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3
Cutting-Edge Efforts to Advance
MCM Regulatory Science
NONCLINICAL APPROACHES TO ASSESSING EFFICACY
A challenge facing developers of MCMs is how to increase the predic-
tive value of nonclinical data, said panel moderator Lauren Black, senior
scientific advisor at Charles River Laboratories. In the absence of clinical
trials, nonclinical data can, for example, help define a human dose regi -
men and predict a reasonable likelihood of human efficacy. In addition
to animal models, other nonclinical tools such as in silico biology and
biomarkers can be employed to inform and advance MCM development.
In Silico Approaches to Efficacy Assessment of MCMs
A systems biology approach to health and disease acknowledges
that there are likely complex molecular mechanisms, with groups of
molecules, genes, proteins, and metabolites working in a coordinated
fashion, that differ in healthy versus diseased states, explained Ramon
Felciano, founder of Ingenuity Systems. These molecular mechanisms
trigger higher-order cellular mechanisms and disease mechanisms that
drive overall physiology (Figure 3-1). Technologies that have emerged
over the past decade or so (e.g., genomics, proteomics, metabolomics)
have generated a flood of new data, driving the need for new types of
analytics such as in silico or computer modeling of biology. These new
data enhance and align with existing knowledge of disease pathways and
mechanisms from the literature.
A typical systems biology approach is philosophically data driven
29
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30 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
Discovery Toxicology Biomarkers Pharmacogenomics
Use understanding Understand
Identify novel
Elucidate biological
of disease mechanisms behind
biomarkers by
mechanisms of
mechanisms to understanding role differential response
drug action and
identify and validate to R(x)
in disease pathways
toxicity
targets
Patient Docetaxel
Disease Prostate
mechanisms cancer
Cellular
mechanisms Apoptosis Angiogenesis
Molecular
mechanisms Fas Vegf
Cancer
Experimental Data Literature and Prior
Computational
Knowledge
Modeling
FIGURE 3-1 In silico modeling of disease mechanisms for drug development.
SOURCE: Ramon Felciano. 2011. Presentation at IOM workshop; Advancing
Regulatory Science for Medical Countermeasure Development.
Figure 3-1
and holistic, Felciano said, using computer-based tools and techniques to
model and understand complex biological function. Experimental designs
are typically comparative in nature (e.g., healthy versus disease, disease
versus treatment, dose response). The complexity and volume of the data
that are generated by these approaches typically require fairly sophisti-
cated computational and statistical modeling for analysis and prediction.
Research teams are often interdisciplinary by necessity, with therapeutic
area researchers, computer scientists, statisticians, and others working
together.
Primary benefits of this approach, Felciano said, include better under-
standing of disease progression, generation of novel hypotheses for thera-
peutic or diagnostic targets (i.e., biomarkers), and characterization of
plausible mechanisms that correlate with these diagnostic and prognostic
markers.
Compared with other therapeutic areas, there has been relatively little
research done in the area of MCMs using a systems biology approach, Fel-
ciano noted. He cited one retrospective study of yellow fever vaccine that
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CUTTING-EDGE EFFORTS
demonstrates the potential of in silico approaches to modeling. Querec
and colleagues (2009) used a systems biology approach to identify early
gene signatures that predicted immune response in humans to the yellow
fever vaccine.
There are several challenges to using in silico techniques in MCM
efficacy studies, Felciano said. As this is a new field, no dominant model -
ing formalisms have yet emerged, and there is a lot of new math being
generated alongside the new data. Some of the “-omics” technologies are
still relatively new, and there are issues to be addressed, for example:
measurement accuracy and reproducibility, false positive results, and cost
effectiveness. Felciano noted that FDA has a Voluntary Exploratory Data
Submission (VXDS) program in which industry submits candidate data-
sets that FDA can use to evaluate the regulatory applicability of these new
approaches. Other challenges are that systems biology experiments are
complex and interdisciplinary, requiring substantial time, interdisciplin -
ary expertise, and resources for analysis. Thus far, there are few successful
applications of in silico techniques to infectious disease. In addition, there
are few good predictive models to bridge animal data to humans.
To leverage in silico modeling for MCM development, Felciano said
there is a need for more VXDS submissions for clinical infectious disease
and MCM studies, with an emphasis on proposals including genomic
markers of efficacy. Secondly, Felciano recommended a public “genome-
to-phenome” database that characterizes, at a systems biology level, how
existing animal models are representative of given target endpoints and
underlying mechanisms. This would allow for assessment of concordance
between existing “well-characterized animal models” and in silico links
between molecular systems and animal study endpoints. Finally, Felciano
recommended the collection and integration of quantitative data on
human and animal model immunity in normal, vaccinated, and infected
individuals. This would allow for analysis of efficacy and common mark -
ers of response for existing treatments between humans and animals, as
well as between animal models.
In summary discussion, participants discussed clinical trial simula -
tions, in which virtual patients are put through in silico trials, a process
that allows a company to model a wide range of trial designs and analysis
methods, with the goal of reaching programmatic decisions more quickly,
more cheaply, and with greater certainty. To leverage in silico modeling,
there is a need for more complete, shared databases of human and animal
study data (e.g., genome-to-phenome information; data on human and
animal model immunity in normal, vaccinated, and infected individuals).
It was also suggested that a Bayesian, model-based, predictive frame-
work should be developed that would essentially create in silico animals
and a virtual human. It was noted that such a project would require per-
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32 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
missions, funding, and collaborations on the scale of IBM’s Watson project
or the Manhattan Project.
