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For our discussion about failures and costs, I've pulled some data from a publication from scientists at Eli Lilly, and that reference is in the bottom left of this slide. The probability of a lead being advanced from the pre-clinical stage (animal testing) and into phase 1 trials is 69%. The most challenging step in all these transitions is the movement from phase 2 to phase 3 with a probability of success of only 34%. This is why phase 2 trials, especially 2b, are called pivotal trials. This is the first time you can establish efficacy in humans, and a lot of assumptions -- assumptions all the way back to the target selection -- are then established once you show efficacy in phase 2. If you work through these probabilities from phase 1 through approval, the overall success from an IND, which is start at phase 1, to approval is 11.7%, or approximately 1 in 8.5 molecules. These data are somewhat dated and only from one company but the trend is still valid. People often cite a clinical trial success rate of about 10-15%. Let's look at the cost on the bottom of the screen. The pie chart shows the fraction of total costs based on each development stage. What is the total cost to bring one drug to market? In 2016 the total cost to bring one drug into the marketplace was estimated as 2.6 billion USD by Joseph DiMasi of Tufts University. That figure includes not just the cost of the successful drug but also the approximately 7.5 other molecules that failed in clinical trials. What are some lessons from these numbers? Well, many molecules advance into phase 2 only to fail -- 34% success rate -- because of lack of efficacy. Such a failed molecule will have accumulated over 2/3 of the development costs just to fail in phase 2. The lesson is clear. Improve efficacy confirmation earlier so that fewer doomed, costly molecules are lost during phase 2 trials. This idea has been called... fail fast, fail cheap. Identify bad leads earlier so resources may be spent on more promising molecules. If successful, such an effort would reduce the cost of developing drugs and also likely reduce the cost of drugs in the marketplace. The drug industry is aware of this opportunity, and researchers are actively working to improve predictions of human efficacy in early discovery stages.