Poster Presenter

Modeling Using QSAR/QSPR As A Tool For Drug Discovery
E.C. Ibezim, P.R. Duchowicz and E.A. Castro
Nigeria

A large plethora of drugs have currently been synthesized and their pharmacological activities elucidated. Of this lot, a great number of their inherent pharmacological actions are not yet known. There still exists a greater number of yet-to-be synthesized substances that have great potentials as remedies for the wide array of prevalent diseases among mankind today. Modelling, through Quantitative Structure Activity /Property Relationships (QSAR/QSPR) has proved a very reliable and timely mode of theoretically projecting possible synthetic materials and elucidating their potential benefits as therapeutic agents. It is an alternative way for overcoming the absence of experimental measurements for biological system. It has as its ultímate role, the proposal of a model capable of estimating the activities of compounds by relying on the assumption that those resulting effects are a consequence of the molecular structure. Ever since the pioneering studies by Hansch, the use of QSAR/QSPR has become helpful in understanding chemical-biological interactions in drug and pesticide research as well as in various areas of toxicology. In this technique, structures are translated into so called ´Descriptors´, describing different relevant features of the compounds, through mathematical formula obtained from the chemical graph theory, information theory, quantum mechanims etc. Greater than a thousand of such descriptors are so far available and one has to decide how to select those that characterise the property under consideration in the best possible manner. A large class of drugs has so far been studied including the quinoxaline antitubercular agents, analgesics, and antihypertensives. A lot more classes are yet to be delved into. This technique would be very helpful in arresting the problems associated with the control of diseases especially the orphan diseases as well as the problem of drug resistance presently common with conventional anti-infective drug classes.

References

1. Vincente, E., Duchowicz, P. R., Castro, E. A. and Monge, A. (2009) QSAR analysis for quinoxaline-2-carboxylate 1,4- di-N- oxides as antimycobacterial agents, J. Mol. Graphics Model. 28: 28-36.

2. Hansch, C. and Leo, A. (1995) Employing QSAR, Fundamentals and Applications in Chemistry and Biology, American Chemical Society, Washington DC, USA

3. Verma, R. P. and Hansch, C. (2004) Chembiochem, 5: 1188.

4. Katritzky, A. R., Lobanov, V. S. and Karelson, M. (1995) Chem Soc. Rev. , 24: 279.

5. Carta, A., Paglietti, G., Nikookar, M. E. R., Sanna, P., Sechi, L. and Zanetti, S. (2002) Eur. J. Med. Chem., 37: 355.

6. Carta, A., Loriga, M., Paglietti, G., Mattana, A., Fiori, P. L., Mollicotti, P., Sechi, L. and Zanetti, S. (2004) Eur. J. Med. Chem. 39: 195.