- Email: [email protected]
Reconsidering phenotypic heart failure drug discovery
Drug Discovery Today: Therapeutic Strategies
Vol. 9, No. 4 2012
Editors-in-Chief Raymond Baker – formerly University of Southampton, UK and Merck Sharp & Dohme, UK Eliot Ohlstein – AltheRx Pharmaceuticals, USA DRUG DISCOVERY
TODAY THERAPEUTIC
STRATEGIES
Heart failure
Reconsidering phenotypic heart failure drug discovery John R. Toomey*, John J. Upson GlaxoSmithKline, Metabolic Pathways and Cardiovascular Therapy Area, Heart Failure Discovery Performance Unit, 709 Swedeland Rd, King of Prussia, PA 19406, United States
With a few notable exceptions, heart failure (HF) drug development has faced substantial attrition while translating preclinical discoveries into clinical efficacy. The high attrition has prompted a reconsideration of
Section editor: Robert Willette – Heart Failure Drug Discovery Performance Unit, GSK Pharmaceuticals, King of Prussia, USA.
the target selection process of drug discovery. The industry standard of selecting drug targets based upon guidance from the published scientific literature has proven of limited reliability and has prompted a resurgent interest in phenotypic drug discovery (PDD). PDD, which has been reported to have an improved drug development success rate, offers an experimental route to drug target selection. In PDD, the drug targets, the disease relevant biological mechanisms and the drugs themselves are all selected by the phenotypic screen. For HF, with highly reproducible pathologic cellular phenotypes and recently developed human cell lines, the infrastructure necessary for PDD programs is well established. Despite an urgent necessity to improve drug discovery strategies, the challenges posed by PDD to both the capabilities and culture of the pharmaceutical industry are formidable. Introduction The declining efficiency of the drug discovery industry has been well documented [1] (Fig. 1). Despite steadily increased investment, and a wide range of scientific and managerial initiatives *Corresponding author.: J.R. Toomey ([email protected]) 1740-6773/$ ß 2014 Elsevier Ltd. All rights reserved.
to improve productivity, the annual rate of new drug production has been unchanged in 60 years [2]. Heart failure drug discovery has largely mirrored the efficiency of the overall industry. With the notable exceptions of select antihypertensives and diuretics (b-blockers and renin–angiotensin–aldosterone (RAAS) pathway inhibitors), this therapeutic area has had an extensive track record of drugs failing in clinical development [3–13]. A number of culprits have been identified to help explain the decline in research and development (R&D) efficiency, from increased regulatory requirements in pharmaceutical development to an ever expanding pharmacopeia of approved medicines, raising the efficacy and differentiation barriers to new product entries. However, given that most drugs fail due to efficacy limitations [14], the data suggest that a key culprit is insufficient scientific insight to accurately discern the basis of complex disease and to reliably translate that knowledge into therapy. It is difficult to reconcile the spectacular last half-century of advances in molecular biology, combinatorial chemistry, screening, computational technologies, and clinical translation capabilities, with the decline in R&D efficiency. How could this explosion of new knowledge [15] with an ever increasing clarity of the molecular mechanisms governing physiology not translate into improved efficiency? The sustained and deep efficiency decline has forced investigators to search for more relevant and more translational scientific input, and to question the reductionist assumptions of target-centric drug discovery [16–18].
http://dx.doi.org/10.1016/j.ddstr.2014.01.003
e199
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Vol. 9, No. 4 2012
R&D efficiency (drugs per $billion R&D spend)
100 30 10 3 1 0.3 1950
1960
1970
1980
1990
2000
2010
Years Drug Discovery Today: Therapeutic Strategies
Figure 1. Research and development efficiency: The number of new drugs approved by the US Food and Drug Administration per billion dollars (inflation adjusted) spent on research and development (R&D) from 1950 to 2010. These data re-plotted from Scannell et al. [1].
