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s_r_constantin's profile
Sarah Constantin
Sarah Constantin
Sarah Constantin
@s_r_constantin

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Sarah Constantin

@s_r_constantin

Math/ML/data-science person now working on solving aging...and helping with COVID19?! Founder, LRI and Daphnia Labs. Married to @oscredwin

Be
srconstantin.posthaven.com
Joined February 2019

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    1. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      If each stage of testing costs proportionally more (and thus can be applied to proportionally fewer drugs in parallel), each increment of improvement in drug screening accuracy has *exponential* improvements in cost-per-successful-drug-candidate.

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    2. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      Yet, "better drug discovery platforms" are perceived (at least by traditional pharma and clinical-stage biotech investors) as low-value, risky, and unserious. Pharma business development execs I've talked to have said that their companies don't license new screening tech at all!

      2 replies 0 retweets 2 likes
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    3. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      It's true that drug discovery has been trending towards bigger and bigger screens for decades, without reducing R&D costs per successful drug at all. ("Eroom's Law.") But I think this is adequately explained by poor predictive validity.

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    4. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      In order to test whether your bigger, cheaper screen will help select better drug candidates, you have to measure its ability to predict outcomes at much later stages of the development process.

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    5. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      My impression is that this is hard to coordinate in large organizations with legacy technology; you have to get separate departments (say, validation and discovery) to integrate their data.

      2 replies 0 retweets 2 likes
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    6. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      Data integration across departments in large orgs is a HARD human and technical problem. I used to work at Palantir; this was literally the whole job of our company, and our clients fought us tooth and nail.

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    7. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      "Make early stage drug discovery more predictive of later preclinical efficacy" is hard for Big Pharma to pull off, but because of institutional/organizational/technical-debt problems, *not* because it's intrinsically hard scientifically.

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    8. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      It's an *easy* statistical problem to evaluate how good your screening is at predicting efficacy outcomes, identify which screening steps are poor predictors, and try to improve them. It's just a hard *social* problem in big orgs.

      1 reply 0 retweets 5 likes
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    9. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      This seems like a classical example of "disruption" in the strict sense; there are innovations that big existing companies can't do, not because the people at those companies are stupid, but because the cost of switching their internal tech and processes is enormous.

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    10. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      A biotech company that's built on data integration from the ground up, such that each screening stage is optimizing for continuous improvement in *predictive validity*, not number of hits, and has predictive validity metrics as OKRs --

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      Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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      that kind of company would *actually* have incentives aligned to switch to improved screening methods as they become practical. The thing to optimize is not "cost per hit" but "cost per hit that succeeds at the next stage of testing."

      7:40 AM - 1 Jun 2019
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      • michael vassar Anais-9 😷 corvus frugilegus Natália Mendonça
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        2. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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          What we need is for clinical-stage investors to understand this logic. It's not about any one screening technology, which ultimately may succeed or fail in producing better clinical results. There are endless arguments about the validity of different screening or animal models.

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        3. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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          The point is, the *general class* of improvements in screening platforms is where *all* the money is, and we need biotech companies structured end-to-end around predictive validity.

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        4. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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          (Well-known examples of improvements in predictive validity: drugs validated against human genetic targets are more likely to succeed in the clinic. Also, compounds discovered through phenotypic screening are a majority of successful first-in-class drugs.)

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        5. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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          "Optimize predictive validity" seems like really solid logic to me, and I expect it to seem common-sense to a lot of tech people and scientists, but I expect it sounds really "out there" to seasoned biotech execs, so I especially welcome critical feedback from them.

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        6. End of conversation
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        2. Plague Misha‏ @drethelin 1 Jun 2019
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          Replying to @s_r_constantin

          So what you’re saying is you want agricultural scientists to start working in pharma?

          1 reply 0 retweets 0 likes
        3. Sarah Constantin‏ @s_r_constantin 1 Jun 2019
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          Replying to @drethelin

          do ag scientists do this?

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