| Summary: | Purpose. Binding of drugs to plasma proteins is a common physiological occurrence which may have a profound effect on both pharmacokinetics and pharmacodynamics. The early prediction of plasma protein binding (PPB) of new drug candidates is an important step in drug development process. The present study is focused on the development of quantitative structure – pharmacokinetics relationship (QSPkR) for the negative logarithm of the free fraction of the drug in plasma (pfu) of basic drugs. Methods. A dataset includes 220 basic drugs, which chemical structures are encoded by 176 descriptors. Genetic algorithm, stepwise regression and multiple linear regression are used for variable selection and model development. Predictive ability of the model is assessed by internal and external validation. Results. A simple, significant, interpretable and predictive QSPkR model is constructed for pfu of basic drugs. It is able to predict 59% of the drugs from an external validation set within the 2-fold error of the experimental values with squared correlation coefficient of prediction 0.532, geometric mean fold error (GMFE) 1.94 and mean absolute error (MAE) 0.17. Conclusions. PPB of basic drugs is favored by the lipophilicity, the presence of aromatic C-atoms (either non-substituted, or involved in bridged aromatic systems) and molecular volume. The fraction ionized as a base fB and the presence of quaternary C-atoms contribute negatively to PPB. A short checklist of criteria for high PPB is defined, and an empirical rule for distinguishing between low, high and very high plasma protein binders is proposed based. This rule allows correct classification of 69% of the very high binders, 71% of the high binders and 91% of the low binders in plasma.
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