Semi-mechanistic population pharmacokinetic/pharmacodynamic modeling of a Plasmodium elongation factor 2 inhibitor cabamiquine for prevention and cure of malaria.

Cabamiquine is a novel antimalarial agent that demonstrates the potential for chemoprevention and treatment of malaria. In this article, the dose-exposure-response relationship of cabamiquine was characterized using a population pharmacokinetic (PK)/pharmacodynamic (PD) model, incorporating the effects of cabamiquine on parasite dynamics at the liver and blood stages of malaria infection. Modeling was performed sequentially. First, a three-compartmental population PK model was developed, comprising linear elimination, a transit absorption model in combination with first-order absorption, and a recirculation model. Second, this model was expanded into a PK/PD model using parasitemia data from an induced blood stage malaria (IBSM) human challenge model. To describe the parasite growth and killing in the blood, a turnover model was used. Finally, the liver stage parasite dynamics were characterized using data from a sporozoite challenge model (SpzCh), and system parameters were fixed based on biological plausibility. Cabamiquine concentration in the central compartment was used to drive parasite killing at the blood and liver stages. Blood stage minimum inhibitory concentrations (MICb) were estimated at 7.12 ng/mL [95% confidence interval (CI95%): 6.26-7.88 ng/mL] and 1.28 ng/mL (CI95%: 1.12-1.43 ng/mL) for IBSM and SpzCh populations, respectively, while liver stage MICl was lower (0.61 ng/mL; CI95%: 0.24-0.96 ng/mL). In conclusion, a population PK/PD model was developed by incorporating parasite dynamics and drug activity at the blood and liver stages based on clinical data and biological knowledge. This model can potentially facilitate antimalarial agent development by supporting the efficient selection of the optimal dosing regimen.

Covariate modeling in pharmacometrics: General points for consideration.

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

Pharmacometrics in tuberculosis: progress and opportunities.

Tuberculosis (TB) remains one of the leading causes of death by a communicable agent, infecting up to one-quarter of the world's population, predominantly in disadvantaged communities. Pharmacometrics employ quantitative mathematical models to describe the relationships between pharmacokinetics and pharmacodynamics, and to predict drug doses, exposures and responses. Pharmacometric approaches have provided a scientific basis for improved dosing of anti-TB drugs and concomitantly administered antiretrovirals at the population level. The development of modelling frameworks including physiologically based pharmacokinetics, quantitative systems pharmacology and machine learning provides an opportunity to extend the role of pharmacometrics to in-silico quantification of drug-drug interactions, prediction of doses for special populations, dose optimization and individualization, and understanding the complex exposure-response relationships of multi-drug regimens in terms of both efficacy and safety, informing regimen design for future study. This short, clinically focused review explores what has been done, and what opportunities exist for pharmacometrics to impact TB pharmacotherapy.

Model-informed approach for risk management of bleeding toxicities for bintrafusp alfa, a bifunctional fusion protein targeting TGF-ß and PD-L1.

PURPOSE: Bintrafusp alfa (BA) is a bifunctional fusion protein composed of the extracellular domain of the transforming growth factor-ß (TGF-ß) receptor II fused to a human immunoglobulin G1 antibody blocking programmed death ligand 1 (PD-L1). The recommended phase 2 dose (RP2D) was selected based on phase 1 efficacy, safety, and pharmacokinetic (PK)-pharmacodynamic data, assuming continuous inhibition of PD-L1 and TGF-ß is required. Here, we describe a model-informed dose modification approach for risk management of BA-associated bleeding adverse events (AEs). METHODS: The PK and AE data from studies NCT02517398, NCT02699515, NCT03840915, and NCT04246489 (n = 936) were used. Logistic regression analyses were conducted to evaluate potential relationships between bleeding AEs and BA time-averaged concentration (Cavg), derived using a population PK model. The percentage of patients with trough concentrations associated with PD-L1 or TGF-ß inhibition across various dosing regimens was derived. RESULTS: The probability of bleeding AEs increased with increasing Cavg; 50% dose reduction was chosen based on the integration of modeling and clinical considerations. The resulting AE management guidance to investigators regarding temporary or permanent treatment discontinuation was further refined with recommendations on restarting at RP2D or at 50% dose, depending on the grade and type of bleeding (tumoral versus nontumoral) and investigator assessment of risk of additional bleeding. CONCLUSION: A pragmatic model-informed approach for management of bleeding AEs was implemented in ongoing clinical trials of BA. This approach is expected to improve benefit-risk profile; however, its effectiveness will need to be evaluated based on safety data generated after implementation.

