The pipeline diagram makes the process look orderly, but the numbers behind it are brutal. For every medicine that reaches patients, thousands of compounds are made and tested, and the great majority are discarded. The narrowing is not uniform: most compounds are eliminated early and cheaply, during hit finding and lead optimization, but a meaningful fraction survives all the way to human trials and then fails there, where each failure costs far more.
This pattern is called attrition, meaning the rate at which candidates drop out of the pipeline. Attrition is the central economic fact of drug discovery. Because the cost of the late failures must be carried by the few successes, the average cost of bringing one approved drug to market is commonly estimated in the range of one to several billion dollars, and the timeline from first target work to approval typically runs ten to fifteen years.
The practical consequence is that reducing late-stage attrition is worth far more than reducing early-stage cost. A method that eliminates a thousand compounds in the laboratory saves relatively little; a method that correctly predicts which of two candidates will fail in phase 3 saves an enormous amount. This asymmetry is the reason the field pays so much attention to any tool that might improve the reliability of early predictions.