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DRUG DISCOVERY & DEVELOPMENT
From Concept to Reality: What’s Required to
Achieve the Promise Held by Precision Medicine
Summary
Precision medicine, as currently envisioned, has incredible potential for the
treatment of individuals, with improved therapeutic indexes. There is also
considerable potential for drug development, as science-based selection of
well-defined patient populations for clinical trials could improve efficacy, possibly meaning earlier regulatory submissions, better odds of approvals, and
faster product launches. But can this promise be realized? This white paper
looks at the development and requirements of precision medicine.
The slogan “right patient, right drug,
right dose, right time” captures the
goals of precision medicine.
The concept of precision medicine has made its way to the public consciousness,
especially after President Obama announcement of the Precision Medicine Initiative
(PMI), a $215 million investment in to accelerate biomedical research (1). The Initiative
seeks to develop new tools to select the therapies that will work best in individual
patients, with funding being channeled through the National Institutes of Health
(NIH), the Food and Drug Administration (FDA) and the National Cancer
Institute (NCI).
Oncology has been a logical choice for the initial focus of the initiative, given that
the first efforts to identify effective therapies through precision medicine leveraged
knowledge of the genetics of disease, and that cancer is the ultimate “disease of the
genome” (2). However, in the longer term, the ambitious plans are to study other
disease areas as well.
Precision medicine is an evolving concept, and its definition has been refined over
time, with slightly different angles depending on the source. An aspirational slogan
occasionally used is “right patient, right drug, right dose, right time,” and it certainly
captures the overarching goals of precision medicine (3). The National Academy of
Sciences describes precision medicine as “the use of genomic, epigenomic, exposure
and other data to define individual patterns of disease, potentially leading to better
individual treatment” (4), while the NIH defines it as “an emerging approach for disease
treatment and prevention that takes into account individual variability in environment,
lifestyle and genes for each person” (5). Although these definitions may suggest
medical intervention at an individual level, when the term is interpreted in the context
of multiple determinants of health, it is understood to lead to both individual and
subpopulation interventions (6).
At the core of this groundbreaking approach to medicine lies a more quantitative,
mechanism-driven understanding of both progression from health to disease and
response to medical treatments and interventions. This understanding means an
ability to predict progressions, outcomes and variability within them, and to minimize
the impact of such variability, which carries the power to improve the benefit-to-risk
ratio of therapeutic interventions (the therapeutic index). The promise is that precision
medicine would also allow a science-based selection of well-defined patient populations
for clinical trials, enriched for responders, which would enable smaller databases to
support efficacy. This would have the potential for earlier regulatory submissions, better
odds of regulatory approvals based on improved therapeutic indexes, and faster and
more focused product launches (7).
The implementation of precision medicine paradigms will likely result in the
establishment of “precision drug development” programs, as Dr. Janet Woodcock,
Director of the FDA’s Center for Drug Evaluation and Research (CDER), discussed
in a recent article (3). She anticipates that in the next decade, continuing advances
in molecular biology and computational methodologies will allow a tremendous
expansion in the capacity to analyze, predict and prevent drug toxicity and efficacy.
Central to this will be the capability of these methodologies to reliably identify the
increasingly precise deviations from normal physiological stages. These tools and
methodologies will require a more detailed knowledge of human physiology—both
normal and pathological—and this knowledge must encompass multiple levels, from
the molecular and cellular to the whole organism.
Section subtitle
Some examples of precision medicine
therapeutics and interventions have
already been developed by leveraging
knowledge about very large deviations
of the disease from the healthy state.
They have mostly been deviations in
genetic sequences-like mutations driving
some cancers or molecular phenotypes
in such diseases as cystic fibrosis. Some
successful approaches have included the
following strategies:
1. Developing a therapeutic for a targeted
subpopulation of cancer patients
defined by a “driver” mutation, e.g.,
crizotiniv (small molecule ALK kinase
inhibitor) for the treatment of ALKpositive non-small cell lung cancer (8).
2. Developing a novel therapeutic thanks
to the insights gained from the analysis
of human “omics” data sets, e.g.,
alirocumab and evolocumab (PCSK9
inhibitory antibodies) to treat high
levels of low-density lipoprotein (LDL)
cholesterol and the associated cardiac
risk (9).
3. Creating novel anti-inflammatory
drugs based on molecular knowledge
of cytokine signaling pathways that
mediate inflammatory responses,
e.g., tofacitinib (a small molecule JAK
kinase inhibitor) for the treatment of
rheumatoid arthritis (10).
Although these cases required
cutting-edge genetics and molecular
biology tools and techniques, they
were relatively straightforward in
terms of mechanism. However, in
many diseases for which there is an
unmet need for effective therapeutic
interventions, pathophysiology is
complex and multifactorial. Therefore,
experts anticipate that precision drug
development will be increasingly
dependent on computational methods
that can integrate results from human
data and from laboratory and animal
studies, and then generate models of
health status, progression to disease and
outcomes of therapeutic interventions (3).
In addition to novel approaches to target
discovery and validation, precision
drug discovery will require an in-depth
understanding of the consequences of
diseases (and their treatments) at the
molecular, cellular and whole-organism
levels. This will have to be coupled with an
ability to share data and expertise across
disciplines in both private and public
sectors (11). Successful drug discovery
programs will rely on combining the
modulation of the candidate drug
targets in model systems with a better
understanding of the biology of the
potential target. For example, much more
remains to be discovered about the role
of genetic variants in disease onset and
progression, as well as response to drugs.
New technologies, such as “tissues on a
chip,” and new computational tools will be
required to elucidate complex biological
mechanisms and to test different
hypotheses. Perhaps most critically,
precision drug development will depend
on the development of computational
models of disease and of response to
intervention that can take into account
the large variability in human responses
to medicines and that have the capability
of making reliable predictions.
