Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic end points.

By applying nonlinear mixed-effect (NLME) models, model-integrated evidence (MIE) approaches are able to analyze bioequivalence (BE) data with pharmacokinetic end points that have sparse sampling, which is problematic for non-compartmental analysis (NCA). However, MIE approaches may suffer from inflation of type I error due to underestimation of parameter uncertainty and to the assumption of asymptotic normality. In this study, we developed a MIE BE analysis method that is based on a pre-defined model and consists of several steps including model fitting, uncertainty assessment, simulation, and BE determination. The presented MIE approach has several improvements compared with the previously reported model-integrated methods: (1) treatment, sequence, and period effects are only added to absorption parameters (such as relative bioavailability and rate of absorption) instead of all PK parameters; (2) a simulation step is performed to generate confidence intervals of the pharmacokinetic metrics for BE assessment; and (3) in an effort to maintain type I error, two more advanced parameter uncertainty evaluation approaches are explored, a nonparametric (case resampling) bootstrap, and sampling importance resampling (SIR). To evaluate the developed method and compare the uncertainty assessment methods, simulation experiments were performed for BE studies using a two-way crossover design with different amounts of information (sparse to rich designs) and levels of variability. Based on the simulation results, the method using SIR for parameter uncertainty quantification controls type I error at the nominal level of 0.05 (i.e., the significance level set for BE evaluation) even for studies with small sample size and/or sparse sampling. As expected, our MIE approach for BE assessment exhibited higher power than the NCA-based method, especially as the data becomes sparser and/or more variable.

A pharmacometric multistate model for predicting long-term treatment outcomes of patients with pulmonary TB.

BACKGROUND: Studying long-term treatment outcomes of TB is time-consuming and impractical. Early and reliable biomarkers reflecting treatment response and capable of predicting long-term outcomes are urgently needed. OBJECTIVES: To develop a pharmacometric multistate model to evaluate the link between potential predictors and long-term outcomes. METHODS: Data were obtained from two Phase II clinical trials (TMC207-C208 and TMC207-C209) with bedaquiline on top of a multidrug background regimen. Patients were typically followed throughout a 24 week investigational treatment period plus a 96 week follow-up period. A five-state multistate model (active TB, converted, recurrent TB, dropout, and death) was developed to describe observed transitions. Evaluated predictors included patient characteristics, baseline TB disease severity and on-treatment biomarkers. RESULTS: A fast bacterial clearance in the first 2 weeks and low TB bacterial burden at baseline increased probability to achieve conversion, whereas patients with XDR-TB were less likely to reach conversion. Higher estimated mycobacterial load at the end of 24 week treatment increased the probability of recurrence. At 120 weeks, the model predicted 55% (95% prediction interval, 50%-60%), 6.5% (4.2%-9.0%) and 7.5% (5.2%-10%) of patients in converted, recurrent TB and death states, respectively. Simulations predicted a substantial increase of recurrence after 24 weeks in patients with slow bacterial clearance regardless of baseline bacterial burden. CONCLUSIONS: The developed multistate model successfully described TB treatment outcomes. The multistate modelling framework enables prediction of several outcomes simultaneously, and allows mechanistically sound investigation of novel promising predictors. This may help support future biomarker evaluation, clinical trial design and analysis.

Pharmacogenetic Testing or Therapeutic Drug Monitoring: A Quantitative Framework.

BACKGROUND: Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV]). METHODS: Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV. RESULTS: Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred. CONCLUSIONS: For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects.

Physiologically-based pharmacokinetic models versus allometric scaling for prediction of tyrosine-kinase inhibitor exposure from adults to children.

PURPOSE: Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption. METHODS: Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values. RESULTS: Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations. CONCLUSION: Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.

Item performance of the scale for the assessment and rating of ataxia in rare and ultra-rare genetic ataxias.

The Scale for the Assessment and Rating of Ataxia (SARA) is widely used for assessing the severity and progression of genetic cerebellar ataxias. SARA is now considered a primary end point in several ataxia treatment trials, but its underlying composite item measurement model has not yet been tested. This work aimed to evaluate the composite properties of SARA and its items using item response theory (IRT) and to demonstrate its applicability across even ultra-rare genetic ataxias. Leveraging SARA subscores data from 1932 visits from 990 patients of the Autosomal Recessive Cerebellar Ataxias (ARCA) registry, we assessed the performance of SARA using IRT methodology. The item characteristics were evaluated over the ataxia severity range of the entire ataxia population as well as the assessment validity across 115 genetic ARCA subpopulations. A unidimensional IRT model was able to describe SARA item data, indicating that SARA captures one single latent variable. All items had high discrimination values (1.5-2.9) indicating the effectiveness of the SARA in differentiating between subjects with different disease statuses. Each item contributed between 7% and 28% of the total assessment informativeness. There was no evidence for differences between the 115 genetic ARCA subpopulations in SARA applicability. These results show the good discrimination ability of SARA with all of its items adding informational value. The IRT framework provides a thorough description of SARA on the item level, and facilitates its utilization as a clinical outcome assessment in upcoming longitudinal natural history or treatment trials, across a large number of ataxias, including ultra-rare ones.

