Unified Multi-target Approach for the Rational in silico Design of Anti-bladder Cancer Agents

Author(s): Alejandro Speck- Planche, Valeria V. Kleandrova, Feng Luan, M. N. D. S. Cordeiro

Journal Name: Anti-Cancer Agents in Medicinal Chemistry
(Formerly Current Medicinal Chemistry - Anti-Cancer Agents)

Volume 13 , Issue 5 , 2013

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Bladder cancer (BLC) is a very dangerous and common disease which is characterized by an uncontrolled growth of the urinary bladder cells. In the field of chemotherapy, many compounds have been synthesized and evaluated as anti-BLC agents. The future design of more potent anti-BLC drugs depends on a rigorous and rational discovery, where the computer-aided design (CADD) methodologies should play a very important role. However, until now, there is no CADD methodology able to predict anti-BLC activity of compounds versus different BLC cell lines. We report in this work the first unified approach by exploring Quantitative- Structure Activity Relationship (QSAR) studies using a large and heterogeneous database of compounds. Here, we constructed two multi-target (mt) QSAR models for the classification of compounds as anti-BLC agents against four BLC cell lines. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. We also extracted different substructural patterns which could be responsible for the activity/inactivity of molecules against BLC and we suggested new molecular entities as possible potent and versatile anti-BLC agents.

Keywords: Artificial neural networks, bladder cancer, fragments, in silico design, linear discriminant analysis, molecular descriptors, mt- QSAR, quantitative contributions.

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Article Details

Year: 2013
Published on: 16 May, 2013
Page: [791 - 800]
Pages: 10
DOI: 10.2174/1871520611313050013
Price: $65

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