DNA-encoded chemical libraries of macrocycles

Available online at www.sciencedirect.com

ScienceDirect DNA-encoded chemical libraries of macrocycles William H Connors, Stephen P Hale and Nicholas K Terrett Address Ensemble Therapeutics, Corp., 99 Erie Street, Suite B, Cambridge, MA 02139, United States

synthesized in a combinatorial fashion and collectively screened becomes increasingly attractive.

Corresponding author: Connors, William H ([email protected])

An alternative class of macrocycle is based on peptidic epitopes, in many cases the natural binding region in the PPI’s of interest. Harnessing peptide pharmacophores responsible for binding and the transformation of these into cyclic structures results in peptidomimetic macrocycles [7,8]. Nature has successfully created its own peptidomimetic macrocycles, like the immunosuppressant, cyclosporine A, and the antibiotics Colistin and Nisin. The advantages of macrocyclization over their linear equivalents includes: lower susceptibility to proteolytic cleavage [9,10], increased bioavailability [11] and reduced entropic penalty incurred during binding [12]. Much like natural product macrocycles, developing analogs of synthetic peptidomimetic macrocycles is a formidable challenge, due in part to difficulties in predicting the unique folding mechanisms which are responsible for function, as well as cell permeability [13]. Taking advantage of high-throughput synthesis and screening technologies like DNA-encoded chemistry and affinitybased screening allows for the generation and interrogation of large macrocyclic libraries for hit discovery with subsequent hit-optimization to drug-like molecules occurring downstream.

Current Opinion in Chemical Biology 2015, 26:42–47 This review comes from a themed issue on Next generation therapeutics Edited by Dario Neri and Jo¨rg Scheuermann

http://dx.doi.org/10.1016/j.cbpa.2015.02.004 1367-5931/# 2015 Elsevier Ltd. All rights reserved.

Introduction Modulating protein–protein interactions (PPI’s) has evolved into a major area of therapeutic interest due to its prominence in molecular recognition and signaling pathways [1]. Such interactions take place through contacts over extended, somewhat featureless protein surfaces where ‘hotspots’ critical for binding are widely dispersed [2]. Finding molecules that can adequately disrupt these interactions with sufficient affinity is a challenge given the limited ability of small molecules to block PPI’s. The pharmaceutical industry has successfully targeted PPI’s with biologics, most notably antibodies, which are both highly selective and bind with remarkable affinity. However, due to high cost and an inability to access intracellular targets, finding alternative strategies to biologics would be advantageous. One such strategy involves utilizing macrocycles, which usually possess 12 or more core scaffold atoms and lie in a molecular weight range of 500–2000 Da [3]. These larger, flexible structures allow key functional groups responsible for binding to reach between hot spots on the protein surface. Macrocycles originating from natural products have an established role in the treatment of disease, as illustrated by the powerful antibiotics vancomycin and erythromycin. However, macrocycles are innately structurally complex [4,5] and can present significant synthetic obstacles, in addition to the challenge of finding bioactivity in the first place, which explains why so few natural product analogs enter clinical trials [6]. Couple this with the rise of targeted therapies over the past 10–15 years using antibodies and small molecules identified through high-throughput screens, the benefits of using a system where large numbers of macrocycles can be simultaneously Current Opinion in Chemical Biology 2015, 26:42–47

Harnessing DNA for the synthesis of macrocycles DNA-encoded library technology utilizes individual strands of DNA that contain ‘codon’ regions that individually ‘encode’ the chemical addition of building blocks. Each DNA strand serves as a unique barcode and is linked specifically to one chemical structure or ‘pharmacophore’. In the case of peptidomimetic macrocycles, the building blocks are usually a combination of natural and nonnatural amino acid-like structures. Utilizing split-and-pool DNA synthesis [14] allows for the generation of large DNA-tagged libraries which can be screened in vitro against protein targets. The library ‘hits’ are subsequently isolated so that the DNA code can be translated using next-generation sequencing [15], revealing the structure of the active pharmacophore [16]. These hits are then subsequently synthesized without the coding DNA to undergo biochemical evaluation and validation. DNA-encoding was pioneered by Brenner and Lerner for peptides in 1992 [17,18] and subsequently various researchers have applied this concept for making chemical libraries [19–21]. David Liu’s group at Harvard introduced DNA-templated reactions (also known as DNA-programmed chemistry or DPC) [22,23] to make macrocycles [24,25,26] and Dario Neri’s group at the www.sciencedirect.com

