TY - JOUR
T1 - Elevated acute phase proteins affect pharmacokinetics in COVID-19 trials
T2 - lessons from the CounterCovid - imatinib - study
AU - Bartelink, Imke H
AU - Bet, Pierre M
AU - Widmer, Nicolas
AU - Guidi, Monia
AU - Duijvelaar, Erik
AU - Grob, Bram
AU - Honeywell, Richard
AU - Evelo, Amanda
AU - Tielbeek, Ivo P E
AU - Snape, Sue D
AU - Hamer, Henrike
AU - Decosterd, Laurent A
AU - Bogaard, Harm Jan
AU - Aman, Jurjan
AU - Swart, Eleonora L
N1 - Funding Information: Innovative Medicines Initiative (IMI): Imke (Heleen) (Heleen) Bartelink, Pierre (M) Bet, Erik Duijvelaar, Richard (J) Honeywell, Sue Snape, Harm Jan Bogaard, Jurjan Aman, Eleonora (L) Swart 101005142; Netherlands Organisation for Health Research and Development (ZonMw): Imke (Heleen) (Heleen) Bartelink, Pierre (M) Bet, Erik Duijvelaar, Harm Jan Bogaard, Jurjan Aman 0430012010007. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement number 101005142. This Joint Undertaking receives support from the European Union?s Horizon 2020 research and innovation programme and EFPIA. For more information, see www.imi.europa.eu. We acknowledge the support of ZONMW. IMI Grant numbers: 101005142, ZonMw Grant numbers: 0430012010007. The authors thank Dr. Reinier van Hest and Prof. Dr. Ron Mathot and Kazien Mahmoud for their contribution and advice during preparation of this manuscript. Dr. Amina Haouala is also warmly thanked for providing the authors with the raw data of her previous work on free imatinib PK modeling. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No [101005142]. This Joint Undertaking receives support from the European Union?s Horizon 2020 research. Funding Information: The authors thank Dr. Reinier van Hest and Prof. Dr. Ron Mathot and Kazien Mahmoud for their contribution and advice during preparation of this manuscript. Dr. Amina Haouala is also warmly thanked for providing the authors with the raw data of her previous work on free imatinib PK modeling. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No [101005142]. This Joint Undertaking receives support from the European Union’s Horizon 2020 research. Funding Information: This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement number 101005142. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. For more information, see www.imi.europa.eu . We acknowledge the support of ZONMW. IMI Grant numbers: 101005142, ZonMw Grant numbers: 0430012010007. Publisher Copyright: © 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.
PY - 2021/12
Y1 - 2021/12
N2 - This study aimed to determine whether published pharmacokinetic (PK) models can adequately predict the PK profile of imatinib in a new indication, such as coronavirus disease 2019 (COVID-19). Total (bound + unbound) and unbound imatinib plasma concentrations obtained from 134 patients with COVID-19 participating in the CounterCovid study and from an historical dataset of 20 patients with gastrointestinal stromal tumor (GIST) and 85 patients with chronic myeloid leukemia (CML) were compared. Total imatinib area under the concentration time curve (AUC), maximum concentration (C max) and trough concentration (C trough) were 2.32-fold (95% confidence interval [CI] 1.34–3.29), 2.31-fold (95% CI 1.33–3.29), and 2.32-fold (95% CI 1.11–3.53) lower, respectively, for patients with CML/GIST compared with patients with COVID-19, whereas unbound concentrations were comparable among groups. Inclusion of alpha1-acid glycoprotein (AAG) concentrations measured in patients with COVID-19 into a previously published model developed to predict free imatinib concentrations in patients with GIST using total imatinib and plasma AAG concentration measurements (AAG-PK-Model) gave an estimated mean (SD) prediction error (PE) of −20% (31%) for total and −7.0% (56%) for unbound concentrations. Further covariate modeling with this combined dataset showed that in addition to AAG; age, bodyweight, albumin, CRP, and intensive care unit admission were predictive of total imatinib oral clearance. In conclusion, high total and unaltered unbound concentrations of imatinib in COVID-19 compared to CML/GIST were a result of variability in acute phase proteins. This is a textbook example of how failure to take into account differences in plasma protein binding and the unbound fraction when interpreting PK of highly protein bound drugs, such as imatinib, could lead to selection of a dose with suboptimal efficacy in patients with COVID-19.
AB - This study aimed to determine whether published pharmacokinetic (PK) models can adequately predict the PK profile of imatinib in a new indication, such as coronavirus disease 2019 (COVID-19). Total (bound + unbound) and unbound imatinib plasma concentrations obtained from 134 patients with COVID-19 participating in the CounterCovid study and from an historical dataset of 20 patients with gastrointestinal stromal tumor (GIST) and 85 patients with chronic myeloid leukemia (CML) were compared. Total imatinib area under the concentration time curve (AUC), maximum concentration (C max) and trough concentration (C trough) were 2.32-fold (95% confidence interval [CI] 1.34–3.29), 2.31-fold (95% CI 1.33–3.29), and 2.32-fold (95% CI 1.11–3.53) lower, respectively, for patients with CML/GIST compared with patients with COVID-19, whereas unbound concentrations were comparable among groups. Inclusion of alpha1-acid glycoprotein (AAG) concentrations measured in patients with COVID-19 into a previously published model developed to predict free imatinib concentrations in patients with GIST using total imatinib and plasma AAG concentration measurements (AAG-PK-Model) gave an estimated mean (SD) prediction error (PE) of −20% (31%) for total and −7.0% (56%) for unbound concentrations. Further covariate modeling with this combined dataset showed that in addition to AAG; age, bodyweight, albumin, CRP, and intensive care unit admission were predictive of total imatinib oral clearance. In conclusion, high total and unaltered unbound concentrations of imatinib in COVID-19 compared to CML/GIST were a result of variability in acute phase proteins. This is a textbook example of how failure to take into account differences in plasma protein binding and the unbound fraction when interpreting PK of highly protein bound drugs, such as imatinib, could lead to selection of a dose with suboptimal efficacy in patients with COVID-19.
UR - http://www.scopus.com/inward/record.url?scp=85117701405&partnerID=8YFLogxK
U2 - https://doi.org/10.1002/psp4.12718
DO - https://doi.org/10.1002/psp4.12718
M3 - Article
C2 - 34608769
SN - 2163-8306
VL - 10
SP - 1497
EP - 1511
JO - CPT: Pharmacometrics and Systems Pharmacology
JF - CPT: Pharmacometrics and Systems Pharmacology
IS - 12
ER -