Target based drug discovery (TBDD) A TBDD program begins with a drug target disease hypothesis, followed by a screening and chemical/biological optimization campaign to generate the necessary tools for an in depth target validation interrogation. Definitive target validation with a new chemical/biological entity (NCE/NBE) in an animal disease model may not occur for several years into the program. Therefore, although the costs of new target investigations are borne throughout the early phase of discovery, the risks of poor target selection go unaddressed for years. Most often in this interim period, the target rationale for such an effort is largely predicated on a simplified scientific narrative derived from the published literature. The narrative will ordinarily assign a single target to a putative disease pathway, and then postulate that modulating the target will attenuate the disease. Industry is heavily invested in, if not dominated by, the monogenic disease model with an infrastructure designed to reduce and dissect the molecular, biochemical, and structural biology/chemistry, and drive the engineering of a highly selective NCE/NBEs capable of modulating target proteins. Although most efforts diligently work to establish selectivity over closely related proteins, the concept of selectivity is illusory in that drug interactions with only a tiny fraction of the proteome are ever evaluated. In development quality molecules, selectivity data may be collected on several hundred isolated enzyme, channel, or receptor molecular targets. However, the assay configurations for these targets are generally not remotely similar to the physiologic context which may involve a myriad of proteins functioning in dynamic complexes with untold numbers of other macromolecules. The often quoted approximation of six different molecular target interactions for known marketed and efficacious drugs e200
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can reasonably be considered a conservative estimate [19]. If selectivity over closely related proteins cannot be achieved, then the target narrative may adjust to include the secondary activities as part of the hypothesis testing. In heart failure, angiotensin converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) are quintessential examples of TBDD. Although TBDD has had many successes, the strategy has been unable to sustain growth in the pharmaceutical sector. Only 25% of the Pharmaceutical Research and Manufacturers of American (PhRMA) companies in 1988 still survive today [20]. In hindsight, the pitfalls seem clear. Building a scientific narrative based on the published literature now appears perilous with reports of 75–90% of peer reviewed oncology published findings failing to reproduce in independent confirmation studies [21,22]. The authors of the report herein have observed similar rates of reproducibility while investigating mechanisms related to heart failure (unpublished). Most common chronic diseases are thought to be polygenic [23] with multiple parallel mechanisms and pathways impacting disease progression (including heart failure). Therefore, the monogenic aspiration of TBDD is likely to limit the patient population amenable to the therapy, and importantly constrain the overall efficacy. A polygenic strategy for drug discovery is now well established for oncologic, neurologic, and infectious diseases [24,25]. Overall, the developing consensus is that reductionist TBDD can be a reasonable strategy for the efficient production of new drugs for well-defined monogenic dominated diseases. For polygenic disease, TBDD appears to be unable to reliably translate preclinical efficacy into clinical benefit, and new strategies are urgently needed to develop therapeutic agents with broader integrated pharmacology.
Phenotypic drug discovery (PDD) Given the productivity crisis outlined above there has been renewed interest in PDD, with some analyses showing that drugs derived from PDD have a higher success rate in clinical development [18]. Most current embodiments of phenotypic screening (PS) involve high-throughput in vitro cell based screening formats that track cellular phenotypic endpoints that are considered to be relevant to disease progression. Ideally, PS would be conducted in ex vivo or in vivo formats but the prospects are currently considered prohibitively cumbersome and expensive. The major strength of PS is that the screen itself impartially selects the targets and mechanisms to regulate the phenotype, and does not depend upon a target selection bias based on the published literature. Importantly, by screening in a living environment, many of the potential targets regulating the biological processes of interest are present in the experiment, explaining why PS generally has higher hit rates than conventional screening. The screen output will rank order the potential mechanisms by potency
Vol. 9, No. 4 2012
and magnitude of efficacy, and the most profound mechanisms will spontaneously percolate to the top. No target bias, no mechanism of action, and no monogenic assumptions, are required.
PS technology developments Important technical advances have matured over recent decades to foster a growing enthusiasm for in vitro PS. Automated high content screening technologies make possible rigorous high-throughput multi-parameter screening [26]. In a single screen, multiple endpoints related to signal transduction, morphologic features, cell proliferation, metabolism, cell health, and organelle function can be collected simultaneously. This allows screening for broadly defined profiles of interest; for example, identifying compounds that promote growth factor signaling, block reactive oxygen species generation, but do not promote cell proliferation. Since human primary cells have historically been exceedingly difficult to acquire and have limited proliferative capacity, PS has primarily been restricted to transformed tumor cell lines or non-human tissue sources [27]. Therefore, the aspiration of conducting PDD in human tissue was all but impossible. With the discovery of human induced pluripotent stem cell (hiPSC) methodology [28], and the capacity of hiPSCs to be controllably differentiated down multiple lineages, for example, neurons and cardiomyocytes, the possibilities of an unlimited source of more physiologically relevant cell types for PS emerged [29]. The subsequent introduction of commercially available hiPSC derived differentiated cell lines translated the discovery into readily available quality controlled reagents for potential use in PS. One major source of consternation in PS is the lack of target knowledge related to hits coming out of a screen. Even if hits are interacting with multiple unrelated targets, at least some target knowledge would both enable the medicinal chemistry lead optimization process and facilitate subsequent biomarker development for preclinical and clinical efficacy studies. Target deconvolution methods have made significant strides in the last decade [30]. One of the most exciting developments is the maturation of stable isotope labeling by amino acids in cell culture (SILAC) [31] based chemical proteomics [32] (CP) for target identification. CP is an affinity chromatography, mass-spectrometry based proteomics method that has demonstrated the capacity to identify small molecule protein interactions. The method has been shown to be sensitive and specific, and to have the potential to bridge the ‘unknown targets’ gap between PS and TBDD.
