Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones
Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of...
Guardado en:
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2014
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09680896_v22_n5_p1568_MartinsAlho http://hdl.handle.net/20.500.12110/paper_09680896_v22_n5_p1568_MartinsAlho |
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paper:paper_09680896_v22_n5_p1568_MartinsAlho |
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dspace |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Antimalarial Antiprotozoan database Antitoxoplasma Antitrichomonas Antitrypanosomal Classification model Cytotoxicity In silico study In vitro assay Leishmanicide Machine learning-based QSAR Non-stochastic and stochastic linear indices TOMOCOMD-CARDD software 1 methyl 7 nitro 4 (5 piperidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 1 methyl 7 nitro 4 (5 pyrrolidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 1 methyl 7 nitro 4 [5 (1,2,3,4 tetrahydroisoquinolin 2 yl)pentyl] 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 (5 azepanylpentyl) 1 methyl 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 (5 azepanylpentyl) 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 [5 (dimethylamino)pentyl] 1 methyl 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 [5 (dimethylamino)pentyl] 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 (5 piperidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 (5 pyrrolidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 [5 (1,2,3,4 tetrahydroisoquinolin 2 yl)pentyl] 3,4 dihydro 1h quinoxalin 2 one hydrobromide antiprotozoal agent chloroquine ligand metronidazole new drug nifurtimox organic compound quinoxalinone derivative unclassified drug antiprotozoal agent quinoxaline derivative accuracy animal cell Apicomplexa article classification cluster analysis combinatorial chemistry conceptual framework controlled study correlation coefficient cytotoxicity data base discriminant analysis drug activity drug approval drug design drug determination drug research drug screening feasibility study flagellate host cell in vitro study linear system mathematical computing molecular biology mouse nonhuman prediction protozoal infection structure activity relation validation study virtual reality chemical structure chemistry cyclization quantitative structure activity relation synthesis Apicomplexa Mastigophora (flagellates) Protozoa Sporozoa Antiprotozoal Agents Cyclization Molecular Structure Quantitative Structure-Activity Relationship Quinoxalines |
| spellingShingle |
Antimalarial Antiprotozoan database Antitoxoplasma Antitrichomonas Antitrypanosomal Classification model Cytotoxicity In silico study In vitro assay Leishmanicide Machine learning-based QSAR Non-stochastic and stochastic linear indices TOMOCOMD-CARDD software 1 methyl 7 nitro 4 (5 piperidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 1 methyl 7 nitro 4 (5 pyrrolidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 1 methyl 7 nitro 4 [5 (1,2,3,4 tetrahydroisoquinolin 2 yl)pentyl] 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 (5 azepanylpentyl) 1 methyl 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 (5 azepanylpentyl) 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 [5 (dimethylamino)pentyl] 1 methyl 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 [5 (dimethylamino)pentyl] 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 (5 piperidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 (5 pyrrolidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 [5 (1,2,3,4 tetrahydroisoquinolin 2 yl)pentyl] 3,4 dihydro 1h quinoxalin 2 one hydrobromide antiprotozoal agent chloroquine ligand metronidazole new drug nifurtimox organic compound quinoxalinone derivative unclassified drug antiprotozoal agent quinoxaline derivative accuracy animal cell Apicomplexa article classification cluster analysis combinatorial chemistry conceptual framework controlled study correlation coefficient cytotoxicity data base discriminant analysis drug activity drug approval drug design drug determination drug research drug screening feasibility study flagellate host cell in vitro study linear system mathematical computing molecular biology mouse nonhuman prediction protozoal infection structure activity relation validation study virtual reality chemical structure chemistry cyclization quantitative structure activity relation synthesis Apicomplexa Mastigophora (flagellates) Protozoa Sporozoa Antiprotozoal Agents Cyclization Molecular Structure Quantitative Structure-Activity Relationship Quinoxalines Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones |
| topic_facet |
Antimalarial Antiprotozoan database Antitoxoplasma Antitrichomonas Antitrypanosomal Classification model Cytotoxicity In silico study In vitro assay Leishmanicide Machine learning-based QSAR Non-stochastic and stochastic linear indices TOMOCOMD-CARDD software 1 methyl 7 nitro 4 (5 piperidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 1 methyl 7 nitro 4 (5 pyrrolidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 1 methyl 7 nitro 4 [5 (1,2,3,4 tetrahydroisoquinolin 2 yl)pentyl] 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 (5 azepanylpentyl) 1 methyl 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 (5 azepanylpentyl) 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 [5 (dimethylamino)pentyl] 1 methyl 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 [5 (dimethylamino)pentyl] 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 (5 piperidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 (5 pyrrolidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 [5 (1,2,3,4 tetrahydroisoquinolin 2 yl)pentyl] 3,4 dihydro 1h quinoxalin 2 one hydrobromide antiprotozoal agent chloroquine ligand metronidazole new drug nifurtimox organic compound quinoxalinone derivative unclassified drug antiprotozoal agent quinoxaline derivative accuracy animal cell Apicomplexa article classification cluster analysis combinatorial chemistry conceptual framework controlled study correlation coefficient cytotoxicity data base discriminant analysis drug activity drug approval drug design drug determination drug research drug screening feasibility study flagellate host cell in vitro study linear system mathematical computing molecular biology mouse nonhuman prediction protozoal infection structure activity relation validation study virtual reality chemical structure chemistry cyclization quantitative structure activity relation synthesis Apicomplexa Mastigophora (flagellates) Protozoa Sporozoa Antiprotozoal Agents Cyclization Molecular Structure Quantitative Structure-Activity Relationship Quinoxalines |
| description |
Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based 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 drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). 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) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of 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 antiprotozoan compounds by using ligand-based virtual screening of 'available' small molecules (with synthetic feasibility) in our 'in-house' library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five 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 depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development 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. © 2014 Elsevier Ltd. All rights reserved. |
| title |
Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones |
| title_short |
Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones |
| title_full |
Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones |
| title_fullStr |
Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones |
| title_full_unstemmed |
Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones |
| title_sort |
antiprotozoan lead discovery by aligning dry and wet screening: prediction, synthesis, and biological assay of novel quinoxalinones |
| publishDate |
2014 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09680896_v22_n5_p1568_MartinsAlho http://hdl.handle.net/20.500.12110/paper_09680896_v22_n5_p1568_MartinsAlho |
| _version_ |
1768542560100286464 |
| spelling |
paper:paper_09680896_v22_n5_p1568_MartinsAlho2023-06-08T15:58:57Z Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones Antimalarial Antiprotozoan database Antitoxoplasma Antitrichomonas Antitrypanosomal Classification model Cytotoxicity In silico study In vitro assay Leishmanicide Machine learning-based QSAR Non-stochastic and stochastic linear indices TOMOCOMD-CARDD software 1 methyl 7 nitro 4 (5 piperidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 1 methyl 7 nitro 4 (5 pyrrolidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 1 methyl 7 nitro 4 [5 (1,2,3,4 tetrahydroisoquinolin 2 yl)pentyl] 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 (5 azepanylpentyl) 1 methyl 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 (5 azepanylpentyl) 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 [5 (dimethylamino)pentyl] 1 methyl 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 4 [5 (dimethylamino)pentyl] 7 nitro 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 (5 piperidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 (5 pyrrolidinopentyl) 3,4 dihydro 1h quinoxalin 2 one hydrobromide 7 nitro 4 [5 (1,2,3,4 tetrahydroisoquinolin 2 yl)pentyl] 3,4 dihydro 1h quinoxalin 2 one hydrobromide antiprotozoal agent chloroquine ligand metronidazole new drug nifurtimox organic compound quinoxalinone derivative unclassified drug antiprotozoal agent quinoxaline derivative accuracy animal cell Apicomplexa article classification cluster analysis combinatorial chemistry conceptual framework controlled study correlation coefficient cytotoxicity data base discriminant analysis drug activity drug approval drug design drug determination drug research drug screening feasibility study flagellate host cell in vitro study linear system mathematical computing molecular biology mouse nonhuman prediction protozoal infection structure activity relation validation study virtual reality chemical structure chemistry cyclization quantitative structure activity relation synthesis Apicomplexa Mastigophora (flagellates) Protozoa Sporozoa Antiprotozoal Agents Cyclization Molecular Structure Quantitative Structure-Activity Relationship Quinoxalines Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based 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 drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). 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) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of 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 antiprotozoan compounds by using ligand-based virtual screening of 'available' small molecules (with synthetic feasibility) in our 'in-house' library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five 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 depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development 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. © 2014 Elsevier Ltd. All rights reserved. 2014 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09680896_v22_n5_p1568_MartinsAlho http://hdl.handle.net/20.500.12110/paper_09680896_v22_n5_p1568_MartinsAlho |