DNA-encoded chemical libraries of macrocycles Connors, Hale and Terrett 43

ETH in Switzerland to make dual-pharmacophore libraries [27]. Both Liu and Neri commercialized their ideas by forming Ensemble Discovery (later Ensemble Therapeutics) and Philochem AG, respectively. Utilizing this technology, Ensemble has successfully identified macrocyclic antagonists for tough-to-drug PPI’s like XIAP (X-Chromosome-linked Inhibitor of Apoptosis Protein) and IL17 (Interleukin 17).

Initially, a P2–P5 XIAP-dedicated library containing 160 K macrocycles was generated by cyclization to form a 1,2,3-triazole ring by way of Huisgen 1,3-dipolar cycloaddition. Key to the tetrapeptide’s ability to bind XIAP is a basic residue at the P1 position; therefore, having cyclization take place between the P2 and a supplemental P5 residue would preserve the functional role of the P1 position. Following affinity-based selections against both BIR domains, the highest enriched hits were individually synthesized and analyzed using Biacore, revealing moderately potent BIR3 binders (IC50 = 1.36 mM), but negligible BIR2 binding. A follow-up library interrogating the initial SAR yielded compounds with improved BIR3 affinity (IC50 = 0.39 mM) and moderate BIR2 binding (IC50 = 4.87 mM). These results prompted a medicinal chemistry campaign for further optimization. Included in these efforts, linear precursors (3) to the macrocycle were likewise analyzed to challenge the cyclization strategy in search for more potent binders. Ultimately, the linears

Hit verification from libraries Applying known pharmacophores to library design

XIAP plays a key role in apoptosis [28,29]. This intracellular protein sequesters pro-apoptotic caspases and thereby prevents programmed cell death [30–32]. Ensemble researchers took advantage of a natural XIAP-binding inhibitor, a tetrapeptide (AVPI, 1) from the N-terminal region of the Smac protein [33], and developed novel P2-P5 (2) and P3-P5 (4) cyclized antagonists (Figure 1) against XIAP’s two caspase binding domains, BIR2 and BIR3 [34]. Figure 1

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Current Opinion in Chemical Biology

Generic structures of library and discrete macrocycles. Beginning with the AVPI tetrapeptide (1), a supplemental P5 group was added in order to generate P2–P5 cyclized library macrocycles (2). Analysis of linear pentapeptides (3) led to a new P3–P5 library series (4). Discrete synthesis of monomeric P3–P5 macrocycles led to the simultaneous generation of dimerized product (5). www.sciencedirect.com

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44 Next generation therapeutics

yielded nanomolar binders against both domains. In addition, a co-crystal structure was obtained with a linear pentapeptide and the BIR2 domain, intimating a better cyclization strategy (Figure 2). As a result, a subsequent P3–P5 library was created, yielding binders with potency near that of the linear precursors and refocused medicinal chemistry efforts. During the final cyclization step of the linear molecules, a small quantity of dimerized product (5) was generated and profiled as these compounds had potential to bind simultaneously to both BIR domains [35–37]. After analysis, these dimers were found to display low nanomolar binding against both BIR domains. Further analysis of the dimers in cell lysate assays revealed the dimeric macrocycle class to be potent activators of caspase 3 release from XIAP compared with the monomeric macrocycles (50 nM IC50 versus 1.4 mM). Additionally, the dimer series was screened against three different cancer cell lines for anti-proliferative activity. Initially measured in the low mM IC50 range, further chemical manipulations improved permeability, translating to dimers with low nM IC50 values (BA Seigal et al., abstract in 248th ACS National Meeting & Exposition, San Francisco CA, August 2014). Hit optimization utilizing biochemical data

Early work in Ensemble’s IL17 cytokine antagonist program is an example of how an empirical screening Figure 2

