Doing it All - How Families are Reshaping Rare Disease Research
The face of rare disease drug discovery and development is changing right before our eyes. The outliers of the past were the plucky parents who summoned up the courage to try to treat their children against all odds. Think of the rare disease focused movies ‘Lorenzo’s Oil’ and ‘Extraordinary Measures’ but now accelerated to develop treatments even quicker. Parents, patient advocates and their collaborators are now capable of doing it all themselves. We think this will have profound implications for everyone from the incumbent rare disease foundations that have held sway for decades to the multibillion dollar rare disease market, BioPharma companies, VCs and angel investors that inhabit this space. The repercussions of this disruption will no doubt impact healthcare in general and ultimately influence how we develop treatments for major diseases as well. We present several lines of evidence for our viewpoint from our personal experiences interacting with many rare disease families and patient advocates in recent years.
KEY WORDSdisruption drug discovery machine learning multiple sulfatase deficiency rare diseases
ACKNOWLEDGMENTS AND DISCLOSURES
We kindly acknowledge extensive discussions with many rare disease parents and their foundations as well as Springboard Enterprises for bringing together rare disease entrepreneurs in 2017. SE acknowledges funding from NIH NINDS 1R41NS089061–02 and 1R41NS092221-01A1, NIH NICHD 1R41HD092110–01 and FY2018 UNC Research Opportunities Initiative (ROI) Award. SE is a co-founder and employee of Collaborations Pharmaceuticals, Inc., Phoenix Nest Inc. and scientific advisory board member for the Pitt Hopkins Research Foundation. EOP is the founder and CEO of Perlara PBC.
- 1.Sachs J. This father founded a medical research startup to save his Kid’s life. Fast Company Available from: https://www.fastcompany.com/40490486/this-father-founded-a-medical-research-startup-to-save-his-kids-life.
- 4.Lane T, Russo DP, Zorn KM, Clark AM, Korotcov A, Tkachenko V, Reynolds RC, Perryman AL, Freundlich JS, Ekins S. Comparing and validating machine learning models for mycobacterium tuberculosis drug discovery. Mol Pharm. 2018 Apr 26. https://doi.org/10.1021/acs.molpharmaceut.8b00083.
- 6.Mandel J, Bertrand V, Lehert P, Chumakov I, Scart-Gres C, Guedj M, et al. A meta-analysis of randomized double-blind clinical trials in CMT1A to assess the change from baseline in CMTNS and ONLS scales after one year of treatment. Orphanet J Rare Dis. 2015;10(1):74.CrossRefPubMedPubMedCentralGoogle Scholar