A Revolution in R&D

tified through the use of animals and tissue cultures,
and so their relevance to human disease is
largely speculative.) In other words, there is no possibility
of failure in target validation, because identified
targets are ipso facto validated. (This is, of
course, no guarantee of their drugability—their
responsiveness to small-molecule intervention.)
It would not be possible to overstate the value of in
vivo human validation. Most of what passes for
target validation today is largely conjectural in
relation to the disease in question.
—Diabetes researcher,
Harvard Medical School
Second, the frequency of the causal polymorphisms
is known at the outset. If a study identifies multiple
genes associated with a particular disease, it will
also reveal their relative culpability. Consider the
example of Alzheimer’s disease, a heritable but
genetically complex disorder. On the one hand,
there are variants in three genes—PS1, PS2, and
APP—that are very rare but very potent: if a person
has any of them, he or she is almost certain to
develop Alzheimer’s. On the other hand, there is
the ApoE4 polymorphism of the ApoE gene, which
has a more modest effect on disease susceptibility
but is much more common in the population at
large, and among patients with Alzheimer’s.
Information of this kind can be useful for predicting
a drug’s potential marketability: although it
might be equally feasible to develop a drug that
influences the rarer variants, a drug targeting
ApoE4 might expect broader effectiveness, and
thus a larger market, and so might take precedence
in further research. (The rarer variants may still be
worth pursuing, using pathway analysis.)
Finally, the nature of the relevant polymorphisms is
known—different disease-inducing mechanisms
among variant forms, for instance. Such information
may help to streamline clinical trials if used by
efficacy-based pharmacogenetics to identify “nonresponders”—patients
who lack the crucial DNA
alteration, and hence are unlikely to experience the
intended effect of a candidate drug—and exclude
them from the trials. (In modeling the potential of
disease genetics, we have included this effect of
efficacy-based pharmacogenetics. See the section
on pharmacogenetics below for further details.)
Depending on the approach taken, cost savings per
drug could be as great as $420 million, with the
potential time savings ranging from 0.7 to about 1.6
years (producing an added $290 million of value
per drug). (See Exhibit 6.) Of the cost savings, the
vast majority would be yielded by the improvements
in success rates: $390 million, consisting of $110
million in validation and $280 million in the clinic.
EXHIBIT 6
DISEASE GENETICS OFFERS GREAT SAVINGS POTENTIAL
Cost to drug
Pre-genomics
Candidate
gene study
Genome-wide scan
Genome-wide scan
plus pathway analysis
Time to drug
Pre-genomics
Candidate
gene study
Genome-wide scan
Genome-wide scan
plus pathway analysis
ID Biology
Target ID Target Validation
0
0
200
5
Chemistry
400
Screening Optimization
10
460
485
455
600
800
13.1
15
14
14.7
Development
16.5
Preclinical Clinical
SOURCES: Industry interviews; scientific literature; public financial data;
BCG analysis.
880
1,000
$M
20
Years
29