Current Opinion in Chemical Biology

Overlay of co-crystal structures from a linear pentapeptide (green) and a P3–P5 macrocycle (orange) with the BIR2 domain. The linear pentapeptide shows the P3 and P5 positions being in close proximity to one another, suggesting P3–P5 macrocycles could be preferential binders. Current Opinion in Chemical Biology 2015, 26:42–47

approach lacking structural insight can successfully identify novel antagonists. IL17 is an anti-inflammatory protein consisting of two homodimers which together bind its receptor, IL17R [38]. Classified as a PPI, this high-value target has prompted the pharmaceutical industry to develop monoclonal antibodies for treatment of various autoimmune disorders such as psoriasis and rheumatoid arthritis [39]. Interest has grown in finding a small molecule alternative; in Ensemble’s case a macrocycle, which can antagonize the dimeric cytokine’s interaction with its receptor. Ensemble researchers screened approximately 3 million macrocycles against IL17. A small 40K-membered sublibrary containing four diversity points showed the strongest SAR pattern of hits (Figure 3a). The selection was repeated again but in the presence of a known anti-IL17 peptide tool having potent affinity and antagonistic to IL17R binding. As a consequence, none of the original library hits were enriched when in the presence of the IL17-binding molecule (Figure 3b), demonstrating ‘on mechanism’ binding of the initial hits. The ensuing medicinal chemistry optimization led to macrocycles having KD values in the low nanomolar range, a 1000fold increase in affinity over the original library hits (KD = 1–5 mM). Early SPR data indicated these highaffinity macrocycles displayed ‘antibody-like’ binding kinetics with very slow dissociation rates and dissociation half-lives similar to an anti-human IL17 monoclonal antibody. Further analysis in FRET assays, followed by cellular assays guided later structural manipulations toward a developmental candidate. Experiments establishing selectivity were completed using a panel of cells stimulated with different cytokines in the presence of a prototype macrocycle. The cells’ response to cytokine stimulation was monitored to observe blocking of other cytokine activity in addition to IL17. Human HT29 and RASF cells stimulated with IL17 in the presence of the prototype macrocycle yielded EC50 values of 0.045 and 0.32 mM, respectively. However, the resulting EC50 values from the other cytokines indicated no blocking of activity (EC50  25 mM), suggesting the macrocycles were specific and selective for IL17. Lastly, in vitro permeability of various macrocycles were tested in PAMPA, then Caco-2 assays where good inherent membrane permeability was observed with A-B values near 5  10 6 cm/s. Currently in PK studies, this program has demonstrated the successful optimization of macrocycles originating from DNA-encoded library hits (Terrett NK, CHI-Drug Discovery Conference, San Diego CA April 2014). Identifying IDE inhibitors

Liu and Saghatelian published the discovery of a 20-membered macrocyclic inhibitor of insulin-degrading enzyme (IDE) [40,41], a long known but difficult drug www.sciencedirect.com

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(a) Enrichment plots showing hits identified as having more than 10-fold enrichment are highlighted within orange oval, in addition to two higherenriched positive control compounds. (b) Control experiment repeating the library selection in the presence of a known anti-IL17 peptide tool, resulting in full occupation of the IL17 binding sites and competition of all library hits, and thus no library member enrichment.

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(a) Chemical structures of the six IDE-binding macrocycles identified from library enrichment plots. The macrocycle building blocks are classified A–D and show undefined alkene stereochemistry. IC50 values of the cis and trans isomers are indicated by a and b suffixes. (b) Synthetically refined macrocycle with 50 nM IDE inhibitory activity. Adapted from [40]. www.sciencedirect.com

