Development and evaluation of a generic population pharmacokinetic model for standard half-life factor VIII for use in dose individualization

McEneny‐King, Alanna; Chelle, Pierre; Foster, Gary; Keepanasseril, Arun; Iorio, Alfonso; Edginton, Andrea N.

doi:10.1007/s10928-019-09634-7

Cited by 31 publications

(50 citation statements)

References 37 publications

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“…The BSV for one FVIII seems to be equivalent to the BSV generated with many brands of FVIII, as mentioned by McEneny-King et al [21]. This is confirmed by the fact that the inclusion of a covariate brand or structure did not significantly reduce the BSV.…”

Section: Discussionsupporting

confidence: 70%

“…The population parameters were comparable to those that were estimated in the published generic SHL FVIII model [21]. Our model had a correlation between V1 and Cl and, despite the heterogeneity of 'coagulation analyser-APTT reagent', it presented a combined error model.…”

Section: Discussionsupporting

confidence: 69%

“…We externally compared the individual parameters predictions of our model while using those that were estimated with the generic SHL model of McEneny-King et al [21]. The results confirmed that their model is capable of predicting individual brands outside the original database, EHL.…”

Section: Discussionsupporting

confidence: 60%

“…Mc Eneny-King et al [21] developed a generic population PK model for SHL FVIII. They pooled in their model seven FVIII: Advate, Kovaltry, Kogenate, Novoeight, Refacto AF, Emoclot, and Octonate.…”

Section: External Comparison Of Half-life Estimates Between Our Modelmentioning

confidence: 99%

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Real Life Population Pharmacokinetics Modelling of Eight Factors VIII in Patients with Severe Haemophilia A: Is It Always Relevant to Switch to an Extended Half-Life?

et al. 2020

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We retrospectively analysed the data files of 171 adults and 87 children/adolescents with severe haemophilia, except for 14 patients (moderate; minor) (1), to develop a global population pharmacokinetic (PK) model for eight factors VIII (FVIII) that could estimate individual PK parameters for targeting the desired level of FVIII activity (FVIII:C); and (2) to compare half-life (HL) in patients switching from a standard half-life (SHL) to an extended half-life (EHL) and evaluate the relevance of the switch. One-stage clotting assay for the measurement of FVIII activity (FVIII:C, IU/mL) was used for population PK modelling. The software, Monolix version 2019R1, was used for non-linear mixed-effects modelling. A linear two-compartment model best described FVIII:C. The estimated PK parameters (between-subject variability) were: 2640 mL (23.2%) for volume of central compartment (V1), 339 mL (46.8%) for volume of peripheral compartment (V2), 135 mL/h for Q (fixed random effect), and 204 mL/h (34.9%) for clearance (Cl). Weight, age, and categorical covariate EHL were found to influence Cl and only weight for V1. This model can be used for all of the FVIII cited in the study. Moreover, we demonstrated, in accordance with previous studies, that Elocta had longer half-life (EHL) than SHL (mean ratio: 1.48) as compared to Advate, Factane, Kogenate, Novoeight, and Refacto.

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Section: Discussionsupporting

confidence: 70%

Section: Discussionsupporting

confidence: 69%

Section: Discussionsupporting

confidence: 60%

Section: External Comparison Of Half-life Estimates Between Our Modelmentioning

confidence: 99%

See 2 more Smart Citations

Real Life Population Pharmacokinetics Modelling of Eight Factors VIII in Patients with Severe Haemophilia A: Is It Always Relevant to Switch to an Extended Half-Life?

et al. 2020

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“…As this resulted in a considerable increase of OFV (158 points) and the magnitude of unexplained IIV in V c more than doubled, LBW was preferred. This finding is in line with the recent study by McEneny-King et al [15].…”

Section: Covariate Analysis and Final Modelsupporting

confidence: 94%

Favorable Pharmacokinetic Characteristics of Extended-Half-Life Recombinant Factor VIII BAY 94-9027 Enable Robust Individual Profiling Using a Population Pharmacokinetic Approach

