I spent a couple of weeks building a model to predict which oncology drugs would advance from Phase 2 to Phase 3. It didn’t work. Walk-forward AUC sat between 0.50 and 0.55 across every feature combination I tried: pathway position, publication sentiment, genetic evidence, novel-MOA flags, sponsor identity. None of them generalised out of sample. But there was a chart I liked too much to let die at the same time. ...
I wanted to think about potential metrics for modelling POS - the following is a living document, open iterative work on this subject. Related posts will update on findings, failures and further ideas to be tested! Predicting probability of success (POS) in biotech should start with biology. Evaluating the mechanism of action (MOA) of therapeutic targets, differentiating between upstream and downstream interventions, and, how the position of a target within a biological pathway influences POS. Most models overweight market size, management quality, or phase transition statistics and underweight whether the mechanism actually has durable control over the disease system. The key question is where the target sits within the network. ...