured the observed profiles in two different CYP2D6 genotypes and all predicted DDI ratios for AUC and Cmax were included within the acceptance range. Consequently, the present model successfully captured the pharmacokinetic alterations of venlafaxine and its active metabolite according to CYP2D6 genetic polymorphism as well as the DDIs between venlafaxine and two CYP inhibitors. The present model can be used to predict the pharmacokinetics of venlafaxine and its active metabolite considering different races, ages, coadministered drugs, and CYP2D6 activity of individuals and it can contribute to individualized pharmacotherapy of venlafaxine.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms

BACKGROUND

Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug.

PURPOSE

A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK).

METHODS

The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values.

RESULTS

In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups.

CONCLUSIONS

In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

A Physiologically Based Pharmacokinetic Modeling Approach to Assess the Potential for Drug Interactions Between Trofinetide and CYP3A4-Metabolized Drugs

PURPOSE

Trofinetide is the first drug to be approved by the US Food and Drug Administration for use in the treatment of patients with Rett syndrome, a multisystem disorder requiring multimodal therapies. Cytochrome P450 (CYP) 3A4 metabolizes >50% of therapeutic drugs and is the CYP isozyme most commonly expressed in the liver and intestines. In vitro studies suggest the concentration of trofinetide producing 50% inhibition (IC50) of CYP3A4 is >15 mmol/L; that concentration was much greater than the target clinical concentration associated with the maximal intended therapeutic dose (12 g). Thus, trofinetide has a low potential for drug-drug interactions in the liver. However, there is potential for drug-drug interactions in the intestines given the oral route of administration and expected relatively high concentration in the gastrointestinal tract after dose administration.

METHODS

Using a validated physiologically based pharmacokinetic (PBPK) model, deterministic and stochastic simulations were used for assessing the PK properties related to exposure and bioavailability of midazolam (sensitive index substrate for CYP3A4) following an oral (15 mg) or intravenous (2 mg) dose, with and without single-dose and steady-state (12 g) coadministration of oral trofinetide.

FINDINGS

Following coadministration of intravenous midazolam and oral trofinetide, the PK properties of midazolam were unchanged. The trofinetide concentration in the gut wall was >15 mmol/L during the first 1.5 hours after dosing. With the coadministration of oral midazolam and trofinetide, the model predicted increases in fraction of dose reaching the portal vein, bioavailability, Cmax, and AUCinf of 30%, 30%, 18%, and 30%, respectively.

IMPLICATIONS

In this study that used a PBPK modeling approach, it was shown that CYP3A4 enzyme activity in the liver was not affected by trofinetide coadministration, but trofinetide was predicted to be a weak inhibitor of intestinal CYP3A4 metabolism after oral administration at therapeutic doses.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

The Pharmacokinetics of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone

Finerenone, a selective and nonsteroidal antagonist of the mineralocorticoid receptor, has received regulatory approval with the indication of cardiorenal protection in patients with chronic kidney disease associated with type 2 diabetes. It is rapidly and completely absorbed and undergoes first-pass metabolism in the gut wall and liver resulting in a bioavailability of 43.5%. Finerenone can be taken with or without food. The pharmacokinetics of finerenone are linear and its half-life is 2 to 3 h in the dose range of up to 20 mg. Cytochrome P450 (CYP) 3A4 (90%) and CYP2C8 (10%) are involved in the extensive biotransformation of finerenone to pharmacologically inactive metabolites, which are excreted via both renal (80%) and biliary (20%) routes. Moderate or severe renal impairment, or moderate hepatic impairment result in area-under-the-curve increases of finerenone (< 40%), which do not require a dose adjustment per se, as the starting dose is based on estimated glomerular filtration rate (eGFR) and titrated according to serum potassium levels and eGFR decline. No relevant effects of age, sex, body size or ethnicity on systemic finerenone exposure were identified. Modulators of CYP3A4 activity were found to affect finerenone exposure, consistent with its classification as a sensitive CYP3A4 substrate. Serum potassium should be monitored during drug initiation or dosage adjustment of either a moderate or weak CYP3A4 inhibitor or finerenone, and the dose of finerenone should be adjusted as appropriate. Its use with strong inhibitors is contraindicated and strong or moderate inducers of CYP3A4 should be avoided. Finerenone has no potential to affect relevant CYP enzymes and drug transporters.

Drug-drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model

Ziritaxestat, an autotaxin inhibitor, was under development for the treatment of idiopathic pulmonary fibrosis. It is a substrate of cytochrome P450 3A4 (CYP3A4) and P-glycoprotein and a weak inhibitor of the CYP3A4 and OATP1B1 pathways. We developed a physiologically based pharmacokinetic (PBPK) network interaction model for ziritaxestat that incorporated its metabolic and transporter pathways, enabling prediction of its potential as a victim or perpetrator of drug-drug interactions (DDIs). Concurrently, we evaluated CYP3A4 autoinhibition, including time-dependent inhibition. In vitro information and clinical data from healthy volunteer studies were used for model building and validation. DDIs with rifampin, itraconazole, voriconazole, pravastatin, and rosuvastatin were predicted, followed by validation against a test dataset. DDIs of ziritaxestat as a victim or perpetrator were simulated using the final model. Predicted-to-observed DDI ratios for the maximum plasma concentration (Cmax ) and the area under the plasma concentration-time curve (AUC) were within a two-fold ratio for both the metabolic and transporter-mediated simulated DDIs. The predicted impact of autoinhibition/autoinduction or time-dependent inhibition of CYP3A4 was a 12% decrease in exposure. Model-based predictions for ziritaxestat as a victim of DDIs with a moderate CYP3A4 inhibitor (fluconazole) suggested a 2.6-fold increase in the AUC of ziritaxestat, while multiple doses of a strong inhibitor (voriconazole) would increase the AUC by 15-fold. Efavirenz would yield a three-fold decrease in the AUC of ziritaxestat. As a perpetrator, ziritaxestat was predicted to increase the AUC of the CYP3A4 index substrate midazolam by 2.7-fold. An overarching PBPK model was developed that could predict DDI liability of ziritaxestat for both CYP3A4 and the transporter pathways.

