Population pharmacokinetics of adebrelimab - Support of alternative flat dose regimen in extensive-stage small-cell lung cancer.

Adebrelimab, a novel anti-PD-L1 antibody, has been approved by the National Medical Products Administration of China as an intravenous infusion for use in combination with carboplatin and etoposide as first-line treatment for extensive-stage small-cell lung cancer in 2023. A two-compartment model with empirical time-varying CL for adebrelimab was established based on data from 263 patients receiving body weight-based doses from two clinical studies. Significant covariate effects of baseline body weight, albumin levels, tumor size, neutrophil counts, and presence of anti-drug antibodies were identified on CL of debrelimab, none of which were clinically significant or warranted dose adjustment. The degree of decrease in CL was higher in patients who responded to treatment with adebrelimab than in non-responders. Adebrelimab exposures (AUC, Ctrough, or Cmax) were not identified as a statistically significant factor related to efficacy or safety endpoint in the exposure-response analysis. Distribution of simulated exposure metrics from the flat dose regimen (1200 mg q3w) was similar to the marketed weight-based dosing regimen (20 mg/kg q3w), supporting the alternative flat dose regimen in the clinic.

Evaluation of the potential impact on pharmacokinetics of various cytochrome P450 substrates of increasing IL-6 levels following administration of the T-cell bispecific engager glofitamab.

Glofitamab is a novel T cell bispecific antibody developed for treatment of relapsed-refractory diffuse large B cell lymphoma and other non-Hodgkin's lymphoma indications. By simultaneously binding human CD20-expressing tumor cells and CD3 on T cells, glofitamab induces tumor cell lysis, in addition to T-cell activation, proliferation, and cytokine release. Here, we describe physiologically-based pharmacokinetic (PBPK) modeling performed to assess the impact of glofitamab-associated transient increases in interleukin 6 (IL-6) on the pharmacokinetics of several cytochrome P450 (CYP) substrates. By refinement of a previously described IL-6 model and inclusion of in vitro CYP suppression data for CYP3A4, CYP1A2, and 2C9, a PBPK model was established in Simcyp to capture the induced IL-6 levels seen when glofitamab is administered at the intended dose and dosing regimen. Following model qualification, the PBPK model was used to predict the potential impact of CYP suppression on exposures of various CYP probe substrates. PBPK analysis predicted that, in the worst-case, the transient elevation of IL-6 would increase exposures of CYP3A4, CYP2C9, and CYP1A2 substrates by less than or equal to twofold. Increases for CYP3A4, CYP2C9, and CYP1A2 substrates were projected to be 1.75, 1.19, and 1.09-fold following the first administration and 2.08, 1.28, and 1.49-fold following repeated administrations. It is recommended that there are no restrictions on concomitant treatment with any other drugs. Consideration may be given for potential drug-drug interaction during the first cycle in patients who are receiving concomitant CYP substrates with a narrow therapeutic index via monitoring for toxicity or for drug concentrations.

Entrectinib dose confirmation in pediatric oncology patients: pharmacokinetic considerations.

PURPOSE: Entrectinib is a central nervous system-active potent inhibitor of tropomyosin receptor kinase (TRK), with anti-tumor activity against neurotrophic NTRK gene fusion-positive tumors. This study investigates the pharmacokinetics of entrectinib and its active metabolite (M5) in pediatric patients and aims to understand whether the pediatric dose of 300 mg/m2 once daily (QD) provides an exposure that is consistent with the approved adult dose (600 mg QD). METHODS: Forty-three patients aged from birth to 22 years were administered entrectinib (250-750 mg/m2 QD) orally with food in 4-week cycles. Entrectinib formulations included capsules without acidulant (F1) and capsules with acidulant (F2B and F06). RESULTS: Although there was interpatient variability with F1, entrectinib and M5 exposures increased dose dependently. Lower systemic exposures were observed in pediatric patients receiving 400 mg/m2 QD entrectinib (F1) versus adults receiving either the same dose/formulation or the recommended flat dose of 600 mg QD (~ 300 mg/m2 for a 70 kg adult) due to suboptimal F1 performance in the pediatric study. The observed pediatric exposures following 300 mg/m2 QD entrectinib (F06) were comparable to those in adults receiving 600 mg QD. CONCLUSIONS: Overall, the F1 formulation of entrectinib was associated with lower systemic exposure in pediatric patients compared with the commercial acidulant formulation (F06). Systemic exposures achieved in pediatric patients with the F06 recommended dose (300 mg/m2) were within the known efficacious range in adults, confirming the adequacy of the recommended dose regimen with the commercial formulation.

