Prediction-corrected Visual Predictive Checks Research Articles

Population Pharmacokinetics and Limited Sampling Strategy of Mycophenolate Mofetil for Indian Patients with Lupus Nephritis

Background: Mycophenolic acid is widely used to treat lupus nephritis (LN). However, it exhibits complex pharmacokinetics with large interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model and a 3-sample limited sampling strategy (LSS) to optimize therapeutic drug monitoring in Indian patients with LN. Methods: Five blood samples from each LN patient treated with mycophenolic acid were collected at steady-state predose and 1, 2, 4, and 6 hours postdose. Demographic parameters were tested as covariates to explain interindividual variability. PopPK analysis was performed using Monolix and the stochastic approximation expectation-maximization algorithm. An LSS was derived from 500 simulated pharmacokinetic (PK) profiles using maximum a posteriori Bayesian estimation to estimate individual PK parameters and area under the curve (AUC). The LSS-calculated AUC was compared with the AUC calculated using the trapezoidal rule and all the simulated samples. Results: A total of 51 patients were included in this study. Based on the 245 mycophenolic acid concentrations, a 1-compartmental model with double absorption using gamma distributions best fitted the data. None of the covariates improved the model significantly. The model was internally validated using diagnostic plots, prediction-corrected visual predictive checks, and bootstrapping. The best LSS included samples at 1, 2, and 4 hours postdose and exhibited good performances in an external dataset (root mean squared error, 21.9%; mean bias, −4.20%). Conclusions: The popPK model developed in this study adequately estimated the PK of mycophenolic acid in adult Indian patients with LN. This simple LSS can optimize TDM based on the AUC in routine practice.

Predictive Performance of a Gentamicin Pharmacokinetic Model in Term Neonates with Perinatal Asphyxia Undergoing Controlled Therapeutic Hypothermia.

Model validation procedures are crucial when population pharmacokinetic (PK) models are used to develop dosing algorithms and to perform model-informed precision dosing. We have previously published a population PK model describing the PK of gentamicin in term neonates with perinatal asphyxia during controlled therapeutic hypothermia (TH), which showed altered gentamicin clearance during the hypothermic phase dependent on gestational age and weight. In this study, the predictive performance and generalizability of this model were assessed using an independent data set of neonates with perinatal asphyxia undergoing controlled TH. The external data set contained a subset of neonates included in the prospective observational multicenter PharmaCool Study. Predictive performance was assessed by visually inspecting observed-versus-predicted concentration plots and calculating bias and precision. In addition, simulation-based diagnostics, model refitting, and bootstrap analyses were performed. The external data set included 323 gentamicin concentrations of 39 neonates. Both the model-building and external data set included neonates from multiple centers. The original gentamicin PK model predicted the observed gentamicin concentrations with adequate accuracy and precision during all phases of controlled TH. Model appropriateness was confirmed with prediction-corrected visual predictive checks and normalized prediction distribution error analyses. Model refitting to the merged data set (n = 86 neonates with 935 samples) showed accurate estimation of PK parameters. The results of this external validation study justify the generalizability of the gentamicin dosing recommendations made in the original study for neonates with perinatal asphyxia undergoing controlled TH (5 mg/kg every 36 or 24 h with gestational age 36-41 and 42 wk, respectively) and its applicability in model-informed precision dosing.

Population Pharmacokinetic Modeling of Cotadutide: A Dual Agonist Peptide of Glucagon-Like Peptide and Glucagon Receptors Administered to Participants with Type II Diabetes Mellitus, Chronic Kidney Disease, Obesity and Non-Alcoholic Steatohepatitis.

Cotadutide is a dual glucagon-like peptide-1 (GLP-1) and glucagon (GCG) receptor agonist peptide. The objective of this analysis was to develop a population pharmacokinetic (popPK) model of cotadutide, and to identify any potential effect on the PK from intrinsic and extrinsic covariates. The popPK analysis utilized a non-linear mixed-effects modeling approach using the data from 10 clinical studies in different participant categories following once-daily subcutaneous dose administration ranging from 20 to 600μg. Additionally, the covariates affecting cotadutide exposure were quantified, and the model performance was evaluated through the prediction-corrected visual predictive checks. A one-compartment model with first-order absorption and elimination adequately described the data as confirmed via visual predictive check plots and parameter plausibility. The mean values for cotadutide apparent clearance (CL/F), apparent volume of distribution (V/F), absorption rate constant (Ka), and half-life were 1.05 L/h, 20.0 L, 0.38 h-1, and 13.3 hours, respectively. Covariate modeling identified body weight, alanine transaminase, albumin, anti-drug antibody (ADA) titer values, formulation strength and injection device, and participant categories as significant covariates on PK parameters, where ADAs have been identified to decrease cotadutide clearance. The model demonstrated that a 150-kg participant was estimated to have 30% lower for both AUC and Cmax and a 66 kg participant was estimated to have 35% higher for both AUC and Cmax relative to a reference individual with a median weight of 96 kg. A popPK model was developed for cotadutide with cotadutide clinical data, and the impact of the statistically significant covariates identified was not considered clinically meaningful. The popPK model will be used to evaluate exposure-response relationships for cotadutide clinical data.

