Poster Presenter

A Novel Qsar Model for Modelling and Predicting Tnf-α Inhibition by Small Molecules
Antreas Afantitis, Georgia Melagraki, Olga Igglessi-Markopoulou and George Kollias
Cyprus

Recent reports from several major pharmaceutical companies indicate that there is a significant interest for the identification of small-molecule antagonists of rheumatoid arthritis (RA). Small-molecule inhibitors could provide a less-expensive, orally administered alternative to parenteral biologic agents. Macromolecular TNF-α inhibitors, such as soluble TNF-α receptor Enbrel and the TNF-α specific monoclonal antibody Remicade have been shown to be useful for the treatment of inflammatory and autoimmune diseases such as RA. Current market for RA medicines is estimated at €7 billion annually; this is expected to approach €9 billion by 2011.

In this work, we have selected from the literature approximately a large database of small molecules which were recently evaluated as inhibitors of TNF-α. The first major result is the development of an accurate and reliable QSAR model involving physicochemical and structural descriptors that are able to predict successfully TNF-α inhibition. The accuracy of the proposed in silico model is illustrated using the following evaluation techniques: cross-validation, validation through an external test set and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. The selected physicochemical descriptors serve as a first guideline for the design of novel and potent TNF-α inhibitors.