Combined Semi-mechanistic Target-Mediated Drug Disposition and Pharmacokinetic-Pharmacodynamic Models of Alirocumab, PCSK9, and Low-Density Lipoprotein Cholesterol in a Pooled Analysis of Randomized Phase I/II/III Studies.

BACKGROUND AND OBJECTIVES: Alirocumab is a cholesterol-lowering monoclonal antibody targeting proprotein convertase subtilisin kexin type 9 (PCSK9) indicated in the prevention of cardiovascular risk and exhibiting target-mediated drug disposition (TMDD). The aim of this work was to develop an integrated pharmacokinetic-pharmacodynamic model to describe the interaction of alirocumab with PCSK9 and its impact on the evolution of low-density lipoprotein cholesterol (LDL-C) levels and explore labeling specification for subpopulations. METHODS: Using data collected from nine phase I/II/III clinical studies (n = 527, subcutaneous or intravenous administration), a TMDD model considering the quasi-steady-state approximation was developed to characterize the interaction dynamics of alirocumab and PCSK9, combined with an indirect pharmacodynamic model describing the inhibition of LDL-C by PCSK9 in a one-step approach using nonlinear-mixed effects modeling. A "full fixed effects modeling" strategy was implemented to quantify parameter-covariate relationships. RESULTS: The model captures the interaction between alirocumab and its target PCSK9 and how this mechanism drives LDL-C depletion, with an estimation of the associated between-subject variability of model parameters and the quantification of clinically relevant parameter-covariate relationships. Co-administration of statins was found to increase the central volume of distribution of alirocumab by 1.75-fold (5.6 L versus 3.2 L) and allow for a 14% greater maximum lipid-lowering effect (88% versus 74%), highlighting the synergy of action between anti-PCSK9 therapeutic antibodies and statins toward lowering LDL-C plasma levels. Baseline levels of PCSK9 were found to be related to the amplitude of LDL-C variations by increasing the concentration of free PCSK9 necessary to reach half its capacity of inhibition of LDL-C degradation. CONCLUSION: The maximum effect of alirocumab is achieved when free PCSK9 concentration is close to zero, as seen mostly after 150 mg every 2 weeks (Q2W) or 300 mg every 4 weeks (Q4W), indicating that there would be no additional clinical benefit of increasing the dose higher than these recommended dosing regimens.

Efficacy and safety exposure-response analyses of entrectinib in patients with advanced or metastatic solid tumors.

PURPOSE: Entrectinib is an anti-cancer agent that inhibits TRKA/B/C, ROS1, and ALK. Secondary pharmacokinetic (PK) exposure parameters for entrectinib derived from a previously described population PK model were used to characterize exposure-response relationships in patients treated with entrectinib. METHODS: Data were pooled from Phase 1 and 2 studies of entrectinib (600-800 mg/day in adults, 250-750 mg/m2/day in children) in 293 patients with NTRK-, ROS1-, or ALK-positive, locally advanced or metastatic tumors. Efficacy was evaluated by the changes in sum of target lesion diameters and best overall response defined by RECIST1.1. A longitudinal nonlinear mixed-effect model described the relationship between entrectinib exposure and tumor size data in patients with ROS1-positive non-small-cell lung cancer (NSCLC) or NTRK fusion-positive solid tumors. The relationship between exposure and treatment-emergent (TEAEs) or serious (SAEs) adverse events was assessed by logistic regression in all patients for whom secondary PK parameter estimates were derived. RESULTS: Among the 89 patients with evaluable efficacy data included in the exposure-efficacy analysis, 73% (65/89) achieved a complete or partial response. Entrectinib exposure distribution was similar in responders and non-responders. Model-described tumor shrinkage rates were 8-12 times greater than growth rates in both ROS-1-positive NSCLC patients and NTRK fusion-positive solid tumor patients, with no relationship between exposure and these rates. The probability of experiencing a Grade ≥ 3 TEAE or SAE increased with exposure, primarily at doses > 600 mg/day. CONCLUSION: These analyses supported that entrectinib at 600 mg/day provides an acceptable benefit-risk ratio in adults with NTRK-, ROS1-, or ALK-positive tumors, considered as rare disease.

