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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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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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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(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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"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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So what you’re saying is you want agricultural scientists to start working in pharma?
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do ag scientists do this?
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