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46 Next generation therapeutics

discovery target [42,43]. Analysis of enrichment plots following in vitro selection of the library revealed six macrocycles possessing consistent SAR. The six macrocycles were synthesized and assayed for activity, revealing the best macrocycle (6b) to have an IC50 value of 60 nM (Figure 4a). The cyclization of this library involved Wittig olefination generating mixtures of the cis and trans olefins which were later separated. Comparing the 12 synthesized isomer pairs, the best binders were those with trans configuration. Additionally, 30 analogs intended to challenge the structural requirements needed for binding were prepared. Systematic variation eventually led to a macrocyclic inhibitor with the slightly lower IC50 value of 50 nM (6bK, Figure 4b), which was subsequently profiled by a panel of in vivo studies and found to be >1000-fold selective over other metalloproteases [44]. A crystal structure was obtained of catalytically inactive cysteine-free human IDE bound to the original library hit 6b and the macrocycle was found to occupy an IDE binding pocket separate, but near, the catalytic zinc ion. This suggests that 6b could prevent substrate binding and thus prevent IDE’s ability to unfold peptides for cleavage. Overall, when administered in vivo, the enhanced library macrocycle 6bK contributed to improved glucose tolerance during intake of meals, as well as a significant drop in blood sugar levels in both lean and obese mice [45,46], highlighting the potential for treating diabetes by targeting IDE.

Conclusion Historically, high-throughput screening (HTS) libraries of conventional small molecules have been used to identify leads for readily druggable enzymes and GPCRs. However, this approach has yielded little success in finding molecules possessing sufficient size to disrupt protein–protein interfaces, leading to the belief that accessing new chemical space beyond the traditional ‘Rule of Five’ molecules is necessary [47]. Peptidomimetic-based macrocycles are considered viable modulators due to their ability to mimic natural peptides or specific secondary structure in proteins, while retaining the pharmacological versatility to make them a desirable class of drug-like molecules. Generating large numbers of peptidomimetic-based macrocycles using DNA-encoded technology can be valuable for the discovery of molecules that modulate PPI’s. Key to this is the inclusion of a sufficient number of diversity elements as well as new molecular scaffolds to enable building blocks to reach additional three-dimensional chemical space [48]. The challenge ahead remains their ability to access intracellular targets and optimization into orally bioavailable drug molecules. Currently, the most advanced clinical stage synthetic macrocycle has been generated by researchers at Polyphor in Switzerland, which is currently in Phase II trials. Albeit not discovered using DNA-encoded technology, having a synthetic macrocycle advance into the clinic lends credence to their potential as viable drug Current Opinion in Chemical Biology 2015, 26:42–47

candidates. Progress in areas such as: establishing rules for how macrocycles bind to proteins, better functional readouts correlating target binding inside cells, and in silico computational methods to forecast membrane-permeable conformations, should collectively lead to more focused chemical strategies and advance synthetic peptidomimetic macrocycles into a viable class of druggable molecules.

References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as:  of special interest  of outstanding interest 1.

Wells JA, McClendon CI: Reaching for the high-hanging fruit in drug discovery at protein–protein interfaces. Nature 2007, 450:1001-1009.

2.

Arkin MR, Wells JA: Small-molecule inhibitors of protein– protein interactions: progressing towards the dream. Nat Rev 2004, 3:301-317.

3.

Marsault E, Peterson ML: Macrocycles are great cycles: applications, opportunities and challenges of synthetic macrocycles in drug discovery. J Med Chem 2011, 54:19612004.

4.

Driggers EM, Hale SP, Lee J, Terrett NK: The exploration of macrocycles for drug discovery – an underexploited structural class. Nat Rev 2008, 7:608-624.

5. 

Giordanetto F, Kihlberg J: Macrocyclic drugs and clinical candidates: what can medicinal chemists learn from their properties. J Med Chem 2014, 57:278-295. A comprehensive survey covering the properties of roughly 100 macrocyclic drugs, including those currently in clinical development. The authors divide the macrocycles into subclasses where individual physicochemical properties are discussed.

6.

Butler MS: Natural products to drugs: natural products derived compounds in clinical trials. Nat Prod Rep 2005, 22:162-195.

7.

Vagner J, Qu H, Hruby VJ: Peptidomimetics, a synthetic tool of drug discovery. Curr Opin Chem Biol 2008, 12:292-296.

8.

White CJ, Yudin AK: Contemporary strategies for peptide macrocyclization. Nat Chem 2011, 3:509-524.

9.

Brownlees CH, Williams J: Peptidases, peptides, and the mammalian blood–brain barrier. J Neurochem 1993, 60:793-803.

