Visual Predictive Checks Research Articles

Population pharmacokinetic and exploratory exposure\u2013response analysis of the fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection in patients with HER2-positive early breast cancer in the FeDeriCa study

PurposeTo characterize pertuzumab pharmacokinetics (PK) in FeDeriCa (NCT03493854: fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection [PH FDC SC] versus intravenous pertuzumab plus trastuzumab); derive individual pertuzumab exposures in the PH FDC SC arm for subsequent pertuzumab exposure–response (ER) analyses; compare observed trastuzumab PK with predicted exposures from a previous SC trastuzumab model; assess whether pertuzumab affects trastuzumab PK; evaluate pertuzumab exposure–efficacy and –safety relationships and support the approved SC dosing regimen.MethodsPopulation pharmacokinetic modeling and simulations were used to describe the data. Standard goodness-of-fit diagnostics and prediction-corrected visual predictive checks were used for model performance assessment. Covariates were included from previously reported models. ER analysis was conducted using logistic regression.ResultsSC pertuzumab PK was described adequately by a two-compartment model with first-order absorption; significant covariates included in the final model were albumin, lean body weight, and Asian region; however, these appeared not to be clinically relevant. Trastuzumab concentrations were described adequately by the previous model; there was no evidence of a pertuzumab effect on trastuzumab PK as part of PH FDC SC and higher model-predicted pertuzumab exposure was not associated with differences in pathologic complete response rate or an increased probability of selected grade ≥ 3 adverse events of interest.ConclusionThe approved PH FDC SC dose [loading: 1200/600 mg pertuzumab/trastuzumab (15 mL); maintenance: 600 mg pertuzumab/trastuzumab (10 mL) and 2000 U/mL recombinant human hyaluronidase every 3 weeks] provides a positive benefit–risk profile with comparable efficacy and safety to intravenous pertuzumab plus trastuzumab.

356-P: Population Pharmacokinetic Modeling of Dasiglucagon in Subjects with Type 1 Diabetes Mellitus

To quantify the impact of subject covariates on pharmacokinetics (PK) of the novel, ready-to-use glucagon analog dasiglucagon, we developed a population PK model. Dasiglucagon pharmacokinetics was dose linear in the tested dose range (0.1 to 1.0 mg) and described by a 1-compartment model with first-order absorption and elimination, including an absorption lag time and standard allometric scaling by body weight. The final model included injection site, age as covariates on bioavailability, and MDRD eGFR and sex as covariates on apparent clearance and absorption rate constant, respectively. The model also accounted for reduced drug content at end of shelf-life storage. Based on visual predictive checks the model reliably predicted observed data. The effect of covariates on dasiglucagon exposure measured by area under the concentration time curve (AUC) are shown in the figure below (predicted [95% CI]). AUCs are relative to the AUC predicted for a typical subject aged ≥ 24 years, weighing 78 kg with normal kidney function (eGFR 95 mL/min/1.73m2) and receiving a 0.6 mg dose of dasiglucagon in the abdomen. The model-predicted effects of PK covariates on dasiglucagon AUC generally fell within the observed AUC from the analysis population receiving 0.6 mg dasiglucagon (red markers, median, and 5th-95th percentile interval, n = 321) and observed across the trials to result in reliable recovery from hypoglycemia.View largeDownload slideView largeDownload slide DisclosureC. B. Knudsen: Employee; Self; Zealand Pharma A/S, Employee; Spouse/Partner; Novo Nordisk A/S, Stock/Shareholder; Self; Zealand Pharma A/S, Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. A. H. Valeur: Employee; Self; Zealand Pharma A/S. S. Maarbjerg: Employee; Self; Zealand Pharma A/S, Employee; Spouse/Partner; Zealand Pharma A/S, Stock/Shareholder; Self; Novo Nordisk A/S, Stock/Shareholder; Spouse/Partner; Zealand Pharma A/S. R. Tehranchi: Employee; Self; Zealand Pharma A/S. P. Diderichsen: Consultant; Self; Boehringer Ingelheim International GmbH, Zealand Pharma A/S, Employee; Self; Certara Netherlands bv. H. Witjes: Employee; Self; Certara.

Population Pharmacokinetic Model of N-acetylmannosamine (ManNAc) and N-acetylneuraminic acid (Neu5Ac) in Subjects with GNE Myopathy.

