Pharma’s Cutting Edge

As you’re likely to have read by now, FDA is holding an advisory committee hearing tomorrow, June 13^th, to discuss rimonabant (formerly Acomplia; now Zimulti?), the cannabinoid CB1 receptor inverse agonist developed by Sanofi-Synthelabo, now Sanofi-Aventis. The rimonabant NDA was granted an approvable letter for the weight loss-related indications in February 2006. The advisory committee is being held to review in a public forum all information on the drug, including the additional information S-A supplied to address the issues raised in the approvable letter, which were heretofore undisclosed publicly.

I see that no shortage of pundits is commenting on the briefing documents FDA released yesterday on its web site and the markets have already reacted. That’s okay. Not everything deserving of a post by me is necessarily uninteresting to the mainstream press. So, let’s have a quick look and await the outcome together.

 The efficacy of rimonabant has not really been in doubt since the sponsor began releasing data from its RIO pivotal trials in 2005.  The drug causes people who are obese to lose weight, probably through diminished appetite and food intake, although I don’t believe the specific effector mechanism(s) of weight loss, nor the exact pharmacological actions accounting for it, has been demonstrated in clinical studies. The weight-loss effect is dose-related and is clinically meaningful at 20 mg daily. It’s also a modest effect with a 1 year weight drop of about 11 pounds overall relative to placebo (the heaviest subjects, those with a BMI above 40, lost the most weight), which was similar in the 4 pivotal studies. In year two a good bit of the weight lost was regained. In those with lipid disorders, hypertension or diabetes, the 20-mg dose of rimonabant either improved or did not worsen surrogate markers of benefit. The antihyperglycemic effect in the RIO Diabetes study was particularly good, with placebo-subtracted HbA1c reduction of 0.7%, despite a higher proportion of rimonabant users using lower doses of concomitant metformin or sulfonylurea.

Not surprisingly, the safety profile of rimonabant has been slower to emerge.  Earlier disclosures did not include an integrated look at all treatment groups side by side. When viewed this way, the distinction between the 20-mg dose and placebo re psychiatric events psychiatric events is clear. A total of 25 preferred AE terms related to psychiatric symptoms were reported by at least 5 subjects in the 20-mg treatment group. A preferred term represents a coded adverse event, essentially a coded translation of a verbatim AE report, although not one that is as rich in information content. Of the 25 preferred terms, the frequency of reported AEs was numerically higher in the 20-mg rimonabant group as compared with placebo for 24 of them. Only 1 category (decreased libido) was associated with a lower frequency of AE reports by subjects assigned to rimonabant. The relative risk of a psychiatric AE was roughly two for the 20-mg group compared with placebo when all events were combined. Also, rimonabant was associated with nearly four-fold higher frequency of associated psychiatric symptoms not captured under the preferred terms per se. Te above is convincing evidence of a generalized CNS folks; I don’t recall a drug not intended to treat psychiatric disease with such a one-sided AE profile. That said, a list of adverse event frequencies hardly gets at risk; it speaks volumes about likely tolerability and propensity to prescribe, but the committee is going to be focused on the benefits of rimonabant in relation to its risks (the draft questions of the committee have also been published).

Apparently, in its 2006 approvable letter, FDA asked Sanofi-Aventis to investigate suicidality further and asked for data to support a meta analysis by the Columbia University group of Posner.   The committee had done this recently with SSRIs in pediatric patients.  S-A did a concurrent analysis using similar methods.  Both studies found essentially the same thing: rimonabant doubles the incidence of suicidal ideation (i.e. thinking about suicide).  S-A undertook additional analyses to demonstrate that nearly half (40%) of the cases of suicidal ideation were reported among patients who self-reported depression at baseline, regardless of assigned therapy.  Presumably, S-A is looking to see if they can get by with restrictions against prescribing rimonabant in depressed patients if the alternative is non-approval.

A number of other related safety issues will be discussed tomorrow, but the bottom line for most of the panelists will undoubtedly be determined by their assessments of the risk of suicide versus the benefit of weight loss and its attendant benefits (on diabetes primarily).  They will be reminded, probably numerous times, that S-A is in the midst of conducting a large cardiovascular outcomes study with rimonabant.  Should they wait before seeing its results before giving the thumbs up?  Or should they give their blessings now and argue for strenuous warnings against its use in depressed patients?  What type of risk management strategy can guard against excessive population risk of suicide?  Would it have to look just like the Accutane strategy?  If so, would S-A even launch under such restrictions?  This is a  fascinating story waiting to play out.

