Predicting the aqueous pKa of benzimidazoles and opioids
\(^{1}\) Thompson Rivers University
\(^{2}\) Universitat de Girona
The acid-base properties of a molecule determine many of its physicochemical and biological characteristics, such as solubility and rate of absorption. The extent to which a molecule will be protonated or deprotonated can be determined by means of its pKa. Thus, this value is of paramount importance when considering the pharmacology of a given compound. We decided to revisit a series of studies which Brown et al. [1,2] did on the acid-base properties of benzimidazoles. They made use of several approaches to correctly predict the pKa values of these molecules, backing up the results with experimental data. First, they employed different acid-base equilibria, but for much better results a quantitative structure-properties relationship (QSPR) equation was derived, where a set of descriptors is related to known properties of the compounds that are being explored. They came up with an equation which properly predicted the aqueous pKa values of simple benzimidazoles. Given that since the publication of these results new experimental values for the pKa of benzimidazoles have appeared in the literature, we have decided to test once again the equation, and improve it in case it failed.
Following the previous procedure, we also decided to study the possibility of predicting the aqueous pKa of opioids. Recently, Alexander et al. [3] published their work on fluorinated derivatives of morphine. It has been shown that reducing the pKa of opioids could diminish their side effects, as these would selectively target inflamed tissues, which have a lower pH (6-6.5). Placing fluorine atoms close to the basic nitrogen would lower its pKa value. We thought that it would be of interest to devise a method to properly predict the pKa of opioids, so that new fluorinated analogues could be screened before being synthesized. Various pKa predicting approaches are tested using experimental values found in the literature to be able to predict new fluorinated opioids able to better target inflamed tissues.
[1] T. N. Brown, N. Mora-Diez, J. Phys. Chem. B 2006, 110, 9270-9279.
[2] T. N. Brown, N. Mora-Diez, J. Phys. Chem. B 2006, 110, 20546-20554.
[3] N. Alexander, M. Augenstein, A. M. Sorensen, C. Garcia, A. Greene, A. W. Harrison, Chem. Phys. Lett. 2021, 777, 138723.