The Role of Biomarker Development
The incorporation of precision medicine
approaches into drug development
will depend on the identification and
validation of biomarkers that allow
relevant patient stratification and reliable
monitoring of drug responses. The
combination of biomarker information
with clinical phenotypes, at individual
or subpopulation levels, is expected
to be strongly synergistic with other
complementary approaches, such as
clinical risk assessments and therapeutic
drug monitoring, and will be critical
for the development of successful
targeted therapeutics exhibiting a higher
therapeutic index when prescribed to the
right individual patient or
patient population.
Biomarkers include a wide range of
measurable indicators that can be
used to assess normal and pathogenic
biological processes, or responses
to therapeutic interventions. Under
a precision medicine approach, new
biomarkers with increased specificity
and sensitivity (revealing physiological
3
Precision drug discovery requires the
ability to share data and expertise
across disciplines in both public and
private sectors.
differences between patients, stages
of disease progression or response to
treatment) will support the development
of safer medications targeted to defined
patient subpopulations. In the context of
clinical development, there will be a need
for stratification biomarkers to support
the categorization of patients during
the screening phase and for responsemonitoring biomarkers to assess
drug safety and efficacy once the drug
treatment has been initiated.
Patient stratification biomarkers can
include diagnostic, prognostic and
predictive markers. Effective stratification
biomarkers will ensure that only patients
with the required driving phenotype or
genotype enter the trial or, alternatively,
that the patients are distributed equally
across the different treatment arms.
Even if no stratification biomarkers are
available before a clinical trial, the use
of banked biological samples can allow
retrospective analysis whenever a subset
of patients shows response, with the aim
of identifying a marker characteristic
to the responding group. In practice,
stratification biomarkers are most likely
to be developed as in vitro companion
diagnostic tests or devices. “Companion
diagnostics” refers to a class of tests
that the FDA has determined to be
essential for the safe and effective use
of a particular therapeutic drug or class
of drugs. They are usually developed in
parallel with its corresponding therapeutic
and used in pivotal clinical trials. Most
currently marketed companion diagnostic
tests are patient stratification
biomarkers (12).
Response biomarkers are widely used
in early-stage clinical trials to help
understand the mechanism of action
and pharmacodynamic effects of drugs.
Currently, there is only one approved
companion diagnostic test based on a
response biomarker, and the challenge
for the coming years is to develop and
implement new biomarkers that can
be used to evaluate the response and
progression of targeted therapies.
While the use of biomarkers for patient
stratification and as surrogate endpoints is already helping to advance
clinical trial design, industry experts
recognize that the availability of a wider
range of qualified response biomarkers
would expedite drug development
and help deliver true precision
medicine. The establishment of clearly
defined regulatory frameworks for the
qualification of new biomarkers and
companion diagnostic devices will be
central to the implementation of precision
medicine approaches.
Access to genetic and phenotypic information
from specific subpopulations is becoming very
attractive to drug developers.
The Importance of New Genetic Bioresources
The use of genetics-based drug discovery strategies has represented a very successful
approach for the development of therapeutics. Indeed, the use of human genetic data
for the selection of drug targets or medical indications can almost double the chance
of success of a drug development program compared to those lacking a genetic basis
(13). Thus, access to genetic and phenotypic information from specific subpopulations
is becoming very attractive to drug development companies, many of which have
started to partner with health care systems and private companies with curated genetic
bioresources or large databases of genomic information. The goal of these partnerships
is to identify potential therapeutic targets and to develop biomarker assays and targeted
drugs. One such example is the recent partnership between Regeneron Pharmaceutical
and the Geisinger Health System (14), which will assemble one of the largest U.S.
populations of participants for the analysis and sequencing of genetic material and
comparison to long-term health outcomes. During the initial five-year collaboration
phase, samples from over 100,000 consenting volunteers collected by Geisinger will be
sequenced and genotyped by Regeneron to generate de-identified genomic data. The
size and scope of the study are meant to allow precision in identifying and validating
the associations between genes and human disease.
On the other hand, government agencies and academic investigators are exploring
new joint opportunities to generate new genetic bioresources and the necessary
analytical tools to extract relevant information. For example, the PMI itself includes
the generation of a cohort of one million U.S. volunteers for longitudinal research,
including genetic studies. Programs such as this can incentivize the formation of new
collaboration and business models bringing together scientific and technological
knowledge from different sectors, while enabling greater access to the genomic data of
large clinical research cohorts.
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Conclusion
Precision medicine, as currently envisioned, will use big data cohorts to support
genomic research. Thanks to the PMI and other initiatives, such data resources are
already under construction. It will require the characterization of patients through
new measurements of health state, like the use of wearable technology, as well as
the characterization of comprehensive sets of genomic, transcriptomic, proteomic
and other “omic” technologies. It will also include the combination of biomarker and
molecular information for very specific clinical profiles of narrowly defined disease
subpopulations, or even for individual patients.
As some experts highlight, the impetus for the implementation of precision medicine
strategies in drug development and clinical practice stems multiple factors:
•Advocacy from patients, physicians, regulators and funding bodies, based on their
interest to see higher treatment response rates with lower therapeutic risks
•A growing appreciation of the complex heterogeneity that underlies diseases
•Decreasing pharmaceutical R&D productivity
•Fnancial constraints for a demonstrated added value in health care (15)
To achieve the promise held by precision medicine, a new generation of therapeutics
will be required, developed under a precision drug development model. Although the
gap between this vision and its practical application remains wide, it will rapidly close
over the next 10 years as the industry, regulatory agencies and different stakeholders
embrace and support this new model.
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