BackgroundGNE myopathy is a rare genetic muscle disease resulting from deficiency in an enzyme critical for the biosynthesis of N-acetylneuraminic acid (Neu5Ac, sialic acid). The uncharged Neu5Ac precursor, N-acetylmannosamine (ManNAc), is under development as an orphan drug for treating GNE myopathy.MethodsA semi-mechanistic population pharmacokinetic model was developed to simultaneously characterize plasma ManNAc and its metabolite Neu5Ac following oral administration of ManNAc to subjects with GNE myopathy. Plasma ManNAc and Neu5Ac pharmacokinetic data were obtained from two clinical studies (ClinicalTrials.gov identifiers NCT01634750, NCT02346461) and were simultaneously modeled using NONMEM.ResultsManNAc and Neu5Ac plasma concentrations were obtained from 34 subjects with GNE myopathy (16 male, 18 female, median age 39.5 years). The model parameter estimates included oral absorption rate (ka) = 0.256 h−1, relative bioavailability relationship with dose (F-Dose) slope = −0.405 (where F = 1 for 6-g dose), apparent clearance (CLM/F) = 631 L/h, volume of distribution (VM/F) = 506 L, Neu5Ac elimination rate constant (kout) = 0.283 h−1, initial ManNAc to Neu5Ac conversion (SLP0) = 0.000619 (ng/mL)−1 and at steady-state (SLPSS) = 0.00334 (ng/mL)−1, with a rate-constant of increase (kinc) = 0.0287 h−1. Goodness-of-fit plots demonstrated an acceptable and unbiased fit to the plasma ManNAc and Neu5Ac concentration data. Visual predictive checks demonstrated reasonable agreement between the 5th, 50th, and 95th percentiles of the observed and simulated data.ConclusionsThis population pharmacokinetic model can be used to evaluate ManNAc dosing regimens and to calculate Neu5Ac production and exposure following oral administration of ManNAc in subjects with GNE myopathy.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40268-021-00343-6.

Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases

The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio(obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).

Inverse probability of censoring weighting for visual predictive checks of time-to-event models with time-varying covariates.

When constructing models to summarize clinical data to be used for simulations, it is good practice to evaluate the models for their capacity to reproduce the data. This can be done by means of Visual Predictive Checks (VPC), which consist of several reproductions of the original study by simulation from the model under evaluation, calculating estimates of interest for each simulated study and comparing the distribution of those estimates with the estimate from the original study. This procedure is a generic method that is straightforward to apply, in general. Here we consider the application of the method to time-to-event data and consider the special case when a time-varying covariate is not known or cannot be approximated after event time. In this case, simulations cannot be conducted beyond the end of the follow-up time (event or censoring time) in the original study. Thus, the simulations must be censored at the end of the follow-up time. Since this censoring is not random, the standard KM estimates from the simulated studies and the resulting VPC will be biased. We propose to use inverse probability of censoring weighting (IPoC) method to correct the KM estimator for the simulated studies and obtain unbiased VPCs. For analyzing the Cantos study, the IPoC weighting as described here proved valuable and enabled the generation of VPCs to qualify PKPD models for simulations. Here, we use a generated data set, which allows illustration of the different situations and evaluation against the known truth.

Population Pharmacokinetics of Vixotrigine in Healthy Volunteers and Subjects with Trigeminal Neuralgia, Painful Lumbosacral Radiculopathy and Erythromelalgia.

Vixotrigine is a voltage and use dependent sodium channel blocker currently under development for treatment of various neuropathic pain indications. The objective of this work was to develop a population pharmacokinetic model and assess effects of various covariates on pharmacokinetic parameters of vixotrigine. Plasma concentration-time data from 12 Phase 1 or 2 studies were included in the analyses. The data were obtained following administration of single or multiple doses of vixotrigine in healthy volunteers and patients. One- and two-compartment pharmacokinetic models were evaluated as base structural pharmacokinetic models. The inclusion of selected covariates was assessed using a stepwise backward elimination approach (α = 0.001) once the base/full model was developed. The predictive ability of the model was evaluated using a visual predictive check (VPC). The final model was used to evaluate effect of covariates on exposure of vixotrigine. A total of 10,263 pharmacokinetic samples collected from 465 subjects were included in the analyses. The pharmacokinetics of vixotrigine was adequately described by a two-compartment model with two transit absorption compartments and first-order elimination. Predictability of the model was also established by VPC. The final model included covariates of age, weight and carbamazepine co-administration on clearance, weight on central volume of distribution, food on absorption rate constant and formulation and Japanese race on bioavailability. None of the covariates identified had a clinically relevant effect, as impact on area under the plasmaconcentration-timecurve (AUC) and maximum plasma concentration (Cmax) was within ± 25%. The model characterizes the pharmacokinetics of vixotrigine well, and the exposure of vixotrigine was comparable between healthy subjects and patients. None of the covariates evaluated have a clinically relevant impact on the pharmacokinetics of vixotrigine.

Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and adolescents.

Various population pharmacokinetic (PopPK) models for vancomycin in children and adolescents have been constructed to optimize the therapeutic regimen of vancomycin. However, little is known about their predictive performance when extrapolated to different clinical centres. Therefore, the aim of this study was to externally validate the predictability of vancomycin PopPK model when extrapolated to different clinical centres and verify its applicability in an independent data set. The published models were screened from the literature and evaluated using an external data set of a total of 451 blood concentrations of vancomycin measured in 220 Chinese paediatric patients. Prediction- and simulation-based diagnostics and Bayesian forecasting were performed to evaluate the predictive performance of the models. Ten published PopPK models were assessed. Prediction-based diagnostics showed that none of the investigated models met all the standards (median prediction error (MDPE) ≤ ±20%, median absolute prediction error (MAPE) ≤30%, PE% within ±20% (F20 ) ≥35% and PE% within ±30% (F30 ) ≥50%), indicating unsatisfactory predictability. In simulation-based diagnostics, both the visual predictive checks (VPC) and the normalized prediction distribution error (NPDE) indicated misspecification in all models. Bayesian forecasting results showed that the accuracy and precision of individual predictions could be significantly improved with one or two prior observations, but frequent monitoring might not be necessary in the clinic, since Bayesian forecasting identified that greater number of samples did not significantly improve the predictability. Model 3 established by Moffett et al showed better predictability than other models. The 10 published models performed unsatisfactorily in prediction- and simulation-based diagnostics; none of the published models was suitable for designing the initial dosing regimens of vancomycin. Pharmacokinetic characteristics and covariates, such as weight, renal function, age and underlying disease should be taken into account when extrapolating the vancomycin model. Bayesian forecasting combined with therapeutic drug monitoring based on model 3 can be used to adjust vancomycin dosing regimens.

Application of a Physiologically Based Pharmacokinetic Model to Characterize Time-dependent Metabolism of Voriconazole in Children and Support Dose Optimization.

Background: Voriconazole is a potent antifungal drug with complex pharmacokinetics caused by time-dependent inhibition and polymorphisms of metabolizing enzymes. It also exhibits different pharmacokinetic characteristics between adults and children. An understanding of these alterations in pharmacokinetics is essential for pediatric dose optimization. Objective: To determine voriconazole plasma exposure in the pediatric population and further investigate optimal dosage regimens. Methods: An adult and pediatric physiologically based pharmacokinetic (PBPK) model of voriconazole, integrating auto-inhibition of cytochrome P450 3A4 (CYP3A4) and CYP2C19 gene polymorphisms, was developed. The model was evaluated with visual predictive checks and quantitative measures of the predicted/observed ratio of the area under the plasma concentration-time curve (AUC) and maximum concentration (Cmax). The validated pediatric PBPK model was used in simulations to optimize pediatric dosage regimens. The probability of reaching a ratio of free drug (unbound drug concentration) AUC during a 24-h period to minimum inhibitory concentration greater than or equal to 25 (fAUC24h/MIC ≥ 25) was assessed as the pharmacokinetic/pharmacodynamic index. Results: The developed PBPK model well represented voriconazole's pharmacokinetic characteristics in adults; 78% of predicted/observed AUC ratios and 85% of Cmax ratios were within the 1.25-fold range. The model maintained satisfactory prediction performance for intravenous administration in pediatric populations after incorporating developmental changes in anatomy/physiology and metabolic enzymes, with all predicted AUC values within 2-fold and 73% of the predicted Cmax within 1.25-fold of the observed values. The simulation results of the PBPK model suggested that different dosage regimens should be administered to children according to their age, CYP2C19 genotype, and infectious fungal genera. Conclusion: The PBPK model integrating CYP3A4 auto-inhibition and CYP2C19 gene polymorphisms successfully predicted voriconazole pharmacokinetics during intravenous administration in children and could further be used to optimize dose strategies. The infectious fungal genera should be considered in clinical settings, and further research with large sample sizes is required to confirm the current findings.

Physiologically based pharmacokinetic/pharmacodynamic model for the prediction of morphine brain disposition and analgesia in adults and children.

