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Aripiprazole and ziprasidone are atypical antipsychotic drugs with the effect on positive and negative symptoms of schizophrenia, mania, and mixed states of bipolar disorder. Hansen's solubility parameters, δ d , δ p , and δ h , which account for dispersive, polarizable, and hydrogen bonding contributions to the overall cohesive energy of a compound, are often used to assess pharmacokinetic properties of drugs. However, no data exist of solubility parameters for the drugs of interest in this study. Therefore, in the present study, partial least square regression (PLS), artificial neural networks (ANNs), regression trees (RT), boosted trees (BT), and random forests (RF) were applied to estimate Hansen's solubility parameters of ziprasidone, aripiprazole, and their impurities/metabolic derivatives, targeting their biopharmaceutical classes and absorption routes. A training set of 47 structurally diverse and pharmacologically active compounds and 290 molecular descriptors and pharmaceutically important properties were used to build the prediction models. The modeling approaches were compared by the sum of ranking differences, using the consensus values as a reference for the unknowns and the experimentally determined values as a gold standard for the calibration set. In both instances, the PLS models, together with ANNs, demonstrated better performance than RT, BT and especially RF.Based on the best scored models, we were able to pinpoint the most probable absorption sites for each drug and the corresponding metabolite, i.e., the upper parts of the gastrointestinal tract, small intestine, or absorption along entire length of gastrointestinal tract. KEYWORDS artificial neural network (ANN), drug absorption, Hansen's solubility parameters, partial least square regression (PLS), sum of ranking difference (SRD)

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For this study, 31 compounds, including 16 imidazoline/α-adrenergic receptor (IRs/α-ARs) ligands and 15 central nervous system (CNS) drugs, were characterized in terms of the retention factors (k) obtained using biopartitioning micellar and classical reversed phase chromatography (log k and log k, respectively). Based on the retention factor (log k) and slope of the linear curve (S) the isocratic parameter (φ) was calculated. Obtained retention factors were correlated with experimental log BB values for the group of examined compounds. High correlations were obtained between logarithm of biopartitioning micellar chromatography (BMC) retention factor and effective permeability (r(log k/log BB): 0.77), while for RP-HPLC system the correlations were lower (r(log k/log BB): 0.58; r(S/log BB): -0.50; r(φ/P): 0.61). Based on the log k retention data and calculated molecular parameters of the examined compounds, quantitative structure-permeability relationship (QSPR) models were developed using partial least squares, stepwise multiple linear regression, support vector machine and artificial neural network methodologies. A high degree of structural diversity of the analysed IRs/α-ARs ligands and CNS drugs provides wide applicability domain of the QSPR models for estimation of blood-brain barrier penetration of the related compounds.

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A thin-layer chromatographic method for simultaneous determination of ziprasidone and its main impurities was developed and validated. Separation of the examined compounds was performed on chromatographic plates precoated with silica gel 60F 254 and using toluene-methanol-glacial acetic acid, 7.5:0.5:0.5 (v/v/v) as mobile phase. Ascending development mode was performed in the twin-trough chromatographic chamber, which was presaturated with mobile phase vapors for 15 min. The developed chromatographic plates were dried in air and densitometrically scanned at the wavelengths of 250 and 320 nm. Regression coefficient (r≥0.992), recovery (94.94-106.70%), limit of quantification of impurities (25 ng band -1 equivalent to the 0.14% impurity level), and robustness were validated and found satisfactory. The developed method is convenient for quantitative analysis and the purity screening of ziprasidone in pharmaceutical formulations.

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