Item response theory analysis of daytime sleepiness as a symptom of obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a sleep disorder which is linked to many health risks. The gold standard to evaluate OSA in clinical trials is the Apnea-Hypopnea Index (AHI). However, it is time-consuming, costly, and disregards aspects such as quality of life.

Evaluation of covariate effects in item response theory models.

Item response theory (IRT) models are usually the best way to analyze composite or rating scale data. Standard methods to evaluate covariate or treatment effects in IRT models do not allow to identify item-specific effects. Finding subgroups of patients who respond differently to certain items could be very important when designing inclusion or exclusion criteria for clinical trials, and aid in understanding different treatment responses in varying disease manifestations.

Evaluation of postprandial total triglycerides within the TIGG model for characterizing postprandial response of glucose, insulin, and GLP-1.

The TIGG model is the first model to integrate glucose and insulin regulation, incretin effect, and triglyceride (TG) response in the lipoprotein subclasses of chylomicrons and VLDL-V6. This model described the response following a high-fat meal in individuals who are lean, obese, and very obese and provided insights into the possible regulation of glucose homeostasis in the extended period following a meal. Often, total TGs are analyzed within clinical studies, instead of lipoprotein subclasses.

Optimal dosing of gliclazide-A model-based approach.

Gliclazide was approved as a treatment for type 2 diabetes in an era before model-based drug development, and consequently, the recommended doses were not optimised with modern methods. To investigate various dosing regimens of gliclazide, we used publicly available data to characterise the dose-response relationship using pharmacometric models. A literature search identified 21 published gliclazide pharmacokinetic (PK) studies with full profiles.

Altered glucose-dependent secretion of glucagon and ACTH is associated with insulin resistance, assessed by population analysis.

This study aimed to characterize how the dysregulation of counter-regulatory hormones can contribute to insulin resistance and potentially to diabetes. Therefore, we investigated the association between insulin sensitivity and the glucose- and insulin-dependent secretion of glucagon, adrenocorticotropic hormone (ACTH), and cortisol in non-diabetic individuals using a population model analysis. Data, from hyperinsulinemic-hypoglycemic clamps, were pooled for analysis, including 52 individuals with a wide range of insulin resistance (reflected by glucose infusion rate 20-60 min; GIR20-60min).

Pharmacometric modeling of drug adverse effects: an application of mixture models in schizophrenia spectrum disorder patients treated with clozapine.

Clozapine has superior efficacy to other antipsychotics yet is underutilized due to its adverse effects, such as neutropenia, weight gain, and tachycardia. The current investigation aimed to introduce a pharmacometric approach to simultaneously model drug adverse effects, with examples from schizophrenia spectrum patients receiving clozapine. The adverse drug effects were represented as a function of time by incorporating a mixture model to describe individual susceptibility to the adverse effects.

Optimization of trial duration to predict long-term HbA1c change with therapy: A pharmacometrics simulation-based evaluation.

Glycated hemoglobin (HbA1c) is the main biomarker of diabetes drug development. However, because of its delayed turnover, trial duration is rarely shorter than 12 weeks, and being able to predict long-term HbA1c with precision using data from shorter studies would be beneficial. The feasibility of reducing study duration was therefore investigated in this study, assuming a model-based analysis.

Impact of Obesity on Postprandial Triglyceride Contribution to Glucose Homeostasis, Assessed with a Semimechanistic Model.

The integrated glucose-insulin model is a semimechanistic model describing glucose and insulin after a glucose challenge. Similarly, a semiphysiologic model of the postprandial triglyceride (TG) response in chylomicrons and VLDL-V6 was recently published. We have developed the triglyceride-insulin-glucose-GLP-1 (TIGG) model by integrating these models and active GLP-1.

Linking categorical models for prediction of pleasantness score using individual predictions of sweetness and creaminess: An advancement of categorical modeling.

The aim of this work was to develop and evaluate approaches of linked categorical models using individual predictions of probability. A model was developed using data from a study which assessed the perception of sweetness, creaminess, and pleasantness in dairy solutions containing variable concentrations of sugar and fat. Ordered categorical models were used to predict the individual sweetness and creaminess scores and these individual predictions were used as covariates in the model of pleasantness response.

An Item Response Theory-Informed Strategy to Model Total Score Data from Composite Scales.

Composite scale data is widely used in many therapeutic areas and consists of several categorical questions/items that are usually summarized into a total score (TS). Such data is discrete and bounded by nature. The gold standard to analyse composite scale data is item response theory (IRT) models.

Postprandial triglyceride reduction following acute treatment of a selective 5-hydroxytryptamine-2c agonist and characterization using a semi-physiological model.

Aim: To investigate the tolerability, pharmacokinetics (PK) and postprandial triglyceride (TG) response of single, escalating oral doses of a selective 5-hydroxytryptamine-2c (5-HT ) agonist in subjects with overweight/obesity and apply mechanistic population pharmacokinetic-pharmacodynamic modelling to identify a plausible drug mechanism of action.