Morphine is a widely used opioid analgesic, which shows large differences in clinical response in children, even when aiming for equivalent plasma drug concentrations. Age-dependent brain disposition of morphine could contribute to this variability, as developmental increase in blood-brain barrier (BBB) P-glycoprotein (Pgp) expression has been reported. In addition, age-related pharmacodynamics might also explain the variability in effect. To assess the influence of these processes on morphine effectiveness, a multi-compartment brain physiologically based pharmacokinetic/pharmacodynamic (PB-PK/PD) model was developed in R (Version 3.6.2). Active Pgp-mediated morphine transport was measured in MDCKII-Pgp cells grown on transwell filters and translated by an in vitro-in vivo extrapolation approach, which included developmental Pgp expression. Passive BBB permeability of morphine and its active metabolite morphine-6-glucuronide (M6G) and their pharmacodynamic parameters were derived from experiments reported in literature. Model simulations after single dose morphine were compared with measured and published concentrations of morphine and M6G in plasma, brain extracellular fluid (ECF) and cerebrospinal fluid (CSF), as well as published drug responses in children (1 day- 16 years) and adults. Visual predictive checks indicated acceptable overlays between simulated and measured morphine and M6G concentration-time profiles and prediction errors were between 1 and -1. Incorporation of active Pgp-mediated BBB transport into the PB-PK/PD model resulted in a 1.3-fold reduced brain exposure in adults, indicating only a modest contribution on brain disposition. Analgesic effect-time profiles could be described reasonably well for older children and adults, but were largely underpredicted for neonates. In summary, an age-appropriate morphine PB-PK/PD model was developed for the prediction of brain pharmacokinetics and analgesic effects. In the neonatal population, pharmacodynamic characteristics, but not brain drug disposition, appear to be altered compared to adults and older children, which may explain the reported differences in analgesic effect.

Derivation of a Pharmacokinetic Model to Include a Plasma-Derived, von Willebrand Factor-Containing Factor VIII (Koate®-DVI) Concentrate and its Low-Dose Use

Background Population pharmacokinetics (popPK) has been reliably leveraged to generate individual PK in hemophilia patients. Specific popPK models are suited to predict individual PK under a variety of scenarios that may not be captured by clinical trials, allowing for individualized prophylactic treatment. The Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) project generates individually predicted pharmacokinetic profiles relying on concentrate-specific popPK models used for Bayesian forecasting. Objective Specification of a popPK model for the plasma-derived factor VIII (FVIII) concentrate Koate-DVI and its suitability for pharmacokinetic estimation in low-dose scenarios. Methods A popPK model was developed for Koate-DVI WAPPS-Hemo PK data in combination with the existing WAPPS-Hemo Fanhdi/Alphanate model derivation dataset using nonlinear mixed effects modelling, and was validated via cross-validation and prediction-corrected Visual Predictive Checks (pcVPC). Bootstrap and PK outcomes between the Fanhdi/Alphanate and the Fanhdi/Alphanate/Koate models were compared, and the relative error distributions from a limited sampling analysis (LSA) under the latter model for low and normal doses (10 vs 50 IU/kg) and inclusion/exclusion of pre-dose measurements. Results A Fanhdi/Alphanate/Koate model was derived (126 patients, ages 1–71 years) after deeming a Koate-brand covariate not clinically significant, resulting in similar parameter estimates than the Fanhdi/Alphanate model with satisfactory goodness of fit, cross-validation and pcVPC results. Low-dose predictions resulted in a higher accuracy and slightly lower precision of half-life ([Formula: see text]-phase) and time to 2% trough (TAT2%) estimates than normal dose (median absolute bias for half-life: 0.12 vs 0.5%; median interquartile range, IQR: 11.79% vs 9.95%). Precision improved under pre-dose designs by 2 to 3% and remained similar between 5- and 2-sample designs (IQR reduction<1.8%). Conclusions The Fanhdi/Alphanate/Koate model is appropriate for Bayesian forecasting in the WAPPS-Hemo platform, providing a comparable prediction capability for low and normal dosing regimens (10 vs 50 IU/Kg).

Development and Qualification of a Physiologically Based Pharmacokinetic Model of Finasteride and Minoxidil Following Scalp Application.

In this study, we aimed to develop and qualify a PBPK model for scalp application using two drugs with marked differences in physicochemical properties and PK profiles. The parameters related to scalp physiology, drug PK, and formulations were incorporated into a Multi-Phase and Multi-Layer (MPML) Mechanistic Dermal Absorption (MechDermA) model within the Simcyp® Simulator V17. The finasteride PBPK model was linked to its effect on dihydrotestosterone (DHT) levels in plasma and scalp using an indirect response model. Predicted PK (and PD for finasteride) profiles and parameters were compared against the clinically reported data and justified by visual predictive checks and two-fold error criteria for model verification. The PBPK/PD model for finasteride reasonably demonstrated an ability to predict its respective PK and PD profiles, and parameters following scalp application under various clinical scenarios. Using the same scalp physiological input parameters, the minoxidil PBPK model was then developed and satisfactorily qualified with independent clinical datasets. Collectively, these results suggested that the established PBPK model may have broader utility for other topical formulations intended for scalp application.

