www.ScienceTranslationalMedicine.org 6 February 2013 Vol 5 Issue 171 171cm1 1

C O M M E N TA R Y “ ”

THE CURRENT MODEL IS DEFUNCTT ere is abundant evidence that the current drug development system is inadequate, un-sustainable, and failing those who need it most: people af ected by diseases (1). Cur-rent projections estimate that it takes more than 1 billion U.S. dollars and between 10 to 15 years to bring a new drug to market (2, 3). Attrition rates of new drugs entering phase I are as high as 92% (3). Further, many diseases in need of new therapies are under-served by the current drug development model. For example, the vast majority of the estimated rare 7000 genetic diseases lack any available medical therapy, in part because the small and geographically dispersed pa-tient populations make it dif cult to amass suf cient data for clinical and translational research, and a lack of clear reimbursement strategies dilute most f nancial incentives (4). At f rst glance, these challenges may ap-pear to be specif c to Mendelian disorders. However, as genomic tools stratify disease, “common” diseases segregate into hundreds of rarer ones.

Although external pressures—increased regulatory burden and the economic down-turn—undoubtedly hamper the develop-ment and approval of new therapies, a fun-damental problem with drug development arises from the design of the system itself, including challenges in translational re-search, regulatory science, and reimburse-ment. To begin to address these challenges, we used the extensive f ndings of the Insti-tute of Medicine (IOM) (5) as a basis for engaging senior management from (i) 60 biotechnology and pharmaceutical compa-nies, (ii) the U.S. and European government agencies, (iii) health-care payers, (iv) patient advocacy groups, and (v) academic scien-

tists and administrators in surveys, salons, and structured interviews. As a result of this information gathering, we propose a new model for depicting the drug development process that ref ects the primary concerns of these stakeholders: that the ecosystem is larger than any one stakeholder group and needs to be better networked.

e current drug development system is a ref ection of a deeply ingrained culture that was useful in the age in which it was estab-lished: the industrial age, which was char-acterized by a scarcity of raw materials and robust competition in all industries. Now, information is the major commodity, and its abundance necessitates a shif from competi-tion to open-source network models. ese types of models have been successful in other information industries: music, publishing, semiconductors, and sof ware development. Models are important because they convey fundamental truths about a system and give resources (both human and material) a struc-ture around which to coalesce.

A NEW MODELDrug development is most of en depicted as a closed linear path with divided segments from target identif cation to approved com-pound. However, this linear pipeline is a gross oversimplif cation of a complex pro-cess, and those who are intimately involved in drug development use network models and management tools with parallel, itera-tive, and self-learning components to or-chestrate their projects. Successful drug development in the networked information age requires teams of basic and translational scientists; clinical services; policy, regula-tory, and reimbursement specialists; and consumers, patients, and advocates. ese teams require a model that is suf ciently complex but allows these normally disparate players to assemble.

To illustrate these observations, we cre-ated a networked systems model called Nav-

igating the Ecosystem of Translational Sci-ences (NETS) (Fig. 1). is model is not the only possible model of drug development but is of ered as a representation that ref ects a culture of openness and transparency, seeks to alleviate misaligned incentives, ac-knowledges the nuances of the process, and provides a map for creating an open, col-laborative, and coordinated system for drug development in the 21st century.

NETWORK APPROACH e NETS model of drug development provides a systems and network perspec-tive that transcends a focus on traditional components. Systems thinking requires that drug development be viewed as parts of a whole, and the network perspective is manifested in a collection of intercon-nected processes, with iterative feedback loops, rather than a series of discrete steps. By placing emphasis on connections rather than boundaries, the system becomes more integrated and ef cient.

For example, in the NETS model an approved compound is a junction rather than an end point or discrete boundary line. e junction links the end of one re-search study with the beginning of another and promotes essential data collection in a postmarket phase for the f rst product and perhaps new development for a second one. e resources used to provide care to pa-tients can be harnessed and repurposed to generate needed clinical data, for example, to fuel outcome studies or biomarker re-search to distinguish responders from non-responders. In doing so, therapeutic devel-opment and health care become a single learning system capable of using the clinical data it generates to improve patient care (6). e system also incentivizes more ef cient research strategies, such as clinical trials that require fewer participants and less time to establish ef cacy before receiving provi-sional approval for continued surveillance in all participants (7). In short, this network approach emphasizes synergies that can be exploited for improved ef ciency.

