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One of the most difficult aspects of investing is the continuous pledge to double-check assumptions, scrutinize management’s capital deployment plans and second-guess yourself by examining risk-reward on a dynamic basis.

I’ve been watching Neoprobe (NEOP) for a few years and have never had more than a tiny position for a few days at a time. When I saw several consecutive bullish analyst reports and scheduled New York roadshows, I reasoned the company was preparing for a capital raise. I read the initiations and enjoyed the lofty estimates and high assumed probability of achieving those estimates.

One thing I didn’t immediately notice was scrutiny of phase III results. Some Phase III data are cut and dry. Some Phase III data seem cut and dry and are not. I am going to spend some time showing you why I think both Lymphoseek Phase III trials failed to meet their primary endpoint. This means what you might think it means – Neoprobe will not receive FDA approval for Lymphoseek because of what I’m going to outline. This probably wouldn’t have been brought to light until FDA decision time without my discoveries.

I’ve read every public statement ever made by Neoprobe. Again and again and again. SEC filings, news releases, presentations, database searches, everything. I started pulling on this thread when I saw the 1/2010 presentation (viewable at my fund’s website – and the 12/2010 presentation had different patient sample sizes for the Phase III NEO03-05 study. Think about that for a second. Clinical trials should have one sample size. The number of patients enrolled in a study is generally not subject to change. This time will be different.

In the January 12th, 2010 OneMedPlace presentation, the company notes that n=179 patients were “enrolled for safety” and 158 patients were “evaluated for efficacy”. This is not the way a clinical trial works. You can’t separate which patients you feel are viable for safety or efficacy. Everyone has to be in both. This is the ITT (intent-to-treat) principle. There are deviations from this standard, and we’ll go through them in a minute. But this is how the process began. The presentation, on page 14, continues to explain that the ITT population consisted of n=158 and the per-protocol population consisted of n=156. Again, this flies against the face of my experience and basic clinical science – the ITT population is always composed of all patients enrolled in the trial. The high p-value of 0.04 for the ITT group also stuck out at me. That is awfully close to the “limit” of p=0.05. I always get nervous when a company has a pivotal trial near p=0.05!

With this in mind, in the December 2010 at Neoprobe’s Analyst Day, page 21 indicates, again, that n=179 patients were “enrolled for safety” (a phrase that makes my head hurt) and n=136 were “evaluated for efficacy”. Until now, I hadn’t really noted any red flags. But the clearly changed n=158 to n=136 from presentation to presentation would make even a colorblind analyst fret.

It was at this point I decided to do a full statistic review of both pivotal trials. The way I see it, NEO03-05 had three pools of patients: n=179 “ITT” (this is my definition, Neoprobe’s is “enrolled for safety”), n=158 “modified ITT” (this is my definition, Neoprobe’s is “ITT”) and n=136 “per-protocol” (this is my definition, Neoprobe’s is sometimes “ITT” and sometimes not). Defining ITT is important. It usually has one definition: all patients assigned to a treatment group. In other words, patients who have been screened, are eligible, gave consent and have been processed as enrolled in the study should be in the ITT group. This is relatively “holy” in clinical science. Anything past this point is unusual and the FDA doesn’t appreciate strange ITT definitions (witness the recent Biomimetics debacle). Some companies try to throw out patients that get lost to follow-up. This is the most common type of removed patient from ITT to mITT (or if the company insists on referring to it as ITT). Similar types of patients include patients enrolled but never randomized, patients who never got any treatment, etc.

So who were the missing ITT to mITT patients in the Lymphoseek studies? The company explained very carefully on a March 12th, 2010 conference call (the transcript of this call is not publicly available) that “The ITT population was prospectively designed as patients who consented to participate in the clinical study, who were injected with Lymphoseek, and who provided at least one lymph node that contained the vital blue dyes”. The company has confirmed that all n=21 “missing” patients were those that did not provide a blue dye node. Should these patients have been included in the ITT analysis? Let’s put that in the back of our heads while we go to another related topic.