Yesterday’s Hearing on FDA’s Role in Evaluating Safety of Avandia held by the U.S. House Committee on Oversight and Government Reform made for entertaining TV.  Most of the elements of a good courtroom drama were on display:  a protaganist whose interests in ethics and science keep him too busy to pay attention to the effects his pronouncements have on financial markets (played by Dr. Steve Nissen); a fact-challenged villain (the TZEs?) out to destroy the reputation of said protagonist by exposing his lust for glory (played by Rep. Darrell Issa); and a captive forum filled with minor characters who fill the backstory of the hero, challenge the villain, and stand at the ready to interject the bits of comic relief and moralizing needed for pacing.  On the downside, the third act was very weak, and I missed the caustic irony and Adamsesque speechifying of James Spader’s character on Boston Legal (hey…why not invite Mr. Spader and a couple of BL writers to sit in on some of these hearings; it would surely boost rating).

For those of you who didn’t catch the action live, you can watch it using the above link.  Other than the entertainment value, though, there was much of substance to take away from the event.  One of the take aways of some interest was that FDA Commish von Eschenbach stated, after several minutes of hemming and hawing that FDA has all the power it needs to enforce existing rules governing DTC advertising.  It simply lacks the resources to do the job properly.  Other than that, much of what was said by FDA staff and the Congressmen present was rehash of what we’ve heard surrounding the PDUFA renewal debates.  There were some hints that an appetite exists among FDA and in Congress to rethink the extent of premarketing risk assessments for chronic-use drugs intended for large populations, but I wasn’t convinced that this event was enough impetus to keep the ball rolling.  Regular readers know that I support such rethinking.

I also wanted to at least try to clarify the panelists’ (esp. Bruce Psaty’s) response to the question that Rep. Issa kept asking everyone he could corner regarding the exclusion of zero-event studies from the Nissen meta-analysis.  Issa kept asserting that exclusion of the zero-event studies (i.e. those without any events of myocardial infarction, MI, or death) from the meta-analysis was inappropriate, because it reduced the apparent incidence of MI.  It is correct that eliminating zero-event studies would reduce the apparent MI incidence rate (i.e. the rate of appearance of new MI events during the observation intervals).  However, the meta-analysis used by Nissen did not use the incidence rate of MI in the rosiglitazone group and compare it with the incidence rates in the other treatment groups.  If it had, the authors would reported an incidence rate ratio (IRR), or a relative risk, which displays the ratio of probabilities of observing the outcome for each treatment comparison.  In order to use the relative risk, the meta-analysis should have access to every study and all observations, and, to get the best estimate, the observation interval should be the same among the pooled studies.  Given these restrictions, use of the relative risk can be problematic in the real world.  Nissen couldn’t be sure these conditions would be met by his analysis, so he wisely chose to calculate an odds ratio for each of the treatment comparisons, which isn’t subject to as many restrictions for proper interpretation. 

The odds ratio as used in the paper is simply the ratio of the odds of observing an event in each of the various treatment groups compared the pooled studies.  It is calculated by dividing the number of events by the number of non-events in each group then calculating the ratio between groups.  In other words, if there were 10 total observations in one group and 6 were MI or death events, the odds of an MI or death event in that group would be 6/4 or 1.5.  If the same total observations (10) occurred in a second group, but only 4 MI or death events were observed, the odds ratio would be:  (6/4)/(4/6)=2.25.  If the observation interval were the same in each study and the relative risk were calculated instead, here is what it would be for the same data:  (6/10)/(4/10)=1.5.  So, in this example, the odds ratio would be higher the relative risk, making the problem appear worse then it might otherwise be perceived (as Issa accused Nissen of doing).  But this example uses a common event that occurs in around half of the patients.  Instead, if we consider a relatively uncommon event that occurs in less than 2% of patients (like in the Nissen paper), we’ll see a different result.  Let’s say that the total observations in each group is 100.  In group A, the event occurs in 2 patients and does not occur in 98.  In group B, the event occurs in 1 patient and does not occur in 99.  The odds ratio for A:B is:  (2/98)/(1/99)=2.02.  The relative risk for A:B is (2/100)/(1/100)=2.00.  Now, you can see that the odds ratio closely approximates the relative risk.  This is simply a reflection of the nature of numbers, fractions in particular, where a risk reduction of 20% (i.e. a rel. risk of 0.8) is not analogous to a risk increase of 20% (i.e. a rel risk of 1.2) but is instead analogous to a risk increase of 25% (i.e. rel risk of 1.25, the inverse of 0.8).  I’ve found that this point is easily understood but frequently not considered by clinicians.  Perhaps it is not as easily understood by Republican Congressmen from California ?