Estimation of plasma protein binding of selected antipsychotics using computed molecular properties

The plasma protein binding (PPB) data of twelve antipsychotics (aripiprazole, clozapine, olanzapine, quetiapine, risperidone, sertindole, ziprasidone, chlorpromazine, flupentixol, fluphenazine, haloperidol, zuclopenthixol) were estimated using computed molecular descriptors, which included the electronic descriptor – polar surface area (PSA), the constitutional parameter – molecular weight (Mw), the geometric descriptor – volume value (Vol), the lipophilicity descriptor (logP) and aqueous solubility data (logS), and the acidity descriptor (pKa). The relationships between computed molecular properties of the selected antipsychotics and their PPB data were investigated by simple linear regression analysis. Low correlations were obtained between the PPB data of the antipsychotics and PSA, Mw, Vol, pKa, logS (R <0.30) values, while relatively higher correlations (0.35 < R2 < 0.70) were obtained for the majority of logP values. Multiple linear regression (MLR) analysis was applied to access reliable correlations of the PPB data of the antipsychotics and the computed molecular descriptors. Relationships with acceptable probability values (P<0.05) were established for five lipophilicity descriptors (logP values) with application of the acidity descriptor (pKa) as independent variables: AlogP (R2=0.705), XlogP3 (R2=0.679), ClogP (R2=0.590), XlogP2 (R2=0.567), as well as for the experimental lipophilicity parameter, logPexp (R2=0.635). The best correlations obtained in MLR using AlogP and pKa as independent variables were checked using three additional antipsychotics: loxapine, sulpiride and amisulpride, with the PPB values of 97%, “less than” 40% and 17%, respectively. Their predicted PPB values were relatively close to the literature data. The proposed technique confirmed that lipophilicity, together with acidity significantly influences the PPB of antipsychotics. The described procedure can be regarded as an additional in vitro approach to the modeling of the investigated group of drugs. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 172016]