Since I can remember, I've been doing my own statistics with company clinical trials, when possible. There’s no point in trusting a company to calculate a p-value when you can do it yourself. In the Analyst Day presentation referred to earlier, Neoprobe explains the statistical method with which they calculated the primary endpoint. It’s all well and good to say “the primary endpoint is concordance”, but what was the null hypothesis, the statistical formula used, etc. That information was made available by Neoprobe – the null hypothesis was that concordance was 90%, or 0.9. Rejecting the null hypothesis would require the entire one-sided tail to be greater than (to the right of) 0.9. The Exact Binomial Test, also called the Clopper-Pearson test was used. Calculating the primary endpoint using the Clopper-Pearson method computes a lower bound of 0.901. That’s right, Neoprobe’s lower bound had to be greater than 0.900 and it came in at 0.901. What good luck they must have! One non-concordant node would have resulted in NEO03-05 failing to meet its primary endpoint.

So let’s go back to the n=21 patients that were excluded from this study. N=158/179 (for a difference of n=21) results in blue dye node identification rate of 88%. The literature suggests a blue dye node identification rate of 95% to 100% (Chintamani et al. 2011. 100%, Shirah et al. 2011. 99.1%, Narui et al. 2010. 99%, Krikanova et al. 2010. 95%, Mathelin et al. 2009. 99%). Why were Neoprobe’s studies so far below the normal blue dye node identification rate?

It’s obvious to me that this open label protocol biased the concordance data and the patients who did not provide a blue dye node should be in the ITT group. This calculation would result both pivotal trials to fail (get a Clopper-Pearson calculator and check for yourself!). It’s very rare to not find a blue dye node. My guess is the clinicians were trying for the best data (don’t forget a few centers with long-time ties to Neoprobe enrolled many patients), and patients who weren’t clearly concordant were thrown out. When you can clearly see which patients are concordant, and which patients are not, you can throw out the bad ones and keep the good ones. That’s what happened here. It’s the only explanation which satisfies the below-average blue dye identification rate, the curiously high concordance rate (isn’t a non-blue patient who has positive Lymphoseek nodes non-concordant?), the strange p-values, the lack of disclosure on these patient groups and the lack of a peer-reviewed publication. Not even the ASCO posters have this data (shame on ASCO).

Physicians I've spoken with who do SLNB agree that you generally make a small incision in the axilla and pluck the "hot" nodes with the assistance of the gamma probe. Did the Neoprobe investigators just excise those nodes and not look carefully for the blue nodes? Think about this. There is bias in this protocol if the physicians weren't forced to search deep for blue nodes. This would explain the high amount of no-blue node patients.

The surgical process here allowed for the assurance of a successful trial, regardless of the true results. This is called poor assay sensitivity and clinical trials should be designed with as much sensitivity as possible. It should not be possible to bias a well-designed clinical trial. Instead, surgeons who measured node radioactivity counts could also clearly see the blue dye status of a node. Clinicians who excised one or two high-count “hot” nodes that were not blue could have just stopped looking for blue nodes and thrown the patient out of the study. This would have helped Neoprobe’s results. Remember, just a few nodes difference makes it a pass or fail. Whether or not it happened is something we’ll never know. The idea that it could have happened is more than reason enough to say this was not a well-conducted study and that the n=21 (and similar n= in NEO03-09) should be counted in the ITT population. If one does that, both studies fail. Two failed studies = no approval.

I know this is a highly technical topic that most aren’t used to, and I will try to take as many questions as I can, but recall my primary responsibility is not to my readers. I have a day job! Also, if you want your questions answered, a little bit of an open mind as well as good nature will go a long way. Even the best investors are right just 60% of the time, and there’s a good chance that’s neither you nor myself. I can always stand to be corrected, and I’m never afraid of admitting I’m wrong. Flexibility is a key to success in my business, and I have often changed my mind in the past. With that said, hopefully we can learn and profit with each other. Good luck!

Source: Neoprobe: Did the Pivotal Studies Meet Their Primary Endpoints?

Additional disclosure: My funds and I are short Neoprobe and may change our position at any time, including hedging, reducing, reversing and liquidating the position without any notice or duty to update Seeking Alpha.