Significant Effects of Renal Function on Mycophenolic Acid Total Clearance in Pediatric Kidney Transplant Recipients with Population Pharmacokinetic Modeling.

Mycophenolic acid (MPA) is an immunosuppressant commonly prescribed in pediatric kidney transplantation to prevent graft rejection. Large variabilities in MPA plasma exposures have been observed in this population, which could result in severe adverse effects. The majority of the MPA pharmacokinetic data have been reported in adult populations, whereas information in pediatric patients is still very limited. The objective of this study was to establish a novel, nonlinear mixed-effects model for MPA and investigate the clinical variables affecting MPA population pharmacokinetics in pediatric kidney transplant recipients. Data were collected retrospectively from pediatric kidney transplant patients (≤ 18 years when MPA concentrations were initially collected; on oral administration of mycophenolate mofetil) in Calgary, Alberta, Canada. Nonlinear mixed-effect modeling was conducted using stochastic approximation expectation-maximization in Monolix 2021R2 (Lixoft SAS, France) to determine population pharmacokinetic estimates, interindividual variabilities, and interoccasional variabilities. Covariate models were constructed using the Model Proposal function in Monolix in conjunction with a systematic stepwise inclusion/elimination protocol. The best model was selected based on objective function values, relative standard errors, goodness-of-fit plots, prediction-corrected visual predictive checks, and numerical predictive checks. A total of 50 pediatric kidney transplant patients (25 female) with 219 MPA plasma concentration-time profiles were included. The average age (± standard deviation) and posttransplant time for the sample population were 12.8 ± 4.8 years and 762 ± 1160 days, respectively. The majority of study subjects (i.e., > 85% based on all occasions) were co-administered tacrolimus. A two-compartment, first-order absorption with lag time and linear elimination structural model with lognormal distributed proportional residual errors best described the MPA concentration-time data. The absorption rate constant (2.52 h-1 or 0.042 min-1), lag time (0.166 h or 9.96 min), volumes of distributions of the central (22.8 L) and peripheral (216 L) compartments, and intercompartment clearance (17.6 Lh-1 or 0.293 Lmin-1) were consistent with literature values; whereas total MPA clearance (0.72 Lh-1 or 0.012 Lmin-1) was relatively reduced, likely due to the general lack of cyclosporine interactions and the stabilized graft functions from significantly longer posttransplant time in our sample population. Of the clinical variables tested, only estimated glomerular filtration rate (eGFR) was identified a significant covariate affecting total MPA clearance with a positive, exponential relationship. The final population pharmacokinetic model was successfully evaluated/validated using a variety of complementary methods. We have successfully constructed and validated a novel population pharmacokinetic model of MPA in pediatric kidney transplant patients. A positive, nonlinear relationship between eGFR and total MPA clearance identified in our model is likely attributed to multiple concurrent mechanisms, which warrant further systematic investigations.

External validation of population pharmacokinetic models of vancomycin in postoperative neurosurgical patients.

Vancomycin is commonly used in the prevention and treatment of intracranial infections in postoperative neurosurgical patients with narrow therapeutic window and large pharmacokinetic variations. Several population pharmacokinetic (PPK) models of vancomycin have been established for neurosurgical patients. But comprehensive external evaluation has not been performed for almost all models. The objective of this study was to evaluate the predictive ability of published vancomycin PPK models in adult postoperative neurosurgical patients using an independent dataset. PubMed, Embase and China National Knowledge Internet databases were searched to identify published vancomycin PPK models in adult postoperative neurosurgical patients. Prediction-based and simulation-based diagnostics were used to evaluate model predictability. Bayesian forecasting was used to assess the influence of prior concentration on model prediction performance. A total of 763 vancomycin plasma concentrations from 493 postoperative neurosurgical patients were included in the external dataset. Eight population pharmacokinetic models of vancomycin in postoperative neurosurgical patients were included and evaluated. The model by Zhang et al. exhibited the best predictive performance in prediction-based diagnostics and prediction-corrected visual predictive checks, followed by the model by Shen et al. The predictive performance of other models was not satisfactory. The normalized predictive distribution error test shows that none of the models is suitable to describe our data. The predictive performance of vancomycin models was obviously improved by maximum a posteriori Bayesian forecasting. The published PPK models for adult postoperative neurosurgical patients show extensive variation in predictive performance in our patients. Although it is challenging to recommend initial doses of vancomycin from these predictive models, the combination of model-based prediction and therapeutic drug monitoring can be used for dose optimization.

