The quantitative structure−bioavailability relationship of 232 structurally diverse drugs was studied to evaluate the feasibility of constructing a predictive model for the human oral bioavailability of prospective new medicinal agents. The oral bioavailability determined in human adults was assigned one of four ratings and analyzed in relation to physicochemical and structural factors by the ORMUCS (ordered multicategorical classification method using the simplex technique) method. A systematic examination of various physicochemical parameters relating primarily to absorption, and structural elements which could influence metabolism, was carried out to analyze their effects on the bioavailabilty classification of drugs in the data set. Lipophilicity, expressed as the distribution coefficient at pH 6.5, was found to be a significant factor influencing bioavailability. The observation that acids generally had better bioavailability characterizitics than bases, with neutral compounds between, led to the formulation of a new parameter, Δ log D (log D6.5 − log D7.4), which proved to be an important contributor in improving the classification results. The addition of 15 structural descriptors relating primarily to well-known metabolic processes yielded a satisfactory QSAR equation which had a correct classification rate of 71% (97% within one class) and a Spearman rank correlation coefficient (Rs) of 0.851, despite the diversity of structure and pharmacological activity in the compound set. In leave-one-out tests, an average of 67% of drugs were correctly classified (96% within one class) with an Rs of 0.812. The relationship formulated identified significant factors influencing bioavailability and assigned them quantitative values expressing their contribution. The predictive power of the model was evaluated using a separate test set of 40 compounds, of which 60% (95% within one class) were correctly classified. Since the necessary physicochemical parameters can be calculated or estimated and the structural descriptors are obtained from an inspection of the structure, the model enables a rough estimate to be made of the prospective human oral bioavailability of unsynthesized compounds. Also, the model has the advantage of transparency in that it indicates which factors may affect bioavailabilty and the extent of that effect. This could be useful in designing compounds which are more bioavailable. Refinement of the model is possible as more bioavailability data becomes available. Potential uses are in drug design, prioritization of compounds for synthesis, and selection for detailed studies of early compound leads in drug discovery programs.
Yoshida et al. (Thu,) studied this question.