Using Biomarkers to Connect Animal Systems with Clinical Efficacy
Measurements of biomarker molecules are intended to allow connec -
tion of physiological changes with changes in outcomes or risks, explained
Leigh Anderson, founder and CEO of the Plasma Proteome Institute. Bio -
markers measured in blood and tissue are generally proteins, measured
by immunoassay, or mRNAs (ribonucleic acid), measured using microar-
rays. Anderson offered cardiac troponin as an example of a successful
biomarker; an increase in this protein is indicative of a recent heart attack.
Candidate biomarkers can be identified via in silico modeling stud-
ies, experimental studies, and by analogy with other species. However,
Anderson noted, establishing biomarker validity requires significant
effort, and all methods of hypothesizing biomarkers are extremely failure
prone (> 99 percent attrition).
There are 109 proteins for which tests have been approved or cleared
by FDA, and 96 additional proteins that can be tested for using laboratory-
developed tests (that have not been reviewed by FDA). Approval of new
protein biomarkers occurs at a very slow rate, Anderson noted: about
1.5 new protein biomarkers per year over the last 15 years. This rate, he
said, is insufficient to meet broad clinical needs, without even considering
MCM development.
Part of this dearth of new biomarkers is caused by the lack of a real
pipeline for systematic discovery, development, and marketing of bio -
markers. There are technological issues, including the lack of reliable,
high-throughput assays for most biomarker candidates and the slow pace
of development of new protein assays. There are also challenges in access-
ing large, existing sample sets in which to test the clinical relevance of a
biomarker. The basic understanding of the mechanism for cross-species
extrapolation is also very poor.
An ideal biomarker measurement method would provide certainty
as to analyte structure, Anderson said. It would include internal stan -
dards and would have a method of eliminating interferences. He noted
that mass spectrometry allows for very high-specificity measurements
of proteins, with quantitative accuracy and internal standards, and it is
inherently multiplexable. These assays can be developed very quickly,
and there is the potential that the improved science content could allow
more rapid approval by FDA.
In conclusion, Anderson said the challenges of identifying biomark -
ers for development of MCMs are similar to those for biomarkers for
general clinical use. He emphasized the fundamental need to develop a
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CUTTING-EDGE EFFORTS
biomarker pipeline capable of systematically addressing complex biology.
Biomarkers of efficacy for MCMs must be established in advance for the
species involved in MCM testing. This requires a systematic evaluation of
candidate biomarker homologs across a range of species, something that
has not been done thus far. Success of an MCM biomarker also relies heav-
ily on parallel mechanisms of disease, treatment efficacy, and recovery
across species. Anderson also recommended that biomarker measurement
technology be based on a high-confidence, rapidly approvable analytical
method.
It is now feasible, Anderson said, to make specific, FDA-approvable
assays for all 20,000 human proteins. Despite statistical design challenges,
it is near feasible, he suggested, to test all possible proteins as candidate
biomarkers against a broad range of diseases. If this were done, it could
establish broad parallelism between human and animal systems.
Marietta Anthony of the Critical Path Institute (C-Path) presented
information about efforts of the Predictive Safety Testing Consortium
(PSTC) to achieve FDA qualification of seven renal toxicity biomarkers.
The specific context of use that FDA allowed was for drug induced kidney
injury in GLP rat studies and to support clinical trials. She remarked that
the next step for C-Path is to conduct human clinical studies to assess their
seven renal biomarkers. If the data are found to be important, they will
be submitted to FDA for qualification. Donna Mendrick of the National
Center for Toxicological Research (NCTR) at FDA commented that trans -
lating biomarkers can be extremely challenging. Mendrick noted that for
kidney biomarkers, the gold standard in animal studies is histopathology,
while in the clinic, the gold standard is measurement of serum blood urea
nitrogen (BUN) and creatinine, which become abnormal at a later stage in
disease. Anthony noted that the seven renal biomarkers that were quali -
fied reflect histopathology far more effectively than BUN. Vikram Patel of
the Office of Testing and Research at FDA’s CDER reminded participants
the ultimate proof of efficacy of an MCM only comes when it is used in
humans. In that regard, having a biomarker is very important to help
assess whether the MCM, or which of several MCMs, is effective in an
emergency situation. He expressed concern that very little attention is
being paid to development of biomarkers.
Animal Models of MCM Efficacy
Throughout the workshop a number of participants discussed limita-
tions of animal models.
Michael Kurilla of NIAID set the stage by noting in his remarks in the
session on enterprise stakeholder perspectives (Chapter 1) that animal
models are critical to MCM development; however, most animal models
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34 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
are not suitable for a number of potential reasons. Animal models are
infection models, Kurilla reminded participants, not disease models,
and some infectious diseases are uniquely human diseases (i.e., there
may not be any appropriate animal model). In addition, pathogenesis
differs among various species, animals may not fully model host defense
responses, and the availability of species-specific reagents may preclude
the ability to define correlates. Extensive pathogenesis and natural his -
tory studies are necessary to demonstrate the validity of a particular
species to replicate a human disease. There are also feasibility issues
with conducting pivotal efficacy studies in animal models, including the
development of GLP animal models to support licensure.
Elizabeth Leffel of PharmAthene provided formal remarks about
animal models, and a panel discussion ensued. In developing MCMs
under the Animal Rule, stakeholders need to think of animal studies as
the equivalent of traditional phase I to II clinical trials, said Leffel. Leffel
emphasized that while aspects of animal models can be standardized, ani-
mals cannot be “validated,” just as we cannot validate humans in clinical
trials. She also noted that both humans and animals are heterogeneous
populations, and no model can be 100 percent predictive of what will
happen in humans.