Population pharmacokinetic and exposure-response safety analyses of mecbotamab vedotin (BA3011) in patients with advanced solid tumors.

e15010 Background: Mecbotamab vedotin (BA3011) is antibody-drug conjugate (ADC) consisting of a conditionally active biologic (CAB) anti-AXL humanized (IgG1) monoclonal antibody conjugated to monomethyl auristatin E (MMAE) using a cleavable linker. BA3011 is being developed for the treatment patients with advanced solid tumors including sarcoma with AXL expression. Methods: As of data cutoff date 19 April 2022, BA3011 was administered by intravenous infusion to 67 patients, with doses ranged from 0.3 to 3 mg/kg once every three weeks (Q3W), 1.2 to 1.8 mg/kg once every two weeks (Q2W) and 1.8 mg/kg twice every three weeks (2Q3W). Population pharmacokinetics (popPK) analysis was performed using sequential modeling approach, where a PK model was first developed for BA3011 ADC alone, and then an integrated model was developed for BA3011 ADC and MMAE. The popPK base model for BA3011 ADC and MMAE was used to generate empirical Bayes estimates of PK parameters for patients in Study BA3011-001 and used for the exposure-response (E-R) safety analysis. E-R time-to-event models were developed to characterize the relationship between BA3011 ADC or MMAE exposure and the time to first incidence of adverse events (AEs). Results: A popPK model with two-compartment disposition and parallel linear and nonlinear elimination of BA3011 ADC, formation of MMAE from ADC, and two-compartment disposition and linear elimination of MMAE, adequately described the PK profiles of BA3011 ADC and MMAE in patients based on model evaluation using prediction-corrected visual predictive checks. Key PK parameter estimates are summarized in the table below. In the E-R safety analysis, statistically significant correlations were demonstrated between BA3011 ADC exposure and all safety endpoints explored (TEAE Grade ≥3, neutropenia Grade ≥3, increase in AST or ALT Grade ≥1, anemia Grade ≥2, and peripheral neuropathy Grade ≥1). Correlations were statistically significant between MMAE exposure and TEAE Grade ≥3, neutropenia Grade ≥3, increase in AST or ALT Grade ≥1, but not anemia Grade ≥2 or peripheral neuropathy Grade ≥1. Time-varying Cmax was selected as the most appropriate exposure metric for each model developed, calculated over the past 24 hours, 1 week, or 3 weeks, depending on the model. Conclusions: The popPK relationship between ADC and MMAE were adequately characterized using a sequential modeling approach. BA3011 ADC exposure was correlated with all safety endpoints evaluated whereas MMAE exposure did not demonstrate correlations with anemia or peripheral neuropathy. These models are essential to generate predictions of exposure and safety for dosing regimens to be evaluated in a planned Phase 2 clinical trial. Clinical trial information: NCT03425279 . [Table: see text]

591. Adintrevimab (ADI) Population Pharmacokinetics (PPK) in Phase 1 and Phase 2/3 COVID-19 Prevention and Treatment Study Participants