In vitro and clinical investigations to determine the drug-drug interaction potential of entrectinib, a small molecule inhibitor of neurotrophic tyrosine receptor kinase (NTRK).

Background Entrectinib is a CNS-active, potent inhibitor of tyrosine receptor kinases A/B/C, ROS1 and anaplastic lymphoma kinase approved for use in patients with solid tumors. We describe the in vitro and clinical studies investigating potential entrectinib drug-drug interactions. Methods In vitro studies with human biomaterials assessed the enzymes involved in entrectinib metabolism, and whether entrectinib modulates the activity of the major cytochrome P450 (CYP) enzymes or drug transporter P-glycoprotein. Clinical studies investigated the effect of a strong CYP3A4 inhibitor (itraconazole) and inducer (rifampin) on single-dose entrectinib pharmacokinetics. The effect of entrectinib on sensitive probe substrates for CYP3A4 (midazolam) and P-glycoprotein (digoxin) were also investigated. Results Entrectinib is primarily metabolized by CYP3A4. In vitro, entrectinib is a CYP3A4/5 inhibitor (IC50 2 µM) and a weak CYP3A4 inducer. Entrectinib inhibited P-glycoprotein (IC50 1.33 µM) but is a poor substrate. In healthy subjects, itraconazole increased entrectinib Cmax and AUC by 73% and 504%, respectively, and rifampin decreased entrectinib Cmax and AUC by 56% and 77%, respectively. Single dose entrectinib did not affect midazolam AUC, although Cmax decreased by 34%. Multiple dose entrectinib increased midazolam AUC by 50% and decreased Cmax by 21%. Single dose entrectinib increased digoxin AUC and Cmax by 18% and 28%, respectively, but did not affect digoxin renal clearance. Conclusions Entrectinib is a CYP3A4 substrate and is sensitive to the effects of coadministered moderate/strong CYP3A4 inhibitors and strong inducers, and requires dose adjustment. Entrectinib is a weak inhibitor of CYP3A4 and P-glycoprotein and no dose adjustments are required with CYP3A4/P- glycoprotein substrates.Registration Number (Study 2) NCT03330990 (first posted online November 6, 2017) As studies 1 and 3 are phase 1 trials in healthy subjects, they are not required to be registered.

Population pharmacokinetic analysis of entrectinib in pediatric and adult patients with advanced/metastatic solid tumors: support of new drug application submission.

PURPOSE: Entrectinib (ROZLYTREK®) is a CNS-active, potent, and selective inhibitor of ROS1, TRK A/B/C, and ALK kinase activity. It was recently approved for the treatment of ROS1-positive non-small cell lung cancer and NTRK gene fusion-positive solid tumors. The main objective of this analysis was to characterize the pharmacokinetics (PK) of entrectinib and its main active metabolite, M5. METHODS: A total of 276 cancer patients receiving oral entrectinib were included in the analysis. A model-based population approach was used to characterize the PK profiles of both entities using NONMEM® 7.4. A joint model captures the PK of both entrectinib and M5. The effects of pH modifiers, formulation, weight, age, and sex on model parameters were assessed. Model performance was evaluated using visual predictive checks (VPCs). RESULTS: The absorption of entrectinib was best described using a sequential zero- and first-order absorption model and the disposition with one-compartment model for each entity with linear elimination. Moderate-to-high between-patient variability was estimated in model parameters (from 30.8% for the apparent clearance of entrectinib to 122% for the first-order absorption rate constant). Theory-based allometric scaling using body weight on clearances and volumes and a 28% lower relative bioavailability of the F1 formulation in pediatric patients were retained in the model. The VPC confirmed the good predictive performance of the PopPK model. CONCLUSIONS: A robust population PK model was built and qualified for entrectinib and M5, describing linear PK for both entities. This model was used to support the ROZLYTREK® new drug application.

Physiologically-Based Pharmacokinetic Modelling of Entrectinib Parent and Active Metabolite to Support Regulatory Decision-Making.

