Abstract
Health outcomes of clonal hematologic disorders will be determined by individualized interventions based upon the molecular analysis of patients’ biological specimens. However, many patients risk exclusion from the application of these techniques if their biological specimens are not preserved in a longitudinal manner during the evolution of their disease. We consider these biological samples as assets which belong to patients, not just because these samples might shed light on the appropriate therapy, but because they may have financial value for the patient, especially when associated with appropriate clinical annotation. These assets’ financial valuations can vary and currently patients and tissue suppliers have no tangible means to control the monetization of such assets and the establishment of a specific valuation. Our proposed methodology is a novel biological asset valuation model to assign a specific financial value to each specimen. This provides an opportunity for patients to have control over usage and monetization of their assets, contrary to the current approach where the providers get assigned all the rights de facto.
Methods
The model depends on a variety of extrinsic, intrinsic, and miscellaneous parameters from local and global data sets to produce a single cohesive value. Each parameter is assigned a score over the interval [0, 1], and the scores are weighted, summed, and averaged to obtain a relative valuation. The relative valuation can be then multiplied by a normalization constant kto obtain an actual monetary value. For such a valuation model to be applicable, accurate, and effective, it needs to draw from an ever-expanding field of such parameters. Therefore, to mitigate this issue and streamline improvement to the model, we proposed a “slot-on” model to allow additional parameter scores to be easily introduced into and weighted in the final valuation. Our model additionally supports specimen tagging via parameter scoring thresholds in order to simplify coarse human valuation adjustment of broad specimen types and allow for dynamic real-world changes to valuations that are impossible for the model to capture (new legislation affecting production, etc.).
Results
The valuation model has been tested on several sample patient cases. For example, a well-preserved, well-annotated polycythemia vera marrow sample was valued at 1.246k, while a poorly-reserved, moderately-annotated glioblastoma multiforme glial cell tissue sample was valued at 1.290k. Such a valuation confirms that the polycythemia vera asset is lower in value than the glioblastoma multiforme asset despite a higher quality of annotation.
Conclusion
Our approach and methodology of assigning a value to biological assets has been validated by our case studies and has the potential to change the level of control that patients have over their biological assets, allowing patients to derive the monetary benefits of the distribution of their tissue and, working with their care providers, have better individualized treatment and health outcomes.
Disclosures
No relevant conflicts of interest to declare.
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