Financing Correlated Drug Development Projects

Abstract

Current business models have struggled to support early-stage drug development. In this paper, we study an alternative financing model, the megafund structure, to fund drug discovery. We extend the framework proposed in previous studies to account for correlation between phase transitions in drug development projects, thus making the model a more realistic representation of biopharma research and development. In addition, we update the parameters used in our simulation with more recent estimates of the probability of success (PoS). We find that the performance of the megafund becomes less attractive when correlation between projects is introduced. However, the risk of default and the expected returns of the vanilla megafund remain promising even under moderate levels of correlation. In addition, we find that a leveraged megafund outperforms an equity-only structure over a wide range of assumptions about correlation and PoS.

Financing repurposed drugs for rare diseases: a case study of Unravel Biosciences

“…For example, some drugs may be a “follow-on” of other drugs for the same disease (i.e., “me-too” drugs), thus having a higher in the preclinical phase once the original drug has been proven to be effective and reaches phase 2. The of the drugs composing our portfolio are allowed to have non-zero pairwise correlations in each phase; here, we have assumed a moderate value of , consistent with the specific nature of rare diseases, which do not tend to exhibit high degrees of correlation [ 28 ]. In addition, we assume a non-zero pairwise correlation of 0.2 for the market valuation in each phase for the Gaussian copula across the drugs in the portfolio.…”

“…Applying concepts from classical portfolio theory [ 22 – 25 ], it is also possible to improve the risk profile of a drug development project by considering the development of a portfolio of drugs instead of a single drug compound [ 26 – 28 ]. Rare diseases are generally believed to present low pairwise correlations in part due to their inherent nature as monogenic disorders [ 5 , 29 , 30 ], although this analysis can still be performed when the drugs in a portfolio are pairwise correlated.…”