Predictive performance of parent-metabolite population pharmacokinetic models of (S)-ketamine in healthy volunteers

PurposeThe recent repurposing of ketamine as treatment for pain and depression has increased the need for accurate population pharmacokinetic (PK) models to inform the design of new clinical trials. Therefore, the objectives of this study were to externally validate available PK models on (S)-(nor)ketamine concentrations with in-house data and to improve the best performing model when necessary.MethodsBased on predefined criteria, five models were selected from literature. Data of two previously performed clinical trials on (S)-ketamine administration in healthy volunteers were available for validation. The predictive performances of the selected models were compared through visual predictive checks (VPCs) and calculation of the (root) mean (square) prediction errors (ME and RMSE). The available data was used to adapt the best performing model through alterations to the model structure and re-estimation of inter-individual variability (IIV).ResultsThe model developed by Fanta et al. (Eur J Clin Pharmacol 71:441–447, 2015) performed best at predicting the (S)-ketamine concentration over time, but failed to capture the (S)-norketamine Cmax correctly. Other models with similar population demographics and study designs had estimated relatively small distribution volumes of (S)-ketamine and thus overpredicted concentrations after start of infusion, most likely due to the influence of circulatory dynamics and sampling methodology. Model predictions were improved through a reduction in complexity of the (S)-(nor)ketamine model and re-estimation of IIV.ConclusionThe modified model resulted in accurate predictions of both (S)-ketamine and (S)-norketamine and thereby provides a solid foundation for future simulation studies of (S)-(nor)ketamine PK in healthy volunteers after (S)-ketamine infusion.

Physiologically-based pharmacokinetic model for clozapine in Korean patients with schizophrenia.

Clozapine has been used as a treatment of schizophrenia. Despite its large interindividual variability, few reports addressed the physiologically-based pharmacokinetic modeling and simulation (PBPK M&S) of clozapine in patients. This study aimed to develop a PBPK M&S of clozapine in Korean patients with schizophrenia. PBPK modeling for clozapine was constructed using a population-based PBPK platform, the SimCYP® Simulator (V19; Certara, Sheffield, UK). The PBPK model was developed by optimizing the physiological parameters of the built-in population and compound libraries in the SimCYP® Simulator. The model verification was performed with the predicted/observed ratio for pharmacokinetic parameters and visual predictive checks (VPCs) plot. Simulations were performed to predict toxicities according to dosing regimens. From published data, 230 virtual trials were simulated for each dosing regimen. The predicted/observed ratio for the area under the curve and peak plasma concentration was calculated to be from 0.78 to 1.34. The observation profiles were within the 5th and 95th percentile range with no serious model misspecification through the VPC plot. A significant impact on age and gender was found for clozapine clearance. The simulation results suggested that 150 mg twice a day and 150 mg three times a day of clozapine have toxicity concerns. In conclusion, a PBPK model was developed and reasonable parameters were made from the data of Korean patients with schizophrenia. The provided model might be used to predict the pharmacokinetics of clozapine and assist dose adjustment in clinical settings.

Nonlinear Mixed Effects Modeling of Deterministic and Stochastic Dynamical Systems in Wolfram Mathematica

Nonlinear mixed effects (NLME) modeling is a powerful tool to analyze time-series data from several individual entities in an experiment. In this paper, we give a brief overview of a package for NLME modeling in Wolfram Mathematica entitled NLMEModeling, implementing the first-order conditional estimation method with sensitivity equation-based gradients for parameter estimation. NLMEModeling supports mixed effects modeling of dynamical systems where the underlying dynamics are described by either ordinary or stochastic differential equations combined with observation equations with flexible observation error models. Moreover, NLMEModeling is a user-friendly package with functionality for parameter estimation, model diagnostics (such as goodness-of-fit analysis and visual predictive checks), and model simulation. The package is freely available and provides an extensible add-on to Wolfram Mathematica.

