costly as it moves downstream, it is important to anticipate immunogenicity as far upstream as possible in the drug discovery
process. Organizations wisely strive to minimize costs by predicting and preventing any type of toxicity early (see Figure 1).
Researchers can use predictive analytics to identify immunogenicity in biotherapeutics during the discovery phase of drug
development. Predictive sciences enable researchers to mine
data, make predictions, and gather actionable insight to move development forward or discard failures. Predictive tools and virtual
experiments can be combined with real experiments to provide
scientists with more key performance indicators (KPIs) that drive
“The best qualification of a prophet is to have a good memory,”
English statesman George Savile said centuries ago. His insight is
relevant to today’s predictive sciences, which depend on past data
for reliability. The value of a predictive approach depends on the
quality and variety of the data supporting it. As such, companies
that want to include predictive analytics in their drug development process should encourage data standardization and pre-competitive data sharing. Information systems need to be easily
accessible to a wide range of diverse users and support collaboration among specialists with unique expertise.
TO GO FAR, GO TOGETHER
Attrition rate is a problem in the pharmaceutical industry.1 A
process that spawns thousands of molecules in the research
phase typically concludes with only a few viable therapeutic products. This increases the cost of bringing new drugs to
market, which leads to patients paying higher prices for the
medications they need. When so much depends on scientists
making the right decision at the right moment, it is essential
to provide them with as much information as possible so they
can be precise.
Predictive tools are based on established algorithms that have
been validated over time. It is not difficult for statistical analysts
to build predictive models. But the degree to which the answer
from a model can be trusted depends on what data is used. More
data results in more reliable predictive models. There is an abun-
dance of biological data in a variety of different formats in enter-
prises and research organizations across the industry. But much
of it is sequestered behind company firewalls. A key challenge
today is to aggregate more of this data in a standardized format
to improve predictability.
Is it reasonable to expect companies to voluntarily share pro-
prietary data to advance scientific knowledge for everyone? Se-
cure third-party platforms could make it possible to collect and
centralize biological data from participating organizations to cre-
ate predictive models. Data would be protected, so companies
would only have access to data they provide while benefitting
from the collective knowledge of all contributors. If such a strat-
egy can improve scientific accuracy for the entire industry and
reduce the cost of medicine for patients, everyone wins.
Initiatives such as the Pistoia Alliance (www.pistoiaalliance.
org) and Allotrope Foundation ( www.allotrope.org/) are successful precedents for this type of cooperation among life science organizations. In these projects, experts from life science companies
and other groups come together to share pre-competitive strategies for establishing a common data format and ontology for the
industry (see Figure 2).
LET’S GET PERSONAL
When researchers discover a drug candidate that has the potential to cure a large percentage of the patient population, they
don’t discard it when studies reveal immunogenicity. The story
is far from over. They look for ways to decrease immunogenicity
without affecting the efficacy of the treatment. Active ingredients
affect different segments of the human population in different
ways, which raises further questions. Is the therapeutic defined
Anticipating any toxic property during the upstream steps of the drug discovery
process avoids useless and costly studies in downstream steps.
Secure third-party platforms could make it possible to collect and centralize
biological data from participating organizations to create predictive models and
improve scientific accuracy.