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A novel high-throughput pepT1 transporter assay differentiates between substrates and antagonists
Teresa N Faria1, Julita K Timoszyk, Terry R Stouch
1Biopharmaceutics Research and Development, Macromolecular Structure/CADD, and Lead Discovery, Pharmaceutical Research Institute, Bristol-Myers Squibb Company, New Brunswick, New Jersey 08903-0191, USA. teresa.faria@bms.com
Molecular Pharmaceutics
|April 19, 2005
Summary
A new high-throughput assay accurately distinguishes peptide transporter 1 (PepT1) substrates from binders, aiding drug development for oral absorption. This robust method uses membrane potential changes to identify drug candidates that activate PepT1.
Area of Science:
- Pharmacology
- Molecular Biology
- Drug Discovery
Background:
- The peptide transporter 1 (PepT1) is crucial for oral drug absorption.
- Developing assays to differentiate PepT1 substrates from binders is vital for drug development.
- Existing methods for this distinction are often low-throughput.
Purpose of the Study:
- To develop a high-throughput, robust functional assay for PepT1.
- To distinguish PepT1 binders from substrates for drug candidate prediction.
- To enable identification and prediction of drug candidate activation via PepT1.
Main Methods:
- Transfection of MDCK cells with rPepT1 for stable expression.
- Development of a miniaturized 96-well (scalable to 384-well) assay.
- Measurement of membrane depolarization using a voltage-sensitive fluorescent indicator upon PepT1 activation.
Main Results:
- The assay demonstrated dose-dependent membrane depolarization with glycylsarcosine (EC50 = 0.49 mM).
- It accurately distinguished 38 known PepT1 substrates, binders, and nonbinders.
- The assay proved sensitive, robust, and capable of differentiating substrates from antagonists.
Conclusions:
- A novel, high-throughput assay effectively distinguishes PepT1 substrates from binders and antagonists.
- This assay facilitates drug candidate screening for improved oral absorption.
- It provides a valuable tool for PepT1 structure-activity relationship (SAR) modeling.