1321. Population Pharmacokinetic (PK) and Pharmacokinetic/Pharmacodynamic (PK/PD) Target Attainment Analyses for Dalbavancin in Pediatric Patients

BackgroundDalbavancin is a lipoglycopeptide approved for treating adults with acute bacterial skin and skin structure infections (ABSSSI). It has a terminal half-life of >14 days, which allows for administration as a single-dose regimen. Pediatric studies for dalbavancin include three phase 1 studies and a phase 3 study in patients from birth to 17 years with ABSSSI or neonatal sepsis.MethodsA population PK model was developed using 1124 concentrations from 211 pediatric patients. Allometric scaling of clearance, and volume parameters was included with exponents fixed at 0.75 and 1, respectively. Based on exploratory analysis and prior knowledge, serum albumin was included as a covariate on all PK parameters, and creatinine clearance or estimated glomerular filtration rate (eGFR) was included as a covariate on clearance. eGFR for patients < 2 years accounted for renal maturation. Additional covariates were assessed by stepwise covariate modeling (SCM). The final model was qualified by visual predictive checks (VPCs) and bootstrapping and was used to simulate 1000 PK profiles for various pediatric age groups, ranging from preterm neonates to adolescents. PK/PD target attainment (PTA) was calculated for targets associated with stasis, 1-log kill, and 2-log kill of Staphylococcus aureus in the murine thigh infection model.ResultsDalbavancin PK was well-characterized by a 3-compartment model. SCM did not find any significant covariates besides albumin, weight, and renal function. VPCs demonstrated that the final model has good predictive performance across the full age range. Simulations showed that single-dose regimens of 22.5 mg/kg for patients < 6 years and 18 mg/kg for patients 6 to < 18 years resulted in PTA ≥94% for MICs up to 2 mg/L for the stasis target and up to 0.5 mg/L for the 2-log kill target. PTA for pediatric patients was similar to adults, and exposures (AUCs) were contained within the range for adults administered 1500 mg.ConclusionDalbavancin PK in pediatric patients was well-characterized by a 3-compartment model with allometric scaling of clearance and volume and with albumin and renal function included as covariates. Simulations with the final model demonstrate adequate PTA across the entire age range for the regimens used in the phase 3 pediatric study.DisclosuresTimothy J. Carrothers, ScD, AbbVie (Employee) H. Maxime Lagraauw, PhD, qPharmetra (Employee) Lars Lindbom, PhD, qPharmetra (Employee) Todd Riccobene, PhD, AbbVie (Employee)

Dosing Recommendations for Lamotrigine in Children: Evaluation Based on Previous and New Population Pharmacokinetic Models.

Lamotrigine is a broad-spectrum antiepileptic drug with high interindividual variability in serum concentrations in children. The aims of this study were to evaluate the predictive performance of pediatric population pharmacokinetic (PPK) models published on lamotrigine, to build a new model with our monitoring data and to evaluate the current recommended doses. A validation cohort included patients treated with lamotrigine who had a serum level assayed during therapeutic drug monitoring (TDM). PPK models published in the literature were first applied to the validation cohort. We assessed their predictive performance using mean prediction errors, root mean squared errors, and visual predictive checks. A new model was then built using the data. Dose simulations were performed to evaluate the doses recommended. We included 270 lamotrigine concentrations ranging from 0.5 to 17.9mg/L from 175 patients. The median (range) age and weight were 11.8 years (0.8-18 years) and 32.7kg (8-110kg). We tested 6 PPK models; most had acceptable bias and precision but underestimated the variability of the cohort. We built a 1-compartment model with first-order absorption and elimination, allometric scaling, and effects of inhibitor and inducer comedications. In our cohort, 22.6% of trough concentrations were below 2.5mg/L. In conclusion, we proposed a PPK model that can be used for TDM of lamotrigine in children. In our population, a high percentage of children had low trough concentrations of lamotrigine. As the intervals of recommended doses are large, we suggest aiming at the higher range of doses to reach the target concentration.

Population Pharmacokinetics of Phosphocreatine and Its Metabolite Creatine in Children With Myocarditis.

Objective: This study aimed to develop a parent-metabolite joint population pharmacokinetic model to characterize the pharmacokinetic (PK) profile for phosphocreatine (PCr) and its metabolite creatine (Cr) in children with myocarditis and to use this model to study the PK profile of different dosing schemes. Methods: One hundred pediatric patients with myocarditis were enrolled. Blood samples were collected at baseline and approximately 30, 40 or 50, 75, and 180 min after a single dose of phosphocreatine sodium. Plasma PCr and Cr concentrations were determined using an HPLC-MS/MS method. A nonlinear mixed effect model approach was used to build the population pharmacokinetic model. After validation, the model was used for simulations to evaluate the PK profile of different dosing schemes. Results: A total of 997 plasma concentrations (498 for PCr and 499 for Cr) were included in the analysis. A four-compartment chain model (central and peripheral compartments for both PCr and Cr) with the first-order elimination adequately characterized the in vivo process of PCr and Cr. Allometric scaling based on bodyweight was applied to the PK parameters. The covariate analysis identified that the glomerular filtration rate (GFR) was strongly associated with Cr clearance. Bootstrapping and visual predictive checks suggested that a robust and reliable pharmacokinetic model was developed. The simulation results showed that PCr had no accumulation in vivo. With the infusion of PCr, the concentration of Cr increased rapidly. Conclusion: A joint population pharmacokinetic model for PCr and Cr in pediatric patients with myocarditis was successfully developed for the first time.