BACKGROUND AND OBJECTIVE: Entrectinib is a selective inhibitor of ROS1/TRK/ALK kinases, recently approved for oncology indications. Entrectinib is predominantly cleared by cytochrome P450 (CYP) 3A4, and modulation of CYP3A enzyme activity profoundly alters the pharmacokinetics of both entrectinib and its active metabolite M5. We describe development of a combined physiologically based pharmacokinetic (PBPK) model for entrectinib and M5 to support dosing recommendations when entrectinib is co-administered with CYP3A4 inhibitors or inducers. METHODS: A PBPK model was established in Simcyp® Simulator. The initial model based on in vitro-in vivo extrapolation was refined using sensitivity analysis and non-linear mixed effects modeling to optimize parameter estimates and to improve model fit to data from a clinical drug-drug interaction study with the strong CYP3A4 inhibitor, itraconazole. The model was subsequently qualified against clinical data, and the final qualified model used to simulate the effects of moderate to strong CYP3A4 inhibitors and inducers on entrectinib and M5 pharmacokinetics. RESULTS: The final model showed good predictive performance for entrectinib and M5, meeting commonly used predictive performance acceptance criteria in each case. The model predicted that co-administration of various moderate CYP3A4 inhibitors (verapamil, erythromycin, clarithromycin, fluconazole, and diltiazem) would result in an average increase in entrectinib exposure between 2.2- and 3.1-fold, with corresponding average increases for M5 of approximately 2-fold. Co-administration of moderate CYP3A4 inducers (efavirenz, carbamazepine, phenytoin) was predicted to result in an average decrease in entrectinib exposure between 45 and 79%, with corresponding average decreases for M5 of approximately 50%. CONCLUSIONS: The model simulations were used to derive dosing recommendations for co-administering entrectinib with CYP3A4 inhibitors or inducers. PBPK modeling has been used in lieu of clinical studies to enable regulatory decision-making.

Population Pharmacokinetics and Pharmacodynamics of Meropenem in Critically Ill Patients: How to Achieve Best Dosage Regimen According to the Clinical Situation.

BACKGROUND AND OBJECTIVES: Meropenem is frequently used for the treatment of severe bacterial infections in critically ill patients. Because critically ill patients are more prone to pharmacokinetic variability than other patients, ensuring an effective blood concentration can be complex. Therefore, describing this variability to ensure a proper use of this antibiotic drug limits the rise and dissemination of antimicrobial resistance, and helps preserve the current antibiotic arsenal. The aims of this study were to describe the pharmacokinetics of meropenem in critically ill patients, to identify and quantify the patients' characteristics responsible for the observed pharmacokinetic variability, and to perform different dosing simulations in order to determine optimal individually adapted dosing regimens. METHODS: A total of 58 patients hospitalized in the medical intensive care unit and receiving meropenem were enrolled, including 26 patients with renal replacement therapy. A population pharmacokinetic model was developed (using NONMEM software) and Monte Carlo simulations were performed with different dosing scenarios (bolus-like, extended, and continuous infusion) exploring the impact of clinical categories of residual diuresis (anuria, oliguria, and preserved diuresis) on the probability of target attainment (MIC: 1-45 mg/L). RESULTS: The population pharmacokinetic model included five covariates with a significant impact on clearance: glomerular filtration rate, dialysis (continuous and semi-continuous), renal function status, and volume of residual diuresis. The clearance for a typical patient in our population is 4.20 L/h and volume of distribution approximately 44 L. Performed dosing regimen simulations suggested that, for equivalent doses, the continuous infusion mode (with loading dose) allowed the obtaining of the pharmacokinetic/pharmacodynamic target for a larger number of patients (100% for MIC ≤ 20 mg/L). Nevertheless, for the treatment of susceptible bacteria (MIC ≤ 2 mg/L), differences in the probability of target attainment between bolus-like, extended, and continuous infusions were negligible. CONCLUSIONS: Identified covariates in the model are easily accessible information in patient health records. The model highlighted the importance of considering the patient's overall condition (renal function and dialysis) and the pathogen's characteristics (MIC target) during the establishment of a patient's dosing regimen.

Characterization of the pharmacokinetics of entrectinib and its active M5 metabolite in healthy volunteers and patients with solid tumors.