The primary regulatory science tool for animal models is, of course,
the FDA Animal Rule. There is a relatively new draft guidance published
to support the Animal Rule, entitled “Qualification Process for Drug
Development Tools.” This guidance, Leffel clarified, is not a mechanism to
discuss product-specific tools or assays; rather, it is meant to address how
animal models can be applied broadly to more than one drug.
Leffel identified four key regulatory science needs relative to ani -
mal models. First, she said, the essence of the Animal Rule needs to be
consistently defined to product sponsors. There are different interpreta-
tions across FDA divisions, she noted, and sometimes between review-
ers within the same division, of how to apply the Animal Rule. Second,
appropriate review of MCMs based on risk and benefit is needed. These
are high-risk, life-threatening diseases, about which clinical knowledge is
often limited. Third, Leffel noted, there is a need for precompetitive mech-
anisms to share basic animal model information quickly. This includes
shared proof-of-concept studies to avoid duplication (e.g., for NIAID-
sponsored studies, information on basic models for vaccine studies is
available in cross-referenced master files for sponsors). Fourth, as noted
by others, there must be ways to bridge nonclinical models to expected
human outcomes, such as surrogate markers, correlates of protection,
clinical observation in animals, and pathology.
Moving forward, the first priority, Leffel said, is to develop a strategic
plan for applying the Animal Rule. She suggested:
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CUTTING-EDGE EFFORTS
• his includes finalizing the draft guidances to reflect current FDA
T
thinking1 and then applying these standards consistently within
and across divisions at FDA and across sponsors. Areas that could
be standardized by disease should be identified, and those areas
that cannot be should be recognized. The strategy should also
include preparing the MCM enterprise to accept more risk, as well
as adopting provisions to mitigate risk (by, for example, special
licensing conditions such as restricted or conditional licenses).
• second priority, Leffel said, should be to leverage existing initia-
A
tives or form new partnerships to enhance data sharing. There are
a lot of partnerships already in existence, she noted, and we need
to start using them more effectively. She cited the FDA-NIH regu-
latory science initiative as a potential opportunity to allow FDA
to leverage scientific resources from NIH and further engage FDA
scientists in professional development.
• hird, she suggested, licensure review could be expedited by
T
engaging cross-functional expert teams early on. Specifically, Leffel
noted, in addition to meetings between product sponsors and FDA,
it might improve communication further if an FDA scientist could
also be present at the regular meetings between product spon-
sors that have U.S. government contracts and the relevant funding
agency, at least at significant time points or milestones.
• ublic-private partnerships, such as early development partner-
P
ships between industry and DoD and NIH labs, could be effective,
and cross-industry precompetitive collaboration models should
be pursued. Leffel also suggested that the agency should initiate a
risk communication strategy to the public and establish dedicated
cross-divisional review teams to evaluate MCMs under the Animal
Rule.
Animal Model Case Study and Discussion
Drusilla Burns from the Office of Vaccines Research and Review in
FDA’s CBER offered as a case study the pathway to licensure for anthrax
vaccines. Animal models were developed, she said, that were thought to
be appropriately reflective of human disease. It was demonstrated that
an immune marker, anthrax toxin neutralization antibodies, correlated
with protection in the animals, and the protective level of antibody was
1 A participant from FDA clarified the status of the draft Animal Rule guidance. Following
the public comment meeting in November 2010, the draft guidance is undergoing major
revisions and, as such, will not be finalized but will be republished as a draft to allow for
another comment period on the revised guidance.
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36 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
identified. Further studies demonstrated that the assay that is used to
measure these antibodies was species independent, allowing for bridg -
ing to humans (i.e., in a clinical trial, measuring the antibody levels in
humans could be used to predict the potential efficacy in humans). While
this may sound simple, Burns said, it was very resource intensive, involv-
ing convening a workshop, conducting a literature review and interviews
with experts, and forming an interagency animal studies working group
that called upon vaccine manufacturers, academicians, and government
contractors as needed. She emphasized the importance of the scientific
partnerships between FDA scientists and other government scientists or
outside scientists, and the involvement of diverse disciplines.
Judy Hewitt, chief of the Biodefense Research Resources Section at
NIAID, emphasized the importance of qualification of animal models
in a product-neutral manner. Patel of FDA suggested having a control
animal dataset in a national database to which sponsors could compare
their animal test data. Leffel commented that organizations such as the
Alliance for Biosecurity, a public-private partnership, have taken steps
to pursue development of a shared database of anthrax animal model
data; unfortunately, that effort was underfunded. She noted that BARDA
has picked up some of this work in anthrax and is in the early stages of
working with industry partners to conduct meta-analyses on contributed
data. She emphasized that adequate funding is critical to the success of
these types of initiatives.
