Why huge gains in drug discovery tech have led to longer drug development times
The authors argue for using better predictive models to screen drugs at an early stage, because too many drugs are failing at costly clinical trials.<p>In my opinion we already know the best predictive model to use, but we choose not to use it. When Jenner developed the first vaccine back around 1800 he didn&#x27;t use any petri dishes or mathematical models: he just found some nearby children and tested his theory directly.<p>Now you may find this ethically repulsive but from a consequentialist point of view it was the right thing to do at the time. This line of research saved hundreds of millions of lives.<p>I say we have gone too far in the other direction. The problem with failed costly clinical trials is not the failure per se but the cost: in money spent, time, reputation, commercial return, legal consequences, etc. And the hidden cost of how long the whole process takes even if successful.<p>Take the field of monoclonal antibodies. We now have almost off-the-shelf technology to develop drugs that specifically block certain biological mechanisms. But it still takes 10-15 expensive years to progress through different lab and animal models, then healthy humans, before clinical trials on actual patients.<p>Instead we should reduce the cost of human trials: have a network of terminally ill volunteers to test mabs at an early stage. Fail fast to move fast. Use the money saved to offer defined compensation when things go wrong.<p>The drugs that we really want to find are the ones with an obvious effect on real patients. This process would find those drugs much faster, and save lives overall.