BACKGROUND: Entrectinib is an oral, CNS-active, potent inhibitor of tyrosine receptor kinases A/B/C, tyrosine kinase ROS proto-oncogene 1, and anaplastic lymphoma kinase approved for use in patients with solid tumors. We describe 3 clinical studies, including one investigating the single/multiple dose pharmacokinetics of entrectinib in patients and two studies in healthy volunteers investigating the absorption/distribution/metabolism/excretion (ADME) of entrectinib, its relative bioavailability, and effect of food on pharmacokinetics. METHODS: The patient study is open-label with dose-escalation and expansion phases. Volunteers received entrectinib (100-400 mg/m2, and 600-800 mg) once daily with food in continuous 28-day cycles. In the ADME study, volunteers received a single oral dose of [14C]entrectinib 600 mg. In the third study, volunteers received single doses of entrectinib 600 mg as the research and marketed formulations in the fasted state (Part 1), and the marketed formulation in the fed and fasted states (Part 2). Entrectinib and its major active metabolite M5 were assessed in all studies. RESULTS: Entrectinib was absorbed in a dose-dependent manner with maximum concentrations at ~4 h postdose and an elimination half-life of ~20 h. Entrectinib was cleared mainly through metabolism and both entrectinib and metabolites were eliminated mainly in feces (minimal renal excretion). At steady-state, the M5-to-entrectinib AUC ratio was 0.5 (with 600 mg entrectinib research formulation in patients). The research and marketed formulations were bioequivalent and food had no relevant effect on pharmacokinetics. CONCLUSIONS: Entrectinib is well absorbed, with linear PK that is suitable for once-daily dosing, and can be taken with or without food.

Physiologically Based Absorption Modelling to Explore the Impact of Food and Gastric pH Changes on the Pharmacokinetics of Entrectinib.

Entrectinib is a potent and selective tyrosine kinase inhibitor (TKI) of TRKA/B/C, ROS1, and ALK with both systemic and CNS activities, which has recently received FDA approval for ROS1 fusion-positive non-small cell lung cancer and NTRK fusion-positive solid tumors. This paper describes the application of a physiologically based biophamaceutics modeling (PBBM) during clinical development to understand the impact of food and gastric pH changes on absorption of this lipophilic, basic, molecule with reasonable permeability but strongly pH-dependent solubility. GastroPlus™ was used to develop a physiologically based pharmacokinetics (PBPK) model integrating in vitro and in silico data and dissolution studies and in silico modelling in DDDPlus™ were used to understand the role of self-buffering and acidulant on formulation performance. Models were verified by comparison of simulated pharmacokinetics for acidulant and non-acidulant containing formulations to clinical data from a food effect study and relative bioavailability studies with and without the gastric acid-reducing agent lansoprazole. A negligible food effect and minor pH-dependent drug-drug interaction for the market formulation were predicted based on biorelevant in vitro measurements, dissolution studies, and in silico modelling and were confirmed in clinical studies. These outcomes were explained as due to the acidulant counteracting entrectinib self-buffering and greatly reducing the effect of gastric pH changes. Finally, sensitivity analyses with the verified model were applied to support drug product quality. PBBM has great potential to streamline late-stage drug development and may have impact on regulatory questions.

Population Pharmacokinetic/Pharmacodynamic Analysis of Alirocumab in Healthy Volunteers or Hypercholesterolemic Subjects Using an Indirect Response Model to Predict Low-Density Lipoprotein Cholesterol Lowering: Support for a Biologics License Application Submission: Part II.

BACKGROUND: Alirocumab, a human monoclonal antibody against proprotein convertase subtilisin/kexin type 9 (PCSK9), significantly lowers low-density lipoprotein cholesterol levels. OBJECTIVE: This analysis aimed to develop and qualify a population pharmacokinetic/pharmacodynamic model for alirocumab based on pooled data obtained from 13 phase I/II/III clinical trials. METHODS: From a dataset of 2799 individuals (14,346 low-density lipoprotein-cholesterol values), individual pharmacokinetic parameters from the population pharmacokinetic model presented in Part I of this series were used to estimate alirocumab concentrations. As a second step, we then developed the current population pharmacokinetic/pharmacodynamic model using an indirect response model with a Hill coefficient, parameterized with increasing low-density lipoprotein cholesterol elimination, to relate alirocumab concentrations to low-density lipoprotein cholesterol values. RESULTS: The population pharmacokinetic/pharmacodynamic model allowed the characterization of the pharmacokinetic/pharmacodynamic properties of alirocumab in the target population and estimation of individual low-density lipoprotein cholesterol levels and derived pharmacodynamic parameters (the maximum decrease in low-density lipoprotein cholesterol values from baseline and the difference between baseline low-density lipoprotein cholesterol and the pre-dose value before the next alirocumab dose). Significant parameter-covariate relationships were retained in the model, with a total of ten covariates (sex, age, weight, free baseline PCSK9, total time-varying PCSK9, concomitant statin administration, total baseline PCSK9, co-administration of high-dose statins, disease status) included in the final population pharmacokinetic/pharmacodynamic model to explain between-subject variability. Nevertheless, the high number of covariates included in the model did not have a clinically meaningful impact on model-derived pharmacodynamic parameters. CONCLUSIONS: This model successfully allowed the characterization of the population pharmacokinetic/pharmacodynamic properties of alirocumab in its target population and the estimation of individual low-density lipoprotein cholesterol levels.

Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective.

This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.

A Quantitative Systems Pharmacology Platform to Investigate the Impact of Alirocumab and Cholesterol-Lowering Therapies on Lipid Profiles and Plaque Characteristics.

Reduction in low-density lipoprotein cholesterol (LDL-C) is associated with decreased risk for cardiovascular disease. Alirocumab, an antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9), significantly reduces LDL-C. Here, we report development of a quantitative systems pharmacology (QSP) model integrating peripheral and liver cholesterol metabolism, as well as PCSK9 function, to examine the mechanisms of action of alirocumab and other lipid-lowering therapies, including statins. The model predicts changes in LDL-C and other lipids that are consistent with effects observed in clinical trials of single or combined treatments of alirocumab and other treatments. An exploratory model to examine the effects of lipid levels on plaque dynamics was also developed. The QSP platform, on further development and qualification, may support dose optimization and clinical trial design for PCSK9 inhibitors and lipid-modulating drugs. It may also improve our understanding of factors affecting therapeutic responses in different phenotypes of dyslipidemia and cardiovascular disease.

Target-Mediated Drug Disposition Population Pharmacokinetics Model of Alirocumab in Healthy Volunteers and Patients: Pooled Analysis of Randomized Phase I/II/III Studies.

BACKGROUND AND OBJECTIVE: Proprotein convertase subtilisin/kexin type 9 inhibition with monoclonal antibodies such as alirocumab significantly reduces low-density lipoprotein-cholesterol levels ± other lipid-lowering therapies. We aimed to develop and qualify a population pharmacokinetics (PopPK) model for alirocumab in healthy subjects and patients, taking into account the mechanistic target-mediated drug disposition (TMDD) process. METHODS: This TMDD model was developed using a subset of the alirocumab clinical trial database, including nine phase I/II/III studies (n = 527); the model was subsequently expanded to a larger data set of 13 studies (n = 2870). Potential model parameters and covariate relationships were explored, and predictive ability was qualified using a visual predictive check. RESULTS: The TMDD model was built using the quasi-steady-state approximation. The final TMDD-quasi-steady-state model included a significant relationship between distribution volume of the central compartment and disease state: distribution volume of the central compartment was 1.56-fold higher in patients vs. healthy subjects. Separately, application of the model to the expanded data set revealed a significant relationship between linear clearance and statin co-administration: linear clearance was 1.27-fold higher with statins. The good predictive performance of the TMDD model was assessed based on graphical and numerical quality criteria, together with the visual predictive check and comparison of the predictions to those from a PopPK model with parallel linear and Michaelis-Menten clearances (i.e., simplification of the TMDD PopPK model). CONCLUSIONS: This mechanistic TMDD PopPK model integrates the interaction of alirocumab with its target and accurately predicts both alirocumab and total proprotein convertase subtilisin/kexin type 9 concentrations in healthy subjects and patients.

Ocular Drug Distribution After Topical Administration: Population Pharmacokinetic Model in Rabbits.

BACKGROUND AND OBJECTIVE: When eye diseases are treated by topical administration, the success of treatment lies in the effective drug concentration in the target tissue. This is why the drug's pharmacokinetic, in the different substructures of the eye, needs to be explored more accurately during drug development. The aim of the present analysis was to describe by rabbit model, the distribution of a drug after ocular instillation in the selected eye tissues and fluids. METHODS: By a top-down population approach, we developed and validated a population pharmacokinetics (PopPK) model, using tissue concentrations (tear, naso-lacrymal duct, cornea and aqueous humor) of a new src tyrosine kinase inhibitor (FV-60165) in each anterior segment's tissue and fluid of the rabbit eye. Inter-individual variability was estimated and the impact of the formulation (solution or nanosuspension) was evaluated. RESULTS: The model structure selected for the eye is a 4-compartment model with the formulation as a significant covariate on the first-order rate constant between tears and the naso-lacrymal duct. The model showed a good predictive performance and may be used to estimate the concentration-time profiles after single or repeated administration, in each substructure of the eye for each animal included in the analysis. CONCLUSIONS: This analysis allowed describing the distribution of a drug in the different selected tissues and fluids in the rabbit's eyes after instillation of the prodrug as a solution or nanosuspension.