Abstract Background ADI is a fully human IgG1 monoclonal antibody engineered to have an extended half-life with high potency and broad neutralization against SARS-CoV-2 and other SARS-like coronaviruses with pandemic potential. Our objective was to develop a PPK model that describes the serum ADI concentration-time profile following intravenous (IV) and intramuscular (IM) administration. Methods The ADI PPK model was developed on PK data from a Phase 1 single-ascending dose study (24 adults, IV and IM) and from Phase 2/3 COVID-19 prevention (EVADE; 659 adults, IM) and treatment (STAMP; 189 adults, IM) studies. 1,486 PK samples were included in the analysis. The impact of covariates (e.g. body weight, age, and baseline viral load) on ADI serum disposition were evaluated. Prediction-corrected visual predictive check (PC-VPC) plots were used to qualify the PPK model. Participant-specific ADI exposure estimates were generated using individual post hoc PK parameters. Results The PPK model is comprised of 2 systemic compartments, zero-order infusion for IV administration and first-order absorption for IM administration and provided a robust fit to the data based on the PC-VPC plots and goodness-of-fit plots (data not shown). Body weight influenced clearance, intercompartmental clearance, and central and peripheral volume compartments. The relationship between body weight and clearance was not suggestive of the need for dose adjustment over the population weight range studied (38.6 to 178.7 kg). There was no apparent impact of baseline viral load or age on ADI clearance. The median [range] half-lives in days by study; Phase 1 (α1.71 [1.18-2.46]; β 125 [117-149]), Phase 2/3 prevention (α 1.86 [0.640-3.13]; β 136 [105-209]), and Phase 2/3 treatment (α 1.89 [0.631-3.01]; β 136 [108-206]). The population mean IM bioavailability estimate was 90.5%. The figure shows the PPK model median (90% confidence interval) concentration-time profile following a single 300 mg IM ADI dose by study. Conclusion The PPK model provided a precise and unbiased fit to the observed ADI concentration-time data and will be useful for future PK-pharmacodynamic analyses. Moreover, ADI demonstrated high IM bioavailability and a median terminal elimination half-life of 125 to 136 days. Disclosures Christopher M. Rubino, PharmD, Adagio Therapeutics: Grant/Research Support|Amplyx Pharmaceuticals, Inc: Grant/Research Support|AN2 Therapeutics: Grant/Research Support|Antabio SAS: Grant/Research Support|Arcutis Biotherapeutics, Inc: Grant/Research Support|B. Braun Medical Inc.: Grant/Research Support|Basilea Pharmaceutica: Grant/Research Support|Boston Pharmaceuticals: Grant/Research Support|Bravos Biosciences: Ownership Interest|Celdara Medical LLC: Grant/Research Support|Cidara Therapeutics Inc: Grant/Research Support|Cipla USA: Grant/Research Support|Crestone Inc: Grant/Research Support|CXC: Grant/Research Support|Debiopharm International SA: Grant/Research Support|Entasis Therapeutics: Grant/Research Support|Evopoint Biosciences Co.: Grant/Research Support|Fedora Pharmaceuticals: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|Hoffmann-La Roche: Grant/Research Support|ICPD: Ownership Interest|ICPD Biosciences, LLC.: Ownership Interest|Insmed Inc.: Grant/Research Support|Iterum Therapeutics Limited: Grant/Research Support|Kaizen Bioscience, Co.: Grant/Research Support|KBP Biosciences USA: Grant/Research Support|Lassen Therapeutics: Grant/Research Support|Matinas Biopharma: Grant/Research Support|Meiji Seika Pharma Co., Ltd.: Grant/Research Support|Melinta Therapeutics: Grant/Research Support|Menarini Ricerche S.p.A: Grant/Research Support|Mutabilis: Grant/Research Support|Nabriva Therapeutics AG: Grant/Research Support|Novartis Pharmaceuticals Corp.: Grant/Research Support|Paratek Pharmaceuticals, Inc.: Grant/Research Support|PureTech Health: Grant/Research Support|Sfunga Therapeutics: Grant/Research Support|Spero Therapeutics,: Grant/Research Support|Suzhou Sinovent Pharmaceuticals Co.: Grant/Research Support|TauRx Therapeutics: Grant/Research Support|Tetraphase Pharmaceuticals: Grant/Research Support|tranScrip Partners: Grant/Research Support|Utility Therapeutics: Grant/Research Support|Valanbio Therapeutics, Inc.: Grant/Research Support|VenatoRx: Grant/Research Support|Wockhardt Bio AG: Grant/Research Support Anthony P. Cammarata, M.S., Adagio Therapeutics, Inc.: Grant/Research Support|Amplyx Pharmaceuticals, Inc.: Grant/Research Support|AN2 Therapeutics: Grant/Research Support|Antabio SAS: Grant/Research Support|Arcutis Biotherapeutics, Inc.: Grant/Research Support|B. Braun Medical Inc.: Grant/Research Support|Basilea Pharmaceutica: Grant/Research Support|Boston Pharmaceuticals: Grant/Research Support|Celdara Medical LLC: Grant/Research Support|Cidara Therapeutics Inc.: Grant/Research Support|Cipla USA: Grant/Research Support|Crestone Inc.: Grant/Research Support|CXC: Grant/Research Support|Debiopharm International SA: Grant/Research Support|Entasis Therapeutics: Grant/Research Support|Evopoint Biosciences Co.: Grant/Research Support|Fedora Pharmaceuticals: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|Hoffmann-La Roche: Grant/Research Support|Insmed Inc.: Grant/Research Support|Iterum Therapeutics Limited: Grant/Research Support|Kaizen Bioscience, Co.: Grant/Research Support|KBP Biosciences USA: Grant/Research Support|Lassen Therapeutics: Grant/Research Support|Matinas Biopharma: Grant/Research Support|Meiji Seika Pharma Co., Ltd.: Grant/Research Support|Melinta Therapeutics: Grant/Research Support|Menarini Ricerche S.p.A.: Grant/Research Support|Mutabilis: Grant/Research Support|Nabriva Therapeutics AG: Grant/Research Support|Novartis Pharmaceuticals Corp.: Grant/Research Support|Paratek Pharmaceuticals, Inc.: Grant/Research Support|PureTech Health: Grant/Research Support|Sfunga Therapeutics: Grant/Research Support|Spero Therapeutics: Grant/Research Support|Suzhou Sinovent Pharmaceuticals Co.: Grant/Research Support|TauRx Therapeutics: Grant/Research Support|Tetraphase Pharmaceuticals: Grant/Research Support|tranScrip Partners: Grant/Research Support|Utility Therapeutics: Grant/Research Support|Valanbio Therapeutics, Inc.: Grant/Research Support|VenatoRx: Grant/Research Support|Wockhardt Bio AG: Grant/Research Support Paul Ambrose, PharmD, Adagio Therapeutics: Employee|Adagio Therapeutics: Stocks/Bonds|Institute for Clinical Pharmacodynamics: President Pete Schmidt, MD, Adagio Therapeutics: Employee|Adagio Therapeutics: Stocks/Bonds Myra Popejoy, PharmD, Adagio Therapeutics: Employee|Adagio Therapeutics: Stocks/Bonds Jean Gong, PhD, Adagio Therapeutics: Employee|Adagio Therapeutics: Stocks/Bonds Xia Pu, PhD, Adagio Therapeutics: Employee|Adagio Therapeutics: Stocks/Bonds.

Prospective population pharmacokinetic study of tacrolimus in adult recipients early after liver transplantation: A comparison of Michaelis-Menten and theory-based pharmacokinetic models.

