Thursday, July 14, 2011
1. Booting up the computers.
David Swinney's paper in Nature Reviews Drug Discovery suggests ways to
develop and use complex assays that efficiently identify a target and decrease
late-stage attrition,1 some biotechs think a better approach to increasing
R&D productivity would rely on computational methods to identify entire
networks of interacting proteins that drive disease.
"A key reason for late-stage failure may well be the focus of many
developers on single therapeutic targets as the key drivers of disease. That
approach ignores a fundamental fact of biology: most diseases are driven by a
highly complex network of interacting pathways that can, in effect, compensate
for the effects at any one target. Therefore it is unlikely that focusing on a
single target within that network will give the strongest or safest therapeutic
effect," said Malcolm Young, CEO of e-Therapeutics Ltd.
with that problem, Young and colleagues at e-Therapeutics are taking a network
pharmacology approach, "which uses computational methods in combination
with in vitro
and in vivo
assays to generate a so-called molecular footprint of the ideal therapeutic for
a given disease," said Young.5
In cancer, for example, "we first of all use a computational step
to identify proteins that could be targeted to induce apoptosis in tumor cells.
That is, from the outset we are focused not on a single target but on multiple
targets or nodes that occur in a network of proapoptotic pathways. An analysis
of that network then yields a molecular footprint or signature, revealing which
node or nodes would be the most effective to hit to get a therapeutic effect.
We then use that signature to help us identify and/or design compounds that hit
all or most of those nodes simultaneously," said Young.
company's lead compound, ETS2101, resulted from that process. The
small molecule is in preclinical testing to treat metastatic cancer. ETS2101's
mechanism is undisclosed.
course, we used phenotypic and target-based screens as part of our
identification of ETS2101, but we did so only in tandem with a rigorous
computational approach that gave us insight into the network complexity
underlying cancer," said Young.
e-Therapeutics uses computational techniques to tackle the biological
complexity of targets, BioLeap Inc. uses the computer to deal with the
complexity associated with target-ligand binding.
our opinion, a key reason for failure in late-stage trials is that the binding
mechanism of ligands to the target has been poorly worked out at the
preclinical stage," said CEO David Pompliano. "There's often too much
of a rush to identify a compound that simply binds with high affinity to the
target, without accounting for subtle binding interactions that determine the
residence time of a compound at its target and, ultimately, affect key clinical
parameters" such as pharmacokinetics and pharmacodynamics.
work out those subtle binding interactions, BioLeap uses computational methods
to generate small molecular weight fragments in silico that are ranked according to their
ability to bind and modulate the X-ray crystal structure of a target. The best
candidates are then synthesized by design chemists and further tested in
phenotypic and target-based assays.
BioLeap is pursuing a variety of preclinical targets to treat multiple
diseases, including cancer, infectious diseases and metabolic diseases.