Literature DB >> 8506849

Penetration of industrial chemicals across the skin: a predictive model.

R H Guy1, R O Potts.   

Abstract

The recently reported dermal absorption and toxicity potential of industrial chemicals is reconsidered using an alternative physicochemically based model of skin penetration. In this model, the outermost, and least permeable, component of the skin [namely, the stratum corneum (SC)] is considered to provide only a lipoidal transport pathway into the body for chemicals that come into contact with the skin. The predictive algorithm of the model is biophysically compatible with known SC properties, and is based on experimental determinations of permeability coefficients through human skin in vitro for nearly 100 compounds of widely divergent physicochemical properties. This simpler prediction results in significantly lower estimates of maximum percutaneous penetration fluxes.

Entities:  

Mesh:

Substances:

Year:  1993        PMID: 8506849     DOI: 10.1002/ajim.4700230505

Source DB:  PubMed          Journal:  Am J Ind Med        ISSN: 0271-3586            Impact factor:   2.214


  5 in total

1.  Quantitative structure-permeation relationships (QSPeRs) to predict skin permeation: a critical evaluation.

Authors:  Sandrine Geinoz; Richard H Guy; Bernard Testa; Pierre-Alain Carrupt
Journal:  Pharm Res       Date:  2004-01       Impact factor: 4.200

2.  A skin permeability model of insulin in the presence of chemical penetration enhancer.

Authors:  K M Yerramsetty; B J Neely; S V Madihally; K A M Gasem
Journal:  Int J Pharm       Date:  2009-12-21       Impact factor: 5.875

3.  Exposure Assessment For Air-To-Skin Uptake of Semivolatile Organic Compounds (SVOCs) Indoors.

Authors:  Javier A Garrido; Srinandini Parthasarathy; Christoph Moschet; Thomas M Young; Thomas E McKone; Deborah H Bennett
Journal:  Environ Sci Technol       Date:  2019-01-09       Impact factor: 9.028

4.  Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment.

Authors:  Abdulkarim Najjar; Ans Punt; John Wambaugh; Alicia Paini; Corie Ellison; Styliani Fragki; Enrica Bianchi; Fagen Zhang; Joost Westerhout; Dennis Mueller; Hequn Li; Quan Shi; Timothy W Gant; Phil Botham; Rémi Bars; Aldert Piersma; Ben van Ravenzwaay; Nynke I Kramer
Journal:  Arch Toxicol       Date:  2022-09-05       Impact factor: 6.168

5.  A deep learning approach for the blind logP prediction in SAMPL6 challenge.

Authors:  Samarjeet Prasad; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2020-01-30       Impact factor: 3.686

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.