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BackgroundDarunavir 800 mg once (q24h) or 600 mg twice (q12h) daily combined with low-dose ritonavir is used to treat HIV-positive pregnant women. Decreased total darunavir exposure (17%-50%) has been reported during pregnancy, but limited data on unbound exposure are available.ObjectivesTo evaluate total and unbound darunavir exposures following standard darunavir/ritonavir dosing and to explore the value of potential optimized darunavir/ritonavir dosing regimens for HIV-positive pregnant women.Patients and methodsA population pharmacokinetic analysis was conducted based on data from 85 women. The final model was used to simulate total and unbound darunavir AUC0-τ and Ctrough during the third trimester of pregnancy, as well as to assess the probability of therapeutic exposure.ResultsSimulations predicted that total darunavir exposure (AUC0-τ) was 24% and 23% lower in pregnancy for standard q24h and q12h dosing, respectively. Unbound darunavir AUC0-τ was 5% and 8% lower compared with post-partum for standard q24h and q12h dosing, respectively. The probability of therapeutic exposure (unbound) during pregnancy was higher for standard q12h dosing (99%) than for q24h dosing (94%).ConclusionsThe standard q12h regimen resulted in maximal and higher rates of therapeutic exposure compared with standard q24h dosing. Darunavir/ritonavir 600/100 mg q12h should therefore be the preferred regimen during pregnancy unless (adherence) issues dictate q24h dosing. The value of alternative dosing regimens seems limited.

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi‐step, challenging, and time‐consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open‐source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter‐individual variability (IIV) model, the inter‐occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development. [ABSTRACT FROM AUTHOR]

Conventional approaches for establishing bioequivalence (BE) between test and reference formulations using non‐compartmental analysis (NCA) may demonstrate low power in pharmacokinetic (PK) studies with sparse sampling. In this case, model‐integrated evidence (MIE) approaches for BE assessment have been shown to increase power, but may suffer from selection bias problems if models are built on the same data used for BE assessment. This work presents model averaging methods for BE evaluation and compares the power and type I error of these methods to conventional BE approaches for simulated studies of oral and ophthalmic formulations. Two model averaging methods were examined: bootstrap model selection and weight‐based model averaging with parameter uncertainty from three different sources, either from a sandwich covariance matrix, a bootstrap, or from sampling importance resampling (SIR). The proposed approaches increased power compared with conventional NCA‐based BE approaches, especially for the ophthalmic formulation scenarios, and were simultaneously able to adequately control type I error. In the rich sampling scenario considered for oral formulation, the weight‐based model averaging method with SIR uncertainty provided controlled type I error, that was closest to the target of 5%. In sparse‐sampling designs, especially the single sample ophthalmic scenarios, the type I error was best controlled by the bootstrap model selection method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This work aimed to evaluate the once-daily antituberculosis treatment as recommended by the new Indian pediatric guidelines. Isoniazid, rifampin, and pyrazinamide concentration-time profiles and treatment outcome were obtained from 161 Indian children with drug-sensitive tuberculosis undergoing thrice-weekly dosing as per previous Indian pediatric guidelines. The exposure-response relationships were established using a population pharmacokinetic-pharmacodynamic approach. Rifampin exposure was identified as the unique predictor of treatment outcome. Consequently, children with low body weight (4-7 kg) and/or HIV infection, who displayed the lowest rifampin exposure, were associated with the highest probability of unfavorable treatment (therapy failure, death) outcome (Punfavorable ). Model-based simulation of optimized (Punfavorable ≤ 5%) rifampin once-daily doses were suggested per treatment weight band and HIV coinfection status (33% and 190% dose increase, respectively, from the new Indian guidelines). The established dose-exposure-response relationship could be pivotal in the development of future pediatric tuberculosis treatment guidelines.

PurposeThe aims of this study were to characterize the concentration-time course of cladribine (CdA) and its main metabolite 2-chloroadenine (CAde), estimate interindividual variability in pharmacokinetics (PK), and identify covariates explaining variability in the PK of CdA.MethodsThis population PK analysis was based on the combined dataset from four clinical studies in patients with multiple sclerosis (MS): three phase I studies, including one food and one drug-drug interaction study, and one phase III clinical study. Plasma and urine concentration data of CdA and CAde were modeled simultaneously.ResultsThe analysis comprised a total of 2619 CdA and CAde plasma and urine concentration observations from 173 patients with MS who received an intravenous infusion or oral tablet doses of CdA as a single agent or in combination with interferon (IFN) β-1a. CdA PK data were best described by a three-compartment model, while a one-compartment model best described the PK of CAde. CdA renal clearance (CLR) was correlated with creatinine clearance (CLCR), predicting a decrease in the total clearance of 19%, 30% and 40% for patients with mild (CLCR = 65 ml/min), moderate (CLCR = 40 ml/min) and severe (CLCR = 20 ml/min) renal impairment, respectively. Food decreased the extent of CdA absorption by 11.2% and caused an absorption delay. Coadministration with IFNβ-1a was found to increase non-CLR (CLNR) by 21%, resulting in an increase of 11% in total clearance.ConclusionsBoth CdA and CAde displayed linear PK after intravenous and oral administration of CdA, with CdA renal function depending on CLCR. Trial registration number for study 25643: NCT00213135.

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. [ABSTRACT FROM AUTHOR]

Safe, effective concomitant treatment regimens for tuberculosis (TB) and HIV infection are urgently needed. Bedaquiline (BDQ) is a promising new anti-TB drug, and efavirenz (EFV) is a commonly used antiretroviral. Due to EFV's induction of cytochrome P450 3A4, the metabolic enzyme responsible for BDQ biotransformation, the drugs are expected to interact. Based on data from a phase I, single-dose pharmacokinetic study, a nonlinear mixed-effects model characterizing BDQ pharmacokinetics and interaction with multiple-dose EFV was developed. BDQ pharmacokinetics were best described by a 3-compartment disposition model with absorption through a dynamic transit compartment model. Metabolites M2 and M3 were described by 2-compartment models with clearance of BDQ and M2, respectively, as input. Impact of induction was described as an instantaneous change in clearance 1 week after initialization of EFV treatment and estimated for all compounds. The model predicts average steady-state concentrations of BDQ and M2 to be reduced by 52% (relative standard error [RSE], 3.7%) with chronic coadministration. A range of models with alternative structural assumptions regarding onset of induction effect and fraction metabolized resulted in similar estimates of the typical reduction and did not offer a markedly better fit to data. Simulations to investigate alternative regimens mitigating the estimated interaction effect were performed. The results suggest that simple adjustments of the standard regimen during EFV coadministration can prevent reduced exposure to BDQ without increasing exposures to M2. However, exposure to M3 would increase. Evaluation in clinical trials of adjusted regimens is necessary to ensure appropriate dosing for HIV-infected TB patients on an EFV-based regimen.

The pharmacokinetics/pharmacodynamics analysis software NONMEM® output provides model parameter estimates and associated standard errors. However, the standard error of empirical Bayes estimates of inter-subject variability is not available. A simple and direct method for estimating standard error of the empirical Bayes estimates of inter-subject variability using the NONMEM® VI internal matrix POSTV is developed and applied to several pharmacokinetic models using intensively or sparsely sampled data for demonstration and to evaluate performance. The computed standard error is in general similar to the results from other post-processing methods and the degree of difference, if any, depends on the employed estimation options.