Physiologically based pharmacokinetic modeling for sequential metabolism: effect of CYP2C19 genetic polymorphism on clopidogrel and clopidogrel active metabolite pharmacokinetics.

Clopidogrel is a prodrug that needs to be converted to its active metabolite (clopi-H4) in two sequential cytochrome P450 (P450)-dependent steps. In the present study, a dynamic physiologically based pharmacokinetic (PBPK) model was developed in Simcyp for clopidogrel and clopi-H4 using a specific sequential metabolite module in four populations with phenotypically different CYP2C19 activity (poor, intermediate, extensive, and ultrarapid metabolizers) receiving a loading dose of 300 mg followed by a maintenance dose of 75 mg. This model was validated using several approaches. First, a comparison of predicted-to-observed area under the curve (AUC)0-24 obtained from a randomized crossover study conducted in four balanced CYP2C19-phenotype metabolizer groups was performed using a visual predictive check method. Second, the interindividual and intertrial variability (on the basis of AUC0-24 comparisons) between the predicted trials and the observed trial of individuals, for each phenotypic group, were compared. Finally, a further validation, on the basis of drug-drug-interaction prediction, was performed by comparing observed values of clopidogrel and clopi-H4 with or without dronedarone (moderate CYP3A4 inhibitor) coadministration using a previously developed and validated physiologically based PBPK dronedarone model. The PBPK model was well validated for both clopidogrel and its active metabolite clopi-H4, in each CYP2C19-phenotypic group, whatever the treatment period (300-mg loading dose and 75-mg last maintenance dose). This is the first study proposing a full dynamic PBPK model able to accurately predict simultaneously the pharmacokinetics of the parent drug and of its primary and secondary metabolites in populations with genetically different activity for a metabolizing enzyme.

Effects of omeprazole and genetic polymorphism of CYP2C19 on the clopidogrel active metabolite.

Clopidogrel is an antiplatelet agent widely used in cardiovascular diseases and an inactive prodrug that needs to be converted to an active metabolite in two sequential metabolic steps. Several CYP450 isoforms involved in these two steps have been described, although the relative contribution in vivo of each enzyme is still under debate. CYP2C19 is considered to be the major contributor to active metabolite formation. In the current study, net CYP2C19 contribution to the active metabolite formation was determined from exposure of the active metabolite in two clinical studies (one phase I study with well balanced genetic polymorphic populations and a meta-analysis with a total of 396 healthy volunteers) at different clopidogrel doses. CYP2C19 involvements were estimated to be from 58 to 67% in intermediate metabolizers (IMs), from 58 to 72% in extensive metabolizers (EMs), and from 56 to 74% in ultrarapid metabolizers (UMs), depending on the study and the dose. For this purpose, a static model was proposed to estimate the net contribution of a given enzyme to the secondary metabolite formation. This static model was compared with a dynamic approach (Simcyp model) and showed good consistency. In parallel, in vitro investigations showed that omeprazole is a mechanism-based inhibitor of CYP2C19 with K(I) of 8.56 µM and K(inact) of 0.156 min(-1). These values were combined with the net CYP2C19 contribution to the active metabolite formation, through a static approach, to predict the inhibitory effect at 80-mg omeprazole doses in EM, IM, and UM CYP2C19 populations, with good consistency, compared with observed clinical values.

Circadian time-effect of orally administered loratadine on plasma pharmacokinetics in mice.

