To continue our discussion of the FDA Workshop on the
development of pathogen-specific antibiotics, I’ll start briefly with a
discussion of statistical issues and approaches that concluded day 1 of the
meeting. Honestly, since it was a fairly
authoritative and intense set of presentations that left me n the dust, I have
little to say other than to refer you to the slides. What was
clear is that there are a number of Bayesian methods that would allow for a
more comprehensive analysis of trials where the numbers are small. Enough said.
The second day of the workshop was the meat of the meeting
and was set-up by our discussion on the first day. John Rex and colleagues invented a fictional
drug they called X-1 that was active only against Pseudomonas aeruginosa. They
then set about trying to design a non-inferiority trial to compare the new drug
to a comparator agent – in this case, meropenem. Why, I asked myself, did he start with a
non-inferiority design. In trying to read John’s mind, I thought the following.
(1) NI trials are the most reliable way to antibiotic approval and have been
used for the approval of new agents (like ceftazidime-avibactam). (2) The
exercise would expose in a very quantitative way the costs and risks of such a
program. (3) The discussion would stimulate thinking about new approaches to
the NI design or to thinking about other designs. You can find all the details about X-1 in the
slide packages
provided for the meeting.
John started with a few assumptions and limitations. The most important was that the trial could
not exceed 1000 patients for cost and time reasons. Given the numbers of
potentially enrollable patients with these infections, mostly HAP/VAP and IAI,
under the usual circumstances of such a trial, you cannot meet the 1000 patient
limitation. The proportion of Pseudomonas infections among enrolled patients is
just too small. So, to meet this goal required several manipulations. First, a wide NI margin of 30% is required
for HAP/VAP and 25% for IAI. He was able
to justify this in that for HAP/VAP he used the FDA endpoint of 28 day all
cause mortality in the microbiologically documented population and for IAI he used cure in the microbiologically documented
population as endpoints based on the FDA’s own guidance documents. The development of agents for patients with
unmet needs specifically states that wider NI margins could be considered and
the margins chosen lie within the crude M1 calculations provided by the FDA. The
design involved the use of X-1 + ertapenem (erta is not active vs. Pseudomonas)
vs. meropenem. Investigators were
allowed to add amikacin to each arm for up to 4 days initially (a potential
confounder). Even with these wide NI margins, to reduce the trial size, John
had to invent a rapid, bedside test for the diagnosis of Pseudomonas in
respiratory secretions or abdominal culture swabs to increase the chances of
culture-positivity. This led to a total of almost 1900 screened patients in the
two indications and 915 enrolled. Then came the results. Based on John’s impeccable math, 175 patients
with Pseudomonas across the two indications were treated divided in a 2:1
randomization schedule. You can see that the numbers are going to be
small. The table shows numbers achieving
the endpoint over the total treated for each treatment group and each
indication. These fit within the prescribed margins. But what if one or two
patients are moved or removed on various sides of this table? It can all rapidly fall apart. What if there is no diagnostic test or if the
test used fails to predict culture positivity?
The numbers then become really small. In all cases, the conclusions
based on the trial, whether positive or negative, are very fragile.
X-1
|
Mero
| |
NP
|
37/48
|
19/24
|
IAI
|
55/69
|
27/34
|
I suggested that we should abandon the NI trial design
concept for such a drug altogether and go towards a superiority design approach
as I have argued consistently in the past. Even here, there are major problems. (1)
External controls are probably required as well as some sort of within-trial
validation set to show that control levels of response are real. (2) Even with
such controls, the trial might not be inferential at the P=.05 level – although
possibly it would be for P=.1 or .2. The
trial would probably have to include amikacin for several days in both arms
thus failing to avoid this potential confounder. This puts the trial at higher risk and the
addition of amikacin would have to be factored in to the external controls one
would use. As I have discussed, such control levels of response could be
established pre-trial – so the calculations could be more informed.
Now imagine the situation where infections are even less
frequent such as would be the case for Acinetobacter
infections.
Paul Ambrose kept coming back to the same theme – and not
without justification. If the trials are
not inferential, wouldn’t a strong pharmacometric argument be a strong
rationale for approval? In the case of
the externally controlled superiority trial, 30 patients enrolled, receiving
the new therapy (X-1 in our hypothetical case), where PK is performed, could
provide evidence of response-exposure relationships and these could be placed
in the context of the preclinical and phase 1 target attainment data available
prior to the start of the “pivotal” trial. I agree with Paul – but would the
regulatory authorities consider the more robust PK data substantial
evidence in combination with everything else to allow approval? I’m not sure –
but I think maybe the FDA is not sure either.
The EMA responded by again pointing out their tools
including conditional approval with regular re-review of ongoing data for key
products like X-1. Although this tool does not exist for the FDA per se, the FDA could approve a drug,
require post-market studies and convene additional advisory committees or take
other action based on emerging post-market data.
While the workshop was unable to establish a clear pathway
forward for pathogen-specific antibiotics, it was clear that the usual pathways
were going to be challenging to say the least. It was also clear that post-approval data collection was going to be an essential piece of any approval for a pathogen-specific product for a Gram negative pathogen. And all of these considerations are now, thanks to the FDA, in the
public domain.
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