Guidelines for the quality control of population pharmacokinetic-pharmacodynamic analyses: an industry perspective

Population pharmacokinetics of imetelstat, a first-in-class oligonucleotide telomerase inhibitor

Imetelstat is a novel, first-in-class, oligonucleotide telomerase inhibitor in development for the treatment of hematologic malignancies including lower-risk myelodysplastic syndromes and myelofibrosis. A nonlinear mixed-effects model was developed to characterize the population pharmacokinetics of imetelstat and identify and quantify covariates that contribute to its pharmacokinetic variability. The model was developed using plasma concentrations from 7 clinical studies including 424 patients with solid tumors or hematologic malignancies who received single-agent imetelstat via intravenous infusion at various dose levels (0.4-11.7 mg/kg) and schedules (every week to every 4 weeks). Covariate analysis included factors related to demographics, disease, laboratory results, renal and hepatic function, and antidrug antibodies. Imetelstat was described by a two-compartment, nonlinear disposition model with saturable binding/distribution and dose- and time-dependent elimination from the central compartment. Theory-based allometric scaling for body weight was included in disposition parameters. The final covariates included sex, time, malignancy, and dose on clearance; malignancy and sex on volume of the central compartment; and malignancy and spleen volume on concentration of target. Clearance in females was modestly lower, resulting in nonclinically relevant increases in predicted exposure relative to males. No effects on imetelstat pharmacokinetics were identified for mild-to-moderate hepatic or renal impairment, age, race, and antidrug antibody status. All model parameters were estimated with adequate precision (relative standard error < 29%). Visual predictive checks confirmed the capacity of the model to describe the data. The analysis supports the imetelstat body-weight-based dosing approach and lack of need for dose individualizations for imetelstat-treated patients.

Characterizing the Nonlinear Pharmacokinetics and Pharmacodynamics of BI 187004, an 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitor, in Humans by a Target-Mediated Drug Disposition Model

BI 187004, a selective small-molecule inhibitor of 11β-hydroxysteroid dehydrogenase-1 (11β-HSD1), displayed complex nonlinear pharmacokinetics (PK) in humans. Following nine single oral doses, BI 187004 exhibited nonlinear PK at low doses and linear PK at higher doses. Notably, substantial hepatic 11β-HSD1 inhibition (50%) was detected in a very low-dose group, achieving a consistent 70% hepatic enzyme inhibition in subsequent ascending doses without any dose-dependent effects. The unusual PK and PD profiles of BI 187004 suggest the presence of pharmacological target-mediated drug disposition (TMDD), arising from the saturable binding of BI 187004 compound to its high-affinity and low-capacity target 11β-HSD1. The non-intuitive dose, exposure, and response relationship for BI 187004 pose a significant challenge in rational dose selection. This study aimed to construct a TMDD model to explain the complex nonlinear PK behavior and underscore the importance of recognizing TMDD in this small-molecule compound. Among the various models explored, the best model was a two-compartment TMDD model with three transit absorption components. The final model provides insights into 11β-HSD1 binding-related parameters for BI 187004, including the total amount of 11β-HSD1 in the liver (estimated to be 8000 nmol), the second order association rate constant (estimated to be 0.102 nM-1h-1), and the first-order dissociation rate constant (estimated to be 0.11 h-1). Our final population PK model successfully characterized the intricate nonlinear PK of BI 187004 across a wide dose range. This modeling work serves as a valuable reference for the rational selection of the dose regimens for BI 187004's future clinical trials.

Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.

Improving pharmacometrics analysis efficiency using DataCheQC: An interactive, Shiny-based app for quality control of pharmacometrics datasets

DataCheQC is an interactive application based on the R Shiny framework developed for the purposes of performing quality control (QC) checks on pharmacometric datasets, and thereby supporting the implementation of model-informed drug development. Features include visual inspection of variables and data entries for errors and/or anomalies, and ensuring structural integrity through comparison with a dataset specification file. The app, which requires no programming knowledge to operate, allows the user to collect all findings into a summary report downloadable directly from the app itself. The source code for the app is freely available on GitHub under an open-source license (https://github.com/DotanOr/DataCheQC) and can also be accessed online (https://dotanor.shinyapps.io/DataCheQC/).

Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA)

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

Population Pharmacokinetics of Isavuconazole in Critical Care Patients with COVID-19-Associated Pulmonary Aspergillosis and Monte Carlo Simulations of High Off-Label Doses

Isavuconazole is a triazole antifungal agent recently recommended as first-line therapy for invasive pulmonary aspergillosis. With the COVID-19 pandemic, cases of COVID-19-associated pulmonary aspergillosis (CAPA) have been described with a prevalence ranging from 5 to 30%. We developed and validated a population pharmacokinetic (PKpop) model of isavuconazole plasma concentrations in intensive care unit patients with CAPA. Nonlinear mixed-effect modeling Monolix software were used for PK analysis of 65 plasma trough concentrations from 18 patients. PK parameters were best estimated with a one-compartment model. The mean of ISA plasma concentrations was 1.87 [1.29-2.25] mg/L despite prolonged loading dose (72 h for one-third) and a mean maintenance dose of 300 mg per day. Pharmacokinetics (PK) modeling showed that renal replacement therapy (RRT) was significantly associated with under exposure, explaining a part of clearance variability. The Monte Carlo simulations suggested that the recommended dosing regimen did not achieve the trough target of 2 mg/L in a timely manner (72 h). This is the first isavuconazole PKpop model developed for CAPA critical care patients underlying the need of therapeutic drug monitoring, especially for patients under RRT.

Pharmacometric model of agalsidase-migalastat interaction in human: a novel mechanistic model of drug-drug interaction between a therapeutic protein and a small molecule

Recently, a new mechanism of drug-drug interaction (DDI) was reported between agalsidase, a therapeutic protein, and migalastat, a small molecule, both of which are treatment options of Fabry disease. Migalastat is a pharmacological chaperone that stabilizes the native form of both endogenous and exogenous agalsidase. In Fabry patients co-administrated with agalsidase and migalastat, the increase in active agalsidase exposure is considered a pharmacokinetic effect of agalsidase infusion but a pharmacodynamic effect of migalastat administration, which makes this new DDI mechanism even more interesting. To quantitatively characterize the interaction between agalsidase and migalastat in human, a pharmacometric DDI model was developed using literature reported concentration-time data. The final model includes three components: a 1-compartment linear model component for migalastat; a 2-compartment linear model component for agalsidase; and a DDI component where the agalsidase-migalastat complex is formed via second order association constant k_on, dissociated with first order dissociation constant k_off, and distributed/eliminated with same rates as agalsidase alone, albeit the complex (i.e., bound agalsidase) has higher enzyme activity compared to free agalsidase. The final model adequately captured several key features of the unique interaction between agalsidase and migalastat, and successfully characterized the kinetics of migalastat as well as the kinetics and activities of agalsidase when both drugs were used alone or in combination following different doses. Most parameters were reasonably estimated with good precision. Because the model includes mechanistic basis of therapeutic protein and small molecule pharmacological chaperone interaction, it can potentially serve as a foundational work for DDIs with similar mechanism.

Design and conduct considerations for studies in patients with impaired renal function

An impaired renal function, including acute and chronic kidney disease and end‐stage renal disease, can be the result of aging, certain disease conditions, the use of some medications, or as a result of smoking. In patients with renal impairment (RI), the pharmacokinetics (PKs) of drugs or drug metabolites may change and result in increased safety risks or decreased efficacy. In order to make specific dose recommendations in the label of drugs for patients with RI, a clinical trial may have to be conducted or, when not feasible, modeling and simulations approaches, such as population PK modeling or physiologically‐based PK modelling may be applied. This tutorial aims to provide an overview of the global regulatory landscape and a practical guidance for successfully designing and conducting clinical RI trials or, alternatively, on applying modeling and simulation tools to come to a dose recommendation for patients with RI in the most efficient manner.