Covariate modeling in pharmacometrics: General points for consideration.

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

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. We present a new method to investigate item-specific effects in IRT models, which is based on inspection of residuals. The method was investigated in a simulation exercise with a model for the Epworth Sleepiness Scale. We also provide a detailed discussion as a guidance on how to build a robust covariate IRT model.

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. Therefore, it is of interest to use patient-reported outcomes like the Epworth Sleepiness Scale (ESS), which measures daytime sleepiness, as surrogate end points. We investigate the link between AHI and ESS, via item response theory (IRT) modeling. Through the developed IRT model it was identified that AHI and ESS are not correlated to any high degree and probably not measuring the same sleepiness construct. No covariate relationships of clinical relevance were found. This suggests that ESS is a poor choice as an end point for clinical development if treatment is targeted at improving AHI, and especially so in a mild OSA patient group.

Predicting the Long-Term Effects of Therapeutic Neutralization of Oncostatin M on Human Hematopoiesis.

Therapeutic neutralization of Oncostatin M (OSM) causes mechanism-driven anemia and thrombocytopenia, which narrows the therapeutic window complicating the selection of doses (and dosing intervals) that optimize efficacy and safety. We utilized clinical data from studies of an anti-OSM monoclonal antibody (GSK2330811) in healthy volunteers (n = 49) and systemic sclerosis patients (n = 35), to quantitatively determine the link between OSM and alterations in red blood cell (RBC) and platelet production. Longitudinal changes in hematopoietic variables (including RBCs, reticulocytes, platelets, erythropoietin, and thrombopoietin) were linked in a physiology-based model, to capture the long-term effects and variability of therapeutic OSM neutralization on human hematopoiesis. Free serum OSM stimulated precursor cell production through sigmoidal relations, with higher maximum suppression (Imax) and OSM concentration for 50% suppression (IC50) for platelets (89.1% [95% confidence interval: 83.4-93.0], 6.03 pg/mL [4.41-8.26]) than RBCs (57.0% [49.7-64.0], 2.93 pg/mL [2.55-3.36]). Reduction in hemoglobin and platelets increased erythro- and thrombopoietin, respectively, prompting reticulocytosis and (partially) alleviating OSM-restricted hematopoiesis. The physiology-based model was substantiated by preclinical data and utilized in exploration of once-weekly or every other week dosing regimens. Predictions revealed an (for the indication) unacceptable occurrence of grade 2 (67% [58-76], 29% [20-38]) and grade 3 (17% [10-25], 3% [0-7]) anemias, with limited thrombocytopenia. Individual extent of RBC precursor modulation was moderately correlated to skin mRNA gene expression changes. The physiological basis and consideration of interplay among hematopoietic variables makes the model generalizable to other drug and nondrug scenarios, with adaptations for patient populations, diseases, and therapeutics that modulate hematopoiesis or exhibit risk of anemia and/or thrombocytopenia.

Comparison of Two Methods for Determining Item Characteristic Functions and Latent Variable Time-Course for Pharmacometric Item Response Models.

There are examples in the literature demonstrating different approaches to defining the item characteristic functions (ICF) and characterizing the latent variable time-course within a pharmacometrics item response theory (IRT) framework. One such method estimates both the ICF and latent variable time-course simultaneously, and another method establishes the ICF first then models the latent variable directly. To date, a direct comparison of the "simultaneous" and "sequential" methodologies described in this work has not yet been systematically investigated. Item parameters from a graded response IRT model developed from Parkinson's Progression Marker Initiative (PPMI) study data were used as simulation parameters. Each method was evaluated under the following conditions: (i) with and without drug effect and (ii) slow progression rate with smaller sample size and rapid progression rate with larger sample size. Overall, the methods performed similarly, with low bias and good precision for key parameters and hypothesis testing for drug effect. The ICF parameters were well determined when the model was correctly specified, with an increase in precision in the scenario with rapid progression. In terms of drug effect, both methods had large estimation bias for the slow progression rate; however, this bias can be considered small relative to overall progression rate. Both methods demonstrated type 1 error control and similar discrimination between model with and without drug effect. The simultaneous method was slightly more precise than the sequential method while the sequential method was more robust towards longitudinal model misspecification and offers practical advantages in model building.

