0:32
The second slide is an
outline of the topics
I'd like to cover in this talk.
It essentially
consists of two parts.
The first part, I'll be
talking about academic targets
and the translational gap.
I'll talk about
chemistry and nutrition,
how it's getting worse with time.
And reductionism, genomics,
HTS, are they to blame?
And in this first
half, some comments
about screening diverse compounds.
And an important point is that
screening diverse compounds
really is the worst
way to discover a drug.
In the second part of the talk, I'll
become a little bit more detailed,
talking about what is
a medicinal chemist?
I'll try to get the point
across that I firmly believe
that medicinal chemistry is a
pattern recognition discipline.
I'll go on to some
discussion of biology
and chemistry networks analysis.
And then I'll finish
up with some comments
about ligand efficiency
and selectivity.
1:34
I'll be talking about drivers
for discovery changes.
So we all know that
there's a problem in terms
of productivity in the
drug discovery process.
And it's helpful to try to
analyze where the problems occur,
especially in the
early discovery phase.
And so one can consider
the issue of attrition.
So the loss of compounds
in sort of three buckets.
So the chemistry bucket, a safety
bucket, and an efficacy bucket.
Now the chemistry bucket is
everything about a compound that
might become a drug
potentially, that
can be predicted just from
the chemistry structure alone.
And that's the bucket
where, a priori,
we have the best success rate at
predicting which are going to be
the good compounds, and which
are going to be the compounds
that we probably want
to stay away from.
And this is the area where rules
and filters come into play.
For example, rules and filters
having to do with physical chemical
properties, or structural
features in a compound
that we might want
to stay away from.
And, overall, we're quite
successful with that.
2/3 of the time,
approximately, we can a priori
predict this is going to
be a good compound, or not
such a good compound.
Now the caveat here is
that the predictivity,
the ADME predictivity-- so
that's absorption, distribution,
metabolism, excretion-- that's
what ADME acronym stands for.
It gets worse as the
compounds lie further
and further outside a RO5 space.
So this 2/3 success rate
is for the-- let's call
it the traditional compounds with
good physical chemical properties.
If you're very, very
high in molecular weight,
or very, very high in
lipophilicity or extremely polar,
then the chemistry
predictivity drops off markedly.
Now in a safety bucket, it's still
reasonable, not quite as good
as the chemistry bucket.
But the safety bucket
is everything that
can go wrong in, say pre-clinical,
in vitro assays or animal toxicity,
and even reaching
into the clinic phase.
And one reason why we're
relatively good at this
is that we have quite a good handle
on the major target organ toxicity.
So for example, we have
a lot of experience,
and we know a lot about
pre-clinical assays and animal
tests for hepatotoxicity
and renal toxicity.
And those would be the two
most common causes of toxicity.
Now where you run into a problem,
is if you have toxicity in some area
where either the experience
internally or the literature
precedent is not very good.
And that's why it's
only at about 50%.
Now the area that really
contributes to loss of compounds
in the discovery
process is efficacy.
And of course, we only
find out about efficacy,
once we get into the clinical
phase, usually Phase 2B.
And this is atrocious.
It's not better than 10%.
And in some areas, it's
a lot of worse than 10%.
And this is where the majority
of compounds are lost.
And this issue of inability
to predict clinical efficacy,
reliably, really is
not getting any better.
And so, it is such a bad situation
that it really has, for example,
senior executives in drug
discovery tearing their hair out.
How are they going to handle it?
And so what has happened is, one
solution that's in play right now
is to tackle efficacy using
academic collaborations.
So the idea is, we probably
will have the greatest success
in clinical efficacy,
if we work in an area
where our biology knowledge
is rich, where we know
as much about the target as
possible, potential target, as much
about the disease as possible.
Well, where are we going
to find those people?
Well, you're going to find them in
academia, because they're supported
in the US by the National
Institutes of Health.
And so, in that sense, having
very close collaborations
with academic, primarily biology
experts, is a real advantage.
And the alternatives, really in
theory, there are alternatives.
But in practice, there aren't.
So for example, many people
believe that systems biology,
the knowledge of how signaling
networks actually exist in a human,
in a disease, if we really
understood that, then we could
rationally, for example,
choose targets and have
a much higher
probability of efficacy.
But despite, you know, now
multiple decades of work,
it's a very, very complex problem.
And we're really not there yet.
So this bottom line of academic
collaborations, many people
believe that target quality is
most likely from rich biology.
And that really means
collaborations, either
with the academics, or potentially
with a small biotech start-up that
has some-- maybe it's a spin
out from an academic profession.