10. Fauche`re JL, Thuriau C: Evaluation of the stability of peptides and pseudopeptides as a tool in peptide drug design. Adv Drug Res 1992, 23:127-159. 11. Woodley JF: Enzymatic barriers for GI peptide and protein delivery. Crit Rev Ther Drug Carrier Syst 1994, 11:61-95. 12. Mallinson J, Collins I: Macrocycles in new drug discovery.  Future Med Chem 2012, 4:1409-1438. Review of the effects of macrocyclization on potency and selectivity. 13. Bockus AT, McEwen CM, Lokey RS: Form and function in cyclic  peptide natural products: a pharmacokinetic perspective. Curr Top Med Chem 2013, 13:821-836. Analysis of known and unknown molecular properties that may offer insight into designing non-rule-of-five compliant molecules. 14. Furka A, Sebestyen F, Asgedom M, Dibo G: General method for rapid synthesis of multicomponent peptide mixtures. Int J Pept Protein Res 1991, 37:487-493. 15. Church GM: Genomes for all. Sci Am 2006, 294:46-54. 16. Hale SP: Screening large compound collections. In A Handbook for DNA-Encoded Chemistry. Edited by Goodnow RA. John Wiley & Sons, Inc.; 2014:281-317. www.sciencedirect.com

DNA-encoded chemical libraries of macrocycles Connors, Hale and Terrett 47

17. Brenner S, Lerner RA: Encoded combinatorial chemistry. Proc Natl Acad Sci U S A 1992, 89:5381-5383. 18. Nielsen J, Brenner S, Janda KD: Synthetic methods for the implementation of encoded combinatorial chemistry. J Am Chem Soc 1993, 115:9812-9813.

Smac/DIABLO to the XIAP BIR 3 domain. Nature 2000, 408:1004-1008. 34. Srinivasula SM, Ashwell JD: IAPs: what’s in a name? Mol Cell 2008, 30:123-135.

19. Clark MA, Acharya RA, Arico-Muendel CC, Belyanskaya SL, Benjamin DR, Carlson NR, Centrella PA, Chiu CH, Creaser SP, Cuozzo JW et al.: Design, synthesis and selection of DNAencoded small-molecule libraries. Nat Chem Biol 2009, 5:647-654.

35. Sun H, Nikolovska-Coleska Z, Lu J, Meagher JL, Yang CY, Qiu S, Tomita Y, Ueda Y, Jiang S, Krajewski K et al.: Design, synthesis, and characterization of a potent, nonpeptide, cell-permeable, bivalent Smac mimetic that concurrently targets both the BIR2 and BIR3 domains in XIAP. J Am Chem Soc 2007, 129:15279-15294.

20. Hansen MH, Blakskjaer P, Petersen LK, Hansen TH, Højfeldt JW, Gothelf KV, Hansen NJV: A yoctoliter-scale DNA reactor for small-molecule evolution. J Am Chem Soc 2009, 131:1322-1327.

36. Nikolovska-Coleska Z, Meagher JL, Jiang S, Yang CY, Qiu S, Roller PP, Stuckey JA, Wang S: Interaction of a cyclic, bivalent smac mimetic with the x-linked inhibitor of apoptosis protein. Biochemistry 2008, 47:9811-9824.

21. Lundorf FT, Dybro, M, Jacobsen SN, Olsen EK, Andersen AL, Holtmann A, Hansen AH, Sorensen AM, Goldbech A, De Leon D et al.: Oligonucleotide- and ligase-based enzymatic encoding methods for efficient synthesis of large libraries. 2007, US Patent Application WO 2007062664.

37. Sun H, Liu L, Lu J, Qiu S, Yang CY, Yi H, Wang S: Cyclopeptide Smac mimetics as antagonists of IAP proteins. Bioorg Med Chem Lett 2010, 20:3043-3046.

