Subscribe to RSS
DOI: 10.1055/s-0033-1351940
In silico prediction and experimental evaluation of furanoheliangolides as potent antitrypanosomal agents
Continuing our studies on QSAR for the activity of sesquiterpene lactones (STL) against Trypanosoma brucei rhodesiense (Tbr) [1], we have extended the set of available IC50 data to 69, and generated a new QSAR model [2] in a similar way as reported previously. Descriptors were calculated from optimized 3D structure models. Compounds were divided into a training- and a test set (46/23). The training set descriptor matrix was submitted to genetic algorithm-based variable selection/multiple linear regression (GA-MLR). The best model regarding internal and external predictions (R2= 0.75, Q2= 0.65, P2(test set)= 0.35) was employed to predict the activities for a database of 1700 STL structures.
Quite notably, among the 71 compounds predicted to be highly active (IC50 ≤0.1µM), 15 were STL of the furanoheliangolide (FH) subclass, which had not been tested for anti-Tbr activity before. Hence, four FH (1-4) were tested in vitro.
Goyazensolide (1), budlein A (2), 15-deoxy-4,5-dihydro-2',3'-epoxygoyazensolide (3) and 4,15-isoatriplicolide tiglate (4) displayed IC50 values of 0.07, 0.07, 0.20 and 0.02µM, respectively. 4 is the most active STL against Tbr found up to present. The lower activity of 3 is due to the absence of the α,β,γ,δ-unsaturated ketone moiety. In agreement with our earlier observations [1], the presence of a second Michael acceptor in addition to the α-methylene-γ-lactone group is required for high anti-Tbr activity.
The experimental results confirm the usefulness of our QSAR model to predict the activity of untested STL. FH represent a new class of very interesting lead compounds against Tbr which deserve further investigations.
References:
[1] Schmidt TJ et al. Molecules, 14, 2062 – 76 (2009)
[2] For all calculations: Molecular Operating Environment 2011.10 (MOE), Chemical Computing Group, Montreal, Canada, http://www.chemcomp.com/
This work is part of the activities of ResNetNPND: www.uni-muenster.de/ResNetNPND/
Support of CCG, Montreal, is gratefully acknowledged.