Predicting drug absorption rates through human skin

Yi. Sun; L.Y. Lam; G.P. Moss; M. Prapopoulou; R.G. Adams; N. Davey; David Gray; Marc Brown

doi:10.1109/IJCNN.2010.5596603

Predicting drug absorption rates through human skin

Yi. Sun
, L.Y. Lam
, G.P. Moss
, M. Prapopoulou
, R.G. Adams
, N. Davey
, David Gray
, Marc Brown

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

115 Downloads (Pure)

Abstract

Predicting the rate at which a substance will pass through human skin and into the bloodstream is a problem of current interest. We use Gaussian Process modeling to train a set of predictors using every combination of six molecular features. We find that only three of the features are needed for our best predictor. This result could be useful in the further analysis of skin permeability.

Original language	English
Title of host publication	IEEE International Joint Conference on Neural Networks (IJCNN) No. 11593964
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-5
ISBN (Print)	978-1-4244-6916-1
DOIs	https://doi.org/10.1109/IJCNN.2010.5596603
Publication status	Published - 2010

Keywords

structure-permeability relationships
percutaneous-absorption

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10.1109/IJCNN.2010.5596603

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title = "Predicting drug absorption rates through human skin",

abstract = "Predicting the rate at which a substance will pass through human skin and into the bloodstream is a problem of current interest. We use Gaussian Process modeling to train a set of predictors using every combination of six molecular features. We find that only three of the features are needed for our best predictor. This result could be useful in the further analysis of skin permeability.",

keywords = "structure-permeability relationships, percutaneous-absorption",

author = "Yi. Sun and L.Y. Lam and G.P. Moss and M. Prapopoulou and R.G. Adams and N. Davey and David Gray and Marc Brown",

note = "“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.{"} “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”",

year = "2010",

doi = "10.1109/IJCNN.2010.5596603",

language = "English",

isbn = "978-1-4244-6916-1",

pages = "1--5",

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AU - Sun, Yi.

AU - Lam, L.Y.

AU - Moss, G.P.

AU - Prapopoulou, M.

AU - Adams, R.G.

AU - Davey, N.

AU - Gray, David

AU - Brown, Marc

N1 - “This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”

PY - 2010

Y1 - 2010

N2 - Predicting the rate at which a substance will pass through human skin and into the bloodstream is a problem of current interest. We use Gaussian Process modeling to train a set of predictors using every combination of six molecular features. We find that only three of the features are needed for our best predictor. This result could be useful in the further analysis of skin permeability.

AB - Predicting the rate at which a substance will pass through human skin and into the bloodstream is a problem of current interest. We use Gaussian Process modeling to train a set of predictors using every combination of six molecular features. We find that only three of the features are needed for our best predictor. This result could be useful in the further analysis of skin permeability.

KW - structure-permeability relationships

KW - percutaneous-absorption

U2 - 10.1109/IJCNN.2010.5596603

DO - 10.1109/IJCNN.2010.5596603

M3 - Conference contribution

SN - 978-1-4244-6916-1

SP - 1

EP - 5

BT - IEEE International Joint Conference on Neural Networks (IJCNN) No. 11593964

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Predicting drug absorption rates through human skin

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Keywords

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