Full random effects models (FREM): A practical usage guide.

The full random-effects model (FREM) is an innovative and relatively novel covariate modeling technique. It differs from other covariate modeling approaches in that it treats covariates as observations and captures their impact on model parameters using their covariances. These unique characteristics mean that FREM is insensitive to correlations between covariates and implicitly handles missing covariate data.

Using forest plots to interpret covariate effects in pharmacometric models.

The inclusion of covariates in pharmacometric models is important due to their ability to explain variability in drug exposure and response. Clear communication of the impact of covariates is needed to support informed decision making in clinical practice and in drug development. However, effectively conveying these effects to key stakeholders and decision makers can be challenging.

Properties of the full random-effect modeling approach with missing covariate data.

During drug development, a key step is the identification of relevant covariates predicting between-subject variations in drug response. The full random effects model (FREM) is one of the full-covariate approaches used to identify relevant covariates in nonlinear mixed effects models. Here we explore the ability of FREM to handle missing (both missing completely at random (MCAR) and missing at random (MAR)) covariate data and compare it to the full fixed-effects model (FFEM) approach, applied either with complete case analysis or mean imputation.

Efficient and relevant stepwise covariate model building for pharmacometrics.

Covariate modeling is an important opportunity for pharmacometrics to influence decision making in drug development. The stepwise covariate model (SCM) building procedure is the most common method for covariate model development. Despite its advantages, the traditional SCM method is known to have long runtimes and the suboptimal ability to select relevant covariates, especially in more complex phase III settings.

Population pharmacokinetic analysis of tepotinib, an oral MET kinase inhibitor, including data from the VISION study.

Purpose: Tepotinib is a highly selective, potent, mesenchymal-epithelial transition factor (MET) inhibitor, approved for the treatment of non-small cell lung cancer (NSCLC) harboring MET exon 14 skipping. Objectives of this population pharmacokinetic (PK) analysis were to evaluate the dose-exposure relationship of tepotinib and its major circulating metabolite, MSC2571109A, and to identify the intrinsic/extrinsic factors that are predictive of PK variability.

Methods: Data were included from 12 studies in patients with cancer and in healthy participants.

A quantitative approach to the choice of number of samples for percentile estimation in bootstrap and visual predictive check analyses.

Understanding the uncertainty in parameter estimates or in derived secondary variables is important in all data analysis activities. In pharmacometrics, this is often done based on the standard errors from the variance-covariance matrix of the estimates. Confidence intervals derived in this way are by definition symmetrical, which may lead to implausible outcomes, and will require translation to generate uncertainties in derived variables.

An introduction to the full random effects model.

The full random-effects model (FREM) is a method for determining covariate effects in mixed-effects models. Covariates are modeled as random variables, described by mean and variance. The method captures the covariate effects in estimated covariances between individual parameters and covariates.

Conditional distribution modeling as an alternative method for covariates simulation: Comparison with joint multivariate normal and bootstrap techniques.

Clinical trial simulation (CTS) is a valuable tool in drug development. To obtain realistic scenarios, the subjects included in the CTS must be representative of the target population. Common ways of generating virtual subjects are based upon bootstrap (BS) procedures or multivariate normal distributions (MVNDs).

A Physiologically-Based Pharmacokinetic Framework for Prediction of Drug Exposure in Malnourished Children.

Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically-based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences.

A PK/PD Analysis of Circulating Biomarkers and Their Relationship to Tumor Response in Atezolizumab-Treated non-small Cell Lung Cancer Patients.

To assess circulating biomarkers as predictors of antitumor response to atezolizumab (anti-programmed death-ligand 1 (PD-L1), Tecentriq) serum pharmacokinetic (PK) and 95 plasma biomarkers were analyzed in 88 patients with relapsed/refractory non-small cell lung cancer (NSCLC) receiving atezolizumab i.v. q3w (10-20 mg/kg) in the PCD4989g phase I clinical trial.

PharmTeX: a LaTeX-Based Open-Source Platform for Automated Reporting Workflow.

Every year, the pharmaceutical industry generates a large number of scientific reports related to drug research, development, and regulatory submissions. Many of these reports are created using text processing tools such as Microsoft Word. Given the large number of figures, tables, references, and other elements, this is often a tedious task involving hours of copying and pasting and substantial efforts in quality control (QC).

Dosing and Switching Strategies for Paliperidone Palmitate 3-Month Formulation in Patients with Schizophrenia Based on Population Pharmacokinetic Modeling and Simulation, and Clinical Trial Data.

Paliperidone palmitate 3-month formulation (PP3M), a long-acting injectable atypical antipsychotic, was recently approved in the US and Europe for the treatment of schizophrenia in adult patients who have already been treated with paliperidone palmitate 1-month formulation (PP1M) for ≥4 months. This article reviews the pharmacokinetic rationale for the approved dosing regimens for PP3M, dosing windows, management of missed doses and treatment discontinuation, switching to other formulations, and dosing in special populations. Approved PP3M dosing regimens are based on the comparisons of simulations with predefined dosing regimens using paliperidone palmitate and oral paliperidone extended release (ER) population pharmacokinetic models (one-compartment model with two saturable absorption processes for PP3M; one-compartment model with parallel zero- and first-order absorption for PP1M; two-compartment model with sequential zero- and first-order absorption for ER) versus clinical trial data.

