How many drug targets are there?

25. Lecture WS 2006/07

Bioinformatics III 1

How many drug targets are there?

In 2002, after the sequencing of the human genome, others arrived at ~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

pharmaceuticals2. And on the basis of 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.



Drug Target: Enzymes



Drug Target: Enzymes II



Drug Target: Enzymes III



Drug Target: Receptors I



Drug Target: Receptors II



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



OutlookA large part of this paper is concerned with the nature of drug targets and the need to consider the dynamics of the drug–targets (plural intended) interactions, as theseconsiderations were used to define what we would eventually count. Many successful drugs have emerged from the simplistic ‘one drug, one target, one disease’ approach that continues to dominate pharmaceutical thinking, and we have generally used this approach when counting targets here. However, there is an increasing readiness to challenge this paradigm. We have discussed its constraints and limitations in light of the emerging network view of targets. The recent progress made in our understanding of biochemical pathways and their interaction with drugs is impressive.

However, it may be that ‘the more you know, the harder it gets’. It is not the final number of targets we counted that is the most important aspect of this Perspective; rather, we stress how considerations about what to count can help us gauge the scope and limitations of our understanding of molecular reaction partners of active pharmaceutical ingredients. Targets are highly sophisticated, delicate regulatory pathways and feedback loops but, at present, we are still mainly designing drugs that can single out and, as we tellingly say, ‘hit’ certain biochemical units — the simple definable, identifiable targets as described here. This is not as much as we might have hoped for, but in keeping with the saying of one of earliest medical practitioners, Hippocrates:“Life is short, and art long; the crisis fleeting; experience perilous, and decision difficult.”Humility remains important in medical pharmaceutical sciences and practice.



Specific example: protein kinasesPhosphorylation of serine, threonine, and tyrosine 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.

Small-molecule inhibitors of the Abelson tyrosine kinase (Abl) and the epidermal growth factor

receptor (EGFR) 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 kinasesStructural and mutagenesis studies have revealed key determinants of kinase inhibitor

selectivity, including a widely exploited filter in the ATP binding site known as the

„gatekeeper“.

A compact gatekeeper (such as threonine) allows bulky aromatic substituents, such as those

found in the Src family kinase inhibitors, PP1 and PP2, to enter a deep hydrophobic pocket. In

contrast, larger gatekeepers (methionine, leucine, isoleucine, or phenylalanine) restrict access

to this pocket. A small gatekeeper provides only partial discrimination between kinase active

sites, however, as ca. 20% of human kinases have a threonine at this position. Gleevec, a

drug used to treat chronic myelogenous leukemia, exploits a threonine gatekeeper in the Abl

kinase domain, yet it also potently inhibits the distantly related tyrosine kinase, c-KIT, as well

as the platelet-derived growth factor receptor (PDGFR).



Selection of gatekeeper residue



Outlook

In this study, we have rationally designed halomethylketone-substituted

inhibitors whose molecular recognition by protein kinases requires the

simultaneous presence of two selectivity filters: a cysteine following the

glycine-rich loop and a threonine in the gatekeeper position.

We estimate that ca. 20% of human kinases have a solvent-exposed cysteine in

the ATP pocket. Because of the structural conservation of the pocket, it should

be possible to predict the orientation of these cysteines.

In addition, there are many reversible kinase inhibitors whose binding modes

have been characterized by x-ray crystallography.

The integration of both types of information should allow the design of scaffolds

that exploit selectivity filters other than the gatekeeper, as well as the

appropriate sites for attaching electrophilic substituents.



Small molecule-kinase interaction map

Figure 1. Competition binding assay for

measuring the interaction between unlinked,

unmodified ('free') small molecules and kinases.

(a) Schematic overview of the assay. The phage-

tagged kinase is shown in blue, 'free' test compound

in green and immobilized 'bait' ligand in red. (b)

Binding assay for p38 MAP kinase. The immobilized

ligand was biotinylated SB202190. The final

concentration of test compounds during the binding

reaction was 10 M. (c) Determination of quantitative

binding constants. Binding of tagged p38 to

immobilized SB202190 was measured as a function

of unlinked test compound concentration. Tagged

p38 kinase was quantified by real-time quantitative

PCR and the results normalized.

