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Expert Opinion on Drug Discovery
ISSN: 1746-0441 (Print) 1746-045X (Online) Journal homepage: http://www.tandfonline.com/loi/iedc20
Open Access Could Transform Drug Discovery: A
Case Study of JQ1
Zeeshaan Arshad, James Smith, Mackenna Roberts, Wen Hwa Lee, Ben
Davies, Kim Bure, Georg A. Hollander, Sue Dopson, Chas Bountra & David
Brindley
To cite this article: Zeeshaan Arshad, James Smith, Mackenna Roberts, Wen Hwa Lee, Ben
Davies, Kim Bure, Georg A. Hollander, Sue Dopson, Chas Bountra & David Brindley (2016)
Open Access Could Transform Drug Discovery: A Case Study of JQ1, Expert Opinion on Drug
Discovery, 11:3, 321-332, DOI: 10.1517/17460441.2016.1144587
To link to this article: http://dx.doi.org/10.1517/17460441.2016.1144587
Accepted author version posted online: 21
Jan 2016.
Published online: 16 Feb 2016.
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Date: 29 April 2016, At: 09:42
EXPERT OPINION ON DRUG DISCOVERY, 2016
VOL. 11, NO. 3, 321–332
http://dx.doi.org/10.1517/17460441.2016.1144587
REVIEW
Open Access Could Transform Drug Discovery: A Case Study of JQ1
Zeeshaan Arshada,b, James Smithc,d, Mackenna Robertsd, Wen Hwa Leea, Ben Daviesc,d, Kim Buree,
Georg A. Hollanderf,g, Sue Dopsonh, Chas Bountraa* and David Brindleyc,d,h,i,j,k*
Downloaded by [University of Southern Indiana] at 09:42 29 April 2016
a
Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, UK; bSchool of Medicine, University of St.
Andrews, St. Andrews, UK; cNuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford,
UK; dThe Oxford – UCL Centre for the Advancement of Sustainable Medical Innovation (CASMI), The University of Oxford, Oxford, UK;
e
Sartorius Stedim, Göttingen, Germany; fDepartment of Biomedicine, University of Basel, and Basel University Children’s Hospital, Basel,
Switzerland; gDepartment of Pediatrics, University of Oxford, Oxford, United Kingdom; hSaid Business School, University of Oxford, Oxford,
UK; iCentre for Behavioral Medicine, UCL School of Pharmacy, University College London, London, UK; jHarvard Stem Cell Institute,
Cambridge, MA, USA; kUSCF-Stanford Center of Excellence in Regulatory Science and Innovation (CERSI), San Fransisco, CA, USA
ABSTRACT
ARTICLE HISTORY
Introduction: The cost to develop a new drug from target discovery to market is a staggering
$1.8 billion, largely due to the very high attrition rate of drug candidates and the lengthy
transition times during development. Open access is an emerging model of open innovation
that places no restriction on the use of information and has the potential to accelerate the
development of new drugs.
Areas Covered: To date, no quantitative assessment has yet taken place to determine the effects and
viability of open access on the process of drug translation. This need is addressed within this study.
The literature and intellectual property landscapes of the drug candidate JQ1, which was made
available on an open access basis when discovered, and conventionally developed equivalents that
were not are compared using the Web of Science and Thomson Innovation software, respectively.
Expert opinion: Results demonstrate that openly sharing the JQ1 molecule led to a greater
uptake by a wider and more multi-disciplinary research community. A comparative analysis of the
patent landscapes for each candidate also found that the broader scientific diaspora of the
publically released JQ1 data enhanced innovation, evidenced by a greater number of downstream patents filed in relation to JQ1. The authors’ findings counter the notion that open access
drug discovery would leak commercial intellectual property. On the contrary, JQ1 serves as a test
case to evidence that open access drug discovery can be an economic model that potentially
improves efficiency and cost of drug discovery and its subsequent commercialization.