Physiologically Based Pharmacokinetic Modeling to Simulate CYP3A4-Mediated Drug-Drug Interactions for Pyrotinib

INTRODUCTION

Pyrotinib is a newly developed tyrosine kinase inhibitor whose in vivo clearance relies heavily on cytochrome P450 3A4 (CYP3A4) activity. Clinical trials are ongoing to explore the effects of coadministration with CYP3A4 perpetrators on pyrotinib exposure. The present study aims to utilize physiologically based pharmacokinetic (PBPK) modeling to predict CYP3A4-based drug interactions of pyrotinib.

METHODS

Pyrotinib PBPK model was developed in the PK-Sim® multicompartmental physiology structure. Physiochemical parameters were obtained from the literature, and clearance-related parameters were optimized by fitting clinical single-dose pharmacokinetic data. Pharmacokinetic parameters from the model output were compared with the observed data to validate the model predictive performance. Using validated CYP3A4 perpetrator models, we conducted PBPK simulations for drug interactions in a virtual population to explore the impacts of comedication with these perpetrators.

RESULTS

The PBPK model accurately describes pyrotinib single- and multi-dose pharmacokinetics. The model also predicts dramatic exposure change of pyrotinib in the presence of itraconazole and rifampicin, though the impact of rifampicin is somewhat underestimated. According to model predictions, coadministration with typical potent or moderate CYP3A4 perpetrators increases pyrotinib concentration by over sixfold, extinguishing the possibility of dose adjustment for pyrotinib. A weak CYP3A4 inhibitor has minimal influence on pyrotinib pharmacokinetics.

CONCLUSION

PBPK modeling provides valuable information to avoid irrational medication when receiving pyrotinib chemotherapy.

Physiologically-based pharmacokinetic modeling for optimal dosage prediction of olaparib when co-administered with CYP3A4 modulators and in patients with hepatic/renal impairment

This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model to predict the maximum plasma concentration (Cmax) and trough concentration (Ctrough) at steady-state of olaparib (OLA) in Caucasian, Japanese and Chinese. Furthermore, the PBPK model was combined with mean and 95% confidence interval to predict optimal dosing regimens of OLA when co-administered with CYP3A4 modulators and administered to patients with hepatic/renal impairment. The dosing regimens were determined based on safety and efficacy PK threshold Cmax (< 12,500 ng/mL) and Ctrough (772-2500 ng/mL). The population PBPK model for OLA was successfully developed and validated, demonstrating good consistency with clinically observed data. The ratios of predicted to observed values for Cmax and Ctrough fell within the range of 0.5 to 2.0. When OLA was co-administered with a strong or moderate CYP3A4 inhibitor, the recommended dosing regimens should be reduced to 100 mg BID and 150 mg BID, respectively. Additionally, the PBPK model also suggested that OLA could be not recommended with a strong or moderate CYP3A4 inducer. For patients with moderate hepatic and renal impairment, the dosing regimens of OLA were recommended to be reduced to 200 mg BID and 150 mg BID, respectively. In cases of severe hepatic and renal impairment, the PBPK model suggested a dosing regimen of 100 mg BID for OLA. Overall, this present PBPK model can determine the optimal dosing regimens for various clinical scenarios involving OLA.

A dual-modal graph learning framework for identifying interaction events among chemical and biotech drugs

Drug-drug interaction (DDI) identification is essential to clinical medicine and drug discovery. The two categories of drugs (i.e. chemical drugs and biotech drugs) differ remarkably in molecular properties, action mechanisms, etc. Biotech drugs are up-to-comers but highly promising in modern medicine due to higher specificity and fewer side effects. However, existing DDI prediction methods only consider chemical drugs of small molecules, not biotech drugs of large molecules. Here, we build a large-scale dual-modal graph database named CB-DB and customize a graph-based framework named CB-TIP to reason event-aware DDIs for both chemical and biotech drugs. CB-DB comprehensively integrates various interaction events and two heterogeneous kinds of molecular structures. It imports endogenous proteins founded on the fact that most drugs take effects by interacting with endogenous proteins. In the modality of molecular structure, drugs and endogenous proteins are two heterogeneous kinds of graphs, while in the modality of interaction, they are nodes connected by events (i.e. edges of different relationships). CB-TIP employs graph representation learning methods to generate drug representations from either modality and then contrastively mixes them to predict how likely an event occurs when a drug meets another in an end-to-end manner. Experiments demonstrate CB-TIP's great superiority in DDI prediction and the promising potential of uncovering novel DDIs.

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.