In summary discussion, it was noted that there is a clear need for a
better understanding of animal models and how to apply them in a vari -
ety of settings. One of the most significant challenges is the extrapolation
of animal immunological and pathophysiological data to the human set-
ting, and participants discussed the need for new approaches to bridge
nonclinical models to expected human outcomes (e.g., surrogate markers,
correlates of protection). A number of workshop participants noted that
it is unlikely one species model will reflect human disease adequately,
and a compartmentalization strategy, pooling data from several species
models, was proposed. Workshop co-chair, Les Benet, Professor in the
Department of Biopharmaceutical Sciences of the University of California,
San Francisco, called attention to a series of five forthcoming papers, part
of the PhRMA initiative on predicting models of efficacy, safety, and com-
pound properties, that found that, for 108 new molecular entities where
both human PK and animal data were available, the animal models were
poor in predicting (Poulin et al., 2011a,b; Ring et al., 2011). There was also
interest in setting up precompetitive mechanisms to share basic animal
model information quickly (including proof of concept studies to avoid
duplication).
Picking up on earlier discussions, Benet suggested that a retrospec -
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CUTTING-EDGE EFFORTS
tive look at historical animal data from approved vaccines, anti-infectives,
and other products could help inform discussions about the Animal Rule.
He proposed looking at the data from animal studies as if that were all
that was available, and making a hypothetical approval decision under
the Animal Rule criteria, and then comparing how well that correlates
with what is known from the human clinical trials that the actual product
approval was based on. In other words, asking “Using all of the predictive
methodologies that we have available today, if we approved this product
under Animal Rule, would we have made the ‘right’ decision?”
In discussion about this proposal, Robert M. Nelson, senior pediat -
ric ethicist at FDA, noted a concern that most animal work is done for
preclinical toxicology purposes, and there may not be a robust enough
dataset around the appropriate animal model for this type of exercise.
A participant from industry countered that they often conduct proof-of-
concept efficacy studies in mice and rats prior to initiating phase II tri -
als in humans. Ed Cox, Director of the Office of Antimicrobial Products
within the Office of New Drugs of CDER, said to keep in mind there are
different types of animal models, those intended to look at an activity
(e.g., pharmacokinetic/pharmacodynamic [PK/PD]) and those that are
intended to mirror the human condition (involving an actual tissue site
where infection would occur and some of the local factors at that site). In
addition, there are models of infection and models of disease. Participants
also noted the challenge and the importance of comparing “apples to
apples” when looking at historical data. Adding to the complexity is the
fact that tests are done by different laboratories with different standards.
Another participant suggested that an alternative approach could be to
conduct a new animal study with a current, approved drug or vaccine, in
an appropriate model, and base the predictions on that data.
Key Messages: Nonclinical Approaches to Assessing Efficacy
In Silico Approaches and Biomarkers
• Clinical trial simulations hold promise for modeling a wide range of trial designs
and analysis methods and could facilitate reaching programmatic decisions
more quickly, more cheaply, and with greater certainty.
• There is a need for a biomarker pipeline capable of systematically addressing
complex biology. Efforts should include systematic evaluation of candidate
biomarker homologs across a range of species.
• “Big science” could be envisioned for new projects, such as:
A Bayesian, model-based, predictive framework could be applied to create a
■
“virtual human”; such a project would require momentum and collaboration
on a large scale.
continued
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38 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
Key Messages Continued
Make specific assays for all 20,000 human proteins; statistical design chal-
■
lenges would need to be overcome.
Animal Models
• Building databases of existing animal models (genome to phenome) could
allow for assessment of concordance between existing “well-characterized
animal models” and in silico links between molecular systems and animal study
endpoints.
• A control animal dataset in a national database would permit comparisons by
sponsors of their animal test data.
• Scientific partnerships, including creation of an “ecosystem” of collaboration
and a multidisciplinary approach, is important for addressing difficult regulatory
science problems in assessing efficacy.
• Funding and substantial resources are essential to sustain interagency, public-
private, and other enterprise partnerships and collaborations.
SAFETY AND REAL-TIME MONITORING
In a public health emergency, some of the MCMs used may be new
molecular entities for which efficacy studies in humans were not done,
and predeployment safety information is limited, said panel moderator,
Carl Peck, of the University of California, San Francisco. He noted that
once a new MCM is deployed, it will be especially important to monitor
for side effects and to confirm effectiveness (so that use of an MCM that
is not effective can be discontinued and further risk of adverse events
reduced).
Toxicology Markers
Robert House, president of DynPort Vaccine Company, presented
about toxicology markers from a vaccine development standpoint, noting
that there are a variety of primary toxicological concerns. Local toxicity
or “reactogenicity,” while not a main concern for small molecules, is a
primary concern in developing vaccines. As with any drug, one must also
be concerned with systemic toxicity. Toxicity testing is performed under
GLP conditions to ensure the cleanest results, using GMP (or GMP-like)
material, in a relevant animal model, House said. For vaccines, a stan -
dard toxicology profile must also include assessment of immunogenicity.
Developmental toxicity and immunotoxicity are also assessed. Vaccine
adjuvants must be tested as if they were a new chemical entity (and as
such are tested twice, alone and as part of the vaccine). Other additives
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CUTTING-EDGE EFFORTS
TABLE 3-1 Prediction of Clinical Outcomes: Preclinical Toxicology
Studies vs. Clinical Studies
Parameter Toxicology Studies Clinical Studies
Survival Yes Yes
Difficult to assess Yes
Pain upon injection
Dependent on animal model Yes
Fever
No good animal models exist Yes
Headache/malaise
Yes Yes
Injection site reactions
Clinical signs Yes Yes
Body weights Yes Useful?
Clinical pathology Yes Yes/not usually
Necropsy, histopathology Yes Generally not
Antibodies Yes Yes
Immunotoxicity Dependent on animal model Yes/not usually
done
SOURCE: Robert House. 2011. Presentation at IOM workshop; Advancing Regulatory Sci -
ence for Medical Countermeasure Development.
that go into vaccines, such as excipients or preservatives, must also be
individually assessed for toxicity. Depending on how a vaccine is admin-
istered, it may also be necessary to assess the toxicology of the adminis-
tration device.
