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The solubility of a model basic drug, nortriptyline (Nor), was investigated as a function of pH in phosphate and/or a chloride-containing aqueous suspension using experimental practices recommended in the previously published “white paper” (Avdeef et al., 2016). The pH-Ramp Shake-Flask (pH-RSF) method, introduced in our earlier work (Marković et al., 2019), was applied.
A multiphasic mass action equilibrium model is used to show that the critical pH in the acid–base disproportionation of a solid salt into its corresponding solid free-base form in aqueous suspensions, widely known as “pHmax”, is incompletely interpreted. It is shown that the traditional thermodynamic model does not predict the invariance of pH and solubility during the salt-to-free-base conversion process in an alkalimetric titration.
This study describes a novel nonlinear variant of the well-known Yalkowsky general solubility equation (GSE). The modified equation can be trained with small molecules, mostly from the Lipinski Rule of 5 (Ro5) chemical space, to predict the intrinsic aqueous solubility, S0, of large molecules (MW > 800 Da) from beyond the rule of 5 (bRo5) space, to an accuracy almost equal to that of a recently described random forest regression (RFR) machine learning analysis.
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