e NETS model highlights activities that benef t from inter- as well as intrastake-holder collaborations. Unlike the traditional model in which rigid boundaries discourage stakeholder interactions, each of the inter-faces between the various “neighborhoods” [highlighted in dif erent colors on the map (Fig. 1)] presents an opportunity for stake-holders to work together in a dynamic net-work. As an example, patient registries and

D R U G D E V E L O P M E N T

An End to the Myth: There Is No Drug Development PipelineKristin Baxter,1,2 Elizabeth Horn,1 Neely Gal-Edd,1 Kristi Zonno,1,3 James O’Leary,1 Patrick F. Terry,4 Sharon F. Terry1*

*Corresponding author. E-mail: sterry@geneticalliance.org

1Genetic Alliance, Washington, DC 20008, USA. 2Booz Allen Hamilton, McLean, VA 22102, USA. 3Myriad Genet-ics, Salt Lake City, Utah 84108, USA. 4PXE International, Washington, DC 20008, USA.

A new map is presented for creating an open, collaborative, and coordinated system for drug development.

by guest on August 26, 2020

http://stm.sciencem

ag.org/D

ownloaded from

www.ScienceTranslationalMedicine.org 6 February 2013 Vol 5 Issue 171 171cm1 2

C O M M E N TA R Y “ ”

Fig. 1. Using NETS to catch new therapies. This model provides a map for creating an open, collaborative, and coordinated system for drug develop-ment in the 21st century. Collaborative activities include basic science and therapeutic target discovery (orange and green), therapeutic discovery and nonclinical research (blue), regulatory science (purple), and clinical research (pink)—all of which impinge on a single goal: Patient access to modern therapeutic strategies. Dotted lines are work-arounds or alternative pathways. A Map with links to resources is available online at www.genetical-liance.org/nets_fullview.

Biomedical

informaticsHypothesis

Cell lines Animal

models

Molecular

pathway

PhenotypesGenotypes

IRB approval

Recruitment

Contractual and

legal agreements

Qualifed

investigators

and study staf

Phase 1 clinical

trials (PIC,

safety, dose)

Phase 3 clinical

trials (safety,

efcacy)

Incorporation

into clinical

practice

Phase 2 clinical

trials (safety,

efcacy, dose)

Participant

enrollment

Updated natural

history

Identifcation of

subpopulations

Responders,

nonresponders

Phase 4

(postmarketing)

Standard

of careDiscontinue

Rejected

application

NDA or BLA New

indication

IND

application

Trial design

Therapeutic

and clinical

end points

Outcomes

measures

Clinical trial

planning and

preparation

Assay

developmentHTS assay

Misses

Compound

libraries

Approved

compound

Unapproved

compounds

(NCE/NME)

Acute

toxicology

Safety Dose

Efcacy / Proof

of concept

cGMP manufacturing

GLP preclinical

studies

PK

(ADME)

Pharmacology

Sustained

product availabilityAccess to

therapies

In vitro / in vivo

pharmacology

Clinical cohort,

development,

clinical research,

and regulatory

requirements

Therapeutic

discovery and

nonclinical

research

Study

sponsor

Researcher/

investigator

Informed

consent

FDA: reporting,

meetings,

amendments

Insurance coverage

and reimbursement

Of label use

Lead

optimization

Repurposing or

new to country

FDA review

Data mining

Dynamic

reconsent

Chemistry,

manufacturing,

and control

Medicinal

chemistry

Non-GLP studies

Long-term

toxicology

(ongoing)

Basic science: Research

and knowledge base

High-throughput

screening

CR

ED

IT: R

EN

DE

RE

D F

RO

M A

UTH

OR

S’ M

AP

BY

Y. H

AM

MO

ND

/SC

IEN

CE

TR

AN

SLA

TIO

NA

L M

ED

ICIN

E

by guest on August 26, 2020

http://stm.sciencem

ag.org/D

ownloaded from

www.ScienceTranslationalMedicine.org 6 February 2013 Vol 5 Issue 171 171cm1 3

C O M M E N TA R Y “ ”biospecimen repositories, which lie at the interface between basic research and clini-cal studies, are two areas that are ripe for a multistakeholder collaborative ef ort (8). Of en, biomedical researchers are not well connected with potential trial participants, and few resources exist to provide transla-tional researchers, in academia or industry, with the f nancial resources needed to sus-tain registries and biorepositories over time or to characterize cohorts to determine the best validated biomarkers.