Approaches to handling missing or "problematic" pharmacology data: Pharmacokinetics

Missing or erroneous information is a common problem in the analysis of pharmacokinetic (PK) data. This may present as missing or inaccurate dose level or dose time, drug concentrations below the analytical limit of quantification, missing sample times, or missing or incorrect covariate information. Several methods to handle problematic data have been evaluated, although no single, broad set of recommendations for commonly occurring errors has been published. In this tutorial, we review the existing literature and present the results of our simulation studies that evaluated common methods to handle known data errors to bridge the remaining gaps and expand on the existing knowledge. This tutorial is intended for any scientist analyzing a PK data set with missing or apparently erroneous data. The approaches described herein may also be useful for the analysis of nonclinical PK data.

Population Pharmacokinetic Model of Oxfendazole and Metabolites in Healthy Adults following Single Ascending Doses

Oxfendazole is a potent veterinary benzimidazole anthelmintic under transition to humans for the treatment of multiple parasitic infectious diseases. The first-in-human study evaluating the disposition of oxfendazole and its metabolites in healthy adults following single ascending oral doses from 0.5 to 60 mg/kg of body weight shows that oxfendazole pharmacokinetics is substantially nonlinear, which complicates correlating oxfendazole dose to exposure. To quantitatively capture the relation between oxfendazole dose and exposure, a population pharmacokinetic model for oxfendazole and its metabolites, oxfendazole sulfone and fenbendazole, in humans was developed using a nonlinear mixed-effect modeling approach. Our final model incorporated mechanistic characterization of dose-limited bioavailability as well as different oxfendazole metabolic processes and provided insight into the significance of presystemic metabolism in oxfendazole and metabolite disposition. Oxfendazole clinical pharmacokinetics was best described by a one-compartment model with nonlinear absorption and linear elimination. Oxfendazole apparent clearance and apparent volume of distribution were estimated to be 2.57 liters/h and 35.2 liters, respectively, at the lowest dose (0.5 mg/kg), indicating that oxfendazole is a low extraction drug with moderate distribution. The disposition of both metabolites was adequately characterized by a one-compartment model with formation rate-limited elimination. Fenbendazole formation from oxfendazole was primarily through systemic metabolism, while both presystemic and systemic metabolism were critical to the formation of oxfendazole sulfone. Our model adequately captured the concentration-time profiles of both oxfendazole and its two metabolites in healthy adults over a wide dose range. The model can be used to predict oxfendazole disposition under new dosing regimens to support dose optimization in humans.

Dose Optimization of Cefpirome Based on Population Pharmacokinetics and Target Attainment during Extracorporeal Membrane Oxygenation

To obtain the optimal dosage regimen in patients receiving extracorporeal membrane oxygenation (ECMO), we developed a population pharmacokinetics model for cefpirome and performed pharmacodynamic analyses. This prospective study included 15 patients treated with cefpirome during ECMO. Blood samples were collected during ECMO (ECMO-ON) and after ECMO (ECMO-OFF) at predose and 0.5 to 1, 2 to 3, 4 to 6, 8 to 10, and 12 h after cefpirome administration. The population pharmacokinetic model was developed using nonlinear mixed effects modeling and stepwise covariate modeling. Monte Carlo simulation was used to assess the probability of target attainment (PTA) and cumulative fraction of response (CFR) according to the MIC distribution. Cefpirome pharmacokinetics were best described by a two-compartment model. Covariate analysis indicated that serum creatinine concentration (SCr) was negatively correlated with clearance, and the presence of ECMO increased clearance and the central volume of distribution. The simulations showed that patients with low SCr during ECMO-ON had lower PTA than patients with high SCr during ECMO-OFF; so, a higher dosage of cefpirome was required. Cefpirome of 2 g every 8 h for intravenous bolus injection or 2 g every 12 h for extended infusion over 4 h was recommended with normal kidney function receiving ECMO. We established a population pharmacokinetic model for cefpirome in patients with ECMO, and appropriate cefpirome dosage regimens were recommended. The impact of ECMO could be due to the change in patient status on consideration of the small population and uncertainty in covariate relationships. Dose optimization of cefpirome may improve treatment success and survival in patients receiving ECMO. (This study has been registered at ClinicalTrials.gov under identifier NCT02581280.).

