Title: Medicinal chemistry meets systems biology
1Medicinal chemistry meets systems biology
John Harris, cjh Consultants (Founder and
consultant to BioFocus)
Cutting Edge Approaches to Drug Design MGMS,
March 2009 School of Oriental and African
Studies, University of London
2Why should drug discoverers bother about
biological networks?
- nearly all drugs can hit more than one effector
target in an organism - not all non-target effectors are off-targets,
metabolic systems or transporters - accumulated genomic/proteomic/analytical
pharmacological knowledge confirm that several
highly efficacious drugs exert their overall
therapeutic effect through a network of effectors - the output of the network determines the drug
profile (i.e. its good points and its bad points)
3How should they respond to the challenges of
biological networks?
- 1970-1990 clinical success driven by
selectivity for single targets (e.g. h2
antagonists, AII inhibitors). Medchem is driven
by isolated enzyme assays or analytical
pharmacology. - 1990-2000 as therapeutic targets become more
challenging, high-throughput screening, fed by
massively combinatorial chemistry, drives
expectations upwards BUT the same technology
demands assay systems even less related to the
constituted organism! - 2000- 2005 unmet expectations drive a much
more focused approach to screening but compounds
are still, essentially, optimised against single
reductionist assays. - 2005- present increasing realisation that
reductionist assays do not predict cell network
responses primary cell screening begins to gain
ground.
4Many clues along the way..
- most of the clinically effective antipsychotics
require polypharmacological mechanisms
(clozapine, a broad-spectrum biogenic amine
ligand, is as effective as 5HT2a selective
atypical antipsychotics such as olanzapine,
ziprasidone, etc. (see Roth et al.,
2004NatureRevDrugDiscovery353)
- in anti-infective therapy, polypharmacology is
common, e.g. Wellcomes Septrin (trimethoprim and
sulfamethoxazole hitting the bacterial
network) or various HIV therapies (NNRTIs and
protease inhibitors)
- more recently, one of the earliest
clinically-successful anticancer kinase
inhibitors, Sutent, has been shown to be one of
the least selective across the kinome
5(Sauer et al., Science (2007), 316, 550) "The
reductionist approach has successfully identified
most of the components and many of the
interactions but, unfortunately, offers no
convincing concepts or methods to understand how
system properties emerge...the pluralism of
causes and effects in biological networks is
better addressed by observing, through
quantitative measures, multiple components
simultaneously and by rigorous data integration
with mathematical models"
Systems Biology and Network Pharmacology are now
very well established BIOLOGICAL activities in
academia and, increasingly, in pharma and
biotech. They are driven by major technology
advances in high-content cell screening, cellular
disease modelling and data handling/knowledge
extraction.
Whither systems medchem?
6How should the medicinal chemist respond?
- Historically screen in a black box
empirical SAR but high relevance and guaranteed
efficacy - Contemporary screen target in isolation
precision SAR but relevance and efficacy
unclear - The compromise take secondary screening into
the cellular context (still much scepticism about
primary cellular screening!) really depends on
the degree to which the cell assays reproduce the
target disease
- So how DO we blend the efficacy lessons of the
past, underpinned by network pharmacology
evidence, with modern screening and secondary
assay technologies? - How much must we change our mindset? After all,
we optimise activity and ADME/PK more or less in
parallel these days is an extra parallel target
or two a quantum leap?
7Kinases show the way forward?
- Clinically effective first generation oncology
drugs (e.g. Sutent, Sorafenib) act at
several/multiple target kinases and mutants - These earlier multiple kinase inhibitors (MKIs)
were discovered serendipitously (see
2006NatureReviewsDrugDisc835) - How do we discover and design MKIs rationally?
(see 2010JMC1413) - The challenges
- Multiple target discovery theoretical and
analytical - Lead discovery cross-screening fragment
re-assembly chemoinformatics - Lead optimisation
- balance of activities into the nearly-unknown
- balance of physicochemical properties
- balance of off-target activities
8It can be done!
