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Computational models of personalized medicine: leading indicators for angiogenesis therapies
Author(s) -
Mac Gabhann Feilim,
Popel Aleksander S.
Publication year - 2010
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.24.1_supplement.593.8
Subject(s) - medicine , clinical trial , angiogenesis , bevacizumab , oncology , disease , vascular endothelial growth factor , personalized medicine , chemotherapy , bioinformatics , vegf receptors , biology
Therapies targeting inhibition of vascular endothelial growth factors (VEGF) have been approved for use in combination with chemotherapy in multiple cancers; more inhibitors of the VEGF pathway are in trial. In comparison, despite promising pre‐clinical data, fourteen clinical trials targeting VEGF upregulation in coronary artery disease or peripheral artery disease have been unsuccessful. Using detailed computational models of VEGF and its receptors in healthy and diseased tissue, we analyze the reasons for the success and failure of pro‐angiogenic and anti‐angiogenic therapies, ranging from intravascular infusion of antibodies to local gene therapy. Intriguingly, using published data on interindividual variability, the model predicts that the outcome of each therapy depends on the individual to whom it is administered; patients can be divided into responders and non‐responders. For example, individuals with low levels of VEGFR1 are predicted to respond better to VEGF gene delivery than others. Further, the route of administration and frequency of delivery can also impact the result, and can be optimized. The computational tools developed here can be used to improve drug development and delivery at all lifecycle stages, from initial trials to post‐approval administration. Support provided by: NIH‐R33‐HL087351, NIH‐R01‐HL079653 and NIH‐R00‐HL093219.