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PurposeBintrafusp alfa (BA) is a bifunctional fusion protein composed of the extracellular domain of the transforming growth factor-β (TGF-β) 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-β is required. Here, we describe a model-informed dose modification approach for risk management of BA-associated bleeding adverse events (AEs).MethodsThe 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-β inhibition across various dosing regimens was derived.ResultsThe 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.ConclusionA 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.

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.

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.

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.

PurposeThe 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).MethodsExternal 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.ResultsA 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).ConclusionsThe 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.

To run the model, one specifies the ODE/solved system and provides initial estimates for the model as described in the nlmixr tutorial. 2 The model then can be solved using the nlme algorithm, or, more optimally, using more advanced algorithms that have been shown to provide more accurate parameter estimates like first‐order conditional estimation with interaction (FOCEI) 3 and stochastic approximation expectation maximization (SAEM). The syntax for fitting a multiple dose theophylline PK is identical to the previous example, except that extra doses were added as well as extra simulated observations: fit.nlme <‐ nlmixr(one.compartment, theo_md, \"nlme\") In pharmacology, the binding of drug molecules with a target (e.g., a receptor on the cell membrane) triggers series of biological changes such as secondary messengers, signal transduction, signal transcription, DNA changes, and protein production. The barriers to interested statisticians engaging with NLME model‐based data analysis have traditionally been high—the gold standard tools used for this are somewhat arcane, use their own terminology and require a great deal of time and experience to learn, inhibiting the ability of pharmacometricians and statisticians to communicate and collaborate.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970’s and early 1980’s and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

Background Targeting the asymptomatic liver stage of Plasmodium infection through chemoprevention could become a key intervention to reduce malaria-associated incidence and mortality. Methods M5717, a Plasmodium  elongation factor 2 inhibitor, was assessed in vitro and in vivo with readily accessible Plasmodium berghei parasites. In an animal refinement, reduction, replacement approach, the in vitro IC 99 value was used to feed a Population Pharmacokinetics modelling and simulation approach to determine meaningful effective doses for a subsequent Plasmodium sporozoite-induced volunteer infection study. Results Doses of 100 and 200 mg would provide exposures exceeding IC 99 in 96 and 100% of the simulated population, respectively. Conclusions This approach has the potential to accelerate the search for new anti-malarials, to reduce the number of healthy volunteers needed in a clinical study and decrease and refine the animal use in the preclinical phase. Graphical Abstract

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.