Background and Objective: Tacrolimus, a calcineurin inhibitor widely used as a potent immunosuppressant to prevent graft rejection, exhibits nonlinear kinetics in patients with kidney transplantation and nephrotic syndrome. However, whether nonlinear drug metabolism occurs in adult patients undergoing liver transplantation remains unclear, as do the main underlying mechanisms. Therefore, here we aimed to further confirm the characteristics of nonlinearity through a large sample size, and determine the potential influence of nonlinearity and its possible mechanisms. Methods: In total, 906 trough concentrations from 176 adult patients (150 men/26 women; average age: 50.68 ± 9.71 years, average weight: 64.54 ± 11.85kg after first liver transplantation) were included in this study. Population pharmacokinetic analysis was performed using NONMEM®. Two modeling strategies, theory-based linear compartmental and nonlinear Michaelis-Menten (MM) models, were evaluated and compared. Potential covariates were screened using a stepwise approach. Bootstrap, prediction-, and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine model stability and predictive performance. Finally, Monte Carlo simulations based on the superior model were conducted to design dosing regimens. Results: Postoperative days (POD), Aspartate aminotransferase (AST), daily tacrolimus dose, triazole antifungal agent (TAF) co-therapy, and recipient CYP3A5*3 genotype constituted the main factors in the theory-based compartmental final model, whereas POD, Total serum bilirubin (TBIL), Haematocrit (HCT), TAF co-therapy, and recipient CYP3A5*3 genotype were important in the nonlinear MM model. The theory-based final model exhibited 234Lh-1 apparent plasma clearance and 11,000L plasma distribution volume. The maximum dose rate (V max ) of the nonlinear MM model was 6.62 mgday-1; the average concentration at steady state at half-V max (K m ) was 6.46ngml-1. The nonlinear MM final model was superior to the theory-based final model and used to propose dosing regimens based on simulations. Conclusion: Our findings demonstrate that saturated tacrolimus concentration-dependent binding to erythrocytes and the influence of daily tacrolimus dose on metabolism may partly contribute to nonlinearity. Further investigation is needed is need to explore the causes of nonlinear pharmacokinetic of tacrolimus. The nonlinear MM model can provide reliable support for tacrolimus dosing optimization and adjustment in adult patients undergoing liver transplantation.

External Evaluation of Risperidone Population Pharmacokinetic Models Using Opportunistic Pediatric Data.

Risperidone is approved to treat schizophrenia in adolescents and autistic disorder and bipolar mania in children and adolescents. It is also used off-label in younger children for various psychiatric disorders. Several population pharmacokinetic models of risperidone and 9-OH-risperidone have been published. The objectives of this study were to assess whether opportunistically collected pediatric data can be used to evaluate risperidone population pharmacokinetic models externally and to identify a robust model for precision dosing in children. A total of 103 concentrations of risperidone and 112 concentrations of 9-OH-risperidone, collected from 62 pediatric patients (0.16–16.8 years of age), were used in the present study. The predictive performance of five published population pharmacokinetic models (four joint parent-metabolite models and one parent only) was assessed for accuracy and precision of the predictions using statistical criteria, goodness of fit plots, prediction-corrected visual predictive checks (pcVPCs), and normalized prediction distribution errors (NPDEs). The tested models produced similarly precise predictions (Root Mean Square Error [RMSE]) ranging from 0.021 to 0.027 nmol/ml for risperidone and 0.053–0.065 nmol/ml for 9-OH-risperidone). However, one of the models (a one-compartment mixture model with clearance estimated for three subpopulations) developed with a rich dataset presented fewer biases (Mean Percent Error [MPE, %] of 1.0% vs. 101.4, 146.9, 260.4, and 292.4%) for risperidone. In contrast, a model developed with fewer data and a more similar population to the one used for the external evaluation presented fewer biases for 9-OH-risperidone (MPE: 17% vs. 69.9, 47.8, and 82.9%). None of the models evaluated seemed to be generalizable to the population used in this analysis. All the models had a modest predictive performance, potentially suggesting that sources of inter-individual variability were not entirely captured and that opportunistic data from a highly heterogeneous population are likely not the most appropriate data to evaluate risperidone models externally.

Evaluation and Application of Population Pharmacokinetic Models for Identifying Delayed Methotrexate Elimination in Patients With Primary Central Nervous System Lymphoma

Objective: Several population pharmacokinetic (popPK) models have been developed to determine the sources of methotrexate (MTX) PK variability. It remains unknown if these published models are precise enough for use or if a new model needs to be built. The aims of this study were to 1) assess the predictability of published models and 2) analyze the potential risk factors for delayed MTX elimination. Methods: A total of 1458 MTX plasma concentrations, including 377 courses (1–17 per patient), were collected from 77 patients who were receiving high-dose MTX for the treatment of primary central nervous system lymphoma in Huashan Hospital. PopPK analysis was performed using the NONMEM® software package. Previously published popPK models were selected and rebuilt. A new popPK model was then constructed to screen potential covariates using a stepwise approach. The covariates were included based on the combination of theoretical mechanisms and data properties. Goodness-of-fit plots, bootstrap, and prediction- and simulation-based diagnostics were used to determine the stability and predictive performance of both the published and newly built models. Monte Carlo simulations were conducted to qualify the influence of risk factors on the incidence of delayed elimination. Results: Among the eight evaluated published models, none presented acceptable values of bias or inaccuracy. A two-compartment model was employed in the newly built model to describe the PK of MTX. The estimated mean clearance (CL/F) was 4.91 L h−1 (relative standard error: 3.7%). Creatinine clearance, albumin, and age were identified as covariates of MTX CL/F. The median and median absolute prediction errors of the final model were -10.2 and 36.4%, respectively. Results of goodness-of-fit plots, bootstrap, and prediction-corrected visual predictive checks indicated the high predictability of the final model. Conclusions: Current published models are not sufficiently reliable for cross-center use. The elderly patients and those with renal dysfunction, hypoalbuminemia are at higher risk of delayed elimination.

