Discussion

Increasing evidence in the field of oncology suggests that combinatorial therapies are clinically advantageous over mono-therapies. Clinical observations indicate increased efficacy, decreased resistance, and rheostatic control of dosing toward multiple targets through a multi-drug strategy. However, these compounds are typically developed independently and dosing strategies for efficacy and safety is not trivial. The ability to test drug combinations in predictive models would sup-port the testing of pre-clinical strategies involving both development compounds and approved drugs. BioMAP Assay Systems are physiologically relevant human disease models that provide multifactorial readouts relevant to compound efficacy and safety evaluated in parallel at any stage of the development pipeline.
We have recently expanded the platform to include predictive host-tumour BioMAP systems employing co-cultures of primary human fibroblasts or endothelial cells and PBMCs with a cancer cell line. These oncology BioMAP models recapitulate the complex interactions between tumour cells, stromal or vascular tissue and recruited, infiltrating immune cells to the tumour. Using these systems we can assess the phenotypic impact of candidate compounds on tumour microenvi-ronment biology to predict in vivo activities and forecast potential clinical outcomes with respect to efficacy and safety. These oncology-focused BioMAP systems capture tumour-mediated activities (angiogenesis, immunosuppression, matrix remodelling) that can be modulated by a number of clinically relevant mono-thera¬pies and drug combinations involving small molecule chemotherapeutics and biologics. This approach may support predictive and physiologically relevant cancer compound development from early discovery through pre-clinical development stages.