Lapatanib designed to hit EGFR and ErbB2 in
order to cover a wider range of tumour types
(see 2005Drugs of the Future1225)
9Target Discovery Approaches
- In silico
- predict therapeutically useful combination of
targets by network modelling and simulation - correlate with known drug profiles, protein
interaction fingerprints, biomarker data - key input from broad chemogenomic databases
which correlate high-quality assay data and in
vivo data (pre-clinical and clinical) with
specific targets - In vitro
- Isolated enzyme profiling is arguably too
reductionist at best can only point to possible
targets or pathways - cell lysate fishing using ligand probes is a
better indication especially if studying
affinities and response time-course (e.g.
Kinaxos KinAffinity, Cellzomes BioBeadsTM - High-content screening in cellular disease
models, tracking networks, not just specific
targets - Counter-screening using characterised probes
10Fesik et al. 2006Oncogene1340 Akt-co-operating
kinases
- A-443654 was counterscreened against 768-O cells
transfected with a kinome-wide (443 kinases 64
orphans) siRNA library - Akt-dependent apoptosis and blockade of critical
Akt signalling pathway nodes were both sensitised
by siRNAs encoding CK3g1 and IMPK (inositol
polyphosphate multikinase)
11Lead Discovery Approaches
- In general, diversity screening against multiple
targets may be even less cost-effective that
against single targets, and key intra-family SAR
is unlikely to be revealed. Pre-filtering based
on overlapping pharmacophores a better bet - A rational approach to MKIs is possible
- Feasibility assessment
- b) Focused screening library and fragment
cross-screening
12Ligand SAR and cross-family common site sequences
Recent evidence supports the view that, within
protein families that have a common site of
action, similar ligands tend to bind to similar
family members (see Bamborough, 2008JMC7898
Vieth, 2005DDT839) BioFocus has developed a
simple roadmap based on the common geometry of
the kinase ATP site (activated state) which
enables quick assessment of multitarget SAR
crossover feasibility
13Feasibility assessment A dual inhibitor of
LimK1 LimK2?
14SoftFocus library screening gave 3 scaffolds but
only two subsite-selective fragment classes
(based on homology model docking)
15Best dual activity for these U-shaped compounds
was 12-fold (in either direction) Subsequent
report showed nanomolar dual (equipotent)
inhibition for a series of linear compounds of
general structure
16Focused library cross-screening
- Despite close sequences, SAR relationships for
similar kinases remain unpredictable, especially
where overt or more subtle differences in binding
mode occur - Many organisations have built up vast datasets
of cross-screened kinase inhibitors which can be
mined for MKI leads - IPR break-out is more of a problem with this
strategy though
17Fragment screening and cross-screening
- Increasingly popular approach well suited to MKI
design - Fragments sample greater chemical space and
allow identification of preferred monomers and/or
monomers which may not be picked up when
screening a more decorated system - Fragments have no pre-determined second
vectors and are able to probe sub-sites more
extensively - Compounds can be grown from common scaffolds
or pre-determined privileged sub-site fragments - Larger fragments can be screened in biochemical
assays
18ThemePair Fragment Libraries
- Small fragments
- 1 component system
- Require affinity-based techniques (HTX-ray, NMR,
SPR)
- ThemePair Fragment library compounds
- 2 component systems
- lead-like compounds, good ligand efficiency,
solubility HC-biochemical screening or affinity
methods
- Traditional focused library compounds
- 3 component systems
- more likely to give potency in biochemical
screens - less likelihood/compound of multitarget SAR
19Addressing multi-targeting in a rational way
using designed fragment libraries such as
ThemePair Fragments
Illustrative simplistic scenario
Primary kinase target Scaffold X, sidechains a-g
Secondary kinase target Scaffold X, sidechains
e-h