Standard preclinical toxicological endpoints include body weights (as
a measure of robust health); clinical observations (are the animals behav -
ing normally); clinical pathology (including hematology, clinical chemis -
try, and other immunogenicity studies); anatomic pathology (including
organ weights and histopathology to assess intended effect at the immune
system target, as well as any effects at other points in the immune system);
and local tolerance.
House compared preclinical toxicology studies to clinical studies in
their ability to predict clinical outcomes (Table 3-1). He noted that several
parameters (in italics)—pain upon injection, fever, headache and malaise,
and injection site reactions—are often considered to be rather subjective
and can be difficult assess in animal models.
Electronic Monitoring of Adverse Events
Kenneth Mandl of the Harvard Medical School Center for Biomedical
Informatics characterized four main sources of clinical electronic health
data:
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54 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
• arly integration of high-throughput data collection in drug and
E
vaccine development as a mechanism to understanding global
impact, off-target effects, and biomarkers for efficacy.
• n silico screening for drug-drug interactions and as a tool for novel
I
drug discovery.
• ncreased interdisciplinary crosstalk between computational scien-
I
tists and bench scientists to define standards for study designs.
In discussion, a participant raised the issue of training the next gen -
eration of the workforce to advance regulatory science outside the context
of a particular product. Katze noted that universities have started offering
interdisciplinary programs in computational biology where previously
there was very little interaction between computer science and biology. A
participant from FDA added that the agency has been putting resources
into science computing capacity and is training agency reviewers and
researchers to be able to use them.
Platform Technology
As an example of the use of platform technology to advance MCM
development, Patrick Iversen, senior vice president of research and inno -
vation at AVI BioPharma, described his company’s approach for the rapid
development RNA-based therapeutics. AVI’s platform is based on the
development of translation-suppressing oligomers that target single-
stranded RNA (which could be from a host cell or from the pathogen),
preventing the assembly of the ribosomal complex on the mRNA tran-
script, thereby preventing the production of a specific protein.
AVI has developed a predictable way of designing the oligomers,
which makes the platform very flexible and allows for very rapid
response. They have defined both the optimal position in the transcript,
and the optimal length of the oligomer, and are developing a database of
oligomers for a growing list of viral and bacterial targets and host genes.
This knowledge base, Iversen predicted, would allow AVI to develop a
putative solution to a new threat in a matter of hours.
Iversen noted that AVI currently has open INDs for oligomers for
Ebola and Marburg viruses. Studies in mice, guinea pigs, and nonhu-
man primates have shown significant protection (i.e., survival). Crossover
studies confirmed the specificity of the oligomers for the intended target
(i.e., the Ebola virus oligomer was not effective against Marburg virus, and
vice versa). Other endpoints investigated included dose-dependent sur-
vival increases, reduction in clinical signs, reduction in viremia, increase
in platelet count, and improvement in both hepatic and renal markers of
toxicity.
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CUTTING-EDGE EFFORTS
In closing, Iverson raised several questions regarding animal stud -
ies and human safety testing. For animal models, he asked, how should
a viral challenge strain be chosen? For example, would it be better to
use Marburg Angola or Marburg Musoke? Quasi-species characteriza-
tion could reveal that there are elements or portions of both viruses in
every outbreak. And the next outbreak will be a new quasispecies. “Deep
sequencing” technology, he suggested, could provide insight into how to
choose challenge strains.
Iversen also questioned whether the use of healthy volunteers for
safety assessment is necessary for MCM development. He noted that in
normal healthy volunteers, the dose limiting toxicity may fall below the
anticipated therapeutic dose. How should that limitation be interpreted;
what distance between anticipated therapeutic benefit and dose limiting
toxicity will be tolerable? Also, how should the size of the required human
safety database be calculated? He asked, if these MCMs will never be used
unless there is an outbreak, and will be used only used under an EUA, is
a human safety database needed?
Discussion
William Fogler, senior director of portfolio planning and analysis
at Intrexon Corporation, pointed out that the need for rapid response
generally occurs under worst-case scenarios, often in association with
compromised infrastructure. While these synthetic, computational, and
platform technologies offer tremendous promise to respond rapidly to
a pathogenic threat, they must be scalable and deliverable under such a
scenario. He suggested that there are additional technologies that exist
in terms of generating DNA vaccines, in which modular components
can be predesigned, stored, and ready to assemble on short notice. Other
modules could be devised in which immune-enhancing agents could be
quickly assembled. These modules in the structure of a DNA vaccine can
be under the control of inducible promoters, so that following injection of
the vaccine, an activating ligand (e.g., a small molecule) would be taken
orally to “turn the vaccine on,” and upon removal of the ligand, it would
be “turned off.” This also offers the possibility of a needle-free vaccine-
boosting mechanism, Fogler said.
Mendrick said that researchers at FDA are looking at these emerging
technologies and are trying to anticipate and solve some of the questions
that may arise. For example, NCTR has a nanotoxicology core facility
that is looking at genetic toxicity assays to evaluate the carcinogenicity
of nanoparticles.
