A Revolution in R&D

Genomics may offer an opportunity for companies
to make the correct decision more often than
before. For one thing, genomics can ultimately provide
more, better, and earlier information, and
good information translates ultimately into high
success rates. For another, the implementation of
genomics approaches will force companies to
rethink their internal decision-making processes.
Genomics-based information, together with the
ability to mine it productively, gives a company an
enormous advantage. Such a company will now be
able to make and execute decisions on targets and
inhibition, therefore, is conditional gene inhibition,
which mimics the partial inhibitory effect of a drug.
Several promising approaches have emerged, including
forward genetics, chemical genomics, and
molecular switches that modulate gene expression,
but their practicality has still to be proved.
Examples abound of genomics companies engaged in
developing these target-validation techniques. Lexicon
Genetics, Exelixis, and Ingenium, for instance,
are using mass mutagenesis on animals such as mice
and zebrafish. In a more focused project, Hypnion is
using forward genetics and other approaches to
understand sleep-wake disorders in mammals.
What benefits lie in wait? By eliminating the false
negatives associated with the current knockout technique,
these new technologies could double or even
triple the number of validated targets, and in that
way save up to $200 million per drug. At the
moment, however, these new kinds of validation
(with the exception of certain chemical genomics
approaches, discussed in the main text) are still
mainly limited to “slow” biology.
Finally, structural biology is used for generating and
analyzing the three-dimensional structure of targets
leads with greater speed and consistency than
before. Guided by more rigorous selection criteria,
the company should go on to improve its success
rates and hence its productivity.
A mere 10 percent improvement in accuracy of
decisions at any stage would confer disproportionately
large benefits. Consider, for example, all the
target/lead pairs that fail just before clinical trials:
if a company were able to decide in just one out of
ten such cases against pursuing the target in the
first place, it would save as much as $100 million
per drug on average. As for INDs that fail clinical
for virtual screening, and is essential to in silico drug
design. Unfortunately, it currently entails protein
crystallization (to prepare the proteins for visualization
by X-ray diffraction), which is a difficult, laborintensive
manual process. Speedier alternatives,
such as NMR spectroscopy, cannot predict overall
protein shape adequately, being restricted to protein
subsegments. As a result, in silico modeling remains
limited in its applicability: the algorithms cannot
boast really high precision for target classes where
no example structures are available.
Several projects, both public and private, are under
way to upgrade structural biology platforms to the
point where they will achieve industrialized scale.
Among the private endeavors is the Novartis Institute
for Functional Genomics, founded by Novartis to
identify and characterize targets using high-throughput
technologies. In the biotech field, Structural
Genomix aims to become a platform provider and
generate revenues by selling protein structures; the
company may also decide to exploit its data inhouse,
and extend into in silico drug design. But it
might be several years before technologies have
developed far enough for the necessary scale effects
to be realized.
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