A multilayer network approach to quantifying biologically-derived systematic risk in biomedical finance
Name
973332641-MIT.pdf
Description
Full printable version
Size
28.49 MB
Format
Adobe PDF
Checksum (MD5)
3eccaefb126dd1b5a9d9c6d7d7f5c612
Author(s)
Walz, Andrew R
Advisor(s)
Olivier de Weck and Andrew Lo.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
Sharply rising disease prevalence and associated healthcare costs are placing an increasingly significant economic burden on society. Biomedical research and industry have struggled to adequately address this challenge, as evidenced by the stagnation and even decline of new therapeutics development success rates. Recent work in the MIT Laboratory for Financial Engineering has explored the potential of using financial engineering in the form of biomedical "megafunds" to help tackle this problem. New methods will be needed to better assess systematic financial risks for these therapeutic project portfolios. This primarily methodological thesis seeks to explore the opportunity to leverage multilayer network models as tools to help measure this risk, specifically the biologically-derived component of risk resulting from project correlations generated through the underlying biological networks. Historical examples of coupling between drug development projects are used to motivate a framework in which project correlations emerge from a combination of indication and target similarity. This framework motivates the construction of a multilayer network model, drawing upon multiple systems biology databases for its construction and using a sample of FDA orphan designations as a representative project set. Using shortest path distance and Random Walk with Restart (RWR) relevance, indication and target similarity between projects are quantitatively evaluated. Comparing average sales correlations to the log of average RWR relevance for classes of compounds reveals notable relationships between correlation and network similarity. This relationship is shown to be stronger for the case of disease relevance (R2 = 0.99) than for target relevance (R2 = 0.93). A potential approach is finally described for integrating biological network similarity with financial models useful for portfolio analysis, and implications on portfolio selection are discussed through synthetic construction of hypothetical orphan drug portfolios..
Description
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 100-106).
Subjects
Engineering and Management Program.
System Design and Management Program.
Engineering Systems Division.
MIT Department
Massachusetts Institute of Technology. Engineering and Management Program
System Design and Management Program.
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