Characterization of exposure-response relationships of ipatasertib in patients with metastatic castration-resistant prostate cancer in the IPATential150 study.

PURPOSE: The exposure-response relationships for efficacy and safety of ipatasertib, a selective AKT kinase inhibitor, were characterized using data collected from 1101 patients with metastatic castration-resistant prostate cancer in the IPATential150 study (NCT03072238). METHODS: External validation of a previously developed population pharmacokinetic model was performed using the observed pharmacokinetic data from the IPATential150 study. Exposure metrics of ipatasertib for subjects who received ipatasertib 400 mg once-daily orally in this study were generated as model-predicted area under the concentration-time curve at steady state (AUCSS). The exposure-response relationship with radiographic progression-free survival (rPFS) was evaluated using Cox regression and relationships with safety endpoints were assessed using logistic regression. RESULTS: A statistically significant correlation between ipatasertib AUCSS and improved survival was found in patients with PTEN-loss tumors (hazard ratio [HR]: 0.92 per 1000 ng h/mL AUCSS, 95% confidence interval [CI] 0.87-0.98, p = 0.011). In contrast, an improvement in rPFS was seen in subjects receiving ipatasertib treatment (HR: 0.84, 95% CI 0.71-0.99, p = 0.038) but this effect was not associated with ipatasertib AUCSS in the intention-to-treat population. Incidences of some adverse events (AEs) had statistically significant association with ipatasertib AUCSS (serious AEs, AEs leading to discontinuation, and Grade ≥ 2 hyperglycemia), while others were associated with only ipatasertib treatment (AEs leading to dose reduction, Grade ≥ 3 diarrhea, and Grade ≥ 2 rash). CONCLUSIONS: The exposure-efficacy results indicated that patients receiving ipatasertib may continue benefiting from this treatment at the administered dose, despite some variability in exposures, while the exposure-safety results suggested increased risks of AEs with ipatasertib treatment and/or increased ipatasertib exposures.

Variability in the population pharmacokinetics of isoniazid in South African tuberculosis patients.

AIM: This study was designed to characterize the population pharmacokinetics of isoniazid in South African pulmonary tuberculosis patients. METHODS: Concentration-time measurements obtained from 235 patients receiving oral doses of isoniazid as part of routine tuberculosis chemotherapy in two clinical studies were pooled and subjected to nonlinear mixed-effects analysis. RESULTS: A two-compartmental model, including first-order absorption and elimination with allometric scaling, was found to describe the observed dose-exposure relationship for oral isoniazid adequately. A mixture model was used to characterize dual rates of isoniazid elimination. Estimates of apparent clearance in slow and fast eliminators were 9.70 and 21.6 l h(-1) , respectively. The proportion of fast eliminators in the population was estimated to be 13.2%. Central volume of distribution was estimated to be 10% smaller in female patients and clearance was found to be 17% lower in patients with HIV. Variability in absorption rate (90%) was completely interoccasional in nature, whereas in relative bioavailability, interoccasional variability (8.4%) was lower than interindividual variability (26%). Oral doses, given once daily according to dosing policies at the time, were sufficient to reach therapeutic concentrations in the majority of the studied population, regardless of eliminator phenotype. Simulations suggested that current treatment guidelines (5 mg kg(-1) ) may be suboptimal in fast eliminators with low body weight. CONCLUSIONS: A population pharmacokinetic model was developed to characterize the highly variable pharmacokinetics of isoniazid in a South African pulmonary tuberculosis patient population. Current treatment guidelines may lead to underexposure in rapid isoniazid eliminators.

