A structure-activity analysis of antagonism of the growth factor and angiogenic activity of basic fibroblast growth factor by suramin and related polyanions

Braddock, Peta; Hu, Dan-ning; Fan, Tai‐Ping; Stratford, Ian J.; Harris, Adrian L.; Bicknell, Roy

doi:10.1038/bjc.1994.172

Cited by 68 publications

(35 citation statements)

References 20 publications

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“…In contrast, the angiogenic activity of non-heparin-binding growth factors such as platelet-derived endothelial cell growth factor was resistant to suramin . In view of the narrow therapeutic window for suramin , we are currently evaluating the anti-angiogenic activity of suramin analogues which are 5 to 10 fold less toxic than suramin (Braddock et al, 1994).…”

Section: Discussionmentioning

confidence: 99%

Suppression of VEGF‐induced angiogenesis by the protein tyrosine kinase inhibitor, lavendustin A

Fan

1995

British J Pharmacology

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3 Daily local administration of 250 ng VEGF165 accelerated the rate of "33Xe clearance from the sponges and induced an intense neovascularisation. This VEGF165-induced angiogenesis was inhibited by daily co-administration of the selective PTK inhibitor, lavendustin A (10 jig), but not its negative control, lavendustin B (10 jig). Blood flow measurements and morphometric analysis of 8-day-old sponges showed that lavendustin A reduced the "33Xe clearance of VEGF165-treated sponges from 32.9 ± 1.5% to 20.9 ± 1.6% and the total fibrovascular growth area from 62.4 ± 6.1% to 21.6 ± 6.8% (n = 1 2, P < 0.05). 4 Co-injection of suramin (3 mg), an inhibitor of heparin-binding growth factors, also suppressed the VEGF165-elicited neovascular response. In contrast, neither lavendustin A nor suramin produced any effect on the basal sponge-induced angiogenesis. 5 When given alone, low doses of VEGF 165 (25 ng) or basic fibroblast growth factor (bFGF; 10 ng) did not modify the basal sponge-induced neovascularisation. However, co-administration of these two peptides to a single sponge together caused a significant increase in the rate of '33Xe clearance and angiogenesis similar to that seen with the high dose of VEGF165 (250 ng) acting alone. This VEGF/ bFGF neovascular response was also blocked by daily co-administration of lavendustin A (10 jig), suramin (3 mg) or a monoclonal anti-bFGF antibody (DG2, I jig), but not lavendustin B (10 g). 6 These results suggest that selective inhibition of PTK could have therapeutic potential in angiogenic diseases where VEGF plays a dominant role. Furthermore, blockade of the angiogenic activity of VEGF and VEGF,/bFGF by suramin reveals an alternative strategy in angiosuppression.

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Section: Discussionmentioning

confidence: 99%

Suppression of VEGF‐induced angiogenesis by the protein tyrosine kinase inhibitor, lavendustin A

Fan

1995

British J Pharmacology

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“…Another task included is mutagenicity prediction [16], which was considered for multi-instance tree and rule learning in [19], based on three different representations of molecules as bags of instances muta-atoms, muta-bonds and muta-chains. We also include the thioredoxin protein identification task [17] and the suramin data, which is another drug activity prediction problem: identifying suramin [4] analogues that can act as anti-cancer agents. Image classification is another important application area of multi-instance learning methods.…”

Section: Experiments On Real-world Datamentioning

confidence: 99%

Propositionalisation of Multi-instance Data Using Random Forests

Frank

Pfahringer

2013

Lecture Notes in Computer Science

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Abstract. Multi-instance learning is a generalisation of attribute-value learning where examples for learning consist of labeled bags (i.e. multisets) of instances. This learning setting is more computationally challenging than attribute-value learning and a natural fit for important application areas of machine learning such as classification of molecules and image classification. One approach to solve multi-instance learning problems is to apply propositionalisation, where bags of data are converted into vectors of attribute-value pairs so that a standard propositional (i.e. attribute-value) learning algorithm can be applied. This approach is attractive because of the large number of propositional learning algorithms that have been developed and can thus be applied to the propositionalised data. In this paper, we empirically investigate a variant of an existing propositionalisation method called TLC. TLC uses a single decision tree to obtain propositionalised data. Our variant applies a random forest instead and is motivated by the potential increase in robustness that this may yield. We present results on synthetic and real-world data from the above two application domains showing that it indeed yields increased classification accuracy when applying boosting and support vector machines to classify the propositionalised data.

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“…Different researchers demonstrated the antagonism character between heparin and suramin and the capacity to inhibit different angiogenic factors by preventing their interaction with cell-surface HSPGs (Danesi et al, 1993;Gagliardi et al, 1992). This activity is due to the capacity of suramin to bind to the heparin-binding site of the proangiogenic factors via the sulfonate groups (Braddock et al, 1994;Middaugh et al, 1992). Suramin was employed in the treatment of conventional chemotherapy unresponsive tumors (Hawkins, 1995), however high doses were necessary to achieve anti-tumor activity which developed intolerable toxicities (Collins et al, 1986;Constantopoulos et al, 1983).…”

Section: Suraminmentioning

confidence: 99%