Designing drug candidates for rare diseases - Noorbatcha, Ibrahim Ali (2012)
Rare diseases, because of their nature are given less attention and as a result patients with such diseases have to undergo lot of hurdles to get proper treatment, even if any such treatment is available. There are several rare diseases for which no treatment is readily available and even it is available, the treatment process is very expensive. Lysosomal storage disease is one of the rare diseases which have been proven to be fatal if not treated properly. Acid β-glucosidase (GlcCerase) is a lysosomal enzyme, which is important in biodegradation of blood cells in human body. Mutation of GlcCerase will lead to Gaucher disease; the most common lysosomal storage disease. Presently, two treatments are available for Gaucher disease patients. The first treatment is by enzyme replacement therapy using recombinant human GlcCerase (CerezymeTM) expressed in Chinese hamster ovary cells. The second treatment is by substrate reduction therapy, in which partial inhibition of glycosphingolipid synthesis is carried out (NBDNJ; ZavescaTM). Current treatment for Gaucher disease is costly. According to available data, the current treatment for Gaucher disease patients is can reach up to US$16,800 for every two weeks. Adding up to the problem, this enzyme replacement therapy, requires high dosage which will increase the overall cost for the treatment. These problems may lead to suffering of Gaucher disease patient which cannot afford the high cost treatment. To rectify the problem, new drugs with lower cost should be designed as an alternative for Gaucher disease patient. With the help of computational molecular modeling, new drug candidate for Gaucher disease treatment can be designed. In this research, we have designed a lead-candidate to act as a potential inhibitor, as a part of substrate reduction therapy for Gaucher disease by using computational docking approach. Lamarckian genetic algorithm is used to locate the potential inhibitor sites in the acid β-glucosidase and strength of the binding is evaluated using potential mean force (PMF) scores. Good correlation between experimental inhibition constant (Ki) and computational binding score is established with the correlation coefficient of 0.782. This correlation can be used to predict the unknown Ki value of the new ligand.