Population pharmacokinetics of the anti-PD-1 antibody camrelizumab in patients with multiple tumor types and model-informed dosing strategy.

Camrelizumab, a programmed cell death 1 (PD-1) inhibitor, has been approved for the treatment of patients with relapsed or refractory classical Hodgkin lymphoma, nasopharyngeal cancer and non-small cell lung cancer. The aim of this study was to perform a population pharmacokinetic (PK) analysis of camrelizumab to quantify the impact of patient characteristics and to investigate the appropriateness of a flat dose in the dosing regimen. A total of 3092 camrelizumab concentrations from 133 patients in four clinical trials with advanced melanoma, relapsed or refractory classical Hodgkin lymphoma and other solid tumor types were analyzed using nonlinear mixed effects modeling. The PKs of camrelizumab were properly described using a two-compartment model with parallel linear and nonlinear clearance. Then, covariate model building was conducted using stepwise forward addition and backward elimination. The results showed that baseline albumin had significant effects on linear clearance, while actual body weight affected intercompartmental clearance. However, their impacts were limited, and no dose adjustments were required. The final model was further evaluated by goodness-of-fit plots, bootstrap procedures, and visual predictive checks and showed satisfactory model performance. Moreover, dosing regimens of 200 mg every 2 weeks and 3 mg/kg every 2 weeks provided similar exposure distributions by model-based Monte Carlo simulation. The population analyses demonstrated that patient characteristics have no clinically meaningful impact on the PKs of camrelizumab and present evidence for no advantage of either the flat dose or weight-based dose regimen for most patients with advanced solid tumors.

Population Pharmacokinetics of Eryaspase in Patients with Acute Lymphoblastic Leukemia or Pancreatic Adenocarcinoma

L-asparaginase (ASNase) is a microbial enzyme and a chemotherapeutic agent that has been a standard component of multi-agent chemotherapy for the treatment of acute lymphoblastic leukemia (ALL). Eryaspase, ASNase encapsulated in red blood cells, is being developed with an improved pharmacokinetic profile compared to some other ASNase products. The purpose of the present analysis was to describe the population pharmacokinetics (Pop PK) of eryaspase and sources of variability in two patient populations - ALL or pancreatic adenocarcinoma (PAC). Eryaspase was administered intravenously at doses of 50, 100, and 150 U/kg to patients enrolled in Phase 1-3 clinical trials in combination with chemotherapy. Either serial or sparse pharmacokinetic sampling was performed in each study. A Pop PK model was fit to the data using Nonlinear Mixed Effects Modeling (NONMEM®) software, and included an assessment of covariates, e.g., demography, disease type, formulation, and immunogenicity. The adequacy of the final Pop PK model and parameter estimates was investigated with a visual predictive check (VPC) and non-parametric bootstrap analysis. Using the final Pop PK model, a simulation was performed for eryaspase 100 U/kg every 2 weeks and included a target trough reference ASNase level of 100 U/L. The Pop PK analysis included 162 patients (94 ALL and 68 PAC) with median age of 56.5 years (range of 2-84 years). Eryaspase concentration-time data were best described by a one compartment model with first order elimination. Population clearance and volume of distribution were 0.29 L/day and 6.12 L, respectively. That equates to a terminal half-life of approximately 15 days. Both clearance and volume of distribution were found to increase with increasing body weight. No other covariate tested was found to influence eryaspase pharmacokinetics, including patient age and gender, patient population (ALL vs PAC), and formulation (native vs recombinant ASNase). VPC showed the final model well captured the data; furthermore, the final model parameters and bootstrap results were in accordance. The effect of positive presence of neutralizing antibodies on eryaspase clearance was inconclusive because of variability within and across studies. The figure below displays the median (solid line) and 95% prediction interval (blue shaded region) of simulated ASNase concentrations with a reference line of 100 U/L. The simulation shows that approximately 95% of patients would exceed the target trough level of 100 U/L following the first and subsequent infusions following administration of 100 U/kg every 2 weeks. In conclusion, the Pop PK analysis provided valuable information in the characterization of eryaspase and factors that influence its variability. As expected for an RBC-type product, the clearance was low, and the volume of distribution was consistent with blood volume. Terminal phase t1/2 was approximately 15 days, and body weight was a significant covariate for both clearance and volume of distribution. Based on simulation, an eryaspase dosing regimen of 100 U/kg every 2 weeks would provide trough ASNase activity levels above 100 U/L for approximately 95% of patients. Eryaspase is currently being investigated in ALL patients with hypersensitivity reactions, as well as a phase 3 clinical study in PAC (Trybeca-1) and a phase 2 study in Triple Negative Breast Cancer (Trybeca-2). Figure Disclosures Hoke: Erytech: Current Employment. Lorenzi:Precision Pathways: Consultancy. El-Hariry:Erytech: Current Employment.

