Previous Article in event
Next Article in event
Antiprotozoan Lead Discovery by Aligning Dry and Wet Screening: Prediction, Synthesis, and Biological Assay of Novel Quinoxalinones
Published: 21 November 2008 by MDPI in The 12th International Electronic Conference on Synthetic Organic Chemistry session Bioorganic Chemistry and Natural Products
Abstract: Protozoan parasites have been one of the most significant public health problems for centuries and several of human infections causes by them are globally massive in their impact. The most of the current drugs used to treat these illness are decades old and have many limitations, including the emergence of drug resistance, severe sideeffects, low-to-medium efficacy, parenteral mode of administration, price, etc. These drugs have been largely neglected for drug development because they affect poor people in poor regions of the world where there is a small market for this kind of drugs. Therefore, nowdays there is a pressing need for identifying and developing new drugbased antiprotozoan therapies. In an effort to overcome this problem, the main purpose of this study is to develop a QSARs-based ensemble classifier for antiprotozoan druglike compounds from a heterogeneous series of compounds. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminat analisis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and nonantiprotozoan compounds, and so as to enable computational screening from virtual combinatorial datasets and/or existing drugs already approved. All studies were carried out taken into account the OECD principle in order for characterizing every obtained QSARs. In first time, a wide-spectrum benchmark database of 680 organic chemicals having great structural variability, 254 of them antiprotozoan agents and 426 compounds having other clinical uses, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. In total, seven discriminant functions were obtained, by using the whole set of atom-based linear indices. All the LDA-based QSAR models show accuracies above 85% in the training set and values of Matthews correlation coefficients (C) varying from 0.70-0.86. The external validation set shows globally rather-good classifications around 80% (92.05% for best equation). Later, we developed a multi-agent QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. Finally, the fusion model was used for the identification of a novel generation of lead-like antiprotozoans by using ligand-based virtual screening of small-molecules ‘available’ (with synthetic feasibility) in our ‘in-house’ library. A new molecular subsystem (quinoxalinones) was then theoretically selected like promising lead series, which were subsequently synthesized, structurally characterized, and experimentally assayed using an in vitro screening that take into consideration a battery of four parasite-based assays. The chemicals 11(12) and 16 are the most active (hits) against apicomplexa (sporozoa) and mastigophora (flagellata) subphylum parasites, respectively. Both compounds had shown rather good activities in the every protozoan in vitro panel and they didn't depict unspecific cytotoxicity to macrophages. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chemicals not included in this study. We conclude that the approach described here seems to be a promising esamble QSAR-clasifier for the molecular discovery of novel classes of broad –antiprotozoan– spectrum drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of protozoan illnesses.
Keywords: In silico Study, TOMOCOMD-CARDD Software, Non-Stochastic and Stochastic Linear Indices, Classification Model, Learning Machine-based QSAR, Antiprotozoan Database, In vitro Assay, Antimalarial, Antitrypanosomal, Antotoxoplasma, Antitrichomonas, Cytotocicity.