Caveats of PDD A central limitation of in vitro PDD is the modeled cellular phenotype. Does the cellular phenotype model a relevant aspect of disease? Assay design is generally based upon a published scientific hypothesis about the relative importance
Drug Discovery Today: Therapeutic Strategies | Heart failure
of major mechanisms, pathways, and cellular phenotypes to disease progression. As with TBDD, this is perilous territory. Although the cells are living and preferably of human origin, they are grown on plastic and in two dimensions, and lack the physiologic cues of multi-lineage neighboring cells, the milieu of soluble secreted factors, and mechanical stress. The iPS technology often generates immature cells of mixed lineage and variable differentiation [33] and their ultimate utility for drug discovery will be determined over the coming decades. Short of clinical outcome studies with drugs derived from specific phenotypic programs, it is not possible to know the drug discovery relevance of any given cellular phenotypic assay. At best, an interim risk mitigation strategy for any PDD program would include an early in vivo validation phase where compounds derived from the phenotypic screen are evaluated in disease relevant in vivo models. Another significant assay limitation of PS is whether the compounds being screened represent enough chemical complexity to engage the desired transformational mechanisms. Ultimately a PS is limited by the chemical complexity of the compounds in the screen. More challenging phenotypes like regeneration may require input from multiple mechanisms and may not be accessible with the limited chemical complexity of a single molecule. For the aforementioned discovery of hiPSCs [28], the requirement was for four transcription factors to compel a fibroblast to become a stem cell, with each transcription factor likely governing 10s to 100s of genes. The notion that a disease phenotype will be meaningfully engaged by single compounds acting through single targets would appear to conflict with the integrated, overlapping, and redundant context of known signaling mechanisms. This is beginning to fuel an interest in screening combinatorial chemical collections [34,35]; however, it remains to be demonstrated whether screening in more complex chemical space can improve access to more profound phenotypic modulation.
Heart failure (HF) and PS HF drug discovery is exceedingly well positioned to capitalize on the progress of PS. HF disease progression has been meticulously characterized and a massive spectrum of pathways and mechanisms around perturbed metabolism, apoptosis, Ca++ cycling, oxidant stress, fibrosis, and inflammation have been identified [36]. Importantly, reagents for configuring high-content screens are readily available for most of the above mechanisms, and as mentioned previously hiPSCderived cardiomyocytes can now be acquired commercially. One PS format that is particularly well suited for HF is the connectivity map (cMAP) concept [37,38]. Over the last several years, cMAP has been introduced as a potential alternative drug discovery model. The cMAP method is based upon connecting human disease with small molecules through the language of gene expression. There are three components necessary for cMAP implementation; (1) a gene-expression www.drugdiscoverytoday.com
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Ischemic HF(mRNA Log fold change)
1.0 0.5 0.0 n = 1818 r = 0.99 P < 0.0001
-0.5 -1.0 -1.0
-0.5
0.0
0.5
1.0
Idiopathic HF (mRNA Log fold change) Drug Discovery Today: Therapeutic Strategies
Figure 2. Gene expression fold changes observed in end-stage human heart failure populations compared to non-failing controls: Ischemic HF gene expression changes averaged over 86 patients. Idiopathic HF gene expression changes averaged from 108 patients. Plot represents 1818 genes. These data are re-plotted from Hannenhalli et al. [40].
signature from human disease; (2) compound elicited gene expression from a relevant cultured cell type on the same signature gene set, and; (3) a pattern matching algorithm designed to query the two datasets and aimed at identifying and ranking compounds that influence the disease signature. The method can be configured to probe a small focused set of genes associated with a particular pathway or 100s to 1000s of genes associated with the broad disease phenotype. The best example of cMAP success to date was reported by Kunkel et al. [39], where ursolic acid was identified by the method as an anti-atrophy agent capable of attenuating skeletal muscle atrophy in vivo. For HF, one of the three components of cMAP is in place. End-stage HF gene expression data have been published [40] for which the fidelity is excellent (Fig. 2). The exceptional gene expression reproducibility is a strong reason to consider the approach for HF drug discovery. The missing cMAP elements for HF are a gene expression database gleaned from screening a pharmaceutical scale compound collection in human cardiomyocytes and the pattern matching algorithm to mine the data. If both were in hand, the method may allow researchers to mine in minutes (in silico) a centralized database for compound effectors of HF.
