Development of Deep Learning Models for Biomarker Identification and Classification

Nearly half of all biomedical publications ever written have been published in the last years the era since the completion of the human genome project The rate of increase is exponential shows no signs of slowing down and can be seen in all of the information sources relevant to precision medicine development This constitutes a nearly insurmountable burden for the drug development and diagnostics professionals who develop precision medicines Next generation automated evaluation of this data that will enable rapid supportable and innovative product development decisions would lead to the development and approval of many more precision medicines resulting in improved public health decreased precision medicine time to market and increased efficiency and profitability for drug development and diagnostics companies Developing precision medicines is exceedingly difficult due to an underlying chicken and egg problem patient populations for whom a drug will be effective cannot be identified without a test but test development is notworth justifying without a drug that is demonstrated to be effective in that population No system for the identification of actionable precision medicine opportunities exists The ideal approach would examine multiple systems of record that impact marketplace decisions be aware of the identify of individual molecular biomarkers mentioned therein as well as bring in technical details such as measurability and measurement method and be accessible to drug development companies looking to define a patient population and diagnostics companies looking to develop a test to provide that definition Therefore the overall goal of this multi phase SBIR project is to capitalize on our preliminary success in building a biomarker database BiomarkerBase that supports this critical interface of precision medicine development decisions Amplionandapos s highly qualified data science Randamp D team will collaborate with Dr Parag Mallick of Stanford University to pursue three Aims train a Deep Learning Model for biomarker identification in clinical trials extend Model application and prove performance for classification of biomarker usage intent and prove that Model identified and classified biomarkers match and expand expert opinions Showing that we can identify biomarkers and their usage classifications in clinical trials and publications will establish the predictive potential of our unique algorithms within the limited scope of this Phase I feasibility project and will set the stage for a larger Phase II demonstration Phase I will provide data that can be incorporated directly into Amplionandapos s BiomarkerBase product and will allow us to assess how well our Model meets user requirements for this data Phase II work will allow us to expand extend this Model to cover additional sources Phase III work with Industry partners will allow us to integrate this commercial service directly into customer work flows Our next generation capabilities will provide a critical linkage at the challenging interface between the diagnostics and drug development efforts and will accelerate the development of novel precision medicines The development of precision medicines requires the synthesis of information from multiple sources to identify the opportunities that are technically feasible and likely to succeed in the marketplace Current methods for analyzing this information are too slow too non specific or too focused on only a small fraction of the information required to make a good decision The overall goal of this project is to develop validate and commercialize Amplionandapos s proprietary algorithms for identifying precision medicine development opportunities from publicly available sources