ROLE FOR DISEASE ADVOCACY ORG ANIZATIONSDisease advocacy organizations (DAOs) are trusted agents and well connected to po-tential clinical research participants. ese groups have a long-term, vested interest in a particular disease, making them ideal can-didates to serve as the stewards of registries and repositories (9–11). DAOs are aware of, and part of the accelerant for, participa-tory research principles; they are leading research by providing funding, generating hypotheses, and developing tools and have several distinct advantages, including an enduring trust relationship with the disease community.

Engaging the DAO during the early stages of drug discovery and preclinical development can shorten timelines and capitalize on the synergies inherent in en-gaging all stakeholders in parallel tracks simultaneously. Likewise, the latent condi-tion-specif c knowledge and insight pres-ent within the advocacy community and advocacy-run registries can be used to both answer research questions and generate new hypotheses (10, 12). e potential impact is profound because decreasing costs overall and shortening the lengths of phases II or III—in which delays of en occur because of an inability to meet targeted enrollment numbers—could potentially reduce the cost of new molecular entities by $200 million or more (13).

REDEFINING DISEASEBreaking down the silos between diseases, which in many cases obscure the common underlying molecular pathology, would al-low pathway and phenotypic therapies to be developed (14). e same type of systems thinking in the NETS model is also needed to improve our understanding of the under-lying biology of disease. Cells and organ-isms are not composed of neat linear path-ways, but rather, are complex systems, made

up of highly interconnected circuitry with multiple feedback loops capable of buf ering a surprising amount of perturbation (15). e distinctions we make between diseases are largely based on their phenotypic char-acteristics and obscure the fact that dif erent diseases can share biological pathways (14). Because underlying biology is shared across diseases, it is important to also approach drug development from the vantage point of understanding biological networks, in addi-tion to the traditional disease-by-disease ap-proach or by continuing to characterize the pathways that have already been extensively characterized.

SHARING DATA AND RESOURCESCommon mechanisms among diseases pave the way for a new approach to drug devel-opment that relies heavily on common in-frastructure, shared tools, and a willingness to reprioritize research so that the aim is to pave the way for better therapies and thus improved patient care. e NETS model makes it readily apparent that addressing challenges in a more networked manner will necessitate shared, open-source infrastruc-ture and tools to address these common obstacles. Shared data sets will be much larger and more powerful than any single data set that individual laboratories or or-ganizations can hope to assemble (15, 16). Common tools made available to all, such as high-quality antibodies, can dramatically improve the breadth and depth of research that can be accomplished (17).

e precompetitive area of clinical trials needs to be expanded to phase IIb so that pharmaceutical companies compete to pro-duce the best drug, rather than competing to identify the best drug targets (18). An extended precompetitive space requires ac-cess to information that previously has been considered proprietary, such as data from preclinical studies and failed clinical trials. Examples from other industries, such as the SEMATECH collaborative ef ort among semiconductor manufacturers, illustrate the strides that can be made through precom-petitive collaborations. e SEMATECH experience has highlighted that stable fund-ing through public-private partnerships, ac-cess to universities in order to draw on the creativity of academia, and providing incen-tives to attract the most quali$ ed people are critical for collaborative ef orts to succeed (19). Drug development needs a sustainable system that requires and rewards precom-petitive collaborations with well-aligned

incentives that bene$ t all participants. En-acting these dramatic changes, however, will not be simple and will require that we make equally dramatic changes to the incentives and reward structure that drives drug devel-opment. Citizen scientists are increasingly interested in sharing data and in transpar-ency (20). is can be a positive catalyst for change in the drug development ecosystem.

ENCOURAGING RISKY EXPLORATIONPublicly funded research reported in the scienti$ c literature provides the foundation for private-sector drug development. Yet, the majority of publicly funded research continues to focus on the proteins that have already been extensively characterized, leav-ing a large pool of potential drug targets un-explored. For example, of the 500 kinases encoded by the human genome, more than 65% of the kinase papers published in 2009 focus on the 50 proteins that were exten-sively studied in the early 1990s (17). us, the vast majority of this research continues to concentrate on a very small subset of the genes and proteins implicated in human dis-ease. e lack of research on the uncharted territory within the human proteome is not from lack of interest. Instead, it originates largely as a by-product of the current public sector funding mechanisms, which require applicants to submit an extensive back-ground and rationale for grant proposals. With little known about this “dark matter” of the proteome, research proposals in these uncharted waters are virtually guaranteed to remain unfunded. us, these unknown proteins remain uncharacterized, limiting the potential pool of drug targets.