Pharmacokinetic Models to Characterize the Absorption Phase and the Influence of a Proton Pump Inhibitor on the Overall Exposure of Dacomitinib

Introduction: Dacomitinib is an epidermal growth factor receptor (EGFR) inhibitor approved for the treatment of metastatic non-small cell lung cancer (NSCLC) in the first line in patients with EGFR activating mutations. Dacomitinib is taken orally once daily at 45 mg with or without food, until disease progression or unacceptable toxicity occurs. Oncology patients often can develop gastroesophageal reflux disease (GERD), which may require management with an acid-reducing agent. Proton pump inhibitors (PPIs), such as rabeprazole, inhibit sodium-potassium adenosine triphosphatase (H⁺/K⁺-ATPase) pumps that stimulate acid secretion in the stomach and have a prolonged pharmacodynamic effect that extends beyond 24 h post-administration. The aim of this work was to characterize the absorption of dacomitinib via modeling with a particular interest in quantifying the impact of rabeprazole on the pharmacokinetics (PK) of dacomitinib. Materials and Methods: The pooled dataset consisted of five clinical pharmacology healthy volunteer studies, which collected serial pharmacokinetic concentration-time profiles of dacomitinib. Non-linear mixed effects modeling was carried out to characterize dacomitinib pharmacokinetics in the presence and absence of the concomitant use of a PPI, rabeprazole. Several absorption models, some more empirical, and some more physiologically based, were tested: transit compartment, first-order absorption with and without lag time, and variations of combined zero- and first-order absorption kinetics models. Results: The presence of a PPI was a significant covariate affecting the extent (F) and rate (ka) of dacomitinib absorption, as previously reported in the dedicated clinical study. A transit compartment model was able to best describe the absorption phase of dacomitinib.

Dexmedetomidine Pharmacokinetics in Neonates with Hypoxic-Ischemic Encephalopathy Receiving Hypothermia

Dexmedetomidine is a promising sedative and analgesic for newborns with hypoxic-ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH). Pharmacokinetics and safety of dexmedetomidine were evaluated in a phase I, single-center, open-label study to inform future trial strategies. We recruited 7 neonates ≥36 weeks' gestational age diagnosed with moderate-to-severe HIE, who received a continuous dexmedetomidine infusion during TH and the 6 h rewarming period. Time course of plasma dexmedetomidine concentration was characterized by serial blood sampling during and after the 64.8 ± 6.9 hours of infusion. Noncompartmental analysis yielded descriptive pharmacokinetic estimates: plasma clearance of 0.760 ± 0.155 L/h/kg, steady-state distribution volume of 5.22 ± 2.62 L/kg, and mean residence time of 6.84 ± 3.20 h. Naive pooled and population analyses according to a one-compartment model provided similar estimates of clearance and distribution volume. Overall, clearance was either comparable or lower, distribution volume was larger, and mean residence time or elimination half-life was longer in cooled newborns with HIE compared to corresponding estimates previously reported for uncooled (normothermic) newborns without HIE at comparable gestational and postmenstrual ages. As a result, plasma concentrations in cooled newborns with HIE rose more slowly in the initial hours of infusion compared to predicted concentration-time profiles based on reported pharmacokinetic parameters in normothermic newborns without HIE, while similar steady-state levels were achieved. No acute adverse events were associated with dexmedetomidine treatment. While dexmedetomidine appeared safe for neonates with HIE during TH at infusion doses up to 0.4 μg/kg/h, a loading dose strategy may be needed to overcome the initial lag in rise of plasma dexmedetomidine concentration.