Conclusion Despite an unprecedented half century of expanded knowledge and capabilities the efficiency of drug discovery continues to decline. The improved ability to dissect and disentangle the pathways and mechanisms that govern pathophysiology has not adequately translated into predicting drug efficacy in the clinic. TBDD appears to do an efficient job of filling the industrial pipeline, but a poor job of reproducibly generating valid medicines. The most important step in drug discovery is widely considered to be the selection of e202
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the therapeutic drug target. Selecting a target that when modulated will translate into a clinically relevant patient benefit on top of the standard-of-care agents is a daunting task. Each generation of target-selected drugs developed independently of each other and decades apart, needs to provoke the complementary and additive physiologic change to be detectable in clinical trials. The data suggest that the current approach is not a sustainable strategy [14], and the two underlying pillars of modern drug discovery, the published scientific literature, and TBDD, are in profound need of reconsideration. PDD is one possible re-addition to the repertoire. By conducting drug discovery in living systems, target prognostication is not necessary and the dependency on published literature is minimized. PS can be viewed as a high-throughput natural selection process for chemical space and biological mechanisms regulating disease phenotypes. The raw data output identifies the most potent and biologically relevant mechanisms. Importantly, since a drug could be impacting multiple targets simultaneously, the concept of singletarget selection becomes less meaningful and target selection falls from the most important step in drug discovery to an enabling step. The challenges to industry posed by PDD are significant. The familiar pharmaceutical discovery model employs tools to effectively optimize drug target interactions; these become less effective with an emphasis on PDD. If target identification is lacking, then more expensive, time consuming, and higher risk cell based optimization schemes would need to be employed. A commitment to succeed with PDD may require different industrialized processes altogether, from highthroughput in vivo assessments to combinatorial screening. Given the inertia of the single target single compound industry mindset, and the deep reductionist roots of scientists, the idea of developing drugs in the absence of target knowledge or in the context of simultaneous multiple independent unknown targets will represent a formidable challenge to future drug discovery.
Conflicts of interest None.
Funding source Authors are employees of GlaxoSmithKline.
Acknowledgments Special thanks to Tim Chendrimada and Bob Willette for editorial assistance.
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3. Vaduganathan M, Greene SJ, Ambrosy AP, Gheorghiade M, Butler J. The disconnect between phase II and phase III trials of drugs for heart failure. Nat Rev Cardiol 2013;10:85–97. 4. Deswal A, Richardson P, Bozkurt B, Mann DL. Results of the randomized aldosterone antagonism in heart failure with preserved ejection fraction trial (RAAM-PEF). J Card Fail 2011;17:634–42. 5. Swedberg K, Young JB, Anand IS, Cheng S, Desai AS, Diaz R, et al. Treatment of anemia with darbepoetin alfa in systolic heart failure. N Engl J Med 2013;368:1210–9. 6. McMurray JJV, Teerlink JR, Cotter G, Bourge RC, Cleland JGF, Johndeau G, et al. Effects of tezosentan on symptoms and clinical outcomes in patients with acute heart failure: The VERITAS Randomized Controlled Trials. JAMA 2007;298:2009–19. 7. O’Connor CM, Starling RC, Hernandez AF, Armstrong PW, Dickstein K, Hasselblad V, et al. Effect of nesiritide in patients with acute decompensated heart failure. N Engl J Med 2011;365:32–43. 8. Mebazaa A, Nieminen MS, Packer M, Cohen-Solal A, Kleber FX, Pocock SJ, et al. Levosimendan vs dobutamine for patients with acute decompensated heart failure: The SURVIVE Randomized Trial. JAMA 2007;297: 1883–91. 9. Gheorghiade M, Konstam MA, Burnett JC, Grinfeld L, Maggioni AP, Swedberg K, et al. Short-term clinical effects of tolvaptan, an oral vasopressin antagonist, in patients hospitalized for heart failure: The EVEREST Clinical Status Trials. JAMA 2013;297:1332–43. 10. Massie BM, O’Connor CM, Metra M, Ponikowski P, Teerlink JR, Cotter G, et al. Rolofylline, an adenosine A1-receptor antagonist, in acute heart failure. N Engl J Med 2010;363:1419–28. 11. Chung ES, Packer M, Lo KH, Fasanmade AA, Willerson JT. Randomized, double-blind, placebo-controlled, pilot trial of infliximab, a chimeric monoclonal antibody to tumor necrosis factor-a, in patients with moderate-to-severe heart failure. Results of the anti-TNF therapy against congestive heart failure (ATTACH) trial. Circulation 2003;107: 3133–40. 12. Udelson JE, Feldman AM, Greenberg B, Pitt B, Mukherjee R, Solomon HA, et al. Randomized, double-blind, multicenter, placebo-controlled study evaluating the effect of aldosterone antagonism with eplerenone on ventricular remodeling in patients with mild-to-moderate heart failure and left ventricular systolic dysfunction. Circ Heart Fail 2013;3:347–53. 13. Kitzman DW, Hundley G, Brubaker PH, Morgan TM, Moore JB, Stewart KP, et al. A randomized double-blind trial of enalapril in older patients with heart failure and preserved ejection fraction: effects on exercise tolerance and arterial distensibility. Circ Heart Fail 2010;3:477–85. 14. Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates. Nat Rev Drug Discov 2004;3:711–5. 15. Drews J. Drug discovery: a historical perspective. Science 2000;287:1960–3. 16. Sams-Dodd F. Target-based drug discovery: is something wrong. Drug Discov Today 2005;10:139–47. 17. Gilbert IH, Leroy D, Frearson JA. Finding new hits in neglected disease projects: target or phenotypic based screening. Curr Top Med Chem 2011;11:1284–91. 18. Swinney DC, Anthony J. How were new medicines discovered. Nat Rev Drug Discov 2011;10:507–19. 19. Mestres J. Data completeness – the Achilles heel of drug–target networks. Nat Biotechnol 2008;26:983–4. 20. LaMattina JL. The impact of mergers on pharmaceutical R&D. Nat Rev Drug Discov 2011;10:559–60. 21. Begley CG, Ellis LM. Raise standards for preclinical cancer research. Nature 2012;483:531–3.
