A Revolution in R&D

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duce targets high in quality but low in quantity, perhaps
just one to five for an average disease. That
would produce at best a 25–30 percent chance of
yielding a drug, given that chemistry and development
remain far from fail-safe. For disease genetics
to live up to its promise, it will need to improve
those odds considerably. And to do that, it will have
to call on a supplementary technique: pathway
analysis.
Disease-susceptibility genes, if identified through
disease genetics, serve not only as targets themselves,
but also as guides to additional targets. The
genes form part of broader disease pathways, and
these pathways contain other molecules that may
serve as targets (perhaps 10 to 15 targets per pathway,
according to experts). These new targets are
identified by pathway analysis, often taking the form
of the study of “simple” experimental systems, such
as those of Drosophila melanogaster or C. elegans (a.k.a.
fruitflies and worms). When studied in the laboratory,
these systems disclose the genetic components
of a given pathway. (Other approaches to pathway
analysis include expression profiling of tissue samples
and studies of protein-protein interaction.)
Implementing pathway analysis will result in a lower
cost per drug on average: it improves efficiency.
Costs are reduced because pathway studies are relatively
inexpensive and fewer human-derived targets
are required—pathway analysis expands the pool of
potential targets tenfold or more. As targets, they
prove to be of high quality, moreover (in keeping
with the disease-susceptibility gene that inspired
their discovery), achieving good success rates in
clinical trials. For although they themselves are not
yet validated in humans or clearly implicated in the
disease, they participate in a pathway that is.
So pathway analysis should give human studies the
requisite boost, with enough targets emerging to
yield a drug more often than not. On the down
side, there are the time and cost of additional animal
research and the loss of some advantages in
clinical trials. Adding pathway analysis, via genetic
studies of Drosophila melanogaster, to indirect
genome-wide association studies would result in a
total savings of $425 million for each drug on average,
regardless of the original human study
approach, though it would add nearly two years of
additional work (producing an additional $255 million
of value per drug).
The first one to do genetic studies takes a huge risk.
If it works, you’ll see everyone running to join the
crowd.
—R&D executive,
leading pharmaceutical company
Implementing Disease Genetics
The savings promised by disease genetics are enormous,
and companies cannot ignore them. But they
cannot ignore the risks either, and will need to exercise
rigorous selectivity and discipline when it comes
to pursuing specific disease genetics studies—which
approaches to adopt, for instance, and which diseases
to explore. Placing bets in this way is going to
be nerve-racking enough. But companies face
another difficult set of choices as well, in the various
operational issues that need to be addressed.
Placing Bets
Some diseases have clear appeal as objects of
genetic research: asthma, Alzheimer’s disease, and
diabetes, for example, being complex diseases that
afflict large populations and obviously contain heritable
factors. They have already become competitive
areas of study. Although opinion is still divided
over the likely impact of disease genetics, substantial
bets are being placed by various companies—
emerging biotechs and established pharmaceutical
companies alike. A few claim they are already seeing
benefits from their investments: following its
alliance with Roche, deCODE genetics claims to
have succeeded in identifying a gene contributing
to cerebrovascular disease; GlaxoSmithKline has
announced finding genes associated with migraine,
Type II diabetes, and psoriasis; and Genset, a
French biotech company, is reported to have identified
genes implicated in prostate cancer and
schizophrenia.
But all companies embarked on, or intent on, pursuing
disease genetics must acknowledge that it is