Exclusion kinase target Scaffold X, Sidechains
a,c,j
Therefore, profitable SAR area for selective
multi-targeted inhibitor is scaffold X combined
with sidechains e,f and g
20In reality, likely that similar scaffolds will
show similar SAR at the themepair fragment level
- Favoured area of space for required hit profile
- Can provide a menu of scaffold and
side-chain/monomer types
21An example BioFocus library TPF11
- large library (ca. gt700 compounds) based on two
cores and 9 scaffolds extensively elaborated at a
single position so that the scaffold becomes the
effective second variable. These compounds are
not reported in SciFinder or by commercial
supplier
22- Lead optimisation where (medchem) going gets
tough! -
- Balance of activities into the nearly-unknown
until more data are available from network
biomarker and enzyme-occupancy studies, balanced
potency is the best guess very high
multipotency may well not be required - Balance of physicochemical properties tricky
for MKIs where structural additivity tends to
correlate with selectivity however, deliberate
choice of overlapping pharmacophores helps
non-oncology applications are more challenging - Balance of off-target activities this issue is
no different in principle to that for so-called
selective kinase inhibitors, of which there are
not many. Isolated enzyme assays are, at best, an
approximate guide to undesirable intra-family
activities. Monitoring cellular target/s activity
against in vitro and in vivo toxicity readouts
are essential in lead optimisation.
23Facilitating parallel lead optimisation
- Parallel optimisation is the ideal
- This is the area of greatest current medchem
caution! Lead optimisation against more than one
non-ADMET/PK target is somewhat foreign to
current practice, at least outside the kinase
area. - Biochemical and cellular kinase assays need to
be run in close conjunction with each other, even
more so than for monovalent kinase inhibitors
HCS technologies are beginning to impact
optimisation in this way. Cellular assays can
also measure inhibitory mechanisms which are
missed by current biochemical methods - Cross-target SARs are, by their nature, more
complex than single-target SARs and compromises
are generally to be expected - Therefore it is very important to qualify these
SAR compromises, preferably in cellular disease
models or even primary cells
24In Oncology MKIs will become the norm in the
kinase inhibitor field combination therapy and
MTDs with kinase and synergising non-kinase drugs
will emerge In Inflammation Certain MKIs will
make it to clinic and safety assessments will be
very interesting. For example, Palau have DD-2, a
dual Jak3/Syk inhibitor in preclinical for
autoimmune diseases and there are unpublished
data for related approaches Whither other
complex multifaceted diseases?
25- Additional references
- Network pharmacology the next paradigm in drug
discovery, A L Hopkins, Nature Chemical Biology,
2008, 682. - What does Systems Biology mean for drug
discovery A Schrattenholz, Vukic Soskic, Current
Medicinal Chemistry, 2008, 1520. - Designed Multiple Ligands. An emerging drug
discovery paradigm Richard Morphy, Zoran
Rankovic, J Med. Chem., 2005, 6523. - The physicochemical challenges of designing
multiple ligands Richard Morphy, Zoran Rankovic,
J Med. Chem., 2006, 4961. - Logic models of pathway biology, Steven
Watterson, Stephen Marshall, Peter Ghazal, Drug
Discovery Today, 2008, 447. - Can we rationally design promiscuous drugs A L
Hopkins, J S Mason, J Overington, Current Opinion
in Structural Biology, 2006, 127. - Discovery of multitarget inhibitors by combining
molecular docking with common pharmacophore
features D Wei, X Jiang, L Zhou, J Chen, Z Chen,
C He, K Yang, Y Liu, J Pei, L Lai, J Med. Chem.,
2008, 7882. - Selectively Nonselective Kinase Inhibition
Striking the Right Balance R Morphy, J Med
Chem.,.2010, 1413.
26Acknowledgements for helpful discussions Rich
ard Morphy (Schering-Plough) Kate Hilyard,
Chris Newton (BioFocus) Ian James (Almac
Biosciences) John Overington (EMBL
Cambridge) Colin Telfer, Finbarr Murphy (Lee
Oncology)