Population pharmacokinetics of tanezumab in phase 3 clinical trials for osteoarthritis pain.

Aims: The aims were to 1) develop the pharmacokinetics model to describe and predict observed tanezumab concentrations over time, 2) test possible covariate parameter relationships that could influence clearance and distribution and 3) assess the impact of fixed dosing vs. a dosing regimen adjusted by body weight.

Methods: Individual concentration-time data were determined from 1608 patients in four phase 3 studies conducted to assess efficacy and safety of intravenous tanezumab.

A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children.

Background: Warfarin is the most widely prescribed anticoagulant for the prevention and treatment of thromboembolic events. Although highly effective, the use of warfarin is limited by a narrow therapeutic range combined with a more than ten-fold difference in the dose required for adequate anticoagulation in adults. An optimal dose that leads to a favourable balance between the wanted antithrombotic effect and the risk of bleeding as measured by the prothrombin time International Normalised Ratio (INR) must be found for each patient.

Warfarin dose prediction in children using pharmacometric bridging--comparison with published pharmacogenetic dosing algorithms.

Purpose: Numerous studies have investigated causes of warfarin dose variability in adults, whereas studies in children are limited both in numbers and size. Mechanism-based population modelling provides an opportunity to condense and propagate prior knowledge from one population to another. The main objectives with this study were to evaluate the predictive performance of a theoretically bridged adult warfarin model in children, and to compare accuracy in dose prediction relative to published warfarin algorithms for children.

A fast method for testing covariates in population PK/PD Models.

The development of covariate models within the population modeling program like NONMEM is generally a time-consuming and non-trivial task. In this study, a fast procedure to approximate the change in objective function values of covariate-parameter models is presented and evaluated. The proposed method is a first-order conditional estimation (FOCE)-based linear approximation of the influence of covariates on the model predictions.

Modeling disease progression in acute stroke using clinical assessment scales.

This article demonstrates techniques for describing and predicting disease progression in acute stroke by modeling scores measured using clinical assessment scales, accommodating dropout as an additional source of information. Scores assessed using the National Institutes of Health Stroke Scale and the Barthel Index in acute stroke patients were used to model the time course of disease progression. Simultaneous continuous and probabilistic models for describing the nature and magnitude of score changes were developed, and used to model the trajectory of disease progression using scale scores.

The use of clinical irrelevance criteria in covariate model building with application to dofetilide pharmacokinetic data.

To characterise the pharmacokinetics of dofetilide in patients and to identify clinically relevant parameter-covariate relationships. To investigate three different modelling strategies in covariate model building using dofetilide as an example: (1) using statistical criteria only or in combination with clinical irrelevance criteria for covariate selection, (2) applying covariate effects on total clearance or separately on non-renal and renal clearances and (3) using separate data sets for covariate selection and parameter estimation. Pooled concentration-time data (1,445 patients, 10,133 observations) from phase III clinical trials was used.

Comparison of proportional and differential odds models for mixed-effects analysis of categorical data.

In this work a model for analyzing categorical data is presented; the differential odds model. Unlike the commonly used proportional odds model, this model does not assume that a covariate affects all categories equally on the log odds scale. The differential odds model was compared to the proportional odds model, by assessing statistical significance and improvement of predictive performance when applying the differential odds model to data previously analyzed using the proportional odds model.

Population pharmacokinetics of oseltamivir when coadministered with probenecid.

Oseltamivir is a potent, selective, oral neuraminidase inhibitor for the treatment and prophylaxis of influenza. Plasma concentrations of the active metabolite, oseltamivir carboxylate, are increased in the presence of probenecid, suggesting that the combination could allow for the use of reduced doses of oseltamivir. To investigate this proposal, we developed a population pharmacokinetic model and simulated the pharmacokinetics of candidate combination regimens of oral oseltamivir (45 mg and 30 mg twice a day) plus oral probenecid (500 mg/6 hourly).

Non-Bayesian knowledge propagation using model-based analysis of data from multiple clinical studies.

The ultimate goal in drug development is to establish the manner of safe and efficacious administration to patients. To achieve this in an efficient way the information contained in the clinical studies should contribute to the increasing pool of accumulated knowledge. The aim of this simulation study is to investigate different knowledge-propagation strategies when the data is analysed using a model-based approach in NONMEM.

Modeling of in vitro drug activity and prediction of clinical outcome in acute myeloid leukemia.

The objectives of this study were to develop a population pharmacodynamic model describing the in vitro drug sensitivity of tumor cells and to relate in vitro parameters to clinical outcome. Cell samples from 179 patients with acute myelocytic leukemia were exposed to cytosine arabinoside and daunorubicin, and cytotoxicity was analyzed using the fluorometric microculture cytotoxicity assay. A sigmoid E(max)-model for daunorubicin and an E(max)-model for cytosine arabinoside described the data.