Translational research for new drugs, medical devices, and diagnostics encompasses aspects of both basic science and clinical research, requiring multidisciplinary skills and resources that are not all readily available in either a basic laboratory or clinical setting alone. We propose that, to be successful, “translational” research ought to be understood as a defined process from basic science through manufacturing, regulatory, clinical testing all the way to market. The authors outline a process which has worked well for them to identify and commercialize academic innovation. The academic environment places a high value on novelty and less value on whether, among other things, data are reproducible, scalable, reimbursable, or have commercial freedom to operate. In other words, when investors, strategic companies, or other later stage stakeholders evaluate academic efforts at translational research the relative lack of attention to clinical, regulatory, reimbursement, and manufacturing and intellectual property freedom to operate almost universally results in more questions and doubts about the potential of the proposed product, thereby inhibiting further interest. This contrasts with industry-based R&D, which often emphasizes manufacturing, regulatory and commercial factors. Academics do not so much choose to ignore those necessary and standard elements of translation development, but rather, they are not built into the culture or incentive structure of the university environment. Acknowledging and addressing this mismatch of approach and lack of common language in a systematic way facilitates a more effective “translation” handoffs of academic project concepts into meaningful clinical solutions help translational researchers more efficiently develop and progress new and better medical products which address validated needs. The authors provide an overview and framework for academic researchers to use which will help them define the elements of a market-driven translational program (1) problem identification and validation; (2) defining the conceptual model of disease; and (3) risk evaluation and mitigation strategies.
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