Physiologically Based Pharmacokinetic Modeling of Bergamottin and 6,7-Dihydroxybergamottin to Describe CYP3A4 Mediated Grapefruit-Drug Interactions

Grapefruit is a moderate to strong inactivator of CYP3A4, which metabolizes up to 50% of marketed drugs. The inhibitory effect is mainly attributed to furanocoumarins present in the fruit, irreversibly inhibiting preferably intestinal CYP3A4 as suicide inhibitors. Effects on CYP3A4 victim drugs can still be measured up to 24 hours after grapefruit juice (GFJ) consumption. The current study aimed to establish a physiologically-based pharmacokinetic (PBPK) grapefruit-drug interaction model by modeling the relevant CYP3A4 inhibiting ingredients of the fruit to simulate and predict the effect of GFJ consumption on plasma concentration-time profiles of various CYP3A4 victim drugs. The grapefruit model was developed in PK-Sim and coupled with previously developed PBPK models of CYP3A4 substrates that were publicly available and already evaluated for CYP3A4-mediated drug-drug interactions. Overall, 43 clinical studies were used for model development. Models of bergamottin (BGT) and 6,7-dihydroxybergamottin (DHB) as relevant active ingredients in GFJ were established. Both models include: (i) CYP3A4 inactivation informed by in vitro parameters, (ii) a CYP3A4 mediated clearance estimated during model development, as well as (iii) passive glomerular filtration. The final model successfully describes interactions of GFJ ingredients with 10 different CYP3A4 victim drugs, simulating the effect of the CYP3A4 inactivation on the victims' pharmacokinetics as well as their main metabolites. Furthermore, the model sufficiently captures the time-dependent effect of CYP3A4 inactivation as well as the effect of grapefruit ingestion on intestinal and hepatic CYP3A4 concentrations.

In-depth analysis of patterns in selection of different physiologically-based pharmacokinetic modeling tools: Part II - Assessment of model reusability and comparison between open and non-open source-code software

Whilst the reproducibility of models in the area of systems biology and quantitative systems pharmacology has been the focus of attention lately, the concept of 'reusability' is not addressed. With the advent of the 'Model Master File' dominating some regulatory discussions on pharmaceutical applications of physiologically-based pharmacokinetic (PBPK) models, reusability becomes a vital aspect of confidence in their use. Herein, we define 'reusability' specifically in the context of PBPK models and investigate the influence of open versus non-open source-code (NOSC) nature of the software on the extent of 'reusability'. Original articles (n = 145) that were associated with the development of novel PBPK models were identified as source models and citations to these reports, which involved further PBPK model development, were explored (n > 1800) for reuse cases of the source PBPK model whether in full or partial form. The nature of source-code was a major determinant of external reusability for PBPK models (>50% of the NOSC models as opposed <25% of open source-code [OSC]). Full reusability of the models was not common and mostly involved internal reuse of the OSC model (by the group who had previously developed the original model). The results were stratified by the software utilised (various), organisations involved (academia, industry, regulatory), and type of reusability (full vs. partial). The clear link between external reuse of models and NOSC PBPK software might stem from many elements related to quality and trust that require further investigation, and challenges the unfounded notion that OSC models are associated with higher uptake for reuse.

Physiologically based pharmacokinetic modeling of levetiracetam to predict the exposure in hepatic and renal impairment and elderly populations

Levetiracetam (LEV) is an anti-epileptic drug approved for use in various populations. The pharmacokinetic (PK) behavior of LEV may be altered in the elderly and patients with renal and hepatic impairment. Thus, dosage adjustment is required. This study was conducted to investigate how the physiologically-based PK (PBPK) model describes the PKs of LEV in adult and elderly populations, as well as to predict the PKs of LEV in patients with renal and hepatic impairment in both populations. The whole-body PBPK models were developed using the reported physicochemical properties of LEV and clinical data. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed blood concentration data and PK parameters. It is recommended that the doses be reduced to ~70%, 60%, and 45% of the adult dose for the mild, moderate, and severe renal impairment populations and ~95%, 80%, and 57% of the adult dose for the Child Pugh-A (CP-A), Child Pugh-B (CP-B), and Child Pugh-C (CP-C) hepatic impairment populations, respectively. No dose adjustment is required for the healthy elderly population, but dose reduction is required for the elderly with organ dysfunction accordingly, on a scale similar to that of adults. A PBPK model of LEV was successfully developed to optimize dosing regimens for special populations.

Leveraging physiologically based pharmacokinetic modeling to optimize dosing for lopinavir/ritonavir with rifampin in pediatric patients

STUDY OBJECTIVE

Treatment of HIV and tuberculosis co-infection leads to significant mortality in pediatric patients, and treatment can be challenging due to the clinically significant drug-drug interaction (DDI) between lopinavir/ritonavir (LPV/RTV) and rifampin. Doubling LPV/RTV results in insufficient lopinavir trough concentrations in pediatric patients. The objective of this study was to leverage physiologically based pharmacokinetic (PBPK) modeling to optimize the adjusted doses of LPV/RTV in children receiving the WHO-revised doses of rifampin (15 mg/kg daily).

DESIGN

Adult and pediatric PBPK models for LPV/RTV with rifampin were developed, including CYP3A and P-glycoprotein inhibition and induction.

SETTING (OR DATA SOURCE)

Data for LPV/RTV model development and evaluation were available from the pediatric AIDS Clinical Trials Group.

PATIENTS

Dosing simulations were next performed to optimize dosing in children (2 months to 8 years of age).

INTERVENTION

Exposure following super-boosted LPV/RTV with 10 and 15 mg/kg PO daily rifampin was simulated.

MEASUREMENTS AND MAIN RESULTS

Simulated parameters were within twofold observations for LPV, RTV, and rifampin in adults and children ≥2 weeks old. The model predicted that, in healthy adults receiving 400/100 mg oral LPV/RTV twice daily (BID), co-treatment with 600 mg oral rifampin daily decreased the steady-state area under the concentration vs. time curve of LPV by 79%, in line with the observed change of 75%. Simulated and observed concentration profiles were comparable for LPV/RTV (230/57.5 mg/m2 ) PO BID without rifampin and 230/230 mg/m2 LPV/RTV PO BID with 10 mg/kg PO daily rifampin in pediatric patients. Sixteen mg/kg of super-boosted LPV (LPV/RTV 1:1) PO BID with 15 mg/kg PO daily rifampin achieved simulated LPV troughs >1 mg/L in ≥93% of virtual children weighing 3.0-24.9 kg, which was comparable with 10 mg/kg PO daily rifampin.