Evaluation of Assumptions Underpinning Pharmacometric Models

Assumptions inherent to pharmacometric model development and use are not routinely acknowledged, described, or evaluated. The aim of this work is to present a framework for systematic evaluation of assumptions. To aid identification of assumptions, we categorise assumptions into two types: implicit and explicit assumptions. Implicit assumptions are inherent in a method or model and underpin its derivation and use. Explicit assumptions arise from heuristic principles and are typically defined by the user to enable the application of a method or model. A flowchart was developed for systematic evaluation of assumptions. For each assumption, the impact of assumption violation ('significant', 'insignificant', 'unknown') and the probability of assumption violation ('likely', 'unlikely', 'unknown') will be evaluated based on prior knowledge or the result of an additional bespoke study to arrive at a decision ('go', 'no-go') for both model building and model use. A table of assumptions with standardised headings has been proposed to facilitate the documentation of assumptions and evaluation of results. The utility of the proposed framework was illustrated using four assumptions underpinning a top-down model describing the warfarin-coagulation proteins' relationship. The next step of this work is to apply the framework to a series of other settings to fully assess its practicality and its value in identifying and making inferences from assumptions.

Reproducible pharmacokinetics

Reproducibility is a highly desired feature of scientific investigation in general, and it has special connotations for research in pharmacokinetics, a vibrant field with over 500,000 publications to-date. It is important to be able to differentiate between genuine heterogeneity in pharmacokinetic parameters from heterogeneity that is due to errors and biases. This overview discusses efforts and opportunities to diminish the latter type of undesirable heterogeneity. Several reporting and research guidance documents and standards have been proposed for pharmacokinetic studies, but their adoption is still rather limited. Quality problems in the methods used and model evaluations have been examined in some empirical studies of the literature. Standardization of statistical and laboratory tools and procedures can be improved in the field. Only a small fraction of pharmacokinetic studies become pre-registered and only 9995 such studies have been registered in ClinicalTrials.gov as of August 2018. It is likely that most pharmacokinetic studies remain unpublished. Publication bias affecting the results and inferences has been documented in case studies, but its exact extent is unknown for the field at-large. The use of meta-analyses in the field is still limited. Availability of raw data, detailed protocols, software and codes is hopefully improving with multiple ongoing initiatives. Several research practices can contribute to greater transparency and reproducibility for pharmacokinetic investigations.

Best Practices to Maximize the Use and Reuse of Quantitative and Systems Pharmacology Models: Recommendations From the United Kingdom Quantitative and Systems Pharmacology Network

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment.

What about confidence intervals? A word of caution when interpreting PTA simulations

OBJECTIVES

In the field of antimicrobial chemotherapy, readers are increasingly confronted with population pharmacokinetic models and the ensuing simulation results with the purpose to improve the efficiency of currently used therapeutic regimens. One such type of analysis is Monte Carlo (MC) simulations in support of dose selection. At the moment, results of these MC simulations consist of predictions for the typical individual/population only. The uncertainty associated with the parameters, from which the simulations are derived, is completely ignored. Here, we highlight the importance of and the need to include parameter uncertainty in PTA simulations.

METHODS

Using MC simulation with parameter uncertainty, we estimated CIs around PTA curves. The added benefit of this approach was illustrated using, on the one hand, a population pharmacokinetic model developed in-house for a β-lactam antibiotic and, on the other hand, results from a previously published PTA analysis.

RESULTS

Our examples illustrate that proper clinical decision-making requires more than the typical PTA curve. Therefore, authors should be encouraged to provide an estimate of the uncertainty along with their simulations and to take this into account when interpreting the results. We feel that CIs around PTA curves provide this information in a comprehensive manner without requiring advanced knowledge on the underlying modelling approaches from the reader.

CONCLUSIONS

We believe that this approach should be advocated by all stakeholders in antibiotic stewardship programmes to safeguard the quality of clinical decision-making in the future.

