University of Manchester
Database of fraction unbound in plasma (fu) measurements.xlsx (46.99 kB)

Database of fraction unbound in plasma (fu) measurements

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posted on 2024-03-01, 13:32 authored by Jokha Al-Qassabi, Shawn Pei Feng Tan, Patcharapan Phonboon, Aleksandra Galetin, Amin Rostami-Hodjegan, Daniel ScotcherDaniel Scotcher

A database of fraction unbound in plasma (fu) measurements collated from literature sources in reference and special populations.

The data was collected and is reported as supplementary material for the manuscript "Facing the facts of altered plasma protein binding: Do current models correctly predict changes in fraction unbound in special populations?" published in Journal of Pharmaceutical Sciences

DOI: 10.1016/j.xphs.2024.02.024

Authors: Jokha Al-Qassabi†,a,b , Shawn Pei Feng Tan†,a , Patcharapan Phonboona , Aleksandra Galetina , Amin Rostami-Hodjegana,c , Daniel Scotchera

J.A.-Q. and S.P.F.T. contributed equally to this manuscript

Affiliations: a Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK; b Current affiliation: University of Technology and Applied Sciences, Oman; c Simcyp Division, Certara UK Limited, Sheffield, UK


Accounting for variability in plasma protein binding of drugs is an essential input to physiologically-based pharmacokinetic (PBPK) models of special populations. Prediction of fraction unbound in plasma (fu) in such populations typically considers changes in plasma protein concentration while assuming that the binding affinity remains unchanged. A good correlation between predicted vs observed fu data reported for various drugs in a given special population is often used as a justification for such predictive methods. However, none of these analyses evaluated the prediction of the fold-change in fu in special populations relative to the reference population. This would be a more appropriate assessment of the predictivity, analogous to drug-drug interactions. In this study, predictive performance of the single protein binding was assessed by predicting fu for alpha-1-acid glycoprotein and albumin bound drugs in hepatic impairment, renal impairment, paediatric, elderly, patients with inflammatory disease, and in different ethnic groups for a dataset of >200 drugs. For albumin models, the concordance correlation coefficients for predicted fu were >0.90 for 16 out of 17 populations with sub-groups, indicating strong agreement between predicted and observed values. In contrast, concordance correlation coefficients for predicted fold-change in fu for the same dataset were <0.38 for all populations and sub-groups. Trends were similar for alpha-1-acid glycoprotein models. Accordingly, the predictions of fu solely based on changes in protein concentrations in plasma cannot explain the observed values in some special populations. We recommend further consideration of the impact of changes in special populations to endogenous substances that competitively bind to plasma proteins, and changes in albumin structure due to posttranslational modifications. PBPK models of special populations for highly bound drugs should preferably use measured fu data to ensure reliable prediction of drug exposure or compare predicted unbound drug exposure between populations knowing that these will not be sensitive to changes in fu.


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