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Conclusion As the stagnation of medical and pharmacological technology practice verges toward social and eco- nomic crisis, greater attention is being paid to a mindset that facilitates access to a tremendous vol- ume of available scientific and clinical data, as well as advanced algorithms that can exploit that infor- mation. This two-part series of papers initiates dis- cussions about the complexities facing drug discov- ery and development. In Part I, we articulated, among other disrup-


tive innovation platforms, the significance and importance of ‘Core Model’, an economic and organisational paradigm for drug discovery and development, that was elucidated through the story-case of the development of the anti-cancer drug bortezomib33. In the current pharmaceutical and health care scenarios, drug leads that may otherwise languish in the laboratory could be fully capitalised via the use of ‘Core Model’ approaches to make it to the patient, saving time, labour and capital. Here in Part II, we introduce a novel EI tech-


nology, namely EBA, a cognition enhancement technology, that determines data relationships by comparing directions of stimuli-induced informa- tion flows (cause-effect relationships) in dynamic interaction-network systems. In doing so, EBA translates vast amounts of data into actionable insights. This information-theory based method- ology uses information characteristics, and unlike


Figure 6


Protein-protein interactions targeted by substances in Phenotype A


AI approaches, it requires fewer data points, is insensitive to noise and reduces system complexi- ty. The inherent logic of the application EBA methodology in analysis of life science data is based on the premise that behaviour of biological systems is founded on emergent properties. We propose innovative network science that address- es the general need for improved applications and advanced algorithms in translational chemical biology. Also discussed is the capability of the EBA plat-


form for identifying substances (botanicals and small molecules) that can modulate resistance of many bacterial strains (gram positive and gram negative) to a wide array of antibiotics. The utility of this approach is demonstrated in the discovery of drug combinations that promise to overcome the menacing resistance of deadly bacteria against multiple antibiotics. Notably, this information theory-based topological data analysis methodolo- gy reduces systems complexity, data noise and scaling issues which are key problems in life sci- ence’s Big Data analytics. The ability to ascertain complex data relationships without the need for supercomputers and human supervision for creat- ing training sets makes this technology particular- ly useful for application in devices, robotics and mobile applications. Thus, SystaMedic Inc’s EBA platform has the potential to become a key tech- nology for identifying efficacious and well-tolerat- ed, differentiated medicines, and is applicable for


50


Drug Discovery World Summer 2018


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