V27 Cellular Drug Network

1
V27 Cellular Drug Network
How many drug targets are there? in 2002 8,000
targets of pharmacological interest, of which
nearly 5,000 could be potentially hit by
traditional drug substances, nearly 2,400 by
antibodies and 800 by protein pharmaceuticals.
Based on ligand-binding studies, 399 molecular
targets were identified belonging to 130 protein
families, and 3,000 targets for small-molecule
drugs were predicted to exist by extrapolations
from the number of currently identified such
targets in the human genome.
2
Drug Target Enzymes
3
Drug Target Enzymes II
4
Drug Target Enzymes III
5
Drug Target Enzymes III
6
Drug Target Receptors I
7
Drug Target Receptors II
8
Drug Target Receptors III
9
Drug Target Receptors III
10
Drug Target Ion channels
11
Drug Target Transport proteins
12
Drug Target DNA/RNA and the ribosome
13
Drug Target Targets of monoclonal antibodies
14
Drug Target Various physicochemical mechanisms
15
Summary
Many successful drugs have emerged from the
simplistic one drug, one target, one disease
approach that continues to dominate
pharmaceutical thinking. However, there is an
increasing readiness to challenge this paradigm
in favor of the emerging network view of targets.
However, it may be that the more you know, the
harder it gets. Targets are highly
sophisticated, delicate regulatory pathways and
feedback loops. But, at present, we are still
mainly designing drugs that can single out and
hit certain biochemical units the simple
definable, identifiable targets.
16
Nature Biotech 25, 1119 (2007)
17
(No Transcript)
18
(No Transcript)
19
(No Transcript)
20
(No Transcript)
21
(No Transcript)
22
(No Transcript)
23
(No Transcript)
24
(No Transcript)
25
Outlook
This analysis of the drug-network network
suggests a need to update the single drugsingle
target paradigm, just as single proteinsingle
function relations are somewhat limited to
accurately describe the reality of
cellular processes. Future attempts at rational
drug design will eventually take into account the
systems effects of a drug on the greater
network upstream and downstream of the actual
drug target, which could pave the way to more
specific drugs for diseases.
26
Specific example protein kinases
Phosphorylation of Ser, Thr, and Tyr residues is
a primary mechanism for regulating protein
function in eukaryotic cells. Protein kinases,
the enzymes that catalyze these reactions,
regulate essentially all cellular processes and
have thus emerged as therapeutic targets for many
human diseases. What are the uses of selective
inhibitors? - Small-molecule inhibitors of the
Abelson tyrosine kinase and the epidermal growth
factor receptor have been developed into
clinically useful anticancer drugs. - Selective
inhibitors can also increase our understanding of
the cellular and organismal roles of protein
kinases. However, nearly all kinase inhibitors
target the adenosine triphosphate (ATP) binding
site, which is well conserved even among
distantly related kinase domains. For this
reason, rational design of inhibitors that
selectively target even a subset of the 491
related human kinase domains continues to be a
daunting challenge.
Cohen et al. Science 308, 1318 (2005)
27
Specific example protein kinases
Structural and mutagenesis studies a key
determinant of kinase inhibitor selectivity is a
structural filter (residue) in the ATP binding
site known as the gatekeeper. A compact
residue at this position (such as Thr in 20 of
all human kinases) allows bulky aromatic
substituents, such as those found in the Src
family kinase inhibitors, PP1 and PP2, to enter a
deep hydrophobic pocket. However, such a small
gatekeeper provides only partial discrimination
between kinase active sites. In contrast, larger
residues (Met, Leu, Ile, or Phe) restrict access
to this pocket. Gleevec is a drug to treat
chronic myelogenous leukemia. It exploits a Thr
gatekeeper in the Abl kinase domain. But it also
potently inhibits the distantly related tyrosine
kinase, c-KIT, as well as the platelet-derived
growth factor receptor (PDGFR).
28
Small molecule-kinase interaction map
Competition binding assay for measuring the
interaction between unlinked, unmodified ('free')
small molecules and kinases.(a) Schematic
overview of the assay. Blue the phage-tagged
kinase Green 'free' test compound in green Red
immobilized 'bait' ligand. (b) Binding assay
for p38 MAP kinase. The immobilized ligand was
biotinylated SB202190. (c) Determination of
quantitative binding constants. Binding of tagged
p38 to immobilized SB202190 was measured as a
function of unlinked test compound concentration.
Fabian et al. Nature Biotech 23, 329 (2005)
29
Small molecule-kinase interaction map
30
Small molecule-kinase interaction map
Each kinase represented in the assay panel is
marked with a red circle. TK, nonreceptor
tyrosine kinases RTK, receptor tyrosine
kinases TKL, tyrosine kinase-like kinases CK,
casein kinase family PKA, protein kinase A
family CAMK, calcium/calmodulin dependent
kinases CDK, cyclin dependent kinases MAPK,
mitogen-activated protein kinases CLK, CDK-like
kinases.
31
Specificity profiles of clinical kinase inhibitors
Circle size is proportional to binding affinity
(on a log10 scale).
32
Distribution of binding constants
For each compound the pKd (-log Kd) was plotted
for all targets identified. Blue primary
targets, Red off-targets in red.
Staurosporine does not have a particular primary
target or targets. The primary targets for
BAY-43-9006 (RAF1) and LY-333531 (PKC ) were not
part of the assay panel.
33
Hierarchical cluster analysis of specificity
profiles
Lighter colors correspond to tighter
interactions. 20 kinase inhibitors profiled
against a panel of 113 different kinases.
34
Summary
Presented was a systematic small molecule-kinase
interaction map for clinical kinase inhibitors
with the aim of providing a more complete
understanding of the biological consequences of
inhibiting particular combinations of kinases.
In future also integrate this information with
results from cell-based or animal studies, and
ultimately with clinical observations. Binding
profiles for larger numbers of chemically diverse
compounds, combined with the phenotypes elicited
by these compounds in biological systems, will
help identify kinases whose inhibition leads to
adverse effects, kinases that are 'safe' to
inhibit and combinations of kinases whose
inhibition can have a synergistic beneficial
effect in particular disease states. ? develop
inhibitors with 'appropriate' specificity that
target multiple kinases involved in the disease
process while avoiding kinases implicated in side
effects.
35
Small molecule-kinase interaction map
The kinase binding profiles also provide valuable
information to guide structural studies. - In
many cases kinases that tightly bind the same
compound have no obvious sequence similarity,
e.g., p38 and ABL(T315I) binding to BIRB-796. -
In other cases, compounds can discriminate
between kinases closely related by sequence, such
as imatinib binding to LCK but not SRC. ABL and
the imatinib-resistant ABL mutants are of
particular structural interest because some
compounds bind with good affinity to all forms
(e.g., ZD-6474), whereas BIRB-796 has a strong
preference for a particular mutant. Key
insights should result from an analysis of
selected co-crystal structures of kinase-compound
combinations identified through profiling
studies. Also, this large, uniform data set may
serve as a valuable training set for
computation-based inhibitor design.
36
Multidrug treatments are increasingly important
in medicine and for probing biological systems.
But little is known about the system properties
of a full drug interaction network. Epistasis
among mutations provides a basis for analysis of
gene function. Similarly, interactions among
multiple drugs provide a means to understand
their mechanism of action. Aim derive a
pairwise drug interaction network.
Yeh et al. Nature Genetics 38, 489 (2006)
37
Different ways of drug interaction
Clustering of individual drugs into functional
classes solely on the basis of properties of
their mutual interaction network. Schematic
illustration of additive, synergistic and
antagonistic interactions between drugs X and Y
by measurements of bacterial growth under the
following conditions no drugs, drug X only,
drug Y only, and both drugs X and Y.
Additive no interaction Synergistic
larger-than-additive effect Antagonistic
smaller-than-additive effect
38
Classification of drug interactions
g ?, gX, gXY growth of wild-type, with drug
X, and with drugs X and Y
This scale maps synthetic lethal interactions to
? -1, additive interactions are mapped to ?
