Solving this one fixes the future of IVF innovation
A group of smart and dedicated IVF entrepreneurs are meeting the challenge of increasing access to surrogate carriers. Great work!
But IVF has another surrogate problem. Lets dig in, starting with a bit of medical history.
I like to talk about different areas of healthcare being stratified along the analog to digital continuum. Mapping the genome and the ease of sequencing make oncology and immunology more precise: tumors that used to be defined by anatomic site and what they look like under light microscopy are now defined by specific genetic sequences. At the other end of the continuum, much of neurology and psychiatry and obstetrics and gynecology retain analog, pattern-recognition classification systems, the names of the diagnoses often characterized as “observation/observation syndrome” — polycystic ovarian syndrome, for example.
In biotechnology, digitalization accelerates innovation. Better characterization of a disease makes solutions more key in a lock and less shotgun. Enrollment criteria for clinical trials becomes more precise and powering more accurate, meaning that conclusive efficacy data can be inferred from smaller, less expensive clinical trials. This in turn makes it easier to rationalize venture capital allocation, independent of the scale of the unmet need being addressed. For women’s health, the exacerbates the well-documented historical lack of funding for diseases and conditions that predominantly affect women, as well as the under-representation of women in trials for conditions that appear gender non-specific.
Digital medicine has great trial endpoints: things you can measure precisely, with little or no inter- and inter- observation variability and no tendency towards observer bias, and a direct relationship to the outcome you want to improve (think overall survival in cancer) or the one step away from the outcome (decreased cholesterol levels leading to lower stroke and myocardial infarction rates) —
In IVF, we are endpoint starved. Heck, we are measurement starved. A few hormones, the sizes of follicles and the lining, number of oocytes, fertilization %, blast %, euploidy/aneuploidy, implantation and live birth. And that’s it.
This makes the search for cause and effect a difficult one, especially since most of the things we want to test out have only one “so what” outcome measurement — and that’s pregnancy. And pregnancy is often so distant in the process from the intervention that we are testing that it’s mathematically impossible to isolate the specific effect of the intervention without an enormous trial, one that digests all of the heterogeneity of an IVF patient population, as well as the vast number of confounders that can affect a cycle outcome after randomization to one or another method of stimulation, or culturing or alteration in laboratory procedure.
Statins were approved and adopted as standard of care long before we had the heart attack and stroke outcome data that proved their effectiveness — because we were able to rely on the surrogate endpoints of lipid and cholesterol levels to demonstrate that they were highly likely to work.
For IVF, what are our surrogate endpoints? Fertilization worked for ICSI (at least for oligo-asthenospermia) and euploidy/aneuploidy and mutation analysis works for certain (but not all) PGT trials; in both cases the surrogate measurement is taken right after the intervention and is relatively free of confounders.)
This surrogate problem is bad for innovators who want to prove that their intervention passes the “so what?” test and get paid for value they create, bad for REI’s and embryologists, who want to do the right things for the right reasons and not the opposite, and bad for patients, who want to get their IVF cycle over without enduring and/or paying for things that don’t work.
OK — that’s our “other” surrogate challenge. In part 2, I’ll address solutions.