CONCLUSIONS

Super-boosted LPV/RTV with 15 mg/kg rifampin achieves therapeutic LPV troughs in HIV/TB-infected simulated children.

Modelling Based Approaches to Support Generic Drug Regulatory Submissions-Practical Considerations and Case Studies

Model informed drug development (MiDD) is useful to predict in vivo exposure of drugs during various stages of the drug development process. This approach employs a variety of quantitative tools to assess the risks during the drug development process. One important tool in the MiDD tool kit is the Physiologically Based Pharmacokinetic Modelling (PBPK). This tool is extensively used to reduce the development cost and to accelerate the access of medicines to the patients. In this work, we provide an overview of PBPK modelling approaches in the generic drug development process, with a special emphasis on the bio-waiver applications. We describe herein approaches and common pitfalls while submitting model based justifications as a response to the regulatory deficiencies during the generic drug development process. With some in-house case studies, we have attempted to provide a clear path for PBPK model based justifications for bio-waivers. With this review, the gap between theoretical knowledge and practical application of modelling and simulation tools for generic drug product development could be potentially reduced.

Management of Drug Interactions with Inducers: Onset and Disappearance of Induction on Cytochrome P450 3A4 and Uridine Diphosphate Glucuronosyltransferase 1A1 Substrates

BACKGROUND

People living with HIV may present co-morbidities requiring the initiation and subsequently the discontinuation of medications with inducing properties. The time to reach maximal enzyme induction and to return to baseline enzyme levels has not been thoroughly characterized.

OBJECTIVE

The aim of this study was to evaluate the onset and disappearance of dolutegravir [uridine diphosphate glucuronosyltransferase (UGT) 1A1 and cytochrome P450 (CYP) 3A4 substrate] and raltegravir (UGT1A1 substrate) induction with strong and moderate inducers using physiologically based pharmacokinetic (PBPK) modeling.

METHODS

The predictive performance of the PBPK model to simulate dolutegravir and raltegravir pharmacokinetics and to reproduce the strength of induction was verified using clinical drug-drug interaction studies (steady-state induction) and switch studies (residual induction). The model was considered verified when the predictions were within 2-fold of the observed data. One hundred virtual individuals (50% female) were generated to simulate the unstudied scenarios. The results were used to calculate the fold-change in CYP3A4 and UGT1A1 enzyme levels upon initiation and discontinuation of strong (rifampicin) or moderate (efavirenz or rifabutin) inducers.

RESULTS

The time for reaching maximal induction and subsequent disappearance of CYP3A4 induction was 14 days for rifampicin and efavirenz but 7 days for rifabutin. The distinct timelines for the moderate inducers relate to their different half-lives and plasma concentrations. The induction and de-induction processes were more rapid for UGT1A1.

CONCLUSIONS

Our simulations support the common practice of maintaining the adjusted dosage of a drug for another 2 weeks after stopping an inducer. Furthermore, our simulations suggest that an inducer should be administered for at least 14 days before conducting interaction studies to reach maximal induction.

Physicochemical characterization and phase II metabolic profiling of echinochrome A, a bioactive constituent from sea urchin, and its physiologically based pharmacokinetic modeling in rats and humans

Echinochrome A, a natural naphthoquinone pigment found in sea urchins, is increasingly being investigated for its nutritional and therapeutic value associated with antioxidant, anticancer, antiviral, antidiabetic, and cardioprotective activities. Although several studies have demonstrated the biological effects and therapeutic potential of echinochrome A, little is known regarding its biopharmaceutical behaviors. Here, we aimed to investigate the physicochemical properties and metabolic profiles of echinochrome A and establish a physiologically-based pharmacokinetic (PBPK) model as a useful tool to support its clinical applications. We found that the lipophilicity, color variability, ultraviolet/visible spectrometry, and stability of echinochrome A were markedly affected by pH conditions. Moreover, metabolic and pharmacokinetic profiling studies demonstrated that echinochrome A is eliminated primarily by hepatic metabolism and that four possible metabolites, i.e., two glucuronidated and two methylated conjugates, are formed in rat and human liver preparations. A whole-body PBPK model incorporating the newly identified hepatic phase II metabolic process was constructed and optimized with respect to chemical-specific parameters. Furthermore, model simulations suggested that echinochrome A could exhibit linear disposition profiles without systemic and local tissue accumulation in clinical settings. Our proposed PBPK model of echinochrome A could be a valuable tool for predicting drug interactions in previously unexplored scenarios and for optimizing dosage regimens and drug formulations.

PBTK modeling of the pyrrolizidine alkaloid retrorsine to predict liver toxicity in mouse and rat

Retrorsine is a hepatotoxic pyrrolizidine alkaloid (PA) found in herbal supplements and medicines, food and livestock feed. Dose-response studies enabling the derivation of a point of departure including a benchmark dose for risk assessment of retrorsine in humans and animals are not available. Addressing this need, a physiologically based toxicokinetic (PBTK) model of retrorsine was developed for mouse and rat. Comprehensive characterization of retrorsine toxicokinetics revealed: both the fraction absorbed from the intestine (78%) and the fraction unbound in plasma (60%) are high, hepatic membrane permeation is dominated by active uptake and not by passive diffusion, liver metabolic clearance is 4-fold higher in rat compared to mouse and renal excretion contributes to 20% of the total clearance. The PBTK model was calibrated with kinetic data from available mouse and rat studies using maximum likelihood estimation. PBTK model evaluation showed convincing goodness-of-fit for hepatic retrorsine and retrorsine-derived DNA adducts. Furthermore, the developed model allowed to translate in vitro liver toxicity data of retrorsine to in vivo dose-response data. Resulting benchmark dose confidence intervals (mg/kg bodyweight) are 24.1-88.5 in mice and 79.9-104 in rats for acute liver toxicity after oral retrorsine intake. As the PBTK model was built to enable extrapolation to different species and other PA congeners, this integrative framework constitutes a flexible tool to address gaps in the risk assessment of PA.

Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling

(1) Purpose: This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the trough concentration (C _trough) of imatinib (IMA) at steady state in patients and to explore the role of free concentration (f _up), α1-acid glycoprotein (AGP) level, and organic cation transporter 1 (OCT1) activity/expression in clinical efficacy. (2) Methods: The population PBPK model was built using physicochemical and biochemical properties, metabolizing and transporting kinetics, tissue distribution, and human physiological parameters. (3) Results: The PBPK model successfully predicted the C _trough of IMA administered alone in chronic phase (CP) and accelerated phase (AP) patients, the C _trough of IMA co-administered with six modulators, and C _trough in CP patients with hepatic impairment. Most of the ratios between predicted and observed data are within 0.70-1.30. Additionally, the recommendations for dosing adjustments for IMA have been given under multiple clinical uses. The sensitivity analysis showed that exploring the f _up and AGP level had a significant influence on the plasma C _trough of IMA. Meanwhile, the simulations also revealed that OCT1 activity and expression had a significant impact on the intracellular C _trough of IMA. (4) Conclusion: The current PBPK model can accurately predict the IMA C _trough and provide appropriate dosing adjustment recommendations in a variety of clinical situations.

Machine Learning-Based Prediction of Digoxin Toxicity in Heart Failure: A Multicenter Retrospective Study

Digoxin toxicity (plasma digoxin concentration ≥0.9 ng/mL) is associated with worsening heart failure (HF). Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of adverse drug reactions. The present study aimed to construct a flowchart using DT analysis that can be used by medical staff to predict digoxin toxicity. We conducted a multicenter retrospective study involving 333 adult patients with HF who received oral digoxin treatment. In this study, we employed a chi-squared automatic interaction detection algorithm to construct DT models. The dependent variable was set as the plasma digoxin concentration (≥ 0.9 ng/mL) in the trough during the steady state, and factors with p < 0.2 in the univariate analysis were set as the explanatory variables. Multivariate logistic regression analysis was conducted to validate the DT model. The accuracy and misclassification rates of the model were evaluated. In the DT analysis, patients with creatinine clearance <32 mL/min, daily digoxin dose ≥1.6 µg/kg, and left ventricular ejection fraction ≥50% showed a high incidence of digoxin toxicity (91.8%; 45/49). Multivariate logistic regression analysis revealed that creatinine clearance <32 mL/min and daily digoxin dose ≥1.6 µg/kg were independent risk factors. The accuracy and misclassification rates of the DT model were 88.2 and 46.2 ± 2.7%, respectively. Although the flowchart created in this study needs further validation, it is straightforward and potentially useful for medical staff in determining the initial dose of digoxin in patients with HF.

Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling

Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim^® and MoBi^® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUC_last and DFI C_max ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUC_last and 21/21 DDI C_max ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUC_last and 18/21 DDI C_max ratios were within the success limits.

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

Development of a Physiologically Based Pharmacokinetic Model for Tegoprazan: Application for the Prediction of Drug-Drug Interactions with CYP3A4 Perpetrators

Tegoprazan is a novel potassium-competitive acid blocker (P-CAB) developed by CJ Healthcare (Korea) for the treatment of gastroesophageal reflux disease and helicobacter pylori infections. Tegoprazan is mainly metabolized by cytochrome P450 (CYP) 3A4. Considering the therapeutic indications, tegoprazan is likely to be administered in combination with various drugs. Therefore, the investigation of drug-drug interactions (DDI) between tegoprazan and CYP3A4 perpetrators is imperative. In the present study, we first aimed to develop a physiologically based pharmacokinetic (PK) model for tegoprazan and its major metabolite, M1, using PK-Sim^®. This model was applied to predict the DDI between tegoprazan and CYP3A4 perpetrators. Clarithromycin, a potent inhibitor of CYP3A4, and rifampicin, a strong inducer of CYP3A4, were selected as case studies. Our results show that clarithromycin significantly increased the exposure of tegoprazan. The area under the concentration-time curve (AUC) and C_max of tegoprazan in the steady state increased up to 4.54- and 2.05-fold, respectively, when tegoprazan (50 mg, twice daily) was coadministered with clarithromycin (500 mg, three times daily). Rifampicin significantly reduced the exposure of tegoprazan. The AUC and C_max of tegoprazan were reduced by 5.71- and 3.51-fold when tegoprazan was coadministered with rifampicin (600 mg, once daily). Due to the high DDI potential, the comedication of tegoprazan with CYP3A4 perpetrators should be controlled. The dosage adjustment for each individual is suggested.

In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools

In the past decade, only a small number of papers have elaborated on the application of physiologically based pharmacokinetic (PBPK) modeling across different areas. In this review, an in-depth analysis of the distribution of PBPK modeling in relation to its application in various research topics and model validation was conducted by text mining tools. Orange 3.32.0, an open-source data mining program was used for text mining. PubMed was used for data retrieval, and the collected articles were analyzed by several widgets. A total of 2699 articles related to PBPK modeling met the predefined criteria. The number of publications per year has been rising steadily. Regarding the application areas, the results revealed that 26% of the publications described the use of PBPK modeling in early drug development, risk assessment and toxicity assessment, followed by absorption/formulation modeling (25%), prediction of drug-disease interactions (20%), drug-drug interactions (DDIs) (17%) and pediatric drug development (12%). Furthermore, the analysis showed that only 12% of the publications mentioned model validation, of which 51% referred to literature-based validation and 26% to experimentally validated models. The obtained results present a valuable review of the state-of-the-art regarding PBPK modeling applications in drug discovery and development and related fields.

Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction

The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( K m $$ {K}_{\mathrm{m}} $$ ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( E T $$ {E}_{\mathrm{T}} $$ ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( F a $$ {F}_{\mathrm{a}} $$ ) is also assumed to be one because F a $$ {F}_{\mathrm{a}} $$ is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a $$ {F}_{\mathrm{a}} $$ was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.

Pharmacogenetic variability and the probability of site of action target attainment during tuberculosis meningitis treatment: A physiologically based pharmacokinetic modeling and simulations study

OBJECTIVE AND METHODS

Our objective was to investigate the role of patient pharmacogenetic variability in determining site of action target attainment during tuberculous meningitis (TBM) treatment. Rifampin and isoniazid PBPK model that included SLCO1B1 and NAT2 effects on exposures respectively were obtained from literature, modified, and validated using available cerebrospinal-fluid (CSF) concentrations. Population simulations of isoniazid and rifampin concentrations in brain interstitial fluid and probability of target attainment according to genotypes and M. tuberculosis MIC levels, under standard and intensified dosing, were conducted.

RESULTS

The rifampin and isoniazid model predicted steady-state drug concentration within brain interstitial fluid matched with the observed CSF concentrations. At MIC level of 0.25 mg/L, 57% and 23% of the patients with wild type and heterozygous SLCO1B1 genotype respectively attained the target in CNS with rifampin standard dosing, improving to 98% and 91% respectively with 35 mg/kg dosing. At MIC level of 0.25 mg/L, 33% of fast acetylators attained the target in CNS with isoniazid standard dosing, improving to 90% with 7.5 mg/kg dosing.

CONCLUSION

In this study, the combined effects of pharmacogenetic and M. tuberculosis MIC variability were potent determinants of target attainment in CNS. The potential for genotype-guided dosing during TBM treatment should be further explored in prospective clinical studies.

Prediction of Drug-Drug-Gene Interaction Scenarios of (E)-Clomiphene and Its Metabolites Using Physiologically Based Pharmacokinetic Modeling

Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug-gene interactions (DGIs), drug-drug interactions (DDIs) and drug-drug-gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted C_max and 80% of AUC_last values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.

Alternatives to rifampicin: A review and perspectives on the choice of strong CYP3A inducers for clinical drug-drug interaction studies

N-Nitrosamine (NA) impurities are considered genotoxic and have gained attention due to the recall of several marketed drug products associated with higher-than-permitted limits of these impurities. Rifampicin is an index inducer of multiple cytochrome P450s (CYPs) including CYP2B6, 2C8, 2C9, 2C19, and 3A4/5 and an inhibitor of OATP1B transporters (single dose). Hence, rifampicin is used extensively in clinical studies to assess drug-drug interactions (DDIs). Despite NA impurities being reported in rifampicin and rifapentine above the acceptable limits, these critical anti-infective drugs are available for therapeutic use considering their benefit-risk profile. Reports of NA impurities in rifampicin products have created uncertainty around using rifampicin in clinical DDI studies, especially in healthy volunteers. Hence, a systematic investigation through a literature search was performed to determine possible alternative index inducer(s) to rifampicin. The available strong CYP3A inducers were selected from the University of Washington DDI Database and their in vivo DDI potential assessed using the data from clinical DDI studies with sensitive CYP3A substrates. To propose potential alternative CYP3A inducers, factors including lack of genotoxic potential, adequate safety, feasibility of multiple dose administration to healthy volunteers, and robust in vivo evidence of induction of CYP3A were considered. Based on the qualifying criteria, carbamazepine, phenytoin, and lumacaftor were identified to be the most promising alternatives to rifampicin for conducting CYP3A induction DDI studies. Strengths and limitations of the proposed alternative CYP3A inducers, the magnitude of in vivo CYP3A induction, appropriate study designs for each alternative inducer, and future perspectives are presented in this paper.

Results From Drug-Drug Interaction Studies In Vitro and In Vivo Investigating the Inhibitory Effect of Finerenone on the Drug Transporters BCRP, OATP1B1, and OATP1B3

BACKGROUND AND OBJECTIVES

In vitro and in vivo studies were performed with the novel, selective, nonsteroidal mineralocorticoid receptor antagonist finerenone to assess the relevance of inhibitory effects on the transporters breast cancer resistance protein (BCRP), organic anion transporting polypeptide 1B1 (OATP1B1), and OATP1B3. These transporters are involved in the disposition of a number of drugs, including statins. Statins are also a frequent comedication in patients receiving finerenone. Therefore, inhibitory effects on BCRP and OATPs are of potential clinical relevance.

METHODS

The effect on the transport of specific substrates of BCRP and OATP1B1/1B3 was assessed in cell-based in vitro assays with finerenone or its metabolites. A fixed-sequence crossover study in 14 healthy male volunteers investigated the effects of finerenone (40 mg once daily) on the pharmacokinetics of the index substrate rosuvastatin (5 mg) administered alone, simultaneously with, or approximately 4 h before finerenone. The effect of finerenone on the endogenous OATP substrates coproporphyrin I and III was also assessed.

