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- Baverel, Paul, et al.
(författare)
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A novel covariate search method intended for PKPD models with nonparametric parameter distributions
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Objective. To develop a new covariate modeling approach adapted for nonparametric parameter distributions and to evaluate its statistical properties in terms of power and type-I error rate of covariate inclusion.Methods. The proposed methodology is articulated around the decomposition of the nonparametric joint density obtained in NONMEM into a set of unique individual probability density distributions. These individual probabilities are then exported into R and used as weighting factors of a generalized additive model (GAM) regressing support points on covariate distributions. A calibration of the method is undertaken by means of 1000 randomization tests automated with GAM analyses to derive a decision criterion based on the Akaike’s information criterion (AIC) given the null hypothesis and a user-defined confidence level α. Statistical properties of the proposed methodology were then evaluated through Monte-Carlo simulations with α=5%. Eight scenarios of 1000 stochastic simulations followed by estimations (SSEs) were performed under FOCE-NONP given a 1-compartment pharmacokinetic model and an informative design. Estimates of the statistical power of inclusion of both a continuous and a categorical covariates with varying correlation strengths on CL were obtained with associated estimates of type-I error rate. A comparison was then intended with likelihood ratio test statistics (LRTs) given FOCE parameter distributions. Errors in estimates of correlation coefficients were further assessed.Results. The methodology was successfully implemented by means of a Perl script calling PsN, NONMEM and R. Estimates of statistical power and type-I error rate of the proposed method were in close agreement with LRT statistics under ideal conditions of hypothesis-testing for the latter, and this, regardless of the correlation strengths and of the attributes of the covariate distribution investigated. Estimates of regression coefficients presented negligible bias and were as precise as the ones obtained with parametric models.Conclusions. The set of covariate analysis tools is extended with a new, calibrated, covariate identification technique intended for nonparametric population models.
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| 2. |
- Fang, Lanyan, et al.
(författare)
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The Role of Model Master Files for Sharing, Acceptance, and Communication with FDA
- 2024
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Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 26:2
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Tidskriftsartikel (refereegranskat)abstract
- With the evolving role of Model Integrated Evidence (MIE) in generic drug development and regulatory applications, the need for improving Model Sharing, Acceptance, and Communication with the FDA is warranted. Model Master File (MMF) refers to a quantitative model or a modeling platform that has undergone sufficient model Verification & Validation to be recognized as sharable intellectual property that is acceptable for regulatory purposes. MMF provides a framework for regulatorily acceptable modeling practice, which can be used with confidence to support MIE by both the industry and the U.S. Food and Drug Administration (FDA). In 2022, the FDA and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop to discuss the best practices for utilizing modeling approaches to support generic product development. This report summarizes the presentations and panel discussions of the workshop symposium entitled "Model Sharing, Acceptance, and Communication with the FDA". The symposium and this report serve as a kick-off discussion for further utilities of MMF and best practices of utilizing MMF in drug development and regulatory submissions. The potential advantages of MMFs have garnered acknowledgment from model developers, industries, and the FDA throughout the workshop. To foster a unified comprehension of MMFs and establish best practices for their application, further dialogue and cooperation among stakeholders are imperative. To this end, a subsequent workshop is scheduled for May 2-3, 2024, in Rockville, Maryland, aiming to delve into the practical facets and best practices of MMFs pertinent to regulatory submissions involving modeling and simulation methodologies.
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| 4. |
- Sundell, Kerstin, et al.
(författare)
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Variable piperaquine exposure significantly impacts protective efficacy of monthly dihydroartemisinin-piperaquine for the prevention of malaria in Ugandan children
- 2015
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Ingår i: Malaria Journal. - : Springer Science and Business Media LLC. - 1475-2875. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Background: Anti-malarial chemoprevention with dihydroartemisinin-piperaquine (DHA/PQ) is a promising tool for malaria control, but its efficacy in children may be limited by inadequate drug exposure. Methods: Children were enrolled in a non directly-observed trial of DHA/PQ chemoprevention in a high transmission setting in Uganda. Children were randomized at 6 months of age to no chemoprevention (n = 89) or monthly DHA/PQ (n = 87) and followed through 24 months of age, with pharmacokinetic sampling performed at variable times following monthly dosing of DHA/PQ. A previously published pharmacokinetic model was used to estimate piperaquine (PQ) exposure in each child, and associations between PQ exposure and the protective efficacy (PE) of DHA/PQ were explored. Results: The incidence of malaria was 6.83 and 3.09 episodes per person year at risk in the no chemoprevention and DHA/PQ arms, respectively (PE 54 %, 95 % CI 39-66 %, P < 0.001). Among children randomized to DHA/PQ, 493 pharmacokinetic samples were collected. Despite nearly 100 % reported adherence to study drug administration at home, there was wide variability in PQ exposure, and children were stratified into three groups based on average PQ exposure during the intervention that was determined by model generated percentiles (low, n = 40; medium, n = 37, and high, n = 10). Gender and socioeconomic factors were not significantly associated with PQ exposure. In multivariate models, the PE of DHA/PQ was 31 % in the low PQ exposure group (95 % CI 6-49 %, P = 0.02), 67 % in the medium PQ exposure group (95 % CI 54-76 %, P < 0.001), and 97 % in the high PQ exposure group (95 % CI 89-99 %, P < 0.001). Conclusions: The protective efficacy of DHA/PQ chemoprevention in young children was strongly associated with higher drug exposure; in children with the highest PQ exposure, monthly DHA/PQ chemoprevention was nearly 100 % protective against malaria. Strategies to ensure good adherence to monthly dosing and optimize drug exposure are critical to maximize the efficacy of this promising malaria control strategy.
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