Changing Body Weight-Based Dosing to a Flat Dose for Avelumab in Metastatic Merkel Cell and Advanced Urothelial Carcinoma.

Avelumab, an anti-programmed death-ligand 1 monoclonal antibody approved for the treatment of metastatic Merkel cell carcinoma and platinum-treated urothelial carcinoma, was initially approved with a 10 mg/kg weight-based dose. We report pharmacokinetic (PK)/pharmacodynamic analyses for avelumab comparing weight-based dosing and a flat 800 mg dose, developed using data from 1,827 patients enrolled in 3 clinical trials (NCT01772004, NCT01943461, and NCT02155647). PK metrics were simulated for weight-based and flat-dosing regimens and summarized by quartiles of weight. Derived exposure metrics were used in simulations of exposure-safety (various tumors) and exposure-efficacy (objective responses; Merkel cell or urothelial carcinoma). Flat dosing was predicted to provide similar exposure to weight-based dosing, with slightly lower variability. Exposure-safety and exposure-efficacy simulations suggested similar benefit:risk profiles for the two dosing regimens. These pharmacometric analyses provided the basis for the US Food and Drug Administration approval of a flat dose of avelumab 800 mg every 2 weeks in approved indications.

Evaluation of the nonparametric estimation method in NONMEM VI: application to real data.

The aim of the study was to evaluate the nonparametric estimation methods available in NONMEM VI in comparison with the parametric first-order method (FO) and the first-order conditional estimation method (FOCE) when applied to real datasets. Four methods for estimating model parameters and parameter distributions (FO, FOCE, nonparametric preceded by FO (FO-NONP) and nonparametric preceded by FOCE (FOCE-NONP)) were compared for 25 models previously developed using real data and a parametric method. Numerical predictive checks were used to test the appropriateness of each model. Up to 1000 new datasets were simulated from each model and with each method to construct 90% and 50% prediction intervals. The mean absolute error and the mean error of the different outcomes investigated were computed as indicators of imprecision and bias respectively and formal statistical tests were performed. Overall, less imprecision and less bias were observed with nonparametric methods than with parametric methods. Across the 25 models, t-tests revealed that imprecision and bias were significantly lower (P < 0.05) with FOCE-NONP than with FOCE for half of the NPC outcomes investigated. Improvements were even more pronounced with FO-NONP in comparison with FO. In conclusion, when applied to real datasets and evaluated by numerical predictive checks, the nonparametric estimation methods in NONMEM VI performed better than the corresponding parametric methods (FO or FOCE).

Population Pharmacokinetic Analysis of Bintrafusp Alfa in Different Cancer Types.

INTRODUCTION: Bintrafusp alfa, an innovative first-in-class bifunctional fusion protein composed of the extracellular domain of the TGF-ßRII receptor (a TGF-ß "trap") fused to a human IgG1 monoclonal antibody blocking programmed death ligand 1, has shown promising antitumor activity and manageable safety. METHODS: To support the dosing strategy for bintrafusp alfa, we developed a population pharmacokinetics model using a full covariate modeling approach, based on pharmacokinetic and covariate data from 644 patients with various solid tumors who received bintrafusp alfa intravenously in two clinical studies. RESULTS: A two-compartmental linear model best described bintrafusp alfa concentrations, and no time-varying clearance was identified. Using this model, the estimated clearance was 0.0158 l/h (relative standard error, 4.1%), and the central and peripheral volume of distribution were 3.21 l (relative standard error, 3.2%) and 0.483 l (relative standard error, 9.8%), respectively. The estimated mean elimination half-life of bintrafusp alfa was 6.93 days (95% CI 4.69-9.65 days). Several intrinsic factors (bodyweight, albumin, sex, and tumor type) were found to influence bintrafusp alfa pharmacokinetics, but none of these covariate effects was considered clinically meaningful and no dosage adjustments are recommended. Notably, simulations from the model suggested less variability in exposure metrics with flat dosing versus weight-based dosing. CONCLUSIONS: Pharmacokinetic analysis of bintrafusp alfa supports the use of a flat dose regimen in further clinical trials (recommended phase 2 dose: 1200 mg every 2 weeks). TRIAL REGISTRATION: ClinicalTrials.gov identifiers: NCT02517398 and NCT02699515. FUNDING: Merck Healthcare KGaA as part of an alliance between Merck Healthcare KGaA and GlaxoSmithKline.

Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages.

nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.

Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.

The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.

Time-Varying Clearance and Impact of Disease State on the Pharmacokinetics of Avelumab in Merkel Cell Carcinoma and Urothelial Carcinoma.

Avelumab, a human anti-programmed death ligand 1 immunoglobulin G1 antibody, has shown efficacy and manageable safety in multiple tumors. A two-compartment population pharmacokinetic model for avelumab incorporating intrinsic and extrinsic covariates and time-varying clearance (CL) was identified based on data from 1,827 patients across three clinical studies. Of 14 tumor types, a decrease in CL over time was more notable in metastatic Merkel cell carcinoma and squamous cell carcinoma of the head and neck, which had maximum decreases of 32.1% and 24.7%, respectively. The magnitude of reduction in CL was higher in responders than in nonresponders. Significant covariate effects of baseline weight, baseline albumin, and sex were identified on both CL and central distribution volume. Significant covariate effects of black/African American race, C-reactive protein, and immunogenicity were found on CL. None of the covariate or time-dependent effects were clinically important or warranted dose adjustment.

Population pharmacokinetics of rifampin in pulmonary tuberculosis patients, including a semimechanistic model to describe variable absorption.

This article describes the population pharmacokinetics of rifampin in South African pulmonary tuberculosis patients. Three datasets containing 2,913 rifampin plasma concentration-time data points, collected from 261 South African pulmonary tuberculosis patients aged 18 to 72 years and weighing 28.5 to 85.5 kg and receiving regular daily treatment that included administration of rifampin (450 to 600 mg) for at least 10 days, were pooled. A compartmental pharmacokinetic model was developed using nonlinear mixed-effects modeling. Variability in the shape of the absorption curve was described using a flexible transit compartment model, in which a delay in the onset of absorption and a gradually changing absorption rate were modeled as the passage of drug through a chain of hypothetical compartments, ultimately reaching the absorption compartment. A previously described implementation was extended to allow its application to multiple-dosing data. The typical population estimate of oral clearance was 19.2 liters x h(-1), while the volume of distribution was estimated to be 53.2 liters. Interindividual variability was estimated to be 52.8% for clearance and 43.4% for volume of distribution. Interoccasional variability was estimated for CL/F (22.5%) and mean transit time during absorption (67.9%). The use of single-drug formulations was found to increase both the mean transit time (by 104%) and clearance (by 23.6%) relative to fixed-dose-combination use. A strong correlation between clearance and volume of distribution suggested substantial variability in bioavailability, which could have clinical implications, given the dependence of treatment effectiveness on exposure. The final model successfully described rifampin pharmacokinetics in the population studied and is suitable for simulation in this context.

Open innovation: Towards sharing of data, models and workflows.