Harvey Rubin, executive director of the Institute for Strategic Threat
Analysis and Response at the University of Pennsylvania, emphasized
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56 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
that computational biology is not simple mathematics. The scale of com -
putational biology spans angstroms to kilometers, and nanoseconds to
millennia, he said. The processes are very complicated, and include,
for example, deterministic, stochastic, continuous, discrete, or hybrid
processes. With regard to organization, the system could be structured,
unstructured, or homogeneous. There are complexities and interdepen-
dencies that make modeling biological systems especially difficult, Rubin
said. Motivations to do complicated mathematical modeling include the
need to predict something (e.g., protein structure, epidemiologic pat-
terns), to design something (e.g., new molecular structures, new control -
lers and regulators, new phenotypes), or to interpret something (e.g., data,
patterns).
Rubin highlighted several research priorities that can help populate
some of these mathematical models:
• here are many model-specific questions that need to be answered,
T
such as what are the effects of interventions on infectivity, and
what are the effects of disease and interventions on immunocom-
promised hosts?
• here is also general research needed on organizational structures,
T
risk communication strategies, interdependencies (e.g., how the
environment, economics, or politics impact the model), and health
impact information.
• lso to be resolved is who should be funding this work—NIH, the
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National Science Foundation, DARPA, FDA, or industry.
DIAGNOSTICS
Significant resources are dedicated to identifying and characterizing
an emerging biological threat, said Daniel Wattendorf, program manager
in the Defense Sciences Office of DARPA, but rarely is there subsequent
broad distribution of new diagnostic assays for the identified threat to
point-of-care settings. In cases where the decision to quarantine or treat is
time sensitive, the turnaround time to ship samples to a reference labora -
tory is prohibitive.
Wattendorf cited several barriers to more rapidly fielding diagnos -
tics for emerging threats. In some cases, the diagnostics platforms have
not been made suitable for use in distributed settings. As an example,
Wattendorf pointed out that PCR has been in use since 1983, yet no PCR-
based diagnostic test is approved for a physician office setting. Addition-
ally, if diagnostic tests are not already in place before an emergency, it is
very difficult to get physicians to employ them in a crisis if they do not
have prior experience with the test or have not been shown evidence of
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utility. In the absence of specific diagnostic tests for emerging threats,
there is interest in developing panels of early detection biomarkers that
could detect a host immune response before an individual begins to
exhibit symptoms of a disease.
Sample collection is another challenge for diagnostic testing. Cur-
rent biospecimen collection generally involves collection of wet samples,
such as through test tubes, which requires that the patient have access
to medical personnel (e.g., a phlebotomist) who can collect the sample,
and which also may require cold storage. There is also the option of tak-
ing dried blood spots on filter paper, but according to Wattendorf such
samples have limited use. In this regard, Wattendorf suggested that a
role for regulatory science would be the development of new formats for
simple, self-collected biospecimens, formats that would be optimized for
specimen source (blood, urine, etc.) and analyte class (specific proteins,
types of RNA, etc.), and would be stable during storage to facilitate func -
tional assays.
Wattendorf also noted that currently, teams of experts travel to a site,
collect samples, and return to CDC or the DoD to run tests and identify
the new threat. He suggested that, instead of moving the sample, it could
be possible to move the data electronically. The use of highly multiplexed
platforms could facilitate local testing, and the data could then be sent
to a central facility for analysis. This would be faster and would provide
distributed diagnostic capability where there is unmet need.
In summary, Wattendorf listed several questions for discussion:
• an universal sample storage formats be developed for dried or
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near-dried self-collected biospecimens that show equivalence to
fresh samples?
• an highly multiplexed protein or molecular diagnostic platforms
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be developed that are suitable for use in a physician office base
setting, from which data could then be sent for interpretation by
highly trained laboratorians at a remote site?
• re measurements of immune or metabolic status useful in the
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absence of a diagnostic test for a specific pathogen? If so, what
should be measured? Could it be measured at the point of care?
And, as it is not specific to a given disease, what would be the
regulatory pathway?
In the panel discussion, Charles Daitch, CEO of Akonni Biosystems,
said that from a technical perspective, the capability to communicate from
remote sites to a central facility already exist, and it would be straightfor-
ward to develop and implement ways to communicate using either raw
or processed data. Sally Hojvat of CDRH concurred and suggested that
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58 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
this would be covered under existing regulations that address electronic
records and the transfer of data from an instrument at a clinical site to a
central facility for analysis (21 CFR 11). She cautioned that it would be
necessary to demonstrate the accuracy and traceability of the results of a
test performed remotely by an unqualified individual.
Panel moderator Bruce Burlington, an independent consultant, ques-
tioned how it could be determined that an immune status test was rel -
evant for many different illnesses. Would test developers need to under-
take a variety of disease challenges? Hojvat responded that it could be
considered more of a prognostic type of marker, and such data would be
one way FDA could begin to assess the test. With regard to its commercial
value, Daitch said that the market for such a test is not obvious. A test that
predicts, based on immune status, that someone is in the early stages of
an infectious disease might be useful, for example, for astronauts about
to go on the space shuttle or for troops about to be deployed, he said.
Burlington added that it could also be used in an epidemic for health care
workers or other first responders.
Participants discussed the potential for commercial assays on mul-
tiplex platforms to be used as epidemiological surveillance tools (as
opposed to diagnostic tests where results are reported back to the patient).
Hojvat suggested that companies could aid the surveillance effort by
developing cassettes for biothreats for their multiplex systems. Daitch
and David Ecker, founder of Ibis Biosciences, agreed it would be possible,
but noted that key challenges would be validation of the test for broad
groups of organisms and ensuring that data could be transferred over a
secure network to somebody who has the capability to interpret the data
correctly.
