Article Preview
TopIntroduction
Currently, cancer treatment remains a challenge owing to the wide variety of cancers and the similarity between normal and tumor cells. Thus, novel chemical entities and biological targets for the treatment are constantly being investigated (Ganesan, 2007).
Among these targets is human thymidine phosporylase (TP), a multifunctional protein that has an important role in pyrimidine nucleoside metabolism and is associated with the maintenance of healthy mitochondria and the recovery of cells from pathological stress. However, TP is overexpressed in a wide range of human cancers (bladder, colorectal, esophageal, pancreatic, and others) and has been associated with the promotion of tumorigenesis, angiogenesis, metastasis, and the inhibition of apoptosis. TP is thus useful for metabolic activation in loco of capecitabine (Xeloda®) and doxifluridine, two 5-fluouracil prodrugs (Figure 1) (Jain, Rasheed, & Kalman, 2010; Toi, Rahman, Bando, & Chow, 2005; Norman et al., 2004; Ye & Zang, 2013).
Figure 1. Structures of capecitabine and doxifluridine
As various types of anticancer treatments (e.g. chemotherapy and radiotherapy) stimulate TP expression, probably owing to their pro-angiogenic and antiapoptotic properties, the suppression of this protein could hypothetically slow down or suppress the recovery of cancer cells, thus providing beneficial supplementary effects (Yano et al., 2004; Toi, Rahman, Bando, & Chow, 2005). Lonsurf® (TAS-102, Taiho Pharmaceuticals Inc.) is approved in Japan for the treatment of unresectable advanced or recurrent colorectal cancer. One of the active principles is 5-chloro-6-[(2-iminopyrrolidin-1-yl) methyl]uracil (TPI or tipracil) (Figure 2), which is formulated in association with 5-trifluoromethyl-2’-deoxyuridine (Jain, Rasheed, & Kalman, 2010). However, despite its great therapeutic value, tipracil is still the only TP inhibitor clinically available.
In the field of computer-aided molecular design (CADD), the quantitative structure-activity relationship (QSAR) method is useful in lead optimization, since it increases the probability of finding novel drug candidates by avoiding the synthesis and biological screening of few potential molecules and thus saving time and money (Roy, Kar, & Das, 2015). This method considers that a specific biological activity can vary as a function of molecular descriptors encoding information about the chemical structure of a set of molecules. Thus, a model containing the calculated descriptors could be used to predict the responses of new compounds (Ribeiro & Ferreira, 2003; Molfetta, Bruni, Rosseli, & Silva, 2007).