Reactive oxygen species assay-based risk assessment of drug-induced phototoxicity: classification criteria and application to drug candidates.

We have previously demonstrated that the phototoxic potential of chemicals could be partly predicted by the determination of reactive oxygen species (ROS) from photo-irradiated compounds. In this study, ROS assay strategy was applied to 39 marketed drugs and 210 drug candidates in order to establish provisional classification criteria for risk assessment of drug-induced phototoxicity. The photosensitizing properties of 39 model compounds consisting of phototoxic and non-phototoxic chemicals, as well as ca. 210 drug candidates including 11 chemical series were evaluated using ROS assay and the 3T3 neutral red uptake phototoxicity test (NRU PT). With respect to marketed drugs, most phototoxic drugs tended to cause type I and/or II photochemical reactions, resulting in generation of singlet oxygen and superoxide. There seemed to be a clear difference between phototoxic drugs and non-phototoxic compounds in their abilities to induce photochemical reactions. A plot analysis of ROS data on the marked drugs provided classification criteria to discriminate the photosensitizers from non-phototoxic substances. Of all drug candidates tested, 35.2% compounds were identified as phototoxic or likely phototoxic on the basis of the 3T3 NRU PT, and all ROS data for these phototoxic compounds were found to be over the threshold value. Furthermore, 46.3% of non-phototoxic drug candidates were found to be in the subthreshold region. These results verify the usefulness of the ROS assay for understanding the phototoxicity risk of pharmaceutical substances, and the ROS assay can be used for screening purposes in the drug discovery stage.

QSAR study on permeability of hydrophobic compounds with artificial membranes.

We previously reported a classical quantitative structure-activity relationship (QSAR) equation for permeability coefficients (P(app-pampa)) by parallel artificial membrane permeation assay (PAMPA) of structurally diverse compounds with simple physicochemical parameters, hydrophobicity at a particular pH (logP(oct) and |pK(a)-pH|), hydrogen-accepting ability (SA(HA)), and hydrogen-donating ability (SA(HD)); however, desipramine, imipramine, and testosterone, which have high logP(oct) values, were excluded from the derived QSAR equation because their measured P(app-pampa) values were lower than calculated. In this study, for further investigation of PAMPA permeability of hydrophobic compounds, we experimentally measured the P(app-pampa) of more compounds with high hydrophobicity, including several pesticides, and compared the measured P(app-pampa) values with those calculated from the QSAR equation. As a result, compounds having a calculated logP(app-pampa)>-4.5 showed lower measured logP(app-pampa) than calculated because of the barrier of the unstirred water layer and the membrane retention of hydrophobic compounds. The bilinear QSAR model explained the PAMPA permeability of the whole dataset of compounds, whether hydrophilic or hydrophobic, with the same parameters as the equation in the previous study. In addition, PAMPA permeability coefficients correlated well with Caco-2 cell permeability coefficients. Since Caco-2 cell permeability is effective for the evaluation of human oral absorption of compounds, the proposed bilinear model for PAMPA permeability could be useful for not only effective screening for several drug candidates but also the risk assessment of chemicals and agrochemicals absorbed by humans.

Relationships between structure and high-throughput screening permeability of diverse drugs with artificial membranes: application to prediction of Caco-2 cell permeability.

To evaluate the absorption of drugs with diverse structures across a membrane via the transcellular route, their permeability was measured using the parallel artificial membrane permeation assay (PAMPA). The permeability coefficients obtained by PAMPA were analyzed using a classical quantitative structure-activity relationship (QSAR) approach with simple physicochemical parameters and 3D-QSAR, VolSurf. We formulated correlation equations for diverse drugs similar to the equation obtained for peptide-related compounds in our previous study. The hydrogen-bonding ability of molecules, not only the hydrogen-accepting ability but also the hydrogen-donating ability, in addition to hydrophobicity at a particular pH, was significant in determining variations in PAMPA permeability coefficients. Based on this result, an in silico good prediction model for the passive transcellular permeability of diverse structural compounds was obtained. The artificial lipid-membrane permeability coefficients of the drugs, except salicylic acid, were well correlated with the Caco-2 permeability in a previous report suggesting the importance of absorption by the transcellular mechanism for these drugs.