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...

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AimsTofacitinib is an oral, small molecule JAK inhibitor being investigated for ulcerative colitis (UC). In a phase 2 dose‐ranging study, tofacitinib demonstrated efficacy vs. placebo as UC induction therapy. In this posthoc analysis, we aimed to compare tofacitinib dose and plasma concentration as predictors of efficacy and identify covariates that determined efficacy in patients with UC.MethodsOne‐ and two‐compartment pharmacokinetic models, with first‐order absorption and elimination, were evaluated to describe plasma tofacitinib concentration‐time data at baseline and week 8. Relationships between tofacitinib exposure (dose, average plasma drug concentration during a dosing interval at steady state [Cav,ss] and trough plasma concentration at steady state [Ctrough,ss]) and week 8 efficacy endpoints were characterized using logistic regression analysis. Baseline disease, demographics, prior and concurrent UC treatment were evaluated as covariates.ResultsPlasma tofacitinib concentrations increased proportionately with dose and estimated oral clearance, and Cav,ss values were not significantly different between baseline and week 8. Dose, Cav,ss and Ctrough,ss performed similarly as predictors of efficacy based on statistical criteria for model fit and comparison of model predictions for each endpoint. Individual Cav,ss values were similar between clinical remitters and nonremitters at predicted efficacious doses (10 and 15 mg twice daily). Baseline Mayo score was a significant determinant of efficacy. Predicted differences from placebo in clinical remission at 10 mg twice daily for patients with baseline Mayo score >8 and ≤8 were 39% (95% CI: 7–70) and 21% (–2–50), respectively.ConclusionsExposure–response characterization demonstrated the potential of tofacitinib 10 and 15 mg twice daily as induction therapy for UC without monitoring of plasma drug concentrations for dose optimization.

With increased costs of drug development the need for efficient studies has become critical. A key decision point on the development pathway has become the proof of concept study. These studies must provide clear information to the project teams to enable decision making about further developing a drug candidate but also to gain evidence that any effect size is sufficient to warrant this development given the current market environment. Our case study outlines one such proof of concept trial where a new candidate therapy for neuropathic pain was investigated to assess dose-response and to evaluate the magnitude of its effect compared to placebo. A Normal Dynamic Linear Model was used to estimate the dose-response--enforcing some smoothness in the dose-response, but allowing for the fact that the dose-response may be non-monotonic. A pragmatic, parallel group study design was used with interim analyses scheduled to allow the sponsor to drop ineffective doses or to stop the study. Simulations were performed to assess the operating characteristics of the study design. The study results are presented. Significant cost savings were made when it transpired that the new candidate drug did not show superior efficacy when compared placebo and the study was stopped.