Population Pharmacokinetics of Cotadutide in Subjects with Type 2 Diabetes.

Cotadutide is a balanced dual glucagon-like peptide-1/glucagon receptor agonist under development for the treatment of nonalcoholic steatohepatitis and chronic kidney disease with type 2 diabetes. The objectives of the analysis were to characterize the population pharmacokinetics of cotadutide following daily subcutaneous injection in subjects with type 2 diabetes and to evaluate the effect of demographic and clinical variables of interest on cotadutide pharmacokinetics. This study analyzed 8834 plasma concentrations of cotadutide from 759 subjects with type 2 diabetes who received daily subcutaneous doses from 20 to 600 μg from six clinical studies. The impact of covariates on cotadutide pharmacokinetics was quantified, and body weight effect on cotadutide exposure was further evaluated using a simulation approach. The model performance was evaluated through prediction-corrected visual predictive checks. A one-compartment model with first-order absorption and elimination described cotadutide pharmacokinetic data well. The mean values for cotadutide apparent clearance, apparent distribution volume, absorption rate constant, and half-life were 1.04 L/h (interindividual variability [IIV]: 26.5%), 18.7 L (IIV: 28.7%), 0.343 h-1 (IIV: 38.6%), and 12.9 h, respectively. Higher body weight, lower albumin, and higher alanine aminotransferase were associated with an increase in cotadutide clearance, while an increase in anti-drug antibody titers was associated with a decrease in cotadutide clearance. These statistically significant effects were not considered clinically significant and did not warrant dose adjustment. Effects of other tested baseline covariates (age, sex, body mass index, hemoglobin A1c, renal function, duration of diabetes) were not found to statistically significantly affect cotadutide pharmacokinetics. Cotadutide pharmacokinetics was adequately described by a one-compartment linear model with first-order absorption and elimination. Body weight-based dosing is not necessary for cotadutide based on the simulation using the final population pharmacokinetic modeling. This model will be used to evaluate exposure-response relationships for efficacy and safety in different indications that are being studied for cotadutide.

External Evaluation of a Bayesian Warfarin Dose Optimization Based on a Kinetic-Pharmacodynamic Model.

Warfarin is a representative anticoagulant with large interindividual variability. The published kinetic-pharmacodynamic (K-PD) model allows the prediction of warfarin dose requirement in Swedish patients; however, its applicability in Japanese patients is not known. We evaluated the model's predictive performance in Japanese patients with various backgrounds and relationships using Bayesian parameter estimation and sampling times. A single-center retrospective observational study was conducted at Tokyo Women's Medical University, Medical Center East. The study population consisted of adult patients aged >20 years who commenced warfarin with a prothrombin time-international normalized ratio (PT-INR) from June 2015 to June 2019. The published K-PD model modified by Wright and Duffull was assessed using prediction-corrected visual predictive checks, focusing on clinical characteristics, including age, renal function, and individual prediction error. The external dataset included 232 patients who received an initial warfarin daily dose of 3.2 ± 1.28 mg with 2278 PT-INR points (median [range] follow-up period of 23 d [7-28]). Prediction-corrected visual predictive checks carried a propensity for underprediction. Additionally, age >60 years, body mass index ≤25 kg/m2, and estimated glomerular filtration rate ≤60 mL/min/1.73 m2 had a pronounced tendency to underpredict PT-INR. However, Bayesian prediction using four prior observations reduced underprediction. To improve the prediction performance of these special populations, further studies are required to construct a model to predict warfarin dose requirements in Japanese patients.

1105. Population Pharmacokinetic Analyses for Tebipenem After the Administration of Tebipenem Pivoxil Hydrobromide

1105. Population Pharmacokinetic Analyses for Tebipenem After the Administration of Tebipenem Pivoxil Hydrobromide

An Enterohepatic Recirculation Population Pharmacokinetic and MIC-1 Pharmacokinetic-Pharmacodynamic Model for the MDM2 Inhibitor Navtemadlin (KRT-232) in Healthy Subjects