RESULTS

Based on in vitro findings and threshold values proposed in regulatory guidelines, finerenone appeared to be a potentially relevant inhibitor of all three transporters. Relevant inhibition could also not be ruled out for the finerenone metabolites M1a (OATP1B1) and M3a (OATP1B1 and OAT1B3), which prompted an investigation into the relevance of these findings in vivo. After administration on a background of finerenone 40 mg, all point estimates of area under the curve ratios (114.47% [rosuvastatin], 99.62% [coproporphyrin I; simultaneous], and 105.28% [rosuvastatin; 4 h separation]) and maximum concentration ratios (111.24% [rosuvastatin], 101.22% [coproporphyrin I], 89.14% [coproporphyrin III; simultaneous], and 96.84% [rosuvastatin; 4 h separation]) of the investigated substrates were within 80.0-125%. In addition, the 90% confidence intervals of the ratios were within the conventional no-effect boundaries of 80.0% and 125% for rosuvastatin after temporally separated administration, and for coproporphyrin I and III.

CONCLUSION

Administration of finerenone 40 mg once daily confers no risk of clinically relevant drug-drug interactions with substrates of BCRP, OATP1B1, or OATP1B3. The potential for relevant inhibition of these transporters suggested by in vitro findings was not confirmed in vivo.

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.

Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim^®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUC_last ratios and 9/12, 5/5 and 7/7 of DGI C_max ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUC_ss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.

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.

Development of Physiology Based Pharmacokinetic Model to Predict the Drug Interactions of Voriconazole and Venetoclax

PURPOSE

Venetoclax (VEN), an anti-tumor drug that is a substrate of cytochrome P450 3A enzyme (CYP3A4), is used to treat leukemia. Voriconazole (VCZ) is an antifungal medication that inhibits CYP3A4. The goal of this study is to predict the effect of VCZ on VEN exposure.

METHOD

Two physiological based pharmacokinetics (PBPK) models were developed for VCZ and VEN using the bottom-up and top-down method. VCZ model was also developed to describe the effect of CYP2C19 polymorphism on its pharmacokinetics (PK). The reversible inhibition constant (Ki) of VCZ for CYP3A4 was calibrated using drug-drug interaction (DDI) data of midazolam and VCZ. The clinical verified VCZ and VEN model were used to predict the DDI of VCZ and VEN at clinical dosing scenario.

RESULT

VCZ model predicted VCZ exposure in the subjects of different CYP2C19 genotype and DDI related fold changes of sensitive CYP3A substrate with acceptable prediction error. VEN model can capture PK of VEN with acceptable prediction error. The DDI PBPK model predicted that VCZ increased the exposure of VEN by 4.5-9.6 fold. The increase in VEN exposure by VCZ was influenced by subject's CYP2C19 genotype. According to the therapeutic window, VEN dose should be reduced to 100 mg when co-administered with VCZ.

CONCLUSION

The PBPK model developed here could support individual dose adjustment of VEN and DDI risk assessment. Predictions using the robust PBPK model confirmed that the 100 mg dose adjustment is still applicable in the presence of VCZ with high inter-individual viability.

A Physiologically Based Pharmacokinetic and Pharmacodynamic Model of the CYP3A4 Substrate Felodipine for Drug-Drug Interaction Modeling

The antihypertensive felodipine is a calcium channel blocker of the dihydropyridine type, and its pharmacodynamic effect directly correlates with its plasma concentration. As a sensitive substrate of cytochrome P450 (CYP) 3A4 with high first-pass metabolism, felodipine shows low oral bioavailability and is susceptible to drug–drug interactions (DDIs) with CYP3A4 perpetrators. This study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) parent–metabolite model of felodipine and its metabolite dehydrofelodipine for DDI predictions. The model was developed in PK-Sim^® and MoBi^® using 49 clinical studies (94 plasma concentration–time profiles in total) that investigated different doses (1–40 mg) of the intravenous and oral administration of felodipine. The final model describes the metabolism of felodipine to dehydrofelodipine by CYP3A4, sufficiently capturing the first-pass metabolism and the subsequent metabolism of dehydrofelodipine by CYP3A4. Diastolic blood pressure and heart rate PD models were included, using an E_max function to describe the felodipine concentration–effect relationship. The model was tested in DDI predictions with itraconazole, erythromycin, carbamazepine, and phenytoin as CYP3A4 perpetrators, with all predicted DDI AUC_last and C_max ratios within two-fold of the observed values. The model will be freely available in the Open Systems Pharmacology model repository and can be applied in DDI predictions as a CYP3A4 victim drug.

Physiologically based pharmacokinetic modeling of brivaracetam and its interactions with rifampin based on CYP2C19 phenotypes

Brivaracetam (BRV), a third-generation antiepileptic drug (AED), is primarily metabolized through amidase hydrolysis and CYP2C19-mediated hydroxylation in vivo. This study utilized physiologically based pharmacokinetic (PBPK) modeling to explore the pharmacokinetics of BRV and drug interactions between BRV and rifampin (RIF), a CYP2C19 inducer, based on CYP2C19 genetic polymorphisms. A PBPK model of BRV was developed in the general population and in individuals with different CYP2C19 phenotypes by adjusting catalytic rate constants (k_cat), and the model was validated with observed clinical data. The model was then extrapolated to predict BRV steady-state plasma concentration in individuals with different CYP2C19 phenotypes, with or without coadministration of RIF. The developed model adequately described BRV exposure in the abovementioned populations. The predicted steady-state area under the curve (AUC_τ-ss) increases by 20% in heterozygous extensive metabolizers (hEMs) and 55% in poor metabolizers (PMs), compared to homozygous extensive metabolizer (EMs). When coadministered with RIF, the model predicted the most significant magnitude of drug-drug interaction (DDI) in EMs, while the exposure change of BRV was minimal in PMs. Referencing the recommended concentration for therapeutic drug monitoring (TDM), we concluded that the current clinical maintenance dose of BRV is acceptable regardless of CYP2C19 polymorphisms and coadministration with RIF.

