Prediction of Half-Life Extension of Peptides via Serum Albumin Binding: Current Challenges.

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Abstrakt

Tytuł:: Prediction of Half-Life Extension of Peptides via Serum Albumin Binding: Current Challenges.
Autorzy:: Hijazi Y; R&D, Drug Metabolism and Pharmacokinetics, Sanofi-Aventis Deutschland GmbH, Industriepark Höchst, Bldg. H 831, C 0442, 65926, Frankfurt, Germany. .
Źródło:: European journal of drug metabolism and pharmacokinetics [Eur J Drug Metab Pharmacokinet] 2021 Mar; Vol. 46 (2), pp. 163-172. Date of Electronic Publication: 2021 Jan 01.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Publication: <2010- >: Paris : Springer France
Original Publication: Paris, Edifor.
MeSH Terms:: Models, Biological*
Serum Albumin/*metabolism
Animals ; Drug Administration Schedule ; Drug Development ; Half-Life ; Humans ; Peptides/pharmacokinetics
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Substance Nomenclature:: 0 (Peptides)
0 (Serum Albumin)
Entry Date(s):: Date Created: 20210102 Date Completed: 20211101 Latest Revision: 20211101
Update Code:: 20240105
DOI:: 10.1007/s13318-020-00664-y
PMID:: 33386550
: Czasopismo naukowe

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The development of peptide therapeutics has increased enormously in recent decades. Many of the peptide drugs and antibody fragments that lack Fc backbone have a short half-life in circulation. In general, the half-life supports the design of the dosing regimen and frequency of administration, which are key aspects in the discovery of peptide drugs intended for long duration of action. Less frequent administration such as weekly or monthly can improve compliance and adherence to therapy. Serum albumin binding is a key approach to extend the half-life of peptide drugs. Despite the evidence of half-life prolongation of a variety of peptide drugs via albumin, quantitative prediction for humans is still a key question. Challenges in the measurement of albumin binding and in understanding the clearance mechanisms can limit quantitative prediction. We integrated pharmacokinetic concepts and albumin binding across species in a quantitative model to be used as a tool for prediction of half-life. Preliminary validation on a limited dataset indicated a good correlation between predicted and observed values. Further development of more quantitative models is warranted.

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