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EP20426
Poster Title: Combination of in Vitro Caco-2 and Aqueous Solubility Screens with in Silico Physiological Modelling for the Prediction of Human Intestinal Absorption in Early Drug Discovery
Submitted on 19 Dec 2013
Author(s): S. Thomas, F.A. Brightman, H.J. Gill, B. Pufong and D.L. Cheney
Affiliations: Cyprotex Discovery Ltd
Poster Views: 1,496
Submitted on 19 Dec 2013
Author(s): S. Thomas, F.A. Brightman, H.J. Gill, B. Pufong and D.L. Cheney
Affiliations: Cyprotex Discovery Ltd
Poster Views: 1,496
Abstract: Commercial drug discovery is a process of design, optimization and selection of candidate compounds with bothappropriate pharmacological activity and favourable absorption, distribution, metabolism and elimination (ADME)properties.
With the increasing application of high-throughput assays for the determination of the in vitro ADME properties ofcompounds in lead identification and optimization, there is a growing need for efficient and cost-effective methods for interpreting theresulting data to enable well-informed selection to be made for compound progression.
Summary: With the increasing application of high-throughput assays for the determination of the in vitro ADME properties of compounds in lead identification and optimization, there is a growing need for efficient and cost-effective methods for interpreting theresulting data to enable well-informed selection to be made for compound progression.
With the increasing application of high-throughput assays for the determination of the in vitro ADME properties ofcompounds in lead identification and optimization, there is a growing need for efficient and cost-effective methods for interpreting theresulting data to enable well-informed selection to be made for compound progression.
Summary: With the increasing application of high-throughput assays for the determination of the in vitro ADME properties of compounds in lead identification and optimization, there is a growing need for efficient and cost-effective methods for interpreting theresulting data to enable well-informed selection to be made for compound progression.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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