Dagmar M Hajducek1, Pierre Chelle1, Cedric Hermans2, Alfonso Iorio3, Alanna McEneny-King1, Jacky Yu1, Andrea Edginton1. 1. School of Pharmacy, University of Waterloo, Waterloo, ON, Canada. 2. Haemostasis and Thrombosis Unit, Division of Haematology, Cliniques universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium. 3. McMaster-Bayer Endowed Research Chair for Clinical Epidemiology of Congenital Bleeding Disorders, Department of Medicine, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada.
Abstract
BACKGROUND: The Web-Accessible Population Pharmacokinetic Service (WAPPS) project generates individually predicted pharmacokinetic (PK) profiles and tailored prophylactic treatment regimens for haemophilic patients, which rely on a set of population PK (PopPK) models providing concentrate-specific priors for the Bayesian forecasting methodology. AIM: To describe the predictive performance of the WAPPS PopPK models in use on the WAPPS-Hemo platform. METHODS: Data for modelling include dense PK data obtained from industry sponsored and independent PK studies, and dense and sparse data accumulated through WAPPS-Hemo. WAPPS PopPK models were developed via non-linear mixed-effect modelling taking into account the effects of covariates and between-individual-and sometimes between-occasion-variability. Model evaluation consisted of (a) prediction-corrected Visual Predictive Check (pcVPC), (b) Limited Sampling Analysis (LSA) and (c) repeated hold-out cross-validation. RESULTS: Thirty-three WAPPS PopPK models built on data from 3188 patients (ages 1-78 years) under treatment by factor VIII or IX products (FVIII, FIX) were evaluated. Overall, models exhibit excellent performance characteristics. The pcVPC shows that the observed PK data fall within acceptable 90% interpercentile predictive bands. A slight overprediction beyond the expected half-life, an anticipated result of using sparse data, occurs for some models. The LSA results in lower than 3% of relative error for FVIII and FIX products and 16% for engineered FIX products. Cross-Validation analysis yields relative errors lower than 1.5% and 1.4% in estimates of half-life and time to 0.02 IU/mL, respectively. CONCLUSION: The WAPPS-Hemo models consistently showed excellent performance characteristics for the intended use for Bayesian forecasting of individual PK profiles.
BACKGROUND: The Web-Accessible Population Pharmacokinetic Service (WAPPS) project generates individually predicted pharmacokinetic (PK) profiles and tailored prophylactic treatment regimens for haemophilic patients, which rely on a set of population PK (PopPK) models providing concentrate-specific priors for the Bayesian forecasting methodology. AIM: To describe the predictive performance of the WAPPS PopPK models in use on the WAPPS-Hemo platform. METHODS: Data for modelling include dense PK data obtained from industry sponsored and independent PK studies, and dense and sparse data accumulated through WAPPS-Hemo. WAPPS PopPK models were developed via non-linear mixed-effect modelling taking into account the effects of covariates and between-individual-and sometimes between-occasion-variability. Model evaluation consisted of (a) prediction-corrected Visual Predictive Check (pcVPC), (b) Limited Sampling Analysis (LSA) and (c) repeated hold-out cross-validation. RESULTS: Thirty-three WAPPS PopPK models built on data from 3188 patients (ages 1-78 years) under treatment by factor VIII or IX products (FVIII, FIX) were evaluated. Overall, models exhibit excellent performance characteristics. The pcVPC shows that the observed PK data fall within acceptable 90% interpercentile predictive bands. A slight overprediction beyond the expected half-life, an anticipated result of using sparse data, occurs for some models. The LSA results in lower than 3% of relative error for FVIII and FIX products and 16% for engineered FIX products. Cross-Validation analysis yields relative errors lower than 1.5% and 1.4% in estimates of half-life and time to 0.02 IU/mL, respectively. CONCLUSION: The WAPPS-Hemo models consistently showed excellent performance characteristics for the intended use for Bayesian forecasting of individual PK profiles.
Authors: Victor S Blanchette; Laura Zunino; Viviane Grassmann; Chris Barnes; Manuel D Carcao; Julie Curtin; Shannon Jackson; Liane Khoo; Vladimir Komrska; David Lillicrap; Massimo Morfini; Gabriela Romanova; Derek Stephens; Ester Zapotocka; Margaret L Rand; Jan Blatny Journal: Thromb Haemost Date: 2021-04-14 Impact factor: 6.681
Authors: Pierre Chelle; Dagmar Hajducek; Mohammed Mahdi; Stuart Young; Alfonso Iorio; Josh Silvertown; Andrea Edginton Journal: Res Pract Thromb Haemost Date: 2021-11-02
Authors: Olav Versloot; Emma Iserman; Pierre Chelle; Federico Germini; Andrea N Edginton; Roger E G Schutgens; Alfonso Iorio; Kathelijn Fischer Journal: Hemasphere Date: 2022-03-21
Authors: Tim Preijers; Lisette M Schütte; Marieke J H A Kruip; Marjon H Cnossen; Frank W G Leebeek; Reinier M van Hest; Ron A A Mathôt Journal: Clin Pharmacokinet Date: 2021-01 Impact factor: 6.447