See my previous blog. Today, I want to look at other ways to skin
this cat. These ideas are mostly mine – but again, John Rex was my sounding
board.
As background, I highly recommend viewing the video of the presentation by Ellenberg presented at an NIH conference on trial designs for emerging infectious diseases. It is very informative. The statistical problems for such trials are numerous but hinge on the following assumption that we must try and meet/validate – that the distribution of patients with good vs. poor prognoses are the same in the experimental and control groups. This is a key basis for preferring a randomized trial. In designing trials to address rare infections, rare pathogens, and pathogen-specific indications, the patient numbers may not be able to support a randomized design. We might not even be able to achieve statistical inference with an externally controlled design – but, in my view, this is where we will have to go. According to the paper by Byar (requires subscription) and later Elllenberg, an externally controlled trial design can be justified if the following conditions can be met . .
·
A randomized trial is infeasible because of the
rarity of the condition under study.
·
There must be sufficient experience to ensure
that patients not receiving therapy will have a uniformly poor prognosis.
·
The therapy must not be expected to have
substantial side effects.
·
There must be a justifiable expectation that the
potential benefit to the patient will be sufficiently large to make
interpretation of the results of a non-randomized trial unambiguous.
·
The scientific rationale for the treatment must
be sufficiently strong that a positive result would be widely expected.
I would argue that a new
antibiotic expected to be active against resistant pathogens would meet these
criteria assuming it had been shown to be safe in a sufficient number of
volunteers/patients. The data supporting
a lack of efficacy of antibiotics where the exposure (drug levels) obtained are
not high enough for the MIC (“susceptibility”) of the pathogen are clear and
overwhelming.
Most of the failures of externally
controlled trials to provide reliable results have resulted from inadequate
controls.
·
Controls had been derived from a different time
such that control therapy had changed by the time the actual trial was
conducted;
·
Or supportive care had changed altering
prognosis for controls.
·
Effect size in controls had simply been
underestimated for other reasons.
How can we overcome these
obstacles for antibacterial drugs?
- ·Get your PK/PD house in order. If you buy the UDR vs. XDR argument from the previous blog, then we can use PK/PD to show that pathogens resistant to comparator agents will be effectively treated by our experimental drug. .
- o Have clearly and adequately designed PK/PD targets.
o Make sure you have adequate PK in the population
you intend to treat (possibly studying the PK of the new antibiotic PK as an add-on to the
SOC or comparator to be used as a control in your proposed study).
·
Consider a small, open label phase II study to
help convince physicians and regulators that your new antibiotic will, in fact,
benefit patients as you expect based on PK/PD considerations. This will also
bolster your PK/PD argument and may even provide an early look at efficacy.
·
Define your inclusion/exclusion criteria
early. I would advise being expansive
rather than constrictive here – you don’t want a lot of amendments in the
middle of your pivotal trial – this is not non-inferiority.
·
Carry out a retrospective
(within the previous year or two) observational study of the key patient
population treated with SOC or with comparator drug to define control level of
response. This should be done in centers
likely to participate in the trial to remove center-to-center bias as much as
possible.
·
Early in the trial, carry out a prospective study of SOC or comparator
to validate the assumptions you have made about controls during your
retrospective SOC – obviously this is done in centers actually participating
(and contributing patients to) in the ongoing trial.
The alternatives discussed by
Ellenberg such as cluster-controlled trials and adaptive allocation
randomization designs all require more patients than we will ever have
available to study.
If, in fact, we observe a large
treatment effect early, I would wonder - is it still ethical to continue the
trial? - even if statistical inference
has not yet been achieved?
Based on my previous discussions
with FDA, and John Rex’s feedback, it is possible if not likely that the FDA
will balk at externally controlled trials even though these would be allowed in
the context of their unmet needs guidance. (Then, of course, that section
should be removed). The FDA needs to see -“Substantial evidence of efficacy
through adequate and well-controlled investigations . . .” and they may
consider that the approach I have outlined above will not meet that criteria. (Since those adjectives [subtantial, adequate, well-controlled] are not so well
defined – I’m not sure how they get to that particular place.)
Alternatives that might be more
palatable to FDA might include a non-inferiority trial with wide margins. For example, a trial powered at 80% with a
margin of 25%, even with only 60% evaluable, will only require 67 patients per
arm or 134 patients total. Obviously,
one would still need a 300 patient safety database . . .
Another possibility might be an
altered randomization ratio like 2:1, 3:1 or even 4:1. But here – one might consider approval based
on a P value of 0.1 – what is so magical about P=.05?
Based on these thoughts, for the right drug, where the
population to be studied will be highly restricted, I recommend negotiating a
design with Europe, carrying out your trials and then presenting it to FDA as a
fait accompli.
Finally, I plea once again for congress to stay out of the
way!
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