Synthesis and structure–antituberculosis activity relationship of 1H-indole-2,3-dione derivatives
- 作者：Nilgü ; n Karalı ; Aysel Gü ; rsoy ; Fatma K ; emirli ; Nathaly Shvets ; F. Betü ; l Kaynak ; Sü ; heyla Ö ; zbey ; Vasyl Kovalishyn ; Anatholy Dimoglo
- 关键词：1H-Indole-2 ; 3-diones ; Antituberculosis activity ; Structure– ; activity relationships ; Electronic-topological method (ETM)
- 刊名：Bioorganic and Medicinal Chemistry
- 出版年：2007
- 期刊代码：9_09680896
- 类别：ch
- 出版时间：1 September 2007
- 卷：15
- 期：17
- 页码：5888-5904
- 文件大小：1148 K
摘要
New series of 5-fluoro-1H-indole-2,3-dione-3-thiosemicarbazones 2a–k and 5-fluoro-1-morpholino/piperidinomethyl-1H-indole-2,3-dione-3-thiosemicarbazones 3a–r were synthesized. The structures of the synthesized compounds were confirmed by spectral data, elemental and single crystal X-ray diffraction analysis. The new 5-fluoro-1H-indole-2,3-dione derivatives, along with previously reported 5-nitro-1H-indole-2,3-dione-3-thiosemicarbazones 2l–v, 1-morpholino/piperidinomethyl-5-nitro-1H-indole-2,3-dione-3-thiosemicarbazones 4a–l, and 5-nitro-1H-indole-2,3-dione-3-[(4-oxo-1,3-thiazolidin-2-ylidene)hydrazones] 5a–s, were evaluated for in vitro antituberculosis activity against Mycobacterium tuberculosis H37Rv. Among the tested compounds, 5-nitro-1H-indole-2,3-dione-3-thiosemicarbazones (2p, 2r, and 2s) and its 1-morpholinomethyl derivatives (4a, 4e, 4g, and 4i) exhibited significant inhibitory activity in the primary screen. The antituberculosis activity of molecules with diverse skeletons was investigated by means of the Electronic-Topological Method (ETM). Ten pharmacophores and ten anti-pharmacophores that have been found by this form the basis of the system capable of predicting the structures of potentially active compounds. The forecasting ability of the system has been tested on structures that differ from those synthesized. The probability of correct identification for active compounds was found as equal to 93%in average. To obtain the algorithmic base for the activity prediction, Artificial Neural Networks were used after the ETM (the so-called combined ETM–ANN method). As the result, only 9 pharmacophores and anti-pharmacophores were chosen as the most important ones for the activity. By this, ANNs classified correctly 94.4%, or 67 compounds from 71.