In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids.

Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery

QSAR modeling and chemical space analysis of antimalarial compounds

© 2017, Springer International Publishing Switzerland.Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones

Influence of Clarithromycin on Early Atherosclerotic Lesions after Chlamydia pneumoniae Infection in a Rabbit Model

Chlamydia pneumoniae may play a role in atherogenesis and vascular diseases, and antibiotics may prove useful in these conditions. Three groups of New Zealand White rabbits (24 per group) were infected via the nasopharynx with C. pneumoniae on three separate occasions (2 weeks apart). Group I was untreated and sacrificed at 12 weeks; group II received clarithromycin at 20 mg/kg/day for 8 days, beginning 5 days after each inoculation (early treatment); and group III received a similar dose of clarithromycin starting 2 weeks after the third inoculation and continued for 6 weeks thereafter (delayed treatment). To test for a possible anti-inflammatory effect of clarithromycin, two other groups of uninfected rabbits (12 animals in each) were fed 0.5% cholesterol-enriched chow, and one of these groups was treated with clarithromycin at 30 mg/kg/day for 6 weeks. Of 23 untreated infected rabbits, 8 developed early lesions of atherosclerosis, whereas 2 of the 24 early-treated group II had similar changes (P = 0.036 [75% efficacy]). However, in the delayed-treatment group, group III, 3 of 24 rabbits developed early lesions of atherosclerosis, thus demonstrating 62.5% reduction compared to the untreated controls (P = 0.07 [trend to statistical significance]). C. pneumoniae antigen was detected in 8 of 23 group I (untreated) rabbits versus 1 of 24 of the early-treated (group II) rabbits and 4 of 24 animals in the delayed group III (P = 0.009 and 0.138, respectively). All of the untreated, cholesterol-fed rabbits had moderate to advanced atherosclerosis (grade III or IV); clarithromycin had no effect on reducing the prevalence of but did reduce the extent of atherosclerosis in the cholesterol-fed rabbits by 17% compared to untreated controls. Thus, clarithromycin administration modified C. pneumoniae-induced atherosclerotic lesions and reduced the ability to detect organism in tissue. Early treatment was more effective than delayed treatment

QSAR modeling and chemical space analysis of antimalarial compounds

© 2017, Springer International Publishing Switzerland.Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones

QSAR modeling and chemical space analysis of antimalarial compounds

© 2017, Springer International Publishing Switzerland.Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones

QSAR modeling and chemical space analysis of antimalarial compounds

© 2017, Springer International Publishing Switzerland.Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones