The Issue: Getting an Answer Sooner and Cheaper
Shortening trial time means reaching a decision earlier as to whether a treatment
is effective—and saving money in the process. With old, invasive, inefficient tests
of cure like those we have now for several neglected tropical diseases, follow-up
(and total trial) times remain inefficiently and uneconomically long. While the need
to licence new drugs is urgent for many of the neglected tropical diseases, it frequently
takes 8–10 years from Phase 1 to licensure, and sometimes even longer. Consider visceral
leishmaniasis: it has taken nearly 20 years and at least three different organisations
for paromomycin to find its way to registration in India, and even longer to take
a final decision to terminate the development of sitamaquine.
The need for efficiency is particularly acute in drug development, and specifically
in Phase 2 clinical trials, when one will select the drug dose/schedule to be tested
at a larger scale in the Phase 3 (pivotal) trials. Here, one wants to find out what
works and what doesn't as quickly and economically as possible. Transposing results
from non-clinical studies (in vitro and in vivo experiments) in terms of pharmacokinetic/dynamic
correlation is not easy, so one is often left with a variety of potential doses and
regimens to choose from.
What Would Alternative Study Designs Add?
Adaptive trials designs are increasingly used by pharmaceutical companies to improve
efficiencies in the R&D process [1]. This approach allows the possibility to redesign
the trial (sample size, number of arms, etc.) based on the information acquired through
interim analyses. Sequential and group sequential [1] trials are a special case of
adaptive trials whereby several interim analyses are done in order to complete the
trial earlier (interrupt enrolment) based on the accumulated information.
However, these methods work best for diseases for which treatment response becomes
obvious shortly after treatment rather than having to wait for 6 months (visceral
leishmaniasis), 18 months (onchocerciasis [river blindness]; human African trypanosomiasis
[HAT; sleeping sickness]), or a yet-to-be-defined period for chronic Chagas disease.
Tuberculosis is in the same league (18 months from treatment start for the initial
assessment and another 12 months for final cure), while with “only” 28–63 days of
follow-up, malaria is comparatively much better in this sense. The reason for such
long follow-up times is that patients who initially respond favourably may relapse
later, and such cases cannot yet be predicted by the current tests of cure.
There are several ways to specify early termination procedures (for futility), allow
repeated analyses to be performed on accumulated data, maintain pre-specified α and
β error, or stop the trial as soon as the information is sufficient to reach a conclusion
[2]. These methods can be grouped as: (i) sequential methods (sequential probability
ratio test and triangular test [2], [3]) and (ii) group sequential designs (Peto [4],
Pocock [5], and O'Brien-Fleming [6] methods; α [7], [8] and β [9] spending function;
etc.). This is a domain of ongoing statistical research with existing methods being
improved and new ones developed.
Example: Triangular Test for Visceral Leishmaniasis
We used a triangular design to study different doses and durations of combination
treatments for visceral leishmaniasis in India [10]. Experimental studies had been
inconclusive [11] while toxicology studies had shown the combinations to be safe (preclinical
toxicology studies on several drug combinations have been done, with no major safety
concerns identified [Drugs for Neglected Diseases initiative (DNDi), data on file]).
The trial was designed as a randomized, parallel-arm, non-comparative, open-label
study using the group-sequential triangular test method to reach, with the minimum
number of subjects, an early decision as to which of four regimens should be selected
for additional testing. With a type 1 error α = 5% and power 1−β = 95% assumptions,
considering a failure rate <10% as adequate efficacy (the minimum detectable failure
rate at the β = 5% level) and a failure rate ≥25% as insufficient efficacy, the boundaries
of the test were calculated for H0 (p = p0) and Ha (p<pa) with p0 = 0.25 and pa = 0.10.
Based on simulations, we expected the sample path to cross the H0 rejection line with
an average sample size of 40 patients and the H0 non-rejection line with an average
sample size between 20 and 25 patients. When, after enrolling 45–46 patients per arm,
all treatments appeared equally and highly effective, an additional 45 consecutive
patients were enrolled and non-randomly assigned to a fifth regimen (Figure 1).
10.1371/journal.pntd.0001545.g001
Figure 1
Design of the triangular test for a Phase 2 study of anti-leishmania drug combinations.
All 181 subjects in Groups A–D completed assigned the treatment, and on day 16, 100%
showed parasite-free splenic aspirate smears and fulfilled the criteria for apparent
cure (Figure 2). Following the successful completion of this study in India, DNDi
used this design again in a Phase 2 trial of anti-leishmania drug combinations in
Africa (ClinicalTrials.gov NCT01067443).
10.1371/journal.pntd.0001545.g002
Figure 2
Patient enrolment in a Phase 2 study of visceral leishmaniasis with triangular design.
Using this approach did not result in shortening trial time; the maximum calculated
number of patients was to be enrolled as all treatment regimens proved very effective.
However, an economy was achieved in the number of trial subjects and the time to reach
a conclusion. Though the two methods cannot be strictly compared, a classical single-stage
comparative trial design, with a type 1 error α = 5% and a power 1−β = 95% and the
null hypothesis of 90% efficacy, would require a sample size of 580 patients per arm
to reach the significance level for a regimen with 95% efficacy. The two approaches
test different hypotheses, but, especially for dose-finding purposes, the triangular
test offers clear advantages in screening different treatment regimens.
Better Measures of Treatment Outcomes Are Needed to Make Adaptive Designs Worthier
The main indication for adaptive trial designs such as the triangular design will
indeed be in the futility setting; weeding out ineffective experimental doses in Phase
2 and thus reducing the number of patients at risk of being exposed to ineffective
doses. This will shorten time to decision and moderate expenses. It will be interesting
to see how the triangular design performs in situations like African leishmaniasis
(see above) where treatments tend to be comparatively less effective than in India,
and thus arms could be dropped earlier. The triangular design however would probably
be less useful in diseases like HAT for example, where end-of-treatment outcomes tend
to be less informative.
However, until and unless reliable markers of treatment effects are found, clinical
trials and drug development for neglected tropical diseases will be hampered. More
investments are needed in this area. An expensive marker can be tolerated for drug
development (contrary to patient management, which needs inexpensive, non-invasive
tests) because the net result will be a curtailment of time and overall cost of development.
However, such markers are notoriously difficult and expensive to discover and validate;
attention must be called to this area for the required long-term investments to be
made. Meanwhile, immediate solutions are also needed.
Beyond Traditional Approaches
What can be done now and with limited resources?
Action must be taken to increase awareness of the problem among research funding organizations
and the research community itself for novel solutions to be found and tested. Consideration
should be given also to approaches used in different areas (such as non-transmissible
diseases). It is hoped that this paper will stimulate interest and broaden the debate.
In leishmaniasis treatment trials, as for other diseases, an initial (apparent) cure
can be followed by a relapse (or a re-infection, to complicate matters further), whereby
the final cure rate will be lower than the initial one. So, a fundamental question
is how predictive of final cure the initial response is. The answer may vary with
the outcome, disease, treatment, parasite, and patient population, and thus location
of trial. In the leishmaniasis triangular trial cited here, we used Day 16 for the
decision based on initial cure and 9 months (instead of the customary 6 months) for
final cure. As all the treatment regimens tested were highly effective, Day 16 proved
to be a reliable indicator of success; the same would apply to the other extreme case
of very ineffective treatments (in our study, it would have required about half as
many patients). The problem will reside in treatments that are only partly effective,
which will suppress parasite replication temporarily or kill the majority but not
all the parasites; initially, these will be missed by insensitive diagnostics, only
to rebound to be detected later on during follow-up.
To some extent, available tools may be fit for purpose. For example, with no cheaper
tools in sight, trial sites could be provided with some state-of-the-art tools such
as real-time (RT) PCR, which could predict cures or relapses based on the number of
organisms at the end of treatment with reasonable accuracy [12]. However even RT-PCR
needs to be validated and standardised for the respective diseases. Currently DNDi,
Médecins Sans Frontières (MSF), the World Health Organization (WHO: Special Programme
for Tropical Diseases [TDR], Pan-American Health Organization [PAHO]), and various
researchers are working together towards validating the use of quantitative RT-PCR
as treatment outcome measure in Chagas disease.
But there may be also other options involving imaginative, cost-effective ways of
constructing the evidence base to design trials differently. In this paper we focus
more on Phase 2-type trials, but the concept should be extended to larger pivotal
trials and pragmatic trials as well.
Progress has been made with the design of tuberculosis and malaria treatment trials,
which will specially benefit Phase 2. For tuberculosis, concern has been raised over
the use of early-response methods such as (extended) early bactericidal activity [13]
and serial sputum colony counts (SSCC) [12], [14] to predict efficacy, over shortened
duration of follow-up (how informative are results at 6 months instead of 2 years
[15]), and over more general design issues [16] and use of surrogate endpoints [17].
In malaria, too, research has been done on identifying both optimal duration of follow-up
for establishing final response [18] and also early outcome measures (Day 3) which
are predictive of parasite susceptibility [19].
Some of the examples above show that research question-driven collection and analyses
of databases from previous trials are both useful and cost-effective as a means of
developing newer, evidence-based approaches.
In Summary
Shortening trial time and reducing requirements for patients saves time and money,
and spares patients from unnecessary exposure: there is therefore both an economic
and an ethical motive for rationalizing trial design.
Economies can be found with alternative clinical trial designs, such as adaptive trials
(especially in the futility setting), though these are only partly suited for neglected
tropical diseases, which have inadequate measures of treatment outcomes.
Research is needed into generating better tests of treatment outcomes for neglected
tropical diseases, but sizeable long-term investments are required.
New, imaginative approaches should be investigated that will generate an evidence
base for alternative trial designs.