Background: Murine double minute 2 (MDM2) is the primary negative regulator of the tumor suppressor protein, p53. Navtemadlin (KRT-232), a potent and selective, orally available MDM2 inhibitor restores p53 activity to drive apoptosis of cancer cells in TP53 WT malignancies. Navtemadlin is currently being evaluated in a phase 3 trial of patients with relapsed or refractory myelofibrosis, as well as in numerous phase 1b/2 trials in various hematologic malignancies and solid tumors. Serum macrophage inhibitor cytokine-1 (MIC-1) is a pharmacodynamic (PD) marker of p53-mediated activity in patients treated with navtemadlin (Allard et al. HemaSphere. 2020). Using pharmacokinetic (PK) and PD data from a healthy subject food effect study (Wong et al. Blood. 2020), we developed a population PK (PPK) model that characterized enterohepatic recirculation (EHR) as a half-life extending element in the PK profiles of navtemadlin and its major acyl glucuronide metabolite M1. MIC-1 PD data were incorporated into the model to quantify plasma concentration-driven MIC-1 excursions and to simulate PK and PD across time and dose in healthy subjects.Methods: PPK and PK-PD models were developed using the first-order conditional estimation with interaction (FOCE-I) method in NONMEM 7.4, with model covariates selected using a stepwise forward addition and backward elimination method based on a 5% significance level. Model quality was checked by inspecting model parameters and confidence intervals, as well as standard residual-based and simulation-based diagnostics, and prediction-corrected visual predictive checks. Navtemadlin plasma concentration and MIC-1 serum concentration-time data from the food effect study (KRT-232-105) were modeled (N=30 subjects after a single 60 mg navtemadlin dose). Candidate PPK semi-mechanistic models that described EHR with multi-compartment structures (gut, central, and peripheral compartments for navtemadlin, and central and gallbladder [GB] compartments for M1), first-order elimination, and mealtime effects on GB emptying were tested. Post hoc parameter estimates from the final PPK model were used to generate individual predicted navtemadlin plasma concentrations for the PK-PD model. Based on exploratory plots, the pharmacological mechanism of action of navtemadlin, and a bile acid recycling model (Guiastrennec et al. CPT Pharmacometrics Syst Pharmacol. 2018), an indirect response equation was selected for the MIC-1 effect compartment (Figure 1a).Results: Navtemadlin and M1 plasma concentrations, including a second peak attributed to EHR at ~8-12 h, were well described by a model with central and peripheral compartments, constant basal M1 release rate into bile (KBR BASAL), and incremental mealtime GB emptying rate (KBR MEAL, Figure 1a). Figure 1b shows simulated navtemadlin and M1 amounts in various compartments over time. Median oral clearance of navtemadlin was estimated at 36.35 L/h. Estimated median apparent oral clearance of navtemadlin in healthy subjects was higher than PPK estimates for patients with advanced solid tumors (24.9 L/h [Ma et al. Blood. 2019]). The median central and peripheral volumes of navtemadlin were 159 L and 390 L, respectively. Navtemadlin exposure was higher in healthy female subjects relative to male subjects. Between-subject variability in clearance was 31%. Typical MIC-1 maximum stimulatory effect (S max) was estimated at 6.82, close to the median maximum ratio of MIC-1 to baseline MIC-1 (7.29) in the observed data. SC 50 was estimated at 85.22 ng/mL, with a Hill coefficient of 2.02, indicating a relatively steep increase in MIC-1 serum concentration with increasing navtemadlin concentration. For both PPK and PK-PD models, diagnostic plots confirmed an adequate fit. Subjects with lower baseline MIC-1 had a larger response and reached a maximum MIC-1 concentration later. Older subjects had the largest covariate impact, with a higher MIC-1 response.Conclusion: A two-compartment PPK model with basal and incremental mealtime GB emptying rates captured concentration-time data for navtemadlin and its metabolite M1. EHR was evident and navtemadlin reabsorption following hydrolysis of biliary M1 in the intestine contributed to navtemadlin half-life. An indirect stimulatory PK-PD model effectively described the relationship between navtemadlin and MIC-1 in healthy subjects. [Display omitted] DisclosuresZhang: Certara, Inc.: Current Employment; Milad Pharmaceutical Consulting, LLC.: Ended employment in the past 24 months. Wong: Kartos Therapeutics: Current Employment; AbbVie Biotherapeutics: Current equity holder in publicly-traded company. Slatter: Telios Pharma: Current holder of stock options in a privately-held company; Kartos Therapeutics: Current Employment, Current holder of stock options in a privately-held company; AstraZeneca: Current equity holder in publicly-traded company; Amgen: Divested equity in a private or publicly-traded company in the past 24 months. OffLabel Disclosure:Yes, navtemadlin (KRT-232) is an investigational small molecule MDM2 inhibitor.

Population pharmacokinetics of meropenem in critically ill infant patients

BackgroundPopulation pharmacokinetic analysis in critically ill infants remains a challenge for lack of information. ObjectivesTo determine the population pharmacokinetic parameters of meropenem and evaluate the covariates affecting population pharmacokinetic parameters. MethodsA prospective study was conducted on 35 patients. A total of 160 blood samples were collected and determined free of drug concentrations of meropenem. Population pharmacokinetic data were analyzed using NONMEM software. Internal validation methods, including bootstrapping and prediction-corrected visual predictive checks, were applied to evaluate the robustness and predictive power of the final model. ResultsA one-compartment model with first-order elimination showed the best fit to the data.The typical clearance (CL) values and volume of distribution (Vd) were 1.33 L/h and 2.27 L, respectively. Weight and creatinine clearance were influential covariates for CL, while weight was a significant covariate for Vd of meropenem. The model evaluation results suggested robustness and good predictability of the final model. The standard dosage regimens of meropenem achieved 40% f T>MIC but not enough if a more aggressive target of 80% f T>MIC at MIC value of ≥ 16 µg/mL is desired. ConclusionsThis population pharmacokinetic model could be used for suggesting individualized meropenem dosage regimens in critically ill infants.

