Correlation analysis between food effects on oral drug absorption (food effect) and physicochemical properties is important for efficient drug discovery and contributes to drug design. This study focused on micelle binding and solubilization considering bile micelles in the intestinal fluid. Profiling using about 40 launched drugs demonstrated that those in a high solubilization area (area 1) tended to have a positive food effect, and that drugs exhibiting negative/no food effect tended to coexist in a no/low solubilization area (area 2). In area 1, the solubilization effect by bile micelles was demonstrated quantitatively as an important factor that indicates a positive food effect. In area 2, the relative and quantitative relationships among the membrane permeation rate, dissolution rate, micelle binding and food effect could be clarified by simulation. The improvement of membrane permeability and the suppression of micelle binding are considered to be required to avoid a negative food effect. In conclusion, important factors contributing to the food effect were clarified relatively and quantitatively. Data generated from this profiling may be beneficial to find a solution for negative food effects. Furthermore, this risk assessment of food effects is considered to be a useful tool in rational drug design for drug discovery.

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The blockade of human ether-a-go-go-related gene (hERG) potassium channels is widely regarded as the predominant cause of drug-induced QT prolongation. The correlation analysis between the inhibition of the hERG channel (hERG inhibition) and physicochemical properties was investigated by use of in-house quinolone antibiotics as model compounds. In order to establish a simple prediction model of hERG inhibition, we focused on the comprehensible physicochemical parameters such as lipophilicity (log P) and basicity (pK(a)). At first, the risk associated with increasing log P and pK(a) was examined by statistical analysis. It was demonstrated that the risk associated with increasing log P and pK(a) by one unit, respectively, almost identically increased. Consequently, equal attention should be paid to both parameters on hERG inhibition. Next, a prediction model of hERG inhibition which was represented by log P and pK(a) was investigated. As a result, we built the stepwise discriminant prediction model which took advantage of the risk judgment by zone classification. In conclusion, the impact of log P and pK(a) on hERG inhibition was clarified relatively and quantitatively. The quantitative risk assessment established based on both parameters, was considered to be a practical and useful tool in avoiding hERG inhibition and in the rational drug design for drug discovery, especially in lead optimization. Moreover, we also carried out a trend analysis using a different derivative and demonstrated that both parameters were equally significant for hERG inhibition.