Physico-chemical properties play a crucial role in drug development, with good in vivo solubility being one of the most important factors. Poor solubility often presents a major obstacle in the development of oral drugs and is one of the leading causes for the discontinuation of drug candidates. In recent years, computational models have become increasingly used for solubility prediction. While they offer several advantages over experimental determination, they also come with certain limitations.
Monoamine oxidases (MAOs) are enzymes that catalyze the oxidative deamination of monoamines. They exist in two isoforms, MAO-A and MAO-B, which differ in gene encoding, tissue distribution, active site structure, and substrate/inhibitor specificity. MAO-A inhibitors are used in the treatment of anxiety disorders and depression, while MAO-B inhibitors are used in the treatment of neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease.
In this master's thesis, we predicted the physicochemical properties of selected compounds using two computational models (QikProp and SwissADME) and evaluated the differences between their predictions. As the models are based on different compound datasets and algorithms, their predictions varied. To validate the computational results, we experimentally determined a qualitative estimate of kinetic solubility and compared it with the predicted values. Among the models used, QikProp showed the best correlation with experimental results and was therefore selected for further solubility prediction of target compounds in the proposed synthetic pathway.
Based on these results, we designed new potential MAO-B inhibitors and began developing a synthetic route for pyrazole derivatives. We successfully synthesized protected intermediates of the synthetic pathway. In most synthetic steps, microwave-assisted heating was employed instead of conventional heating, and the formation of C–C bonds was achieved using the Suzuki–Miyaura reaction.
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