Participants also discussed the concept of an evolving label. Perfor-
mance characteristics of a diagnostic test need to be defined in terms of
sensitivity and specificity, but a challenge is how to present that infor-
mation in the label when the background prevalence of what is being
tested for is almost zero. It would be helpful if, as the threat emerges,
new information and data based on use could be made available rapidly.
Hojvat responded that FDA has the technology to do that, and there is an
ongoing electronic labeling project.
In summary discussion, participants observed that it is important
to remember that diagnostics are also MCMs. Several options for more
efficient use of diagnostics were suggested, including the development of
new formats for collection, transport, and stable storage of biospecimens,
and the development of highly multiplexed testing platforms for local
site use, with data then sent electronically to experts at a central facility
for analysis. It was also noted that rapid diagnostics could improve the
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CUTTING-EDGE EFFORTS
efficiency of antimicrobial trials, allowing for enrichment of the popula-
tion with patients infected with resistant organisms.
STATISTICAL TECHNIQUES
The goal of clinical trial simulation in drug development programs is
to reach a decision faster, cheaper, and with greater certainty, explained
Stephen Ruberg, distinguished research fellow and scientific leader in
advanced analytics at Eli Lilly and Company. Companies seek to “kill”
ineffective or unsafe investigational drugs sooner and advance potentially
effective drugs as quickly as possible and at the lowest cost possible.
Clinical trial simulation allows for examination of a broad range of clinical
trial designs, decision rules, and analysis methods. In simulations, models
can be used to create virtual patients that are then randomly selected for
inclusion into in silico clinical trials using sophisticated software tools.
These models for virtual patients can be PK/PD models, empirical sta-
tistical models of response over doses and time, or mechanistic disease
models. Known design and analysis parameters can be controlled (e.g.,
sample size, number of doses or visits, analysis strategies for testing
hypotheses or estimating key drug effect parameters), and a range of pos-
sibilities for unknown parameters and factors that cannot be controlled
can be assessed (e.g., drug effect, true dose response curve, adverse event
rate, placebo response, dropout rate). Dozens of combinations of factors
are typically evaluated with the goal of selecting the design and analysis
parameters that will minimize false positive and false negative findings
in the drug development program.
From a regulatory science perspective, Ruberg said, this will require
training of FDA staff on the use of simulation tools, some of which are
becoming commercially available. FDA statistical and medical reviewers
will have to understand and accept modeling simulation as a tool for
study design. Simulation trial designs generated may not look like classic
trial designs or may not have theoretically or mathematically described
properties, he said. This is of particular concern when designing phase III
trials due to the need to control the type I error (false positive) rate at 0.05.
As this cannot always be done analytically, Ruberg asked whether FDA
will accept simulated results in lieu of analytical proof. He noted that the
FDA draft guidance on adaptive designs3 is a substantial step forward in
helping the industry understand how best to move forward with innova-
tive trial designs. Another topic for consideration is the simulation of the
3 See Guidance for Industry Adaptive Design Clinical Trials for Drugs and Biologics (Draft
Guidance) http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatory
Information/Guidances/ucm201790.pdf (accessed June 9, 2011).
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60 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
sequence of clinical trials spanning an entire clinical drug development
program, which, Ruberg said, companies could realistically be doing in
the next couple of years.
A goal in drug development is to use as much data as possible—
current or historical—to make decisions on drug safety and efficacy. Cur-
rent practice in the vast majority of phase III clinical programs is for each
clinical trial to stand on its own as an independent piece of evidence in
the evaluation of a drug’s effect. This is a frequentist statistical approach.
Eli Lilly, Ruberg said, is currently implementing Bayesian methods for
phase I and phase II trial design and analysis. There are many ways in
which Bayesian methods can be used in clinical drug development. One
example presented by Ruberg is a Bayesian augmented control design, in
which control group data from the current prospective study is supple -
mented with historical control data. This allows for smaller trials (saving
both time and resources) and for more enrolled patients to be allocated
to treatment groups.
While the use of Bayesian statistical methods is a technical topic,
Ruberg opined that the largest barriers to implementation are social.
There will need to be changes in philosophy and mind-set within some
FDA centers and other regulatory agencies around the world. There are
also legitimate scientific debates relative to the choice of historical data to
include in analyses and how to weigh those data relative to data gener-
ated from a new study, he added. From a regulatory science perspective,
Ruberg said that the use of Bayesian approaches for phase III confirma-
tory trials would require in-depth sponsor-agency discussions at the end-
of-phase-II meeting or sooner.
Important to the use of Bayesian approaches is the development of a
comprehensive data element dictionary. Data element standards allow for
more efficient collection of data and routine use of standardized software.
More importantly, common data element standards allow for the simple,
rapid integration of data from multiple sources, facilitating more compre-
hensive statistical analysis in order to draw the best scientific conclusions
possible. Such a dictionary should be maintained by a central authorita -
tive group, Ruberg said, and must be free, broadly accessible in electronic
form, and downloadable for use within IT systems. He acknowledged the
various ongoing standardization efforts (e.g., CDISC, HL7),4 but said that
4 The mission of the global, nonprofit, multidisciplinary Clinical Data Interchange Stan -
dards Consortium (CDISC) is to “develop and support global, platform-independent data
standards that enable information system interoperability to improve medical research and
related areas of health care.” See http://www.cdisc.org/. Health Level Seven International
(HL7) is a nonprofit “standards-developing organization dedicated to providing a com -
prehensive framework and related standards for the exchange, integration, sharing, and
retrieval of electronic health information.” See http://www.hl7.org/ (accessed June 9, 2011).
