PREDICTION OF PERCUTANEOUS-ABSORPTION FROM PHYSICOCHEMICAL DATA - A MODEL-BASED ON DATA OF IN-VITRO EXPERIMENTS

Correlations between in vitro percutaneous absorption data and physico chemical properties of industrial chemicals are evaluated in order to develop predictive mathematical models based on said properties. Percu taneous diffusion of 16 compounds of occupational interest, eight of w hich were polycyclic aromatic hydrocarbons (acenaphthene, anthracene, benzo(a)anthracene, chrysene, phenanthrene, fluorene, naphthalene, pyr ene). six organophosphorus insecticides (acephate, chlorpyrifos, dimet hoate, fenitrothion, methamidophos, omethoate) and two phenoxycarboxyl ic herbicides (2,4-D, MCPA), were tested ill vitro using monkey (Cerco pithecus aetiops) skin. The test apparatus consisted of nine static di ffusion cells with normal saline, gentamycin sulphate and 4% bovine se rum albumin as receiving solution. Test compounds were applied at vari ous concentrations in 30 pi of acetone solution and determined, in the receiving phase, by chemical analysis. Values for ln K-ow (octanol/wa ter partition coefficient) were correlated with experimentally determi ned values of the permeability constant Kp (r = 0.90, P < 0.001) and l ag time (r = 0.81, P < 0.01). Analysis of variance in a model of multi ple linear regression between Kp, In K-ow and water solubility [water] of the compounds, showed that the data had a highly significant fit ( P < 0.0001), A more general model which also included molecular weight (MW) and vapour pressure was evaluated as well, but the tno variables made no substantial difference. Multiple regression analysis between lag time, In K-ow and water was significant (P < 0.0001), whereas intr oduction of vapour pressure and MW as independent variables did not si gnificantly improve the predictive effect on lag time, Our experimenta l system, therefore, enables the values of Kp and lag time to be predi cted with reasonable precision on the basis of In K-ow and [water] val ues, using the algorithm derived from the multiple linear regression e quation. (C) 1998 British Occupational Hygiene Society. Published by E lsevier Science Ltd. All rights reserved.