Recently, NPR had a discussion about the
influence of Placebo effect on consumer product purchases. While the concept of the placebo
effect comes from clinical trials in medical contexts,
the placebo effect also have impact in non-medical research fields. For
example, just by telling the people it is a brand name product (even though it
is not), it affects the results of using the product.
Placebo effect is also called the placebo response. It is a remarkable phenomenon in which
a placebo -- a fake treatment, an inactive substance like sugar, distilled water,
or saline solution -- can sometimes improve a patient's condition simply
because the person has the expectation that it will be helpful. Expectation to
plays a potent role in the placebo effect. The more a person believes they are
going to benefit from a treatment, the more likely it is that they will
experience a benefit.”
Placebos have to be identical in all respects
to the active drug, except that the active drug is absent. This means for oral
placebos that they are not distinguishable by color, size, shape, taste, or
texture. For oral therapy there are several modes of application available:
capsules, tablets, or liquids. Since many active drugs have a distinctive
taste, the use of capsules is most often feasible.
Sometimes, it is not so easy to make the
placebo that are not distinguishable from the active drug. For example, liquids
containing therapeutic protein generate foam but the placebo using buffer
solutions do not. The treatment can be differentiated by observing the foam by
shaking the test products. In this case, the ideal placebo will contain also an
inactive protein in low concentrations such as human albumin. Some clinicians
are very clever in breaking the code; they not only shake or identify the drugs
by taste and smell but even photometers were used to determine differences
between active drug and placebo. For such reasons it is sometimes very
difficult to guarantee blinding.
While majority of the placebos used in the
clinical trials are true inactive substance (at least we think so), there are
times that the placebos may not be true inactive substance. This is especially
true in the clinical trials using the protein therapies, for example, the
therapeutic proteins derived from human plasma.
Table below listed several randomized,
placebo controlled clinical trials using the therapeutic proteins derived from
human plasma. Various concentrations of albumin (also protein) were used as the
placebo assuming that the low dose of albumin has no therapeutic effect.
|
Study
|
Experimental Treatment
|
Choice of Placebo
|
|
IGIV
|
Biological: 0.25% human
albumin solution (Placebo)
|
|
|
IGIV
|
Biological: Placebo
solution: Human Albumin 0.25% - 4 mL/kg
Biological: Placebo solution: Human Albumin 0.25% - 2 mL/kg |
|
|
IgPro20
|
2% human albumin administered by weekly SC
infusions during the SC treatment period of the study
|
|
|
Prolastin
|
Albumin (Human) 20%,
United States Pharmacopeia (USP)
|
|
|
IGIV
|
Placebo will be supplied as 20% or 25% Albumin and diluted
with glucose 5% to a final concentration of 0.1%
|
If the results of these clinical trials are positive
(statistically significant), people will not question the use of albumin as the
placebo. If the results of these clinical trials are negative (not
statistically significant) or the placebo group perform much better than
expected, people may question the adequacy of the use of albumin as the
placebo. This happened in one of the clinical trials in relapse-remitting
multiple sclerosis (RRMS).
Prior
to the clinical trial below, previous studies had shown that the benefit of
IGIV in treating the RRMS. The expected response rate in placebo group is about
50%.
Randomized, Double-Blind, Placebo-Controlled Study to Compare the Effects of Different Dose Regimens of IGIV Chromatography (IGIV-C), 10% Treatment on Relapses in Patients With Relapsing Remitting Multiple Sclerosis
The study results were
published in Fazekas et al (2009) “Intravenous
immunoglobulin in relapsing-remitting multiple sclerosis: a dose-finding trial”.
For the primary efficacy endpoint, after 1 year, the proportion of relapse-free
patients did not differ statistically according to treatment (IVIG 0.2 g/kg:
57%; IVIG 0.4 g/kg: 60%; placebo: 68%). Surprisingly, the response rate in placebo
group is much higher than expected and than what was assumed when the study was
designed. This prompted the discussion of the potential therapeutic effect of
albumin used as the placebo. The article by Hommes et al “IVIG
trials in MS. Is albumin a placebo?” argued that albumin may have
properties that make it unsuitable as placebo.
Since using the albumin as the placebo may
have therapeutic effects, something other than albumin (for example, saline
with Polysorbate) may be an option as the placebo in clinical trials with these
therapeutic proteins. Polysorbete is added to mimic the foam created by the
therapeutic protein. However, the
blinding may still remain as a concern when saline is used as the placebo
because of the difference in foaming. In clinical trials using the therapeutic
proteins, we need to balance the risks of potential unblinding of using Saline
as placebo versus potential placebo effect of using albumin as placebo.
For blinded studies, we always assume that
the blinding is maintained during the course of the study. However, this can be
wrong in many trials where the investigators and/or patients can tell which
treatment group the patients are on by the distinguishable features between the
actual product and the placebo, by the different adverse event profiles, and
others. It is important to assess the potential unblinding by asking the
investigators or patients to guess the treatment assignments – a practice seems
to be reasonable, but pretty much nobody wants to do. If the assessment
indicates the potential unblinding or high percentages of guess-it-right, there
is no easy way to mitigate the impact and make it difficult to interpret the
results from the clinical trial.
After the study, it is a
good practice to compare the results from placebo group back to the original
assumptions. During the design stage of the clinical trial, we usually make assumptions
about the response rate or mean/standard deviation in placebo group. After the
study, it will be interesting to compare the actual results from the placebo
group with the assumptions in the protocol sample size calculation section. I
see the situations where the final results are way different from the
assumptions in the protocol sample size calculation – in this case, we must
have some further investigation what goes wrong and why this happens.