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22. Prinz F, Schlange T, Asadullah K. Believe it or not: how much can we rely on published data on potential drug targets. Nat Rev Drug Discov 2011;10:712. 23. Gibbons GH, Liew CC, Goodarzi MO, Rotter JI, Hsueh WA, Siragy HM, et al. Genetic markers: progress and potential for cardiovascular disease. Circulation 2004;109(Suppl. IV):47–58. 24. Roth BL, Sheffler DJ, Kroeze WK. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat Rev Drug Discov 2004;3:353–9. 25. Sams-Dodd F. Is poor research the cause of the declining productivity of the pharmaceutical industry? An industry in need of a paradigm shift. Drug Discov Today 2013;18(5/6):211–7. 26. Bickle M. The beautiful cell: high content screening in drug discovery. Anal Bioanal Chem 2010;398:219–26. 27. Bush E, Fielitz J, Melvin L, Martinez-Arnold M, McKinsey TA, Plichta R, et al. A small molecular activator of cardiac hypertrophy uncovered in a chemical screen for modifiers of the calcineurin signaling pathway. Proc Natl Acad Sci U S A 2004;101:2870–5. 28. Takahashi K, Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblasts cultures by defined factors. Cell 2006;126:663–76. 29. Grskovic M, Javaherian A, Strulovici B, Daley GQ. Induced pluripotent stem cells – opportunities for disease modeling and drug discovery. Nat Rev Drug Discov 2011;10:915–29. ¨ pen C, Raggiaschi R, Gaviraghi G. Target 30. Terstappen GC, Schlu deconvolution strategies. Nat Rev Drug Discov 2007;6:891–903. 31. Ong S, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 2002;1:376–86. 32. Ong S, Schenone M, Margolin AA, Li X, Do K, Doud KD, et al. Identifying the proteins to which small-molecule probes and drugs bind in cells. Proc Natl Acad Sci U S A 2009;106:4617–22. 33. Ivashchenko CI, Pipes GC, Lozinskaya I, Lin Z, Xu X, Needle S, et al. Human-induced pluripotent stem cell-derived cardiomyocytes exhibit temporal changes in phenotype. Am J Physiol Heart Circ Physiol 2013;305:H913–22. 34. Axelrod M, Ou Z, Brett LK, Zhang L, Lopez ER, Tamayo AT, et al. Combinatorial drug screening identifies synergistic co-targeting of Bruton’s tyrosine kinase and the proteasome in mantle cell lymphoma. Leukemia 2013;1–4. 35. Held MA, Langdon CG, Platt JT, Graham-Steed T, Liu Z, Chakraborty A, et al. Genotype-selective combination therapies for melanoma identified by high throughput drug screening. Cancer Discov 2013;3:52–67. 36. Mudd JO, Kass DA. Tackling heart failure in the twenty-first century. Nature 2008;451:919–28. 37. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel WJ, et al. The Connectivity Map: using gene expression signatures to connect small molecules, genes, and disease. Science 2006;313:1929–35. 38. Lamb J. The Connectivity Map: a new tool for biomedical research. Nat Rev Cancer 2011;7:54–60. 39. Kunkel SD, Suneja M, Ebert SM, Bongers KS, Fox DK, Malmberg SE, et al. mRNA expression signatures of human skeletal muscle atrophy identify a natural compound that increases muscle mass. Cell Metab 2011;13:627– 38. 40. Hannenhalli S, Putt ME, Gilmore JM, Wang J, Parmacek MS, Epstein JA, et al. Transcriptional genomics associates FOX transcription factors with human heart failure. Circulation 2006;114:1269–76.
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e203
Vol. 9, No. 4 2012
Editors-in-Chief Raymond Baker – formerly University of Southampton, UK and Merck Sharp & Dohme, UK Eliot Ohlstein – AltheRx Pharmaceuticals, USA DRUG DISCOVERY
TODAY THERAPEUTIC
STRATEGIES
Heart failure
Reconsidering phenotypic heart failure drug discovery John R. Toomey*, John J. Upson GlaxoSmithKline, Metabolic Pathways and Cardiovascular Therapy Area, Heart Failure Discovery Performance Unit, 709 Swedeland Rd, King of Prussia, PA 19406, United States
With a few notable exceptions, heart failure (HF) drug development has faced substantial attrition while translating preclinical discoveries into clinical efficacy. The high attrition has prompted a reconsideration of
Section editor: Robert Willette – Heart Failure Drug Discovery Performance Unit, GSK Pharmaceuticals, King of Prussia, USA.