External Evaluation of Population Pharmacokinetic Models and Bayes-Based Dosing of Infliximab.

Despite the well-demonstrated efficacy of infliximab in inflammatory diseases, treatment failure remains frequent. Dose adjustment using Bayesian methods has shown in silico its interest in achieving target plasma concentrations. However, most of the published models have not been fully validated in accordance with the recommendations. This study aimed to submit these models to an external evaluation and verify their predictive capabilities. Eight models were selected for external evaluation, carried out on an independent database (409 concentrations from 157 patients). Each model was evaluated based on the following parameters: goodness-of-fit (comparison of predictions to observations), residual error model (population weighted residuals (PWRES), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE)), and predictive performances (prediction-corrected visual predictive checks (pcVPC) and Bayesian simulations). The performances observed during this external evaluation varied greatly from one model to another. The eight evaluated models showed a significant bias in population predictions (from −7.19 to 7.38 mg/L). Individual predictions showed acceptable bias and precision for six of the eight models (mean error of −0.74 to −0.29 mg/L and mean percent error of −16.6 to −0.4%). Analysis of NPDE and pcVPC confirmed these results and revealed a problem with the inclusion of several covariates (weight, concomitant immunomodulatory treatment, presence of anti-drug antibodies). This external evaluation showed satisfactory results for some models, notably models A and B, and highlighted several prospects for improving the pharmacokinetic models of infliximab for clinical-biological application.

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.

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).

Incorporating nonlinear kinetics to improve predictive performance of population pharmacokinetic models for ciclosporin in adult renal transplant recipients: A comparison of modelling strategies

BackgroundCiclosporin has been shown to follow nonlinear pharmacokinetics (PK) in renal transplant recipients who received ciclosporin (NeoralⓇ, Novartis)-based triple immunosuppressive therapy. Some of these nonlinear properties have not been fully considered in population PK (popPK) analysis. Therefore, the aim of this study was to determine the potential influence of nonlinearity and the functional forms of covariates on model predictability as well as to analyse multiple nonlinear factors in the in vivo process. MethodsA total of 2969 ciclosporin whole-blood measurements, including 1328 pre-dose and 1641 2-h post-dose concentrations, were collected from 173 patients who underwent their first renal transplantation. Four popPK models based on different modelling strategies were developed to investigate the discrepancy between empirical and theory-based, linear and nonlinear compartmental kinetic models and empirical formulae on model predictability. Prediction and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine the stability and predictive performance of these four models. ResultsModel predictability improved when nonlinearity was considered. The theory-based nonlinear model which incorporated nonlinear property based on known theoretical relationships performed better than the other two compartmental models. The nonlinear Michaelis-Menten model showed a remarkable improvement in predictive performance compared to the other three compartmental models. The saturated binding of ciclosporin to erythrocytes, auto-inhibition induced by the inhibitory effects of ciclosporin on cytochrome P450 3A4/P-glycoprotein may have contributed to the nonlinearity. Ciclosporin-prednisolone drug interaction should be given serious consideration in clinical settings. ConclusionsIncorporation of nonlinear properties is likely to be a promising approach for improving ciclosporin model predictability. Theory-based modelling is helpful to improve model predictability. However, ciclosporin nonlinear kinetics resources need further investigation.

Development and evaluation of the population pharmacokinetic models for FVIII and FIX concentrates of the WAPPS-Hemo project.

The Web-Accessible Population Pharmacokinetic Service (WAPPS) project generates individually predicted pharmacokinetic (PK) profiles and tailored prophylactic treatment regimens for haemophilic patients, which rely on a set of population PK (PopPK) models providing concentrate-specific priors for the Bayesian forecasting methodology. To describe the predictive performance of the WAPPS PopPK models in use on the WAPPS-Hemo platform. Data for modelling include dense PK data obtained from industry sponsored and independent PK studies, and dense and sparse data accumulated through WAPPS-Hemo. WAPPS PopPK models were developed via non-linear mixed-effect modelling taking into account the effects of covariates and between-individual-and sometimes between-occasion-variability. Model evaluation consisted of (a) prediction-corrected Visual Predictive Check (pcVPC), (b) Limited Sampling Analysis (LSA) and (c) repeated hold-out cross-validation. Thirty-three WAPPS PopPK models built on data from 3188 patients (ages 1-78years) under treatment by factor VIII or IX products (FVIII, FIX) were evaluated. Overall, models exhibit excellent performance characteristics. The pcVPC shows that the observed PK data fall within acceptable 90% interpercentile predictive bands. A slight overprediction beyond the expected half-life, an anticipated result of using sparse data, occurs for some models. The LSA results in lower than 3% of relative error for FVIII and FIX products and 16% for engineered FIX products. Cross-Validation analysis yields relative errors lower than 1.5% and 1.4% in estimates of half-life and time to 0.02IU/mL, respectively. The WAPPS-Hemo models consistently showed excellent performance characteristics for the intended use for Bayesian forecasting of individual PK profiles.