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. Bioinformatics III

2. Drug Target: Enzymes Bioinformatics III

3. Drug Target: Enzymes II Bioinformatics III

4. Drug Target: Enzymes III Bioinformatics III

5. Drug Target: Enzymes III Bioinformatics III

6. Drug Target: Receptors I Bioinformatics III

7. Drug Target: Receptors II Bioinformatics III

8. Drug Target: Receptors III Bioinformatics III

9. Drug Target: Receptors III Bioinformatics III

10. Drug Target: Ion channels Bioinformatics III

11. Drug Target: Transport proteins Bioinformatics III

12. Drug Target: DNA/RNA and the ribosome Bioinformatics III

13. Drug Target: Targets of monoclonal antibodies Bioinformatics III

14. Drug Target: Various physicochemical mechanisms Bioinformatics III

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. Bioinformatics III

16. Nature Biotech 25, 1119 (2007) Bioinformatics III

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25. 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. Bioinformatics III

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 anticancerdrugs. - 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) Bioinformatics III

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). Bioinformatics III

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) Bioinformatics III

29. Small molecule-kinase interaction map Bioinformatics III

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. Bioinformatics III

31. Specificity profiles of clinical kinase inhibitors Circle size is proportional to binding affinity (on a log10 scale). Bioinformatics III

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. Bioinformatics III

33. Hierarchical cluster analysis of specificity profiles Lighter colors correspond to tighter interactions. 20 kinase inhibitors profiled against a panel of 113 different kinases. Bioinformatics III

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. Bioinformatics III

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. Bioinformatics III

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) Bioinformatics III

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 Bioinformatics III

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  > 1. Bioinformatics III

39. The Prism algorithm Bioinformatics III

40. 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 Bioinformatics III

41. Tested drugs Bioinformatics III

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 X+Y, from left to right. The interaction of piperacillin with the 50S ribosomal subunit drug erythromycin is clearly synergistic. Bioinformatics III

43. Different modes of interaction The pairs of antibiotics illustrate synergistic (a), additive (b), antagonistic buffering (c) and antagonistic suppression (d) interactions Bioinformatics III

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 < -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). Bioinformatics III

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 Bioinformatics III

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. Bioinformatics III

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. Bioinformatics III