0, antagonistic buffering to ? 1, and
antagonistic suppression to ? gt 1.
39
The Prism algorithm
40
Classification
(bd) A network (b) of synergistic interactions
and antagonistic interactions between drugs can
be clustered into functional classes that
interact with each other monochromatically. (d)
This classification generates a system-level
perspective of the drug network. (e,f) Two
independent observations indicate whether a new
drug (Z) will be clustered into a particular drug
class (dashed oval) mixed synergistic and
antagonistic intraclass interactions of Z with a
(e, thin dotted green and red lines) and
nonconflicting interclass interactions of Z (e,
dotted thin lines) and a (e, dotted thick lines)
with all other classes. Both intra and interclass
indications are depicted in e, and the drug is
clustered (black arrow) with an existing class.
If drug Z has no such intra- or interclass
association with any existing drug class, the
drug will be clustered in a new class (f).
black circles drugs red lines synergistic
interactions green lines antagonistic
interactions
41
Tested drugs
42
Experimental classification of drug interaction
Experimental classification of drug interactions
into 4 types using bioluminescence measurements
of bacterial growth in the presence of sublethal
concentrations of antibiotics. (a) The pairs of
antibiotics illustrate synergistic interactions.
The number of bacteria (proportional to
bioluminescence counts per second (c.p.s.) is
shown from 2 replicates, for control with no
drugs (f, solid black lines), each single drug
(X, Y blue and magenta lines) and the
double-drug combination (X Y, dashed black
lines).
Insets normalized growth rates (W) with error
bars for ?, X, Y and XY, from left to right.
The interaction of piperacillin with the 50S
ribosomal subunit drug erythromycin is clearly
synergistic.
43
Different modes of interaction
The pairs of antibiotics illustrate synergistic
(a), additive (b), antagonistic buffering (c) and
antagonistic suppression (d) interactions
44
Systematic measurements of pairwise interactions
between antibiotics
(a) Growth measurements and classification of
interaction for all pairwise combinations of
drugs X and Y. Within each panel, the bars
represent measured growth rates for, from left to
right no drugs (f), drug X only, drug Y only and
the combination of the two drugs X and Y (see
inset). Error bars represent variability in
replicate measurements.
The background color of each graph designates the
form of epistasis according to the scale in b
synergistic (red emax lt -0.5 pink -0.5 lt emax
lt -0.25), antagonistic buffering (green 0.5 lt
emin lt 1.15 light green 0.25 lt emin lt 0.5),
antagonistic suppression (blue emin gt 1.15) or
additive (white -0.25 lt emax lt 0.5 and -0.5 lt
emin lt 0.25). Cases that do not fall into any of
these categories are labeled inconclusive (gray
background).
45
Classification into interaction classes
Unsupervised classification of the antibiotic
network into monochromatically interacting
classes of drugs with similar mechanisms of
action. Shown is the unclustered network of
drug-drug interactions. red synergistic links,
green antagonistic buffering, blue
antagonistic suppression
46
Monochromatically interacting functional classes
Prism algorithm classifies drugs into
monochromatically interacting functional classes.
This unsupervised clustering shows good
agreement with known functional mechanism of the
drugs (single letter inside each node).
Bleomycin (BLM), which is believed to affect
DNA synthesis, although its mechanism is not well
understood, cannot be clustered monochromatically
with any other class. The multifunctional drug
nitrofurantoin (NIT) shows non-monochromatic
interactions.
47
Summary
Systems analysis of the drug-drug interaction
network demonstrates that drugs can be classified
according to their action mechanism based on
their interactions with other functional drug
classes. The ability to classify drug function
based solely on phenotypic measurements and
without the tools of biochemistry or microscopy
can provide a simple and powerful method for
screening new drugs with multiple or novel
mechanisms of action. Applying network
approaches to drug interactions may help suggest
new drug combinations and highlight the
importance of gene-environment interactions,
including, in particular, the resistance and
persistence of bacteria to antibiotics and of
cancer cells to antitumor drugs.