the target selection process of drug discovery. The industry standard of selecting drug targets based upon guidance from the published scientific literature has proven of limited reliability and has prompted a resurgent interest in phenotypic drug discovery (PDD). PDD, which has been reported to have an improved drug development success rate, offers an experimental route to drug target selection. In PDD, the drug targets, the disease relevant biological mechanisms and the drugs themselves are all selected by the phenotypic screen. For HF, with highly reproducible pathologic cellular phenotypes and recently developed human cell lines, the infrastructure necessary for PDD programs is well established. Despite an urgent necessity to improve drug discovery strategies, the challenges posed by PDD to both the capabilities and culture of the pharmaceutical industry are formidable. Introduction The declining efficiency of the drug discovery industry has been well documented [1] (Fig. 1). Despite steadily increased investment, and a wide range of scientific and managerial initiatives *Corresponding author.: J.R. Toomey ([email protected]) 1740-6773/$ ß 2014 Elsevier Ltd. All rights reserved.
to improve productivity, the annual rate of new drug production has been unchanged in 60 years [2]. Heart failure drug discovery has largely mirrored the efficiency of the overall industry. With the notable exceptions of select antihypertensives and diuretics (b-blockers and renin–angiotensin–aldosterone (RAAS) pathway inhibitors), this therapeutic area has had an extensive track record of drugs failing in clinical development [3–13]. A number of culprits have been identified to help explain the decline in research and development (R&D) efficiency, from increased regulatory requirements in pharmaceutical development to an ever expanding pharmacopeia of approved medicines, raising the efficacy and differentiation barriers to new product entries. However, given that most drugs fail due to efficacy limitations [14], the data suggest that a key culprit is insufficient scientific insight to accurately discern the basis of complex disease and to reliably translate that knowledge into therapy. It is difficult to reconcile the spectacular last half-century of advances in molecular biology, combinatorial chemistry, screening, computational technologies, and clinical translation capabilities, with the decline in R&D efficiency. How could this explosion of new knowledge [15] with an ever increasing clarity of the molecular mechanisms governing physiology not translate into improved efficiency? The sustained and deep efficiency decline has forced investigators to search for more relevant and more translational scientific input, and to question the reductionist assumptions of target-centric drug discovery [16–18].
http://dx.doi.org/10.1016/j.ddstr.2014.01.003
e199
Drug Discovery Today: Therapeutic Strategies | Heart failure
Vol. 9, No. 4 2012
R&D efficiency (drugs per $billion R&D spend)
100 30 10 3 1 0.3 1950
1960
1970
1980
1990
2000
2010
Years Drug Discovery Today: Therapeutic Strategies
Figure 1. Research and development efficiency: The number of new drugs approved by the US Food and Drug Administration per billion dollars (inflation adjusted) spent on research and development (R&D) from 1950 to 2010. These data re-plotted from Scannell et al. [1].
Target based drug discovery (TBDD) A TBDD program begins with a drug target disease hypothesis, followed by a screening and chemical/biological optimization campaign to generate the necessary tools for an in depth target validation interrogation. Definitive target validation with a new chemical/biological entity (NCE/NBE) in an animal disease model may not occur for several years into the program. Therefore, although the costs of new target investigations are borne throughout the early phase of discovery, the risks of poor target selection go unaddressed for years. Most often in this interim period, the target rationale for such an effort is largely predicated on a simplified scientific narrative derived from the published literature. The narrative will ordinarily assign a single target to a putative disease pathway, and then postulate that modulating the target will attenuate the disease. Industry is heavily invested in, if not dominated by, the monogenic disease model with an infrastructure designed to reduce and dissect the molecular, biochemical, and structural biology/chemistry, and drive the engineering of a highly selective NCE/NBEs capable of modulating target proteins. Although most efforts diligently work to establish selectivity over closely related proteins, the concept of selectivity is illusory in that drug interactions with only a tiny fraction of the proteome are ever evaluated. In development quality molecules, selectivity data may be collected on several hundred isolated enzyme, channel, or receptor molecular targets. However, the assay configurations for these targets are generally not remotely similar to the physiologic context which may involve a myriad of proteins functioning in dynamic complexes with untold numbers of other macromolecules. The often quoted approximation of six different molecular target interactions for known marketed and efficacious drugs e200
www.drugdiscoverytoday.com
can reasonably be considered a conservative estimate [19]. If selectivity over closely related proteins cannot be achieved, then the target narrative may adjust to include the secondary activities as part of the hypothesis testing. In heart failure, angiotensin converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) are quintessential examples of TBDD. Although TBDD has had many successes, the strategy has been unable to sustain growth in the pharmaceutical sector. Only 25% of the Pharmaceutical Research and Manufacturers of American (PhRMA) companies in 1988 still survive today [20]. In hindsight, the pitfalls seem clear. Building a scientific narrative based on the published literature now appears perilous with reports of 75–90% of peer reviewed oncology published findings failing to reproduce in independent confirmation studies [21,22]. The authors of the report herein have observed similar rates of reproducibility while investigating mechanisms related to heart failure (unpublished). Most common chronic diseases are thought to be polygenic [23] with multiple parallel mechanisms and pathways impacting disease progression (including heart failure). Therefore, the monogenic aspiration of TBDD is likely to limit the patient population amenable to the therapy, and importantly constrain the overall efficacy. A polygenic strategy for drug discovery is now well established for oncologic, neurologic, and infectious diseases [24,25]. Overall, the developing consensus is that reductionist TBDD can be a reasonable strategy for the efficient production of new drugs for well-defined monogenic dominated diseases. For polygenic disease, TBDD appears to be unable to reliably translate preclinical efficacy into clinical benefit, and new strategies are urgently needed to develop therapeutic agents with broader integrated pharmacology.
