DDDAS

Last year Spherix undertook an asset-centric growth strategy based on SPX106T, and is now launching a combination-drug discovery platform based on a dynamic data-driven application simulation (DDDAS) approach. In addition to its own combination drugs licensed from the University of Kentucky, Spherix is looking at phase 1 and phase 2 assets to roll-up in this platform.

The DDDAS program began at the National Science Foundation (NSF) and has been more recently extended to the Air Force Office of Scientific Research (AFOSR). DDDAS is now being employed to model complex metabolic disease pathways, testing potential binary therapies in simulations at various combinations of two points in the pathways, choosing the most effective pair-wise combinations. DDDAS is being used now in animal and human studies underway at the University of Kentucky. Recently, researchers at Stanford University began to use a data-driven algorithmic approach to predict drug interactions as well.

DDDAS is a paradigm through which simulations and measurements become a symbiotic feedback control system. DDDAS entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process. Such capabilities promise more accurate analysis and prediction, more precise controls, and more reliable outcomes. The ability of an application simulation to control and guide the measurement process and determine when, where, and how it is best to gather additional data has itself the potential of enabling more effective disease interventions as well as diagnostic measurement methodologies. Furthermore, the incorporation of dynamic inputs into an executing simulation helps create application platforms that can more accurately describe real world, complex metabolic diseases.

The asset-centric growth strategy based on the single combination drug SPX106T is a natural segue to a discovery platform strategy designed to create new combination therapies for complex metabolic diseases. To be successful, an asset-centric growth strategy needs an experienced senior core team, like the team at Spherix and at the University of Kentucky. This team must access the best of the scientific and business ecosystem around the asset, as Spherix is doing now with orphan drugs. Asset-centric virtual business models rely on a broad network of strong academic collaborators, outstanding advisors, and CRO partners. Frequently the single-asset development model realizes its value creation through an acquisition by a larger company. Only occasionally does a single asset model company bring a product all the way to market.

The transition to a discovery platform model requires efficient deployment of resources, including the resources Spherix already has as an asset-centric company. The path to product candidates is known and a key emphasis over the next few years will be validating that the platform generates repeatable advances. The discovery platform model focuses resources on core expertise, as mitigates risk through diversification. Combination drugs may be the best therapies for complex multifactorial diseases. The discovery platform model offers ongoing value creation through licensing one or more drugs in its pipeline, through revenue from sales, as well as through acquisition by another company. For these reasons and more, the adoption of the DDDAS approach to combination drug development is a good strategic move.

Tatonetti, Nicholas P.; Ye, Patrick P.; Daneshjou, Roxana; et al. Data-Driven Prediction of Drug Effects and Interactions. Science Translational Medicine (03/14/12) Vol. 4, No. 125, P. 125