Fabian et al. Nature Biotech 23, 329 (2005)




Each kinase represented in the assay panel is

marked with a red circle. Gene symbols for

kinases in the panel are shown in Figure 5.

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.




Figure 3. Specificity profiles of

clinical kinase inhibitors.

Circle size is proportional to binding

affinity (on a log10 scale).

Binding constants were measured at

least in duplicate for each interaction

identified in the primary screen.



Distribution of binding constants

For each compound the

pKd (-log Kd) was plotted for all

targets identified.

Primary targets, as shown in

Table 1, are in blue, and off-

targets in red.

Staurosporine does not have a

particular primary target or

targets, and 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.

(a) Twenty kinase inhibitors

profiled against a panel of 113

different kinases.



Small molecule-kinase interaction mapWe have described a systematic small molecule−kinase interaction map for clinical kinase

inhibitors. Integration of the information provided here with results from cell-based or animal

studies, and ultimately with clinical observations, should enable a more complete understanding

of the biological consequences of inhibiting particular combinations of kinases.

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.

This knowledge should enable the development of inhibitors with 'appropriate' specificity that

target multiple kinases involved in the disease process while avoiding kinases implicated in side

effects. The ability to rapidly screen compounds against multiple kinases in parallel and the

incorporation of specificity profiling during initial lead discovery and optimization should greatly

facilitate and accelerate the drug development process.



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

(for example, 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, and the large, uniform data set presented here

should serve as a valuable training set for computation-based inhibitor design.

Finally, the use of phage-tagged proteins in quantitative biochemical assays circumvents the

need for conventional protein production and purification, and should help reduce one of the

major bottlenecks in modern proteomics and drug discovery research.



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 interactionSynergistic: larger-than-additive effectAntagonistic: smaller-than-additive effect



Classification of drug interactions

otherwise 0 and for ,min~

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YXXYYXXY

YXXY

YXXY

WWWWWW

WWW

WWW

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

WWW

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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 (red lines) and antagonistic interactions (green lines)

between drugs (black circles) can be clustered into functional classes that interact with each other

monochromatically (that is, with purely synergistic or purely antagonistic interactions between any two

classes; c). This classification generates a system-level perspective of the drug network (d). (e,f) Two

independent observations indicate whether a new drug (Z) will be clustered into a particular drug class

(a, 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).



Tested drugs



Experimental classification of drug interactionFigure 2 Experimental classification of drug interactions into four 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 two 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 f, X, Y and X+Y, from left to

right, respectively. Note the contrast

between the interactions of piperacillin with

the 50S ribosomal subunit drug

erythromycin (a, ERY-PIP, synergistic) and

the 30S ribosomal subunit drug tetracycline

(c, TET-PIP, antagonistic).



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.

(a) The unclustered network of drug-drug

interactions with synergistic (red),

antagonistic buffering (green) and

antagonistic suppression (blue) links.



Monochromatically interacting functional classes(b) Prism algorithm classification of drugs

into monochromatically interacting

functional classes.

This unsupervised clustering shows good

agreement with known functional

mechanism of the drugs (single letter

inside each node; see Table 1).

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.



(c) Larger ellipses show higher-level

classification of DNA gyrase inhibitors (D)

with inhibitors of biosynthesis of DNA

precursors (F) and classification of the

two subclasses of drugs involved in the

inhibition of protein synthesis via the 50S

ribosomal subunit (R).

System-level interactions between the drug classes



Outlook

We provide a complete and systematic analysis of a drug-drug interaction network.

Systems analysis of the 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. Our systems

approach is general in nature and could be applied to other biological systems.

It would be particularly useful if the approach could be generalized to in vivo studies and

to a wider range of phenotypes despite added complexity of host-drug interaction.

Furthermore, 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.

How many drug targets are there?

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