Materials And Methods: This phase 1, single-centre, double-blind, randomized, placebo-controlled, four-period, two-alternating cohorts study evaluated single escalating oral doses ranging from 5 to 130 mg of LY2140112 (LY) in subjects with overweight/obesity (body mass index: 27-39 kg/m ). Postprandial TG response (total TG, chylomicrons and very low-density lipoprotein particles [VLDL]-V6) following a high-fat meal were assessed for 11 h postmeal for each dose level.

Comparison of Precision and Accuracy of Five Methods to Analyse Total Score Data.

Total score (TS) data is generated from composite scales consisting of several questions/items, such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The analysis method that most fully uses the information gathered is item response theory (IRT) models, but these are complex and require item-level data which may not be available. Therefore, the TS is commonly analysed with standard continuous variable (CV) models, which do not respect the bounded nature of data.

Sweet/Fat Preference Taste in Subjects Who are Lean, Obese and Very Obese.

Purpose: This study assessed the perception of sweetness, creaminess, and pleasantness from a sweet/fat preference test in subjects who are lean (BMI: 19-25), obese (BMI: 30-33) or very obese (BMI: 34-40) using categorical modeling.

Methods: Subjects tasted 16 dairy solutions consisting of 0%, 3.5%, 11.

Dose-Response Mixed Models for Repeated Measures - a New Method for Assessment of Dose-Response.

Purpose: In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations.

Methods: The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape.

A Time-to-Event Model Relating Integrated Craving to Risk of Smoking Relapse Across Different Nicotine Replacement Therapy Formulations.

Smoking increases the risk of cancer and other diseases, causing an estimated 7 million deaths per year. Nicotine replacement therapy (NRT) reduces craving for smoking, therefore, increasing an individual's probability to remain abstinent. In this work, we for the first time quantitatively described the relationship between craving and smoking abstinence, using retrospectively collected data from 19 studies, including 3 NRT formulations (inhaler, mouth spray, and patch) and a combination of inhaler and patch.

Optimal Designs for Model-Based Assessment of Insulin Sensitivity and Glucose Effectiveness.

The integrated minimal model allows assessment of clinical diagnosis indices, for example, insulin sensitivity (S ) and glucose effectiveness (S ), from data of the insulin-modified intravenous glucose tolerance test (IVGTT), which is laborious with an intense sampling schedule, up to 32 samples. The aim of this study was to propose a more informative, although less laborious, IVGTT design to be used for model-based assessment of S and S . The IVGTT design was optimized simultaneously for all design variables: glucose and insulin infusion doses, time of glucose dose and start of insulin infusion, insulin infusion duration, sampling times, and number of samples.

Correction to: Evidence-Based Design of Fixed-Dose Combinations: Principles and Application to Pediatric Anti-Tuberculosis Therapy.

In the online supplementary.

Relating Nicotine Plasma Concentration to Momentary Craving Across Four Nicotine Replacement Therapy Formulations.

Tobacco use is a major health concern. To assist smoking cessation, nicotine replacement therapy (NRT) is used to reduce nicotine craving. We quantitatively described the relationship between nicotine pharmacokinetics (PKs) from NRTs and momentary craving, linking two different pharmacodynamic (PD) scales for measuring craving.

A Bounded Integer Model for Rating and Composite Scale Data.

Rating and composite scales are commonly used to assess treatment efficacy. The two main strategies for modelling such endpoints are to treat them as a continuous or an ordered categorical variable (CV or OC). Both strategies have disadvantages, including making assumptions that violate the integer nature of the data (CV) and requiring many parameters for scales with many response categories (OC).

Translation Between Two Models; Application with Integrated Glucose Homeostasis Models.

Purpose: For some biological systems, there exist several models with somewhat different features and perspectives. We propose an evaluation method for NLME models by analyzing real and simulated data from the model of main interest using a structurally different, but similar, NLME model. We showcase this method using the Integrated Glucose Insulin (IGI) model and the Integrated Minimal Model (IMM).

The integrated glucose insulin minimal model: An improved version.

This paper describes the improved integrated minimal model for healthy subjects and patients with type 2 diabetes and the work leading up to this model. The original integrated minimal model characterizes simultaneously glucose and insulin following intravenous glucose tolerance test (IVGTT) in healthy subjects and provides apart from estimates of indices for insulin sensitivity (S) and glucose effectiveness (S), also full simulation capabilities. However, this model was developed using IVGTT data of total glucose and consequently, the model cannot separate hepatic glucose production from glucose disposal.

Variability Attribution for Automated Model Building.

We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method "residual modeling." Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples.

Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment.

Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES).

Using a semi-mechanistic model to identify the main sources of variability of metformin pharmacokinetics.

Metformin pharmacokinetics (PK) is highly variable, and researchers have for years tried to shed light on determinants of inter-individual (IIV) and inter-occasion variability (IOV) of metformin PK. We set out to identify the main sources of PK variability using a semi-mechanistic model. We assessed the influence of subject characteristics, including seven genetic variants.