A Population Pharmacokinetic Model for Concizumab Based on Phase 1 and Phase 2 Trial Data

Introduction Concizumab is a high-affinity anti-tissue factor pathway inhibitor (TFPI) monoclonal antibody in clinical investigation for the subcutaneous (SC) treatment of patients with hemophilia. The data generated from phase 1 and 2 concizumab trials have been used to develop a population pharmacokinetic (PK) model with the aim of supporting dose selection for phase 3 trials. WMethods The objective of this study was to develop a model to describe the PK of concizumab across administration routes in various groups of patients with hemophilia to generate a generally applicable population PK model of concizumab. The model was developed based on available PK data from four phase 1 trials (for both intravenous [IV] and SC concizumab administration) and two phase 2 trials (for SC concizumab administration). Trial populations in the phase 1 trials included both healthy subjects and patients with hemophilia, whilst the phase 2 trials enrolled patients with hemophilia A or B with inhibitors and patients with hemophilia A without inhibitors. A structural population PK model was first developed based on phase 1 data and the final population PK model was then estimated using data from both phase 1 and phase 2 trials. Simulations were performed for phase 3 concizumab exposure using a full parametric simulation (n=10,000), including both inter-individual and intra-individual variability for the selected population. Randomly sampled body weights from a normal distribution with mean and variance corresponding to body weight distribution from phase 2 trials were used to simulate patient profiles. WResults The population PK dataset used for the model comprised 1,504 observations from 119 subjects (89 patients and 30 healthy individuals), with a mean age of 35 years (range: 18-65 years) and mean body weight of 74.4 kg (range: 47.1-130 kg). The PK model parameters were first estimated based on phase 1 data alone, and after fixing the majority in order to ensure robustness of the model only a few parameters were re-estimated based on phase 1 and 2 data combined. The PK model (Figure 1) was evaluated by standard goodness-of-fit plots and qualification assessments. Using visual predictive checks, it was shown that the model was able to reproduce the median and the 5th and 95th percentiles of the observed concizumab concentrations from phase 1 and 2 trials, and so it was deemed suitable for simulation purposes. The PK model suggested a target-mediated drug disposition following concizumab binding to TFPI at the endothelium, and subsequent elimination of the complex to account for the non-linear elimination. WConclusions The developed model accurately described the PK of concizumab delivered at a wide dose range by either SC or IV administration to both healthy subjects and patients with hemophilia A or B with and without inhibitors. The model was used for simulations to select the dosing regimen for subsequent phase 3 studies. Figure 1. Concizumab pharmacokinetic model. Structure of the final concizumab PK model for SC and IV dosing with target-mediated drug disposition via the endothelial TFPI. CL, clearance; doseiv, intravenous dose; dosesc, subcutaneous dose; IV, intravenous; ka, absorption rate constant; kcom, elimination rate constant of the concizumab-TFPI complex; kon and koff, rate constants for binding of concizumab to the endothelial TFPI; ktr, rate constant from the transit compartment; Q, inter-compartmental clearance; Rtot, amount of endothelial TFPI available for concizumab binding; SC, subcutaneous; TFPI, tissue factor pathway inhibitor; V, volume. Figure Disclosures Høyer Rose: Novo Nordisk A/S: Current Employment, Divested equity in a private or publicly-traded company in the past 24 months. Hollensen:Novo Nordisk: Current Employment, Current equity holder in private company, Current equity holder in publicly-traded company. Agersø:Novo Nordisk A/S: Current Employment. Viig Overgaard:Novo Nordisk A/S: Current Employment, Current equity holder in publicly-traded company.