Solms

Iorio

Ahsman³

et al. 2019

Clin Pharmacokinet

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Background Prophylaxis with factor VIII (FVIII) should be individualized based on patient characteristics, including FVIII pharmacokinetics. Population pharmacokinetic (popPK) modeling simplifies pharmacokinetic studies by obviating the need for multiple samples.Objective The objective of this study was to characterize the pharmacokinetics and inter-individual variability (IIV) of BAY 94-9027 in relation to patient characteristics in support of a popPK-tailored approach, including identifying the optimal number and timing of pharmacokinetic samples. Methods Pharmacokinetic samples from 198 males (aged 2-62 years) with severe hemophilia A, enrolled in BAY 94-9027 clinical trials, were analyzed. Baseline age, height, weight, body mass index, lean body weight (LBW), von Willebrand factor (VWF) level, and race were evaluated. A popPK model was developed and used to simulate pharmacokinetic endpoints difficult to observe from measured FVIII levels, including time to maintain FVIII levels above 1, 3, and 5 IU/dL after different BAY 94-9027 doses. Results A one-compartment model adequately described BAY 94-9027 pharmacokinetics. Clearance and central volume of distribution were significantly associated with LBW; clearance was inversely correlated with VWF. Due to the monophasic pharmacokinetics and well-understood IIV sources, identification of patient pharmacokinetics was achievable with sparse blood sampling. Median predicted time to maintain FVIII levels > 1 IU/dL in patients aged ≥ 12 years ranged from 120.1 to 127.2 h after single BAY 94-9027 doses of 45-60 IU/kg. Conclusions This analysis evaluated the pharmacokinetics of BAY 94-9027 and its sources of IIV. Using the model, determination of individual patient pharmacokinetics was possible with few FVIII samples, and a sparse sampling design to support pharmacokinetic-guided dosing was identified. Key PointsA robust population pharmacokinetics (popPK) model was derived for BAY 94-9027 using data from three BAY 94-9027 clinical trials, including 198 patients with severe hemophilia A.BAY 94-9027 pharmacokinetics were adequately described using a one-compartment model in which lean body weight explained much of the inter-patient variability seen in clearance and volume of distribution of the central compartment.The popPK model can be used to accurately determine BAY 94-9027 pharmacokinetic profiles for individual patients using sparse blood sampling.

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A Generative and Causal Pharmacokinetic Model for Factor VIII in Hemophilia A: A Machine Learning Framework for Continuous Model Refinement

Janssen,

Smalbil,

Bennis

et al. 2024

Clin Pharma and Therapeutics

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In rare diseases, such as hemophilia A, the development of accurate population pharmacokinetic (PK) models is often hindered by the limited availability of data. Most PK models are specific to a single recombinant factor VIII (rFVIII) concentrate or measurement assay, and are generally unsuited for answering counterfactual (“what‐if”) queries. Ideally, data from multiple hemophilia treatment centers are combined but this is generally difficult as patient data are kept private. In this work, we utilize causal inference techniques to produce a hybrid machine learning (ML) PK model that corrects for differences between rFVIII concentrates and measurement assays. Next, we augment this model with a generative model that can simulate realistic virtual patients as well as impute missing data. This model can be shared instead of actual patient data, resolving privacy issues. The hybrid ML‐PK model was trained on chromogenic assay data of lonoctocog alfa and predictive performance was then evaluated on an external data set of patients who received octocog alfa with FVIII levels measured using the one‐stage assay. The model presented higher accuracy compared with three previous PK models developed on data similar to the external data set (root mean squared error = 14.6 IU/dL vs. mean of 17.7 IU/dL). Finally, we show that the generative model can be used to accurately impute missing data (< 18% error). In conclusion, the proposed approach introduces interesting new possibilities for model development. In the context of rare disease, the introduction of generative models facilitates sharing of synthetic data, enabling the iterative improvement of population PK models.

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Development and evaluation of a generic population pharmacokinetic model for standard half-life factor VIII for use in dose individualization

Cited by 31 publications

References 37 publications

Real Life Population Pharmacokinetics Modelling of Eight Factors VIII in Patients with Severe Haemophilia A: Is It Always Relevant to Switch to an Extended Half-Life?

Real Life Population Pharmacokinetics Modelling of Eight Factors VIII in Patients with Severe Haemophilia A: Is It Always Relevant to Switch to an Extended Half-Life?

Favorable Pharmacokinetic Characteristics of Extended-Half-Life Recombinant Factor VIII BAY 94-9027 Enable Robust Individual Profiling Using a Population Pharmacokinetic Approach

A Generative and Causal Pharmacokinetic Model for Factor VIII in Hemophilia A: A Machine Learning Framework for Continuous Model Refinement

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