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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.

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.

Pemetrexed disodium is a novel antifolate that exhibits potent inhibitory effects on multiple enzymes in folate metabolism. Phase II/III clinical trials have shown that pemetrexed is effective against various solid tumors. Like methotrexate, pemetrexed may be useful in treatment of primary and secondary brain tumors. In this study, we examined the central nervous system (CNS) distribution of pemetrexed and the interaction with an organic anion transport inhibitor indomethacin. Male Wistar rats were administered pemetrexed by either single intravenous bolus or constant intravenous infusion. Unbound pemetrexed in blood and brain was measured by simultaneous arterial blood and frontal cortex microdialysis sampling. In the i.v. bolus experiments, indomethacin was administered by i.v. bolus (10 mg/kg) followed by i.v. infusion (0.1 mg/kg/h) in a crossover manner. In the infusion experiments, the same dose of indomethacin was administered after a steady state was reached for pemetrexed. CNS distributional kinetics was analyzed by compartmental and noncompartmental methods. Both bolus and infusion studies showed that pemetrexed has a limited CNS distribution. The mean area under concentrationtime curve (AUC) brain /AUC plasma ratio of unbound pemetrexed was 0.078 Ϯ 0.038 in the i.v. bolus study. The pemetrexed steady-state brain-to-plasma unbound concentration ratio after i.v. infusion was 0.106 Ϯ 0.054. The distributional clearance into the brain was approximately 10% of the clearance out of the brain in both the compartmental and noncompartmental analyses. Indomethacin had no effect on either the brain-toplasma AUC ratio or the steady-state brain-to-plasma concentration ratio. The distribution of pemetrexed into the brain is limited, and an efflux clearance process, such as an efflux transporter, may be involved.

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ABSTRACT:The disposition of 3,3-difluoropyrrolidin-1-yl{(2S,4S)-4-[4-(pyrimidin-2-yl)piperazin-1-yl]pyrrolidin-2-yl}methanone (PF-00734200), a dipeptidyl peptidase IV inhibitor that progressed to phase 3 for the treatment of type 2 diabetes, was examined in rats, dogs, and humans after oral administration of a single dose of