Received 19 October 2015
Accepted 18 January 2016
Published online 15 February
2016
1. The need for open access in therapeutic
development
The process of translation as it currently stands is
fundamentally flawed.[1–5] Despite recent advances
in our scientific understanding of disease pathology
coupled with dramatic technological breakthroughs in
patient care, companies have produced relatively few
novel drugs as genuine new treatments for existing
diseases.[6] With high attrition rates and soaring costs
for clinical trials, translational science budgets to
investigate novel drugs as opposed to existing and
alternative treatments are dwindling. This is partly
due to the current restrictive economic climate,
which currently hovers at a mere 3.5% of total basic
research and clinical development spending.[7] It is
clear that industry needs to develop new, sustainable
models for drug discovery to adapt.[8,9]
CONTACT David Brindley
*Joint senior authors
© 2016 Taylor & Francis
[email protected]
KEYWORDS
Open Innovation; Open
Access; Drug Discovery;
Healthcare Translation; JQ1;
Bromodomain; Epigenetics;
SGC (Structural Genomics
Consortium)
The average number of new molecular entities
(NMEs) approved each year has remained static at
22.7 per year between 2002 and 2010,[10] despite the
overall research and development budget doubling
within the same period.[7] Current estimates suggest
the cost of developing a drug to be a staggering $1.8
billion,[11] having increased at an annual rate of 13.4%
per annum since the 1950s.[12] There has been a period
of extensive merger and acquisition (M&A) activity in
recent decades [13] in an attempt to reduce expenditure and contain risks associated with drug development. Meanwhile, the number of new drugs approved
has still stagnated.[7] The patent expiry of blockbuster
drugs, the so-called ‘patent cliff’, for many leading pharmaceutical giants in the early twenty-first century led to
an estimated $113 billion in losses across the industry.
[14] Although disputed by some authors [15], the loss in
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Z. ARSHAD ET AL.
Article highlights
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The process of translation within drug discovery is currently too
costly and inefficient leading to a poor output of new drugs for
patients as well as less than optimal return on investment for
stakeholders especially in key therapeutic areas such as
oncology.
Open access is an emerging model of OI with the potential to
be an economically viable model that could be used by drug
developers.
To investigate the effects open access can have on drug development the literature and intellectual property landscapes of
open access drug candidate JQ1 was compared to conventionally equivalents: PRI 725 and MK 1775.
Our results found that openly sharing the JQ1 molecule led to a
greater uptake by a wider and more multidisciplinary research
community contributing to greater and more impactful research
into the molecule itself as well as its therapeutic target. This has
potential to improve understanding regarding the drugs full
repertoire of effects and uses within the body leading to
improved translation times and reduced attrition rates within
clinical trials.
Contrary to popular belief, the initial free availability of the JQ1
molecule actually led to greater downstream patenting. This
offers clear evidence that open access is a commercially viable
model of drug discovery that has the potential to lead to
improved commercial gain for drug developers.
This box summarizes key points contained in the article.
these revenues also contributes to the soaring costs of
drug development.[16] In addition, our increasing
understanding of disease has led to a desire to treat
diseases of a greater complexity and specificity, with
costly technology, such as monoclonal antibodies.[17]
These approaches often target diseases with low incidence and, therefore, may lead to lower reimbursement, potentially deterring industry investment in
these areas.[6]
While on average it takes 10–15 years to progress
a drug from target discovery to market,[11] many
drug candidates can take as long as 30 years to
develop from bench to first-in-man clinical trials.[18]
Increasing regulation complicating trial design and
authorization requirements are a significant factor.