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data element standards needs to go deeper in terms of specificity, broader
in terms of accommodation of all therapeutic areas and measurements,
and faster in terms of development and deployment.
In closing, Ruberg offered several ideas to advance the use of trial sim-
ulation and Bayesian statistics, and the standardization of data elements:
• or study design, Ruberg suggested adaptive/pooled studies as a
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way to more rapidly and uniformly test compounds. Such studies
use a single trial design with a common control group that allows
companies to insert their drug into a perpetually ongoing trial
(such as the I-SPY 2 breast cancer clinical trial).
• uberg also directed participants to the Drug Information Associa-
R
tion (DIA) Working Group on Bayesian Methods. Ruberg noted that
Bayesian approaches were discussed in a recent National Research
Council (NRC) report on how to handle missing data in clinical
trials (NRC, 2010), and he suggested that the National Academies
conduct a study to evaluate the use of Bayesian methods in clinical
trials, with particular emphasis on phase III confirmatory trials.
In panel discussion, there was much discussion about the use of Bayes-
ian statistical methods for analysis of clinical trials. Jeffrey Wetherington
of GSK said that his company has made significant use of Bayesian meth -
ods for phase II proof-of-concept studies and dose-ranging studies, and
he estimated that use of these methods has saved the company nearly $15
million on study costs over the past year. Similar to Eli Lilly, Wetherington
said, GSK uses augmented control groups, decreasing study sample sizes
by several hundred people. Bayesian methods provide very interpretable
results, he said.
Estelle Russek-Cohen, acting director of the Division of Biostatistics
at CBER, noted that CDRH frequently uses Bayesian analysis in the con -
text of device modification submissions. She added that CBER has seen
submissions with Bayesian and adaptive designs, primarily in phase I
and II studies, many of which have been oncology studies. For phase II
studies, a variety of skill sets are needed when considering the benefits
and risks of the analysis approaches (e.g., medical officers, statisticians).
Russek-Cohen said a concern with historical controls is how far back to
go if the standard of care is changing. A question for consideration is
whether, in the context of MCMs, there is a real and compelling need for
these approaches.
Panel moderator Burlington noted that the toxicology community
routinely uses historical controls, pooling data from control animals from
many experiments. Russek-Cohen responded that pooling of historical
control data is used for safety assessment as there is often not enough
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62 ADVANCING REGULATORY SCIENCE FOR MCM DEVELOPMENT
power in individual studies, but it has not yet been done for efficacy
studies, in part because FDA statutes call for adequate and well-controlled
trials. In a phase II environment, it makes sense for industry to find ways
to pool control information across companies pursuing similar projects.
Wetherington said that drugs such as anti-infectives can have small
niche markets and limited profit margins. Using Bayesian-type designs
for phase III studies, especially when comparing the novel agent to a
well-characterized standard of care, could save time and money, and get
products to patients more quickly.
Participants discussed the potential for use of simulation and Bayes-
ian approaches as the basis for approval of an MCM in anticipation of
future use. Goodman responded that part of the FDA MCM initiative is
to consider novel approaches, and the agency is open to these possibili -
ties. He encouraged developers of MCMs to discuss this with their FDA
review team as part of their product development planning.
Burlington questioned whether FDA could mandate or incentivize
companies to submit their data in conformation with data element stan -
dards. Russek-Cohen responded that implementing standards is part of
the broader FDA initiative. Ruberg suggested that the National Library of
Medicine or FDA could take the lead on pushing forward with data ele -
ment standards. A participant noted that CDISC is approaching standards
development disease by disease. In response, Ruberg suggested that there
could be a working group of experts in MCMs to define what generally
needs to be measured and start discussing standard data elements.
In summary discussion, the statistics of diagnostics and dealing with
false positives was also considered. A participant said that CDRH has
asked developers of new multiplex diagnostic assays to offer ideas about
how to handle false positives, for example if three or four positives were
found where one was expected. A participant said to keep in mind the
primary question the assay is answering: Is it diagnosing an individual
or determining if there is an outbreak? It was noted that there is an inter-
agency meeting being planned on this issue.
Several workshop presenters and discussants noted that Bayesian
statistical methodology can be used for both study design (e.g., supple -
menting the control group data with historical control data) and analy -
sis (of both actual and simulated trials). Workshop participants offered
suggestions for themes and future directions with respect to statistical
methodologies and data analysis. The following individual suggestions
were made:
• raining in Bayesian approaches and causal mechanisms of actions
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will be needed for both scientists and the public.
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• he use of Bayesian approaches would be enhanced by the devel-
T
opment of common data element standards (e.g., to facilitate pool -
ing of data across studies).
• hristian Macedonia, medical sciences advisor to Admiral Mullen,
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the chair of the Joint Chiefs of Staff, raised the idea of electronically
tagging every piece of information obtained in biomedical research
(e.g., date, time, group, unique animal identification, institution) so
data from large multicenter trials could be traced back, even years
later, for further analysis. He likened this to the way electronic data
are broken into packets and tagged for transfer across computer
networks, to be reassembled at the other end.
• here was also interest in platform approaches to health data soft-
T
ware design, for which many applications or “apps” could be
developed. These could be used for collection, management, and
analysis of electronic health data, specifically for monitoring of
adverse events.
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