No abstract

RESULTSThe Pfizer Population Pharmacokinetic Analysis Guidance is included as Supplementary Appendix S1 online. The full content of the guidance and a general workflow are presented in Figure 1 and Figure 2, respectively, and general recommendations are summarized below. It should be noted that the recommendations in the guidance were based on current best practice and state of knowledge. The guidance will be updated and revised on a regular basis as new methodologies are developed and the model-building process is refined. The guidance was written with internal and external references to avoid in-depth technical and theoretical discussion within the guidance itself: the full list of references applicable to the guidance can be found in the Reference section of the Supplementary Appendix S1 online.The guidance itself does not address tool-specific implementation but is primarily focused on outlining the expected population pharmacokinetic (Pop PK) modeling-related processes and procedures that should be undertaken by the analyst. However, guidance recommendations are based on standard tools and relevant terminology, including NON-MEM (ICON Development Solutions, Ellicott City, MD), 1 Perl speaks NONMEM (PsN), 2 and Xpose. 3 Points to consider before conducting a Pop PK analysisPopulation modeling analysis plan. It is recommended that a population modeling analysis plan (PMAP) be developed to prospectively outline the modeling approach before conducting a Pop PK analysis. In addition, the PMAP should be finalized before database lock if the analysis results are to be included in a regulatory submission. A well-prepared PMAP should provide an overview of the purpose of the modeling, prior information used, the choice of studies/data to be included for analysis, the proposed modeling approach, and assumptions made. The level of detail required in the PMAP depends on the intended use of the modeling analysis, as the plan in some cases can be considered a "living document," i.e., updates to the plan can be made as more information becomes available. A PMAP should facilitate writing of the population modeling analysis report (PMAR) in a timely manner upon completion of model development and should be an effective planning tool both for the analyst and for any reviewer to assess whether the original objectives of the analysis were met. cal and statistical summaries of dependent variables and demographics, including covariates, should be completed to help with identifying potential errors. In addition, this will help to identify the base structural model and components of the statistical model, as well as potential covariate relationships and outliers.Below the limit of quantification. It is not uncommon that some concentration data are censored as below the limit of quantification (BLQ) by the bioanalytical laboratory and reported qualitatively in Pop PK data sets. Commonly used approaches for handling BLQ concentrations have been shown to introduce bias in the parameter estimates and to result in model misspecification...

Recent data from immuno-oncology clinical studies have shown the exposure-response (E-R) relationship for therapeutic monoclonal antibodies (mAbs) was often confounded by various factors due to the complex interplay of patient characteristics, disease, drug exposure, clearance, and treatment response and presented challenges in characterization and interpretation of E-R analysis. To tackle the challenges, exposure relationships for therapeutic mAbs in immuno-oncology and oncology are reviewed, and a general framework for an integrative understanding of E-R relationship is proposed. In this framework, baseline factors, drug exposure, and treatment response are envisioned to form an interconnected triangle, driving the E-R relationship and underlying three components that compose the apparent relationship: exposure-driven E-R, baseline-driven E-R, and response-driven E-R. Various strategies in data analysis and study design to decouple those components and mitigate the confounding effect are reviewed for their merits and limitations, and a potential roadmap for selection of these strategies is proposed. Specifically, exposure metrics based on a single-dose pharmacokinetic model can be used to mitigate responsedriven E-R, while multivariable analysis and/or case control analysis of data obtained from multiple dose levels in a randomized study may be used to account for the baseline-driven E-R. In this context, the importance of collecting data from multiple dose levels, the role of prognostic factors and predictive factors, the potential utility of clearance at baseline and its change over time, and future directions are discussed.

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.