Little is known about the chronopharmacokinetics of loratadine, a long-acting tricyclic antihistamine H(1) widely used in the treatment of allergic diseases. Hence, the pharmacokinetics of loratadine and its major metabolite, desloratadine, were investigated after a 20 mg/kg dose of loratadine had been orally administered to comparable groups of mice (n=33), synchronized for three weeks to 12 h light (rest span)/12 h dark (activity span). The drug was administered at three different circadian times (1, 9, and 17 h after light onset [HALO]). Multiple blood samples were collected over 48 h, and plasma concentrations of loratadine and desloratadine were determined by high performance liquid chromatography. There were no significant differences in T(max) of loratadine and desloratadine between treatment-time different groups. However, the elimination half-life (t1/2) of the parent compound and its metabolite was significantly longer (p<0.01) following administration at 9 HALO (t1/2 loratadine and desloratadine 5.62 and 4.08 h at 9 HALO vs. 4.29 and 2.6 h at 17 HALO vs. 3.26 and 3.27 at 1 HALO). There were relevant (p<0.05) differences in C(max) between the three treated groups for loratadine and desloratadine; 133.05+/-3.55 and 258.07+/-14.45 ng/mL at 9 HALO vs. 104.5+/-2.61 and 188.62+/-7.20 ng/mL at 1 HALO vs. 94.33+/-20 and 187.75+/-10.79 ng/mL at 17 HALO. Drug dosing at 17 HALO resulted in highest loratadine and desloratadine total apparent clearance values: 61.46 and 15.97 L/h/kg, respectively, whereas loratadine and desloratadine clearances (CL) were significantly slower (p<0.05) at the other administration times (loratadine and desloratadine CL was 57.3 and 14.22 L/h/kg at 1 HALO vs. 43.79 and 12.89 L/h/kg at 9 HALO, respectively). The area under the concentration-time curve (AUC) of loratadine and desloratadine was significantly (p<0.05) greater following drug administration at 9 HALO (456.75 and 1550.57 (ng/mL) . h, respectively); it was lowest following treatment at 17 HALO (325.39 and 1252.53 (ng/mL) . h, respectively). These pharmacokinetic data indicate that the administration time of loratadine significantly affected its pharmacokinetics: the elimination of loratadine and its major metabolite desloratadine.

Influence of the UGT2B7 promoter region and exon 2 polymorphisms and comedications on Acyl-MPAG production in vitro and in adult renal transplant patients.

INTRODUCTION: The polymorphic enzyme UGT2B7 metabolizes mycophenolic acid into acyl-mycophenolic acid-glucuronide (AcMPAG), a presumably toxic metabolite. This study aimed at investigating in vitro and in vivo the impact on AcMPAG production of: (i) the UGT2B7 gene G-842A single nucleotide polymorphism, in complete linkage disequilibrium with most other known single nucleotide polymorphisms in the promoter region of this gene and with the C802T single nucleotide polymorphism in exon 2 (UGT2B*2); and (ii) of the other immunosuppressants given to renal transplant patients in association with mycophenolate mofetil. METHODS: We compared the production of AcMPAG by human liver microsomes genotyped for the UGT2B7 G-842A and C802T single nucleotide polymorphisms, and plasma AcMPAG concentrations in genotyped renal transplant patients administered mycophenolate mofetil associated with sirolimus (n=40), tacrolimus (n=24) or cyclosporin (n=28) and decreasing doses of corticosteroids, over the first 3 months after transplant. The effect of corticosteroids was also investigated in vitro using rats' liver microsomes. RESULTS: The two polymorphisms studied were in complete reverse linkage disequilibrium. AcMPAG production was 1.25 and 1.56-fold higher in G-842A and -842AA human liver microsomes, respectively, compared with GG-842 human liver microsomes (P=0.01). Enzyme kinetics showed 1.4 and 3.7-fold higher Vmax in the respective pools of human liver microsomes. Km values were 0.20, 0.25 and 0.44 mmol/l for the GG-842, G-842A and -842AA genotypes, respectively. This clear increase in Vmax is in favor of the implication of the promoter region polymorphisms, whereas the slighter increase in Km might be due to the UGT2B7*2 single nucleotide polymorphism. Consistently, the UGT2B7 genotype significantly influenced AcMPAG area under the curve (AUC0-9 h)/dose in patients on sirolimus at months 1 and 3 after transplant (P=0.04 for both). No effect was observed in patients on tacrolimus and possibly also on cyclosporin, maybe owing to pharmacokinetic interaction with mycophenolate. AcMPAG production was increased in corticosteroid-induced rat liver microsomes, consistent with the observed in-vivo decrease of mycophenolic acid metabolites AUC0-9 h/dose with time after transplant. CONCLUSION: Both UGT2B7 polymorphisms and co-medications significantly influenced AcMPAG production, but cyclosporin and tacrolimus hindered the phenotypic impact of this trait.