Abstract:
:Quantitative Structure-Activity (mt-QSAR) techniques may become an important tool for prediction of cytotoxicity and High-throughput Screening (HTS) of drugs to rationalize drug discovery process. In this work, we train and validate by the first time mt-QSAR model using TOPS-MODE approach to calculate drug molecular descriptors and Linear Discriminant Analysis (LDA) function. This model correctly classifies 8258 out of 9000 (Accuracy = 91.76%) multiplexing assay endpoints of 7903 drugs (including both train and validation series). Each endpoint correspond to one out of 1418 assays, 36 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). After that, we determined experimentally, by the first time, the values of EC50 = 21.58 μg/mL and Cytotoxicity = 23.6% for the anti-microbial/anti-parasite drug G1 over Balb/C mouse peritoneal macrophages using flow cytometry. In addition, the model predicts for G1 only 7 positive endpoints out 1251 cytotoxicity assays (0.56% of probability of cytotoxicity in multiple assays). The results obtained complement the toxicological studies of this important drug. This work adds a new tool to the existing pool of few methods useful for multi-target HTS of ChEMBL and other libraries of compounds towards drug discovery.
journal_name
Eur J Med Chemjournal_title
European journal of medicinal chemistryauthors
Tenorio-Borroto E,Peñuelas-Rivas CG,Vásquez-Chagoyán JC,Castañedo N,Prado-Prado FJ,García-Mera X,González-Díaz Hdoi
10.1016/j.ejmech.2013.08.035subject
Has Abstractpub_date
2014-01-24 00:00:00pages
206-20eissn
0223-5234issn
1768-3254pii
S0223-5234(13)00552-7journal_volume
72pub_type
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