Identification of Potential Inhibitors for the Treatment of Alkaptonuria Using an Integrated In Silico Computational Strategy

Abstract

Alkaptonuria (AKU) is a rare genetic autosomal recessive disorder characterized by elevated serum levels of homogentisic acid (HGA). In this disease, tyrosine metabolism is interrupted because of the alterations in homogentisate dioxygenase (HGD) gene. The patient suffers from ochronosis, fractures, and tendon ruptures. To date, no medicine has been approved for the treatment of AKU. However, physiotherapy and strong painkillers are administered to help mitigate the condition. Recently, nitisinone, an FDA-approved drug for type 1 tyrosinemia, has been given to AKU patients in some countries and has shown encouraging results in reducing the disease progression. However, this drug is not the targeted treatment for AKU, and causes keratopathy. Therefore, the foremost aim of this study is the identification of potent and druggable inhibitors of AKU with no or minimal side effects by targeting 4-hydroxyphenylpyruvate dioxygenase. To achieve our goal, we have performed computational modelling using BioSolveIT suit. The library of ligands for molecular docking was acquired by fragment replacement of reference molecules by ReCore. Subsequently, the hits were screened on the basis of estimated affinities, and their pharmacokinetic properties were evaluated using SwissADME. Afterward, the interactions between target and ligands were investigated using Discovery Studio. Ultimately, compounds c and f were identified as potent inhibitors of 4-hydroxyphenylpyruvate dioxygenase.