THE HEAVY LIFT: CULTUREIt is simplistic to say that a conceptualiza-tion will have any meaningful impact on an entire system, particularly one that is so dysfunctional. e NETS model is simply of ered as a means to a more important end: the culture shif required to accelerate ther-apy development. is reimagining requires risk in order to move from traditional inter-actions, competitive lines, intellectual prop-erty claims, and ego to transparent collabo-ration that remains focused on the end goal of accelerating therapy development. What if the current research and development sys-tems are released to organically realign with society’s need for therapies?

e goal of drug development is clear and uncontroversial: safe and ef ective therapies delivered to consumers in a way that is time-

by guest on August 26, 2020

http://stm.sciencem

ag.org/D

ownloaded from

www.ScienceTranslationalMedicine.org 6 February 2013 Vol 5 Issue 171 171cm1 4

C O M M E N TA R Y “ ”ly, cost ef ective, and sustainable. e NETS model provides a framework for thinking about necessary connectivity. To enable suc-cess, each contributor to the process must acknowledge his or her responsibility to the success of the whole. Every part must work together in order to form a cohesive and robust drug development system capable of shepherding therapies from discovery to cure. e public needs to become empowered to transition from subjects into active partici-pants. Financial incentives will follow suc-cesses. e start of this brave new world re-quires leadership from the public and private sector—individuals and organizations willing to take enormous risks to move beyond the status quo. Risks are something individuals, families, and communities suf ering from disease understand very well. It is time to pool the risks and accelerate the bene$ ts.

REFERENCES AND NOTES 1. B. H. Munos, Pharmaceutical innovation gets a little help

from new friends. Sci. Transl. Med. 5, 168ed1 (2013). 2. M. Kessel, The problems with today’s pharmaceutical

business—An outsider’s view. Nat. Biotechnol. 29, 27–33 (2011).

3. B. Hughes, 2009 FDA drug approvals. Nat. Rev. Drug Dis-cov. 9, 89–92 (2010).

4. C. B. Forrest, R. J. Bartek, Y. Rubinstein, S. C. Groft, The case for a global rare-diseases registry. Lancet 377, 1057–1059 (2011).

5. IOM Forum on Drug Discovery, Development, and Trans-lation (www.iom.edu/Activities/Research/DrugForum.aspx); Cancer Policy Forum (www.iom.edu/Activities/Disease/NCPF.aspx); Roundtable on Translating Ge-nomic-Based Research for Health (www.iom.edu/Activities/Research/GenomicBasedResearch.aspx); Rare Disease Study (www.iom.edu/Activities/Research/OrphanProductResearch.aspx).

6. W. A. Goolsby, L. Olsen, M. McGinnis, Clinical data as the basic staple of health learning: Creating and protecting a public good: Workshop summary, IOM Roundtable on Evidence-Based Medicine (Series), Institute of Medicine (2011); www.iom.edu/Reports/2011/Clinical-Data-as-the-Basic-Staple-for-Health-Learning.aspx.

7. A. Roses, “Personalized medicine: Elusive dream or immi-nent reality?”: A commentary. Clin. Pharmacol. Ther. 81, 801–805 (2007).

8. M. A. Hamburg, F. S. Collins, The path to personalized medicine. N. Engl. J. Med. 363, 301–304 (2010).

9. S. F. Terry, C. D. Boyd, Researching the biology of PXE: Partnering in the process. Am. J. Med. Genet. 106, 177–184 (2001).

10. S. F. Terry, P. F. Terry, K. A. Rauen, J. Uitto, L. G. Bercovitch, Advocacy groups as research organizations: The PXE In-ternational example. Nat. Rev. Genet. 8, 157–164 (2007).

11. D. C. Landy, M. A. Brinich, M. E. Colten, E. J. Horn, S. F. Terry, R. R. Sharp, How disease advocacy organizations participate in clinical research: A survey of genetic orga-nizations. Genet. Med. 14, 223–228 (2012).

