Ruxandra Teslo's mental models

Their wordsI told him what I believe: that in the age of AI, medicine will be bottlenecked more than ever by regulation and the grind of clinical trials, and that billions poured into faster pre-clinical research won’t touch that problem, unsexy as it is.

Their wordsEven if AI can help design more effective drugs, timelines will not compress until we solve the operational and regulatory bottlenecks of trials.

Their wordsThat is mostly in the realm of chemistry and is intrinsically far more tractable and predictable; given a validated target, good teams usually reach a decent molecule.

Their wordsBut remember that such data can only come from trials in people (mice are nice, but most animal results simply do not translate.)

Their wordsExcessive manufacturing requirements are a pervasive problem, especially in the field of cell and gene therapy and they are killing countless of promising medicines before they ever reach patients.

Their wordsA capability existing is not the same as a capability being absorbed into institutions, and, perhaps most important of all, translated into outcomes people care about.

Their wordsUntil manufacturing and regulatory risks become easier to underwrite, capital will keep drifting away from one of the most promising technologies of the last century.

Their wordsLastly, the duration of a clinical trial does not merely determine how fast an individual therapy reaches patients. It also shapes which diseases attract serious investment and which do not.

Their wordsThe patent system then further disincentivizes companies from taking target risk, because it rewards novel chemical matter, not novel biology.

Their wordsIn biomedicine we have a problem called target herding: most companies chase the same biological targets because they are de-risked, which means we explore far less of the biological space than we could.