Anti-precision medicine

drugging variability to create drugs that work on all patients

Is there a way to turn a $40B/yr class of drugs that only works on about half of patients into a drug that works for all patients? We think the answer is YES.

Three years ago we set out on a mission to increase the representation of humanity’s biological diversity in precision medicine. Over the last year, we have enrolled over 1,000 patients (including 200 autoimmune patients) in our REEF studies towards understanding the clinical and molecular factors driving individual level variation in disease progression and drug response.

While we initially planned to enable personalized medicine, towards getting the “right drug for the right patient”, there are two emergent problems with this approach:

  1. Some patients may not end up with any options from the set of existing therapies.
  2. At its worst, precision medicine can result in bias. This can be especially true if the criteria for excluding certain patient populations from a therapy is based on prior medical history or demographic criteria.

We are still working towards deploying a clinical test that guides autoimmune patients to the best possible medication for them in a way that minimizes these problems, but, earlier this year, we decided to also try to flip the problem on its head. Specifically, to take an existing therapeutic mechanism of action and add a metaphorical elastic to make it better at being “one size fit all”. Motivated by TNF as a major hub across most autoimmune diseases (Schett et al., NEJM 2021), we sought to improve its efficacy by modulating orthogonal biological pathways.

Today, I’m excited to announce preclinical in vitro results validating a new drug (antibody based TNF inhibition coupled with a small molecule) that targets mechanisms underlying patient variability in response to TNF inhibition. Interestingly enough, this combination resulted in both reduced variability in TNF inhibition across immune cells from different 200 different patients (p<0.003) AND reduced variability in TNF inhibition across cell types within a given patient (p<0.000001).

Biological variability exists across patients and across cell types within a patient. Our working hypothesis from our experiments is that by targeting mechanisms driving the former, we can also impact the later.

Our results suggest this approach may yield drugs that are both more efficacious for a given patient AND less variable across patients.

A skeptic may question the relevance of an in vitro model of human immune cells to quantify efficacy for autoimmunity. Interestingly, the history of the use of TNF inhibition to treat autoimmune diseases started with in vitro efficacy results in mononuclear cells derived from rheumatoid arthritis patients (see timeline below).

Next steps

We’re currently working on taking this combination therapy forward to in vivo results while also:

  1. looking for ways to get to standalone therapies that achieve similar performance
  2. trying a similar approach on other mechanisms (ex: IL12/23)
  3. working with pharma partners on downstream development

Overall, we think this approach of “drugging variability” is uniquely enabled by the platform we have built at Coral and could be a new way of leveraging molecular insights not for sub-typing disease at finer and finer resolution, but rather to create single therapies that work uniformly across the population.

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Atray Dixit

Atray Dixit

CEO@Coral: patient diversity + improving therapies. MIT PhD 2018: single cell seq + CRISPR (Perturb-seq, Shuffle-seq)