Andrew Blauvelt, MD, MBA: In terms of dupilumab [Dupixent] in this long-term trial that I’ve been talking about, 1 of the biggest things is that the long-term trials for dupilumab were dosing every week. The reason for that is that the phase 2 studies indicated that the dosing would likely be 300 mg once weekly, subcutaneously. There was only a little bit of data obtained in the phase 3 program with every other week dosing. Then, what would turn out, however, was that when the FDA [Food and Drug Administration] looked at the phase 3 data, they made the conclusion for the product label. The FDA concluded that the dosing should be every other week. So what happened is you had this long-term trial of dupilumab dosed every week and all this data over time, and yet the FDA-approved dosing was every other week. So that’s one of the big caveats when you look at clinical trial data from dupilumab: it’s all going to be once-weekly dosing. Again, it’s hard to kind of know what to do with that in terms of private practice. We need more, in my opinion, real-world data on that every other week dosing to see how patients do over time. But we do know how they do with once-weekly dosing over time. As far as when to look at patients over time, long-term data and studies are complicated compared to short-term data. With short-term data, you have placebo control, and it’s really easy to make comparisons with the placebo control group for efficacy and safety. In long-term data, you lose that placebo control group, so everything is open-label. That right away tells you that the responses are perhaps biased from the patient point of view and from the doctor point of view because it’s an open-label drug. Everyone is on the drug. Then, there is how you analyze it. If the long-term data has only been analyzed as what’s called “as observed,” that means you only look at the people who are continuing in the trial, which can overinflate the efficacy numbers and make the drug look better than it actually is. On the other end, if you look at long-term data by non-responder imputation, which is a very strict way looking at long-term data, and that’s counting everybody that started at time 0 in the denominator, and always carrying those patients forward in the long-term data, that actually tends to lower the efficacy numbers to perhaps lower than what is real life. So I always say when you look at long-term data in clinical trials, look if it’s as observed or NRI [non-responder imputation] and then, probably, the real situation or the real-life data lies somewhere in between those numbers.

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