22. Gartner ZJ, Liu DR: The generality of DNA-templated synthesis as a basis for evolving non-natural small molecules. J Am Chem Soc 2001, 123:6961-6963. 23. Gartner ZJ, Kanan MW, Liu DR: Multistep small-molecule synthesis programmed by DNA templates. J Am Chem Soc 2002, 124:10304-10306. 24. Gartner ZJ, Tse BN, Grubina R, Doyon JB, Snyder TM, Liu DR: DNA-templated organic synthesis and selection of a library of macrocycles. Science 2004, 305:1601-1605. 25. Kleiner RE, Dumelin CE, Tiu GC, Sakurai K, Liu DR: In vitro selection of a DNA-templated small-molecule library reveals a class of macrocyclic kinase inhibitors. J Am Chem Soc 2010, 132:11779-11791.

38. Song X, Qian Y: IL-17 family cytokines mediated signaling in the pathogenesis of inflammatory diseases. Cell Signal 2013, 25:2335-2347. 39. Tse MT: IL-17 antibodies gain momentum. Nat Rev 2013, 12:815-816. 40. Maianti JP, McFedries A, Foda ZH, Kleiner RE, Du XQ, Leissring MA, Tang WJ, Charron MJ, Seeliger MA, Saghatelian A, Liu DR: Anti-diabetic activity of insulin-degrading enzyme inhibitors mediated by multiple hormones. Nature 2014, 511:94-98. 41. Tse BN, Snyder TM, Shen Y, Liu DR: Translation of DNA into a library of 13,000 synthetic small-molecule macrocycles suitable for in vitro selection. J Am Chem Soc 2008, 130:15611-15626. 42. Duckworth WC, Bennett RG, Hamel FG: Insulin degradation: progress and potential. Endocr Rev 1998, 19:608-624.

26. Georghiou G, Kleiner RE, Pulkoski-Gross M, Liu DR: Highly  specific, bisubstrate-competitive Src inhibitors from DNAtemplated macrocycles. Nat Chem Biol 2012, 8:366-374. Example of a DNA-encoded library serving as a useful starting point for identifying high-affinity binders.

43. Mirsky IA, Broh-Kahn RH: The inactivation of insulin by tissue extracts; the distribution and properties of insulin inactivating extracts. Arch Biochem 1949, 20:1-9.

27. Melkko S, Scheuermann J, Dumelin CE, Neri D: Encoded self-assembling chemical libraries. Nat Biotechnol 2004, 22:568-574.

44. Malito E, Hulse RE, Tang WJ: Amyloid beta-degrading cryptidases: insulin degrading enzyme, presequence peptidase, and neprilysin. Cell Mol Life Sci 2008, 65:2574-2585.

28. Wei Y, Fan T, Yu M: Inhibitor of apoptosis proteins and apoptosis. Acta Biochem Biophys Sin 2008, 40:278-288.

45. Ahren B, Winzell MS, Pacini G: The augmenting effect on insulin secretion by oral versus intravenous glucose is exaggerated by high-fat diet in mice. J Endocrinol 2008, 197:181-187.

29. Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation. Cell 2011, 144:646-674. 30. Wilkinson JC, Cepero E, Boise LH, Duckett CS: Upstream regulatory role for XIAP in receptor-mediated apoptosis. Mol Cell Biol 2004, 24:7003-7014. 31. Chang HY, Yang X: Proteases for cell suicide: functions and regulation of caspases. Microbiol Mol Biol Rev 2000, 64:821-846. 32. Deveraux QL, Reed JC: IAP family proteins—suppressors of apoptosis. Genes Dev 1999, 13:239-252. 33. Liu Z, Sun C, Olejniczak ET, Meadows RP, Betz SF, Oost T, Hermann J, Wu JC, Fesik SW: Structural basis for binding of

www.sciencedirect.com

46. Winzell MS, Ahren B: The high-fat diet-fed mouse: a model for studying mechanisms and treatment of impaired glucose tolerance and type 2 diabetes. Diabetes 2004, 53:S215-S219. 47. Josephson K, Ricardo A, Szostak JW: mRNA display: from basic principles to macrocycle drug discovery. Drug Discov Today 2014, 4:388-399. 48. O’Connor CJ, Beckmann HSG, Spring DR: Diversity-oriented  synthesis: producing chemical tools for dissecting biology. Chem Soc Rev 2012, 41:4444-4456. Review highlighting recent advances in strategy and preparation of diverse collections of compounds.

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