Phenotypic drug discovery (PDD) Given the productivity crisis outlined above there has been renewed interest in PDD, with some analyses showing that drugs derived from PDD have a higher success rate in clinical development [18]. Most current embodiments of phenotypic screening (PS) involve high-throughput in vitro cell based screening formats that track cellular phenotypic endpoints that are considered to be relevant to disease progression. Ideally, PS would be conducted in ex vivo or in vivo formats but the prospects are currently considered prohibitively cumbersome and expensive. The major strength of PS is that the screen itself impartially selects the targets and mechanisms to regulate the phenotype, and does not depend upon a target selection bias based on the published literature. Importantly, by screening in a living environment, many of the potential targets regulating the biological processes of interest are present in the experiment, explaining why PS generally has higher hit rates than conventional screening. The screen output will rank order the potential mechanisms by potency
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and magnitude of efficacy, and the most profound mechanisms will spontaneously percolate to the top. No target bias, no mechanism of action, and no monogenic assumptions, are required.
PS technology developments Important technical advances have matured over recent decades to foster a growing enthusiasm for in vitro PS. Automated high content screening technologies make possible rigorous high-throughput multi-parameter screening [26]. In a single screen, multiple endpoints related to signal transduction, morphologic features, cell proliferation, metabolism, cell health, and organelle function can be collected simultaneously. This allows screening for broadly defined profiles of interest; for example, identifying compounds that promote growth factor signaling, block reactive oxygen species generation, but do not promote cell proliferation. Since human primary cells have historically been exceedingly difficult to acquire and have limited proliferative capacity, PS has primarily been restricted to transformed tumor cell lines or non-human tissue sources [27]. Therefore, the aspiration of conducting PDD in human tissue was all but impossible. With the discovery of human induced pluripotent stem cell (hiPSC) methodology [28], and the capacity of hiPSCs to be controllably differentiated down multiple lineages, for example, neurons and cardiomyocytes, the possibilities of an unlimited source of more physiologically relevant cell types for PS emerged [29]. The subsequent introduction of commercially available hiPSC derived differentiated cell lines translated the discovery into readily available quality controlled reagents for potential use in PS. One major source of consternation in PS is the lack of target knowledge related to hits coming out of a screen. Even if hits are interacting with multiple unrelated targets, at least some target knowledge would both enable the medicinal chemistry lead optimization process and facilitate subsequent biomarker development for preclinical and clinical efficacy studies. Target deconvolution methods have made significant strides in the last decade [30]. One of the most exciting developments is the maturation of stable isotope labeling by amino acids in cell culture (SILAC) [31] based chemical proteomics [32] (CP) for target identification. CP is an affinity chromatography, mass-spectrometry based proteomics method that has demonstrated the capacity to identify small molecule protein interactions. The method has been shown to be sensitive and specific, and to have the potential to bridge the ‘unknown targets’ gap between PS and TBDD.
Caveats of PDD A central limitation of in vitro PDD is the modeled cellular phenotype. Does the cellular phenotype model a relevant aspect of disease? Assay design is generally based upon a published scientific hypothesis about the relative importance
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of major mechanisms, pathways, and cellular phenotypes to disease progression. As with TBDD, this is perilous territory. Although the cells are living and preferably of human origin, they are grown on plastic and in two dimensions, and lack the physiologic cues of multi-lineage neighboring cells, the milieu of soluble secreted factors, and mechanical stress. The iPS technology often generates immature cells of mixed lineage and variable differentiation [33] and their ultimate utility for drug discovery will be determined over the coming decades. Short of clinical outcome studies with drugs derived from specific phenotypic programs, it is not possible to know the drug discovery relevance of any given cellular phenotypic assay. At best, an interim risk mitigation strategy for any PDD program would include an early in vivo validation phase where compounds derived from the phenotypic screen are evaluated in disease relevant in vivo models. Another significant assay limitation of PS is whether the compounds being screened represent enough chemical complexity to engage the desired transformational mechanisms. Ultimately a PS is limited by the chemical complexity of the compounds in the screen. More challenging phenotypes like regeneration may require input from multiple mechanisms and may not be accessible with the limited chemical complexity of a single molecule. For the aforementioned discovery of hiPSCs [28], the requirement was for four transcription factors to compel a fibroblast to become a stem cell, with each transcription factor likely governing 10s to 100s of genes. The notion that a disease phenotype will be meaningfully engaged by single compounds acting through single targets would appear to conflict with the integrated, overlapping, and redundant context of known signaling mechanisms. This is beginning to fuel an interest in screening combinatorial chemical collections [34,35]; however, it remains to be demonstrated whether screening in more complex chemical space can improve access to more profound phenotypic modulation.