12. S. F. Terry, E. J. Horn, J. Scott, P. F. Terry, Genetic Alliance Registry and BioBank: A novel disease advocacy-driven research solution. Per Med 8, 207–213 (2011).

13. S. M. Paul, D. S. Mytelka, C. T. Dunwiddie, C. C. Persinger, B. H. Munos, S. R. Lindborg, A. L. Schacht, How to improve R&D productivity: The pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).

14. S. F. Terry, Accelerate medical breakthroughs by ending

disease earmarks. Nat. Rev. Genet. 11, 310–311 (2010). 15. S. H. Friend, The need for precompetitive integrative bio-

network disease model building. Clin. Pharmacol. Ther. 87, 536–539 (2010).

16. S. H. Friend, Something in common. Sci. Transl. Med. 2, 40ed6 (2010).

17. A. M. Edwards, R. Isserlin, G. D. Bader, S. V. Frye, T. M. Willson, F. H. Yu, Too many roads not taken. Nature 470, 163–165 (2011).

18. A. M. Edwards, C. Bountra, D. J. Kerr, T. M. Willson, Open access chemical and clinical probes to support drug dis-covery. Nat. Chem. Biol. 5, 436–440 (2009).

19. Establishing precompetitive collaborations to stimulate genomics-driven product development—Workshop summary. IOM Roundtable on Translating Genomic-Based Research for Health (2010); www.iom.edu/Re-ports/2010/Establishing-Precompetitive-Collaborations-to-Stimulate-Genomics-Driven-Product-Development.aspx.

20. S. F. Terry, P. F. Terry, Power to the people: Participant ownership of clinical trial data. Sci. Transl. Med. 3, 69cm3 (2011).

Acknowledgments: The men, women, and children who are waiting for therapies keep us focused on seeking more eff ec-tive solutions. The authors acknowledge the many individuals who contributed to the development of NETS, including lead-ers from industry, advocacy organizations, and government agencies in the United States and abroad. Special thanks to C. Austin, D. Baker, F. Collins, S. Groft, and A. Guttmacher for the many fruitful discussions that planted the seeds for NETS.

Citation: K. Baxter, E. Horn, N. Gal-Edd, K. Zonno, J. O’Leary, P. F. Terry, S. F. Terry, An end to the myth: There is no drug devel-opment pipeline. Sci. Transl. Med. 5, 171cm1(2013).

10.1126/scitranslmed.3003505

by guest on August 26, 2020

http://stm.sciencem

ag.org/D

ownloaded from

An End to the Myth: There Is No Drug Development PipelineKristin Baxter, Elizabeth Horn, Neely Gal-Edd, Kristi Zonno, James O'Leary, Patrick F. Terry and Sharon F. Terry

DOI: 10.1126/scitranslmed.3003505, 171cm1171cm1.5Sci Transl Med

ARTICLE TOOLS http://stm.sciencemag.org/content/5/171/171cm1

CONTENTRELATED

http://stm.sciencemag.org/content/scitransmed/6/221/221ed2.fullhttp://stm.sciencemag.org/content/scitransmed/2/52/52cm26.fullhttp://stm.sciencemag.org/content/scitransmed/3/88/88mr1.fullhttp://stm.sciencemag.org/content/scitransmed/5/168/168ed1.fullhttp://stm.sciencemag.org/content/scitransmed/5/173/173fs6.fullhttp://stm.sciencemag.org/content/scitransmed/3/76/76cm10.fullhttp://stm.sciencemag.org/content/scitransmed/5/174/174ed3.fullhttp://stm.sciencemag.org/content/scitransmed/4/143/143cm8.fullhttp://stm.sciencemag.org/content/scitransmed/3/69/69cm3.full

REFERENCES

http://stm.sciencemag.org/content/5/171/171cm1#BIBLThis article cites 17 articles, 3 of which you can access for free

PERMISSIONS http://www.sciencemag.org/help/reprints-and-permissions

Terms of ServiceUse of this article is subject to the

registered trademark of AAAS. is aScience Translational MedicineScience, 1200 New York Avenue NW, Washington, DC 20005. The title

(ISSN 1946-6242) is published by the American Association for the Advancement ofScience Translational Medicine

Copyright © 2013, American Association for the Advancement of Science

by guest on August 26, 2020

http://stm.sciencem

ag.org/D

ownloaded from