Publications by authors named "Barbara W A Vanhoecke"

Invasion and metastasis are responsible for 90% of cancer-related mortality. Herein, we report on our quest for novel, clinically relevant inhibitors of local invasion, based on a broad screen of natural products in a phenotypic assay. Starting from micromolar chalcone hits, a predictive QSAR model for diaryl propenones was developed, and synthetic analogues with a 100-fold increase in potency were obtained.

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The role of host-microbe interactions in the pathobiology of oral mucositis is still unclear; therefore, this study aimed to unravel the effect of irradiation on behavioral characteristics of oral microbial species in the context of mucositis. Using various experimental in vitro setups, the effects of irradiation on growth and biofilm formation of two Candida spp., Streptococcus salivarius and Klebsiella oxytoca in different culture conditions were evaluated.

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In our ongoing exploration of the structure-activity landscape of anti-invasive chalcones, we have prepared and evaluated a number of structurally related (E)- and (Z)-stilbenes. These molecules exhibited an extraordinary high in vitro potency in the chick heart invasion assay, being active up to 10nmolL(-1), a concentration level a 100-fold lower than the lowest effective doses that have been reported for natural analogues. Furthermore, they possess an interesting pharmacological profile in silico.

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In order to get a clearer view on the active geometry of anti-invasive chalcones, we have prepared a number of isoxazoles and related substances as conformationally restrained mimics of 1,3-diarylpropenones, and also of (Z)-stilbenes. In vitro anti-invasive activity data for 3,5-isoxazoles and 4,5-isoxazoles, together with an in silico geometrical comparison, point towards an active conformation for chalcones more resembling their s-trans geometry than the s-cis counterpart.

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Article Synopsis
  • An artificial neural network was used to analyze the anti-invasive activity of 139 compounds, focusing on molecular structure descriptors calculated with CODESSA Pro.
  • The QSAR model achieved a classification accuracy of 71% for the training set and 70% for the validation set, demonstrating good predictive capability.
  • This model can aid in virtually screening large databases of anticancer drugs to predict anti-invasive activity in new compounds.
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