No abstract

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). We present a method, called the bounded integer (BI) model, which utilises the probit function with fixed cut-offs to estimate the probability of a certain score through a latent variable. This method was successfully implemented to describe six data sets from four different therapeutic areas: Parkinson’s disease, Alzheimer’s disease, schizophrenia, and neuropathic pain. Five scales were investigated, ranging from 11 to 181 categories. The fit (likelihood) was better for the BI model than for corresponding OC or CV models (ΔAIC range 11–1555) in all cases but one (∆AIC − 63), while the number of parameters was the same or lower. Markovian elements were successfully implemented within the method. The performance in external validation, assessed through cross-validation, was also in favour of the new model (ΔOFV range 22–1694) except in one case (∆OFV − 70). A residual for diagnostic purposes is discussed. This study shows that the BI model respects the integer nature of data and is parsimonious in terms of number of estimated parameters. Electronic supplementary material The online version of this article (10.1208/s12248-019-0343-9) contains supplementary material, which is available to authorized users.

Imeglimin is an orally administered first‐in‐class drug to treat type 2 diabetes mellitus (T2DM) and is mainly excreted unchanged by the kidneys. The present study aimed to define the pharmacokinetic (PK) characteristics of imeglimin using population PK analysis and to determine the optimal dosing regimen for Japanese patients with T2DM and chronic kidney disease (CKD). Imeglimin plasma concentrations in Japanese and Western healthy volunteers, and patients with T2DM, including patients with mild to severe CKD with an estimated glomerular filtration rate (eGFR) greater than 14 ml/min/1.73 m 2 were included in a population PK analysis. PK simulations were conducted using a population PK model, and the area under concentration‐time curve (AUC) was extrapolated with power regression analysis to lower eGFR. The influence of eGFR, weight, and age on apparent clearance and of dose on relative bioavailability were quantified by population PK analysis. Simulations and extrapolation revealed that the recommended dosing regimen based on the AUC was 500 mg twice daily (b.i.d.) for patients with eGFR 15–45 ml/min/1.73 m 2 , and 500 mg with a longer dosing interval was suggested for those with eGFR less than 15. Simulations revealed that differences in plasma AUCs between Japanese and Western patients at the same dose were mainly driven by a difference in the eGFR and that the plasma AUC after 1000 and 1500 mg b.i.d. in Japanese and Western patients, respectively, was comparable in the phase IIb studies. These results indicate suitable dosages of imeglimin in the clinical setting of T2DM with renal impairment.

Reusing published models saves time; time to be used for informing decisions in drug development. In antihyperglycemic drug development, several published HbA1c models are available but selecting the appropriate model for a particular purpose is challenging. This study aims at helping selection by investigating four HbA1c models, specifically the ability to identify drug effects (shape, site of action, and power) and simulation properties. All models could identify glucose effect nonlinearities, although for detecting the site of action, a mechanistic glucose model was needed. Power was highest for models using mean plasma glucose to drive HbA1c formation. Insulin contribution to power varied greatly depending on the drug target; it was beneficial only if the drug target was insulin secretion. All investigated models showed good simulation properties. However, extrapolation with the mechanistic model beyond 12 weeks resulted in drug effect overprediction. This investigation aids drug development in decisions regarding model choice if reusing published HbA1c models.

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. Data from three studies of healthy individuals with PK measurements of plasma and urine after single dose or at steady‐state were used in this study. In total, 87 subjects were included (16 crossover subjects). Single nucleotide polymorphisms in ATM, OCT1, OCT2, MATE1 and MATE2‐K were investigated as dominant, recessive or additive. A three‐compartment model with transit absorption and renal elimination with a proportional error was fitted to the data using NONMEM 7.3. Oral parameters were separated from disposition parameters as dose‐dependent absolute bioavailability was determined with support from urine data. Clearance was expressed as net renal secretion and filtration, assuming full fraction unbound and fraction excreted. Mean transit time and peripheral volume of distribution were identified as the main sources of variability according to estimates, with 94% IOV and 95% IIV, respectively. Clearance contributed only with 16% IIV. Glomerular filtration rate and body‐weight were the only covariates found to affect metformin net secretion, reducing IIV to 14%. None of the genetic variants were found to affect metformin PK. Based on our analysis, finding covariates explaining absorption of metformin is much more valuable in understanding variability and avoiding toxicity than elimination.

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. However, IRT models require item-level data while sometimes only TS is available. This work investigates models for TS. When an IRT model exists, it can be used to derive the information as well as expected mean and variability of TS at any point, which can inform TS-analyses. We propose a new method: IRT-informed functions of expected values and standard deviation in TS-analyses. The most common models for TS-analyses are continuous variable (CV) models, while bounded integer (BI) models offer an alternative that respects scale boundaries and the nature of TS data. We investigate the method in CV and BI models on both simulated and real data. Both CV and BI models were improved in fit by IRT-informed disease progression, which allows modellers to precisely and accurately find the corresponding latent variable parameters, and IRT-informed SD, which allows deviations from homoscedasticity. The methodology provides a formal way to link IRT models and TS models, and to compare the relative information of different model types. Also, joint analyses of item-level data and TS data are made possible. Thus, IRT-informed functions can facilitate total score analysis and allow a quantitative analysis of relative merits of different analysis methods.

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. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM. Results The DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS). Conclusions DR-MMRM is a promising method for dose-response analysis.

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. Bounded integer (BI) models do respect the data nature but are not as extensively researched. Mixed models for repeated measures (MMRM) are an alternative that requires few assumptions and handles dropout without bias. If an IRT model exists, the expected mean and standard deviation of TS can be computed through IRT-informed functions—which allows CV and BI models to estimate parameters on the IRT scale. The fit, performance on external data and parameter precision (when applicable) of CV, BI and MMRM to analyse simulated TS data from the MDS-UPDRS motor subscale are investigated in this work. All models provided accurate predictions and residuals without trends, but the fit of CV and BI models was improved by IRT-informed functions. The IRT-informed BI model had more precise parameter estimates than the IRT-informed CV model. The IRT-informed models also had the best performance on external data, while the MMRM model was worst. In conclusion, (1) IRT-informed functions improve TS analyses and (2) IRT-informed BI models had more precise IRT parameter estimates than IRT-informed CV models.