Determining the Effects of Chronic Kidney Disease on Organic Anion Transporter1/3 Activity Through Physiologically Based Pharmacokinetic Modeling

BACKGROUND AND OBJECTIVE

The renal excretion of drugs via organic anion transporters 1 and 3 (OAT1/3) is significantly decreased in patients with renal impairment. This study uses physiologically based pharmacokinetic models to quantify the reduction in OAT1/3-mediated secretion of drugs throughout varying stages of chronic kidney disease.

METHODS

Physiologically based pharmacokinetic models were constructed for four OAT1/3 substrates in healthy individuals: acyclovir, meropenem, furosemide, and ciprofloxacin. Observed data from drug-drug interaction studies with probenecid, a potent OAT1/3 inhibitor, were used to parameterize the contribution of OAT1/3 to the renal elimination of each drug. The models were then translated to patients with chronic kidney disease by accounting for changes in glomerular filtration rate, kidney volume, renal blood flow, plasma protein binding, and hematocrit. Additionally, a relationship was derived between the estimated glomerular filtration rate and the reduction in OAT1/3-mediated secretion of drugs based on the renal extraction ratios of ƿ-aminohippuric acid in patients with varying degrees of renal impairment. The relationship was evaluated in silico by evaluating the predictive performance of each final model in describing the pharmacokinetics (PK) of drugs across stages of chronic kidney disease.

RESULTS

OAT1/3-mediated renal excretion of drugs was found to be decreased by 27-49%, 50-68%, and 70-96% in stage 3, stage 4, and stage 5 of chronic kidney disease, respectively. In support of the parameterization, physiologically based pharmacokinetic models of four OAT1/3 substrates were able to adequately characterize the PK in patients with different degrees of renal impairment. Total exposure after intravenous administration was predicted within a 1.5-fold error and 85% of the observed data points fell within a 1.5-fold prediction error. The models modestly under-predicted plasma concentrations in patients with end-stage renal disease undergoing intermittent hemodialysis. However, results should be interpreted with caution because of the limited number of molecules analyzed and the sparse sampling in observed chronic kidney disease pharmacokinetic studies.

CONCLUSIONS

A quantitative understanding of the reduction in OAT1/3-mediated excretion of drugs in differing stages of renal impairment will contribute to better predictive accuracy for physiologically based pharmacokinetic models in drug development, assisting with clinical trial planning and potentially sparing this population from unnecessary toxic exposures.

Development of a Translational Exposure-Bracketing Approach to Streamline the Development of Hormonal Contraceptive Drug Products

Worldwide, 922 million women of reproductive age (or their partners) use some sort of contraception to prevent pregnancy. Oral combined hormonal contraceptives (CHC) typically utilize a combination of a progestin and an estrogen. CHC are potentially at risk to metabolic drug-drug interaction (DDI) via CYP3A4, the main enzyme involved in the oxidative metabolism of ethinyl estradiol (EE) and most progestins, e.g. levonorgestrel (LNG) and drospirenone (DRSP). Recently, the FDA issued a guidance addressing metabolic DDIs in the realm of CHC, establishing an overall class-based recommendation with respect to avoidance of CYP3A4 induction interactions. Given that different progestins have varying magnitudes of fraction metabolized by CYP3A4 (fm_CYP3A4 ), it would be of clinical benefit to determine if all progestins are at the same risk to CYP3A4-mediated metabolic DDI. LNG and DRSP are commonly used progestins that are at the margins of the rifampicin induction effect observed in vivo since they have the relatively lowest and highest fm_CYP3A4 among commonly used CHC formulations containing norgestimate, desogestrel, norgestrel, and norethindrone. Therefore, we applied a multi-pronged strategy, i.e. (1) development of the PBPK models, (2) comparison of the effect of CYP3A inducers and inhibitors on DRSP vs. LNG, and (3) providing the clinical-practice context based on real world data, to explore the difference in DDI risk for oral CHCs.

Physiologically based pharmacokinetic combined JAK2 occupancy modelling to simulate PK and PD of baricitinib with kidney transporter inhibitors and in patients with hepatic/renal impairment

PURPOSE

Our aim is to build a physiologically based pharmacokinetic and JAK2 occupancy model (PBPK-JO) to simultaneously predict pharmacokinetic (PK) and pharmacodynamic (PD) changes of baricitinib (BAR) in healthy humans when co-administrated with kidney transporters OAT3 and MATE2-K inhibitors, and in patients with hepatic and renal impairment.

METHODS

Probenecid and vandetanib were selected as OAT3 and MATE2-K competitive inhibitors, respectively. The PBPK-JO model was built using physicochemical and biochemical properties of BAR, and then verified by observed clinical PK. Finally, the model was applied to determine optimal dosing regimens in various clinical situations.

RESULTS

Here, we have successfully simulated PK and JAK2 occupancy profiles in humans by PBPK-JO model. Moreover, this modelling reproduced every observed PK data, and every mean relative deviation (MRD) was below 2. The simulation suggested that PK of BAR had a significant change (2.22-fold increase), however PD only had a slight increase of 1.14-fold. Additionally, the simulation also suggested that vandetanib was almost unlikely to affect the PK and PD of BAR. In simulations of hepatic and renal impairment patients, the predictions suggested that significant changes in the PK and PD of BAR occurred. However, there was a lower fold increase in JAK2 occupancy than in PK in patients relative to healthy individuals.

CONCLUSION

Administration dose adjustment of BAR when co-administrated with OAT3 inhibitors or in patients with hepatic or renal impairment should combine PK and PD changes of BAR, instead of only considering PK alteration.