Increase in peg-asparaginase clearance as a predictor for inactivation in patients with acute lymphoblastic leukemia.

Asparaginase is an essential component of acute lymphoblastic leukemia (ALL) therapy, yet its associated toxicities often lead to treatment discontinuation, increasing the risk of relapse. Hypersensitivity reactions include clinical allergies, silent inactivation, or allergy-like responses. We hypothesized that even moderate increases in asparaginase clearance are related to later inactivation. We therefore explored mandatory monitoring of asparaginase enzyme activity (AEA) in patients with ALL aged 1-45 years treated according to the ALLTogether pilot protocol in the Nordic and Baltic countries to relate mean AEA to inactivation, to build a pharmacokinetic model to better characterize the pharmacokinetics of peg-asparaginase and assess whether an increased clearance relates to subsequent inactivation. The study analyzed 1631 real-time AEA samples from 253 patients, identifying inactivation in 18.2% of the patients. This inactivation presented as mild allergy (28.3%), severe allergy (50.0%), or silent inactivation (21.7%). A pharmacokinetic transit compartment model was used to describe AEA-time profiles, revealing that 93% of patients with inactivation exhibited prior increased clearance, whereas 86% of patients without hypersensitivity maintained stable clearance throughout asparaginase treatment. These findings enable prediction of inactivation and options for either dose increments or a shift to alternative asparaginase formulations to optimize ALL treatment strategies.

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. A global health dataset (20 421 children) was used to develop a FREM describing the changes of height for age Z-score (HAZ) over time. Simulated datasets (n = 1000) were generated with variable rates of missing (MCAR) covariate data (0%-90%) and different proportions of missing (MAR) data condition on either observed covariates or predicted HAZ. The three methods were used to re-estimate model and compared in terms of bias and precision which showed that FREM had only minor increases in bias and minor loss of precision at increasing percentages of missing (MCAR) covariate data and performed similarly in the MAR scenarios. Conversely, the FFEM approaches either collapsed at ≥ $$ \ge $$ 70% of missing (MCAR) covariate data (FFEM complete case analysis) or had large bias increases and loss of precision (FFEM with mean imputation). Our results suggest that FREM is an appropriate approach to covariate modeling for datasets with missing (MCAR and MAR) covariate data, such as in global health studies.

Model-Informed Precision Dosing to Reduce Vincristine-Induced Peripheral Neuropathy in Pediatric Patients: A Pharmacokinetic and Pharmacodynamic Modeling and Simulation Analysis.

BACKGROUND: Vincristine-induced peripheral neuropathy (VIPN) is a common adverse effect of vincristine, a drug often used in pediatric oncology. Previous studies demonstrated large inter- and intrapatient variability in vincristine pharmacokinetics (PK). Model-informed precision dosing (MIPD) can be applied to calculate patient exposure and individualize dosing using therapeutic drug monitoring (TDM) measurements. This study set out to investigate the PK/pharmacodynamic (PKPD) relationship of VIPN and determine the utility of MIPD to support clinical decisions regarding dose selection and individualization. METHODS: Data from 35 pediatric patients were utilized to quantify the relationship between vincristine dose, exposure and the development of VIPN. Measurements of vincristine exposure and VIPN (Common Terminology Criteria for Adverse Events [CTCAE]) were available at baseline and for each subsequent dosing occasions (1-5). A PK and PKPD analysis was performed to assess the inter- and intraindividual variability in vincristine exposure and VIPN over time. In silico trials were performed to portray the utility of vincristine MIPD in pediatric subpopulations with a certain age, weight and cytochrome P450 (CYP) 3A5 genotype distribution. RESULTS: A two-compartmental model with linear PK provided a good description of the vincristine exposure data. Clearance and distribution parameters were related to bodyweight through allometric scaling. A proportional odds model with Markovian elements described the incidence of Grades 0, 1 and ≥ 2 VIPN overdosing occasions. Vincristine area under the curve (AUC) was the most significant exposure metric related to the development of VIPN, where an AUC of 50 ng⋅h/mL was estimated to be related to an average VIPN probability of 40% over five dosing occasions. The incidence of Grade ≥ 2 VIPN reduced from 62.1 to 53.9% for MIPD-based dosing compared with body surface area (BSA)-based dosing in patients. Dose decreases occurred in 81.4% of patients with MIPD (vs. 86.4% for standard dosing) and dose increments were performed in 33.4% of patients (no dose increments allowed for standard dosing). CONCLUSIONS: The PK and PKPD analysis supports the use of MIPD to guide clinical dose decisions and reduce the incidence of VIPN. The current work can be used to support decisions with respect to dose selection and dose individualization in children receiving vincristine.