Heart failure (HF) and PS HF drug discovery is exceedingly well positioned to capitalize on the progress of PS. HF disease progression has been meticulously characterized and a massive spectrum of pathways and mechanisms around perturbed metabolism, apoptosis, Ca++ cycling, oxidant stress, fibrosis, and inflammation have been identified [36]. Importantly, reagents for configuring high-content screens are readily available for most of the above mechanisms, and as mentioned previously hiPSCderived cardiomyocytes can now be acquired commercially. One PS format that is particularly well suited for HF is the connectivity map (cMAP) concept [37,38]. Over the last several years, cMAP has been introduced as a potential alternative drug discovery model. The cMAP method is based upon connecting human disease with small molecules through the language of gene expression. There are three components necessary for cMAP implementation; (1) a gene-expression www.drugdiscoverytoday.com
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Ischemic HF(mRNA Log fold change)
1.0 0.5 0.0 n = 1818 r = 0.99 P < 0.0001
-0.5 -1.0 -1.0
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Figure 2. Gene expression fold changes observed in end-stage human heart failure populations compared to non-failing controls: Ischemic HF gene expression changes averaged over 86 patients. Idiopathic HF gene expression changes averaged from 108 patients. Plot represents 1818 genes. These data are re-plotted from Hannenhalli et al. [40].
signature from human disease; (2) compound elicited gene expression from a relevant cultured cell type on the same signature gene set, and; (3) a pattern matching algorithm designed to query the two datasets and aimed at identifying and ranking compounds that influence the disease signature. The method can be configured to probe a small focused set of genes associated with a particular pathway or 100s to 1000s of genes associated with the broad disease phenotype. The best example of cMAP success to date was reported by Kunkel et al. [39], where ursolic acid was identified by the method as an anti-atrophy agent capable of attenuating skeletal muscle atrophy in vivo. For HF, one of the three components of cMAP is in place. End-stage HF gene expression data have been published [40] for which the fidelity is excellent (Fig. 2). The exceptional gene expression reproducibility is a strong reason to consider the approach for HF drug discovery. The missing cMAP elements for HF are a gene expression database gleaned from screening a pharmaceutical scale compound collection in human cardiomyocytes and the pattern matching algorithm to mine the data. If both were in hand, the method may allow researchers to mine in minutes (in silico) a centralized database for compound effectors of HF.
Conclusion Despite an unprecedented half century of expanded knowledge and capabilities the efficiency of drug discovery continues to decline. The improved ability to dissect and disentangle the pathways and mechanisms that govern pathophysiology has not adequately translated into predicting drug efficacy in the clinic. TBDD appears to do an efficient job of filling the industrial pipeline, but a poor job of reproducibly generating valid medicines. The most important step in drug discovery is widely considered to be the selection of e202
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the therapeutic drug target. Selecting a target that when modulated will translate into a clinically relevant patient benefit on top of the standard-of-care agents is a daunting task. Each generation of target-selected drugs developed independently of each other and decades apart, needs to provoke the complementary and additive physiologic change to be detectable in clinical trials. The data suggest that the current approach is not a sustainable strategy [14], and the two underlying pillars of modern drug discovery, the published scientific literature, and TBDD, are in profound need of reconsideration. PDD is one possible re-addition to the repertoire. By conducting drug discovery in living systems, target prognostication is not necessary and the dependency on published literature is minimized. PS can be viewed as a high-throughput natural selection process for chemical space and biological mechanisms regulating disease phenotypes. The raw data output identifies the most potent and biologically relevant mechanisms. Importantly, since a drug could be impacting multiple targets simultaneously, the concept of singletarget selection becomes less meaningful and target selection falls from the most important step in drug discovery to an enabling step. The challenges to industry posed by PDD are significant. The familiar pharmaceutical discovery model employs tools to effectively optimize drug target interactions; these become less effective with an emphasis on PDD. If target identification is lacking, then more expensive, time consuming, and higher risk cell based optimization schemes would need to be employed. A commitment to succeed with PDD may require different industrialized processes altogether, from highthroughput in vivo assessments to combinatorial screening. Given the inertia of the single target single compound industry mindset, and the deep reductionist roots of scientists, the idea of developing drugs in the absence of target knowledge or in the context of simultaneous multiple independent unknown targets will represent a formidable challenge to future drug discovery.
Conflicts of interest None.
Funding source Authors are employees of GlaxoSmithKline.
Acknowledgments Special thanks to Tim Chendrimada and Bob Willette for editorial assistance.
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