V27 Cellular Drug Network

27. Lecture WS 2008/09

Bioinformatics III 1

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.

V27 Cellular Drug Network



Drug Target: Enzymes



Drug Target: Enzymes II



Drug Target: Enzymes III



Drug Target: Enzymes III



Drug Target: Receptors I



Drug Target: Receptors II



Drug Target: Receptors III



Drug Target: Receptors III



Drug Target: Ion channels



Drug Target: Transport proteins



Drug Target: DNA/RNA and the ribosome



Drug Target: Targets of monoclonal antibodies



Drug Target: Various physicochemical mechanisms



SummaryMany 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.



Nature Biotech 25, 1119 (2007)



















Outlook

This analysis of the drug-network network suggests a need to update the single

drug–single target paradigm, just as single protein–single 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.



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)



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).



Small molecule-kinase interaction mapCompetition 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)



Small molecule-kinase interaction map



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.



Specificity profiles of clinical kinase inhibitors

Circle size is proportional to binding

affinity (on a log10 scale).



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.



Hierarchical cluster analysis of specificity profiles

Lighter colors correspond to

tighter interactions.

20 kinase inhibitors profiled

against a panel of 113 different

kinases.



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.



Small molecule-kinase interaction mapThe 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.



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)



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



Classification of drug interactions

otherwise 0 and for ,min~

~

YXXYYXXY

YXXY

YXXY

WWWWWW

WWW

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g

gW XYXY

g , gX, gXY : growth of wild-type, with drug X,

and with drugs X and Y

1,min1

,min~

,minFor

YX

YXXY

YXXY

WW

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This scale maps synthetic lethal interactions to = -1,

additive interactions are mapped to = 0,

antagonistic buffering to = 1,

and antagonistic suppression to > 1.



The Prism algorithm



Classification(b–d) 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



Tested drugs



Experimental classification of drug interactionExperimental 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 X+Y, from left

to right.

The interaction of piperacillin with the

50S ribosomal subunit drug erythromycin

is clearly synergistic.



Different modes of interaction

The pairs of antibiotics illustrate synergistic (a), additive (b), antagonistic

buffering (c) and antagonistic suppression (d) interactions



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 < -0.5; pink: -0.5 < emax < -0.25), antagonistic buffering (green: 0.5 < emin < 1.15; light green: 0.25 < emin < 0.5), antagonistic

suppression (blue: emin > 1.15) or additive (white: -0.25 < emax < 0.5 and -0.5 < emin < 0.25). Cases that do not fall into any of these categories are labeled

inconclusive (gray background).



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



Monochromatically interacting functional classesPrism 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.



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.

Documents

V27 Cellular Drug Network