Should Biotech Investors Care About Early Stage Clinical Trials?

Biotech companies put their drugs through several trials to test for different things, including safety and efficacy. But at what point can an investor start to get an idea about how a company's drug is performing?

In this video clip from "The Pharma & Biotech Show," recorded on Feb. 9, Motley Fool contributor Taylor Carmichael asks Dr. Frank David, author of The Pharmagellan Guide to Analyzing Biotech Clinical Trials, if there's anything that can be learned from an early stage trial.

Taylor Carmichael: Frank, let me ask you a question then about an investor. I'm the only non-doctor on this panel. [laughs] Let me ask you a question about an investor looking at clinical trials. Your phase 1 trial is typically safety, and then your phase 2, you might have a little bit of efficacy, and then phase 3 is the pivotal trial, which is efficacy. Is it fair to look at those early stage trials and try to get some feeling about the efficacy of a drug or should you wait to that pivotal trial? Do you have any thoughts on that?

Frank David: I mean, if we just take a step back. Imagine yourself, whether it's in Pfizer or in a small biotech actually running these trials. Why do you do those trials before you run your phase 3 study? Part of it is that there is a mandated amount of safety data that you have to generate from FDA in order to really get permission to run the larger study. But what you do between getting some basic safety data from our first-in-human study and a registration-enabling trial, a pivotal trial, is up to you. From a company perspective, and I think the same is true for an investor perspective, it was all just about successive de-risking and some companies will spend a lot more money and time and energy trying to maximize the amount of information they can get out of those mid-stage trials. Others will not invest much at all and go straight into a big pivotal study.

I think for the external person, for people like us who are seeing these go by, it's a Bayesian approach. You want to continue to reassess your assumptions every time you have new information. When you get little amounts of information out of a phase 2a study, for example, and that can be additional safety data, it can be efficacy data on an endpoint, which is some surrogate endpoint. It could be some proof-of-mechanism data, so something that's going to show that the drug is actually hitting its target. Could be some pharmacologic data in terms of the levels of something changing, whether it's the drug or the level of some target. Those are all valuable, obviously, and they do at some level, de-risks the further trial, which is to say if those had not shown what they did. If you had that phase 2a data that could not demonstrate an effect on the surrogate marker, maybe you'd be a little bit more concerned. On the flip side, how much weight to put on those early findings from those small trials, which usually are using surrogate efficacy markers. I think it's always a challenge. Clearly, when you see a phase 2 study, when you have phase 2 data available from a larger study using an endpoint which is the same as the one that's going to be used in the pivotal trial in a population that's the same as what's going to be studied in the pivotal trial, those are all very comforting signs.