Reporting guidelines for population pharmacokinetic analyses

The purpose of this work was to develop a consolidated set of guiding principles for the reporting of population pharmacokinetic (PK) analyses based on input from a survey of practitioners as well as discussions between industry, consulting, and regulatory scientists. The survey found that identification of population covariate effects on drug exposure and support for dose selection (in which population PK frequently serves as preparatory analysis for exposure-response modeling) are the main areas of influence for population PK analysis. The proposed guidelines consider 2 main purposes of population PK reports: (1) to present key analysis findings and their impact on drug development decisions, and (2) as documentation of the analysis methods for the dual purpose of enabling review of the analysis and facilitating future use of the models. This work also identified 2 main audiences for the reports: (1) a technically competent group responsible for in-depth review of the data, methodology, and results; and (2) a scientifically literate but not technically adept group, whose main interest is in the implications of the analysis for the broader drug development program. We recommend a generalized question-based approach with 6 questions that need to be addressed throughout the report. We recommend 8 sections (Synopsis, Introduction, Data, Methods, Results, Discussion, Conclusions, Appendix) with suggestions for the target audience and level of detail for each section. A section providing general expectations regarding population PK reporting from a regulatory perspective is also included. We consider this an important step toward industrialization of the field of pharmacometrics such that a nontechnical audience also understands the role of pharmacometric analyses in decision making. Population PK reports were chosen as representative reports to derive these recommendations; however, the guiding principles presented here are applicable for all pharmacometric reports including pharmacokinetics/pharmacodynamics and simulation reports.

Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation

This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of "good practice" recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

Establishing Good Practices for Exposure-Response Analysis of Clinical Endpoints in Drug Development

This tutorial aims at promoting good practices for exposure–response (E-R) analyses of clinical endpoints in drug development. The focus is on practical aspects of E-R analyses to assist modeling scientists with a process of performing such analyses in a consistent manner across individuals and projects and tailored to typical clinical drug development decisions. This includes general considerations for planning, conducting, and visualizing E-R analyses, and how these are linked to key questions.

Reporting guidelines for population pharmacokinetic analyses

The purpose of this work was to develop a consolidated set of guiding principles for reporting of population pharmacokinetic (PK) analyses based on input from a survey of practitioners as well as discussions between industry, consulting and regulatory scientists. The survey found that identification of population covariate effects on drug exposure and support for dose selection (where population PK frequently serves as preparatory analysis to exposure–response modeling) are the main areas of influence for population PK analysis. The proposed guidelines consider two main purposes of population PK reports (1) to present key analysis findings and their impact on drug development decisions, and (2) as documentation of the analysis methods for the dual purpose of enabling review of the analysis and facilitating future use of the models. This work also identified two main audiences for the reports: (1) a technically competent group responsible for in-depth review of the data, methodology, and results, and (2) a scientifically literate, but not technically adept group, whose main interest is in the implications of the analysis for the broader drug development program. We recommend a generalized question-based approach with six questions that need to be addressed throughout the report. We recommend eight sections (Synopsis, Introduction, Data, Methods, Results, Discussion, Conclusions, Appendix) with suggestions for the target audience and level of detail for each section. A section providing general expectations regarding population PK reporting from a regulatory perspective is also included. We consider this an important step towards industrialization of the field of pharmacometrics such that non-technical audience also understands the role of pharmacometrics analyses in decision making. Population PK reports were chosen as representative reports to derive these recommendations; however, the guiding principles presented here are applicable for all pharmacometric reports including PKPD and simulation reports.

Establishing best practices and guidance in population modeling: an experience with an internal population pharmacokinetic analysis guidance

This tutorial describes the development of a population pharmacokinetic (Pop PK) analysis guidance within Pfizer, which strives for improved consistency and efficiency, and a more systematic approach to model building. General recommendations from the Pfizer internal guidance and a suggested workflow for Pop PK model building are discussed. A description is also provided for mechanisms by which conflicting opinions were captured and resolved across the organization to arrive at the final guidance.

CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e51; doi:10.1038/psp.2013.26; advance online publication 3 July 2013