Sharing of resources across organisations to support open innovation is an old idea, but which is being taken up by the scientific community at increasing speed, concerning public sharing in particular. The ability to address new questions or provide more precise answers to old questions through merged information is among the attractive features of sharing. Increased efficiency through reuse, and increased reliability of scientific findings through enhanced transparency, are expected outcomes from sharing. In the field of pharmacometrics, efforts to publicly share data, models and workflow have recently started. Sharing of individual-level longitudinal data for modelling requires solving legal, ethical and proprietary issues similar to many other fields, but there are also pharmacometric-specific aspects regarding data formats, exchange standards, and database properties. Several organisations (CDISC, C-Path, IMI, ISoP) are working to solve these issues and propose standards. There are also a number of initiatives aimed at collecting disease-specific databases - Alzheimer's Disease (ADNI, CAMD), malaria (WWARN), oncology (PDS), Parkinson's Disease (PPMI), tuberculosis (CPTR, TB-PACTS, ReSeqTB) - suitable for drug-disease modelling. Organized sharing of pharmacometric executable model code and associated information has in the past been sparse, but a model repository (DDMoRe Model Repository) intended for the purpose has recently been launched. In addition several other services can facilitate model sharing more generally. Pharmacometric workflows have matured over the last decades and initiatives to more fully capture those applied to analyses are ongoing. In order to maximize both the impact of pharmacometrics and the knowledge extracted from clinical data, the scientific community needs to take ownership of and create opportunities for open innovation.

NONMEMory: a run management tool for NONMEM.

NONMEM is an extremely powerful tool for nonlinear mixed-effect modelling and simulation of pharmacokinetic and pharmacodynamic data. However, it is a console-based application whose output does not lend itself to rapid interpretation or efficient management. NONMEMory has been created to be a comprehensive project manager for NONMEM, providing detailed summary, comparison and overview of the runs comprising a given project, including the display of output data, simple post-run processing, fast diagnostic plots and run output management, complementary to other available modelling aids. Analysis time ought not to be spent on trivial tasks, and NONMEMory's role is to eliminate these as far as possible by increasing the efficiency of the modelling process. NONMEMory is freely available from http://www.uct.ac.za/depts/pha/nonmemory.php.

A drug and disease model for lixisenatide, a GLP-1 receptor agonist in type 2 diabetes.

Incretin hormone analogs such as glucagon-like peptide-1 (GLP-1) receptor agonists have emerged as promising new options for the treatment of type 2 diabetes mellitus (T2DM), targeting several of its pathophysiological traits, including reduced insulin sensitivity, inadequate insulin secretion, and loss of ß-cell mass (BCM). This article describes the semi-mechanistic modeling of lixisenatide dose-response over time using fasting plasma glucose (FPG), fasting serum insulin (FSI) and glycated hemoglobin (HbA1c) data from two Phase II and four Phase III clinical trials, for a total of 2470 T2DM patients. Previously published models for FPG, FSI, and BCM as well as HbA1c were adapted and expanded to describe the available data. The model incorporated aspects describing disease progression, standard-of-care, FPG-dependent and -independent HbA1c synthesis, and covariate effects of body size, race, and sex. The final model described lixisenatide effects on ß-cell responsiveness, insulin sensitivity and FPG-independent HbA1c synthesis, was able to describe the observed FPG, FSI, and HbA1c data accurately, and was successful in predicting data from an unseen Phase III clinical study.

Modeling disease progression in acute stroke using clinical assessment scales.

This article demonstrates techniques for describing and predicting disease progression in acute stroke by modeling scores measured using clinical assessment scales, accommodating dropout as an additional source of information. Scores assessed using the National Institutes of Health Stroke Scale and the Barthel Index in acute stroke patients were used to model the time course of disease progression. Simultaneous continuous and probabilistic models for describing the nature and magnitude of score changes were developed, and used to model the trajectory of disease progression using scale scores. The models described the observed data well, and exhibited good simulation properties. Applications include longitudinal analysis of stroke scale data, clinical trial simulation, and prognostic forecasting. Based upon experience in other areas, it is likely that application of this modeling methodology will enable reductions in the number of patients needed to carry out clinical studies of treatments for acute stroke.