A solvatochromatic approach to quantifying formulation effects on dermal permeability

Linear and nonlinear quantitative structure-property relationship modelling of skin permeability

In this work, quantitative structure-property relationship (QSPR) models were developed to estimate skin permeability based on theoretically derived molecular descriptors and a diverse set of experimental data. The newly developed method combining modified particle swarm optimization (MPSO) and multiple linear regression (MLR) was used to select important descriptors and develop the linear model using a training set of 225 compounds. The adaptive neuro-fuzzy inference system (ANFIS) was used as an efficient nonlinear method to correlate the selected descriptors with experimental skin permeability data (log Kp). The linear and nonlinear models were assessed by internal and external validation. The obtained models with three descriptors show good predictive ability for the test set, with coefficients of determination for the MPSO-MLR and ANFIS models equal to 0.874 and 0.890, respectively. The QSPR study suggests that hydrophobicity (encoded as log P) is the most important factor in transdermal penetration.

Quantitative risk assessment methods for cancer and noncancer effects

Human health risk assessments have evolved from the more qualitative approaches to more quantitative approaches in the past decade. This has been facilitated by the improvement in computer hardware and software capability and novel computational approaches being slowly recognized by regulatory agencies. These events have helped reduce the reliance on experimental animals as well as better utilization of published animal toxicology data in deriving quantitative toxicity indices that may be useful for risk management purposes. This chapter briefly describes some of the approaches as described in the guidance documents from several of the regulatory agencies as it pertains to hazard identification and dose-response assessment of a chemical. These approaches are contrasted with more novel computational approaches that provide a better grasp of the uncertainty often associated with chemical risk assessments.