[19,20] Many argue that burdensome regulations are
unnecessarily delaying drug translation: the time
required by an organization to gather data and submit a new drug application (NDA) increased from 3 to
7 years between 1962 and 1989.[21] Lengthening
timescales on returns may also dampen investors’
appetite to provide badly needed funding. By contrast, it is imperative to ensure that patient safety is
tantamount to prevent incidences, such as the thalidomide tragedy in the mid-twentieth century [22],
which resulted in new regulations and, notably, the
Kefauver Harris Amendment requiring a drug’s safety
and efficacy be proven before authorized.[23] A balance must be achieved that preserves patient safety
while retaining reasonable efficiency in translation so
that patients equally may benefit from therapeutic
progress and the translation process does not
become unsustainable.[7]
Compounding these problems is the high attrition
rates of new drug candidates.[24] On average, only
11% of drugs reaching phase I clinical trials gain
regulatory approval,[25] while phase II trials have
also have a staggering 25% attrition rate.[26] More
bleakly, only one in 24 newly investigated NME’s
reach market.[26] The consequential, significant loss
to the industry in terms of capital, other resources
and manpower are detrimental. Among the factors
responsible, is a lack of scientific understanding of
disease pathology, drug targets, toxicity and/or
mechanism of action for treatment efficacy.[27,28]
Beyond the science, however, there are factors that
relate to the current precompetitive model for drug
discovery in the pharmaceutical industry, which conducts unreported research behind closed doors
engendering secrecy while molecular structures are
patented. Multiple corporations, therefore, inadvertently often duplicate chemical compound investigations on the same molecule, thereby wasting money
and resources when the majority will fail and could
have been prevented if results shared.[29] Industry as
well as academic centers justify this superfluous
waste and duplication by the potential it may discover valuable competitive know-how and patentable
drugs notwithstanding the high attrition rates. An
example of this is the massive losses industry faced
while trying to develop an NK1 receptor antagonist
to treat pain.[29] If the industry had worked together,
this problem could potentially have been noticed
earlier – preventing any further wastage of time and
effort. Attrition rates vary meaningfully by therapeutic
area: cardiovascular drug candidates have a 20% success rate compared to 8% [25] in neurodegenerative
disease. This variation suggests different models such
as translation including open access, which may be
better suited to certain research fields than
others.[30]
The discovery of novel drug treatments of neurodegenerative diseases such as Alzheimer’s, therefore, is
potentially a ripe area for open access drug discovery
to share research efforts and avoid duplication.[31]
There are currently five approved drugs to treat
Alzheimer’s disease, which function only to slow
down progression.[32] Despite extensive efforts, with
a heavy focus on amyloid plaques as a target, no new
drug has been brought to market since the approval of
memantine, an N-methyl-D-aspartate receptor antagonist, in 2003.[33] With one of the worst attrition rates in
any therapeutic area at 0.4% during the period 2002–
EXPERT OPINION ON DRUG DISCOVERY
323
Possible Indications
Closed Model
Secrecy = long
lag before
community takes
interest
Serendipity=
uncoordinated
accumulation of
knowledge
•
•
•
•
Knowledge accumulation is linear, repetitive & slow
Longer to de-risk drug candidate
Limited access to global academia
Small, non-pooled funds
Closed
Healthcare Translation
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Possible Indications
Open
Open Probes =
faster adoption
and de-risking/
narrowing of
indication
Each square represents an
arbitrary amount of
effort/resources spent on
validating a new target for
different indications
Open Model
Target
Validation
•
•
•
•
Open access & freedom to operate ensures
much quicker adoption of drug candidate
Knowledge accumulation is exponential, less
repetitive and fast
Unlimited access to global academics
Larger pooled funds
Figure 1. Comparing the closed (upper half) model to the open (lower half) model: the free availability of open access tools in drug
discovery allows the research community to explore the drug candidate in multiple models of disease and report back through
publication. The increased research in the open model can lead to improved understanding of drug candidate and target and thus
lower attrition rates in clinical trials while allowing drug developers to focus on the most promising indications, whilst reducing the
level of effort (open squares), wastage, and duplication present to a greater extent in the closed model (adapted from Lee WH
(2015) Open Access Target Validation Is a More Efficient Way to Accelerate Drug Discovery. Permission granted for use.
2012 [34], there is a dire need to come up with a
solution to this age-related disorder in the context of
the current ageing population.[35]
To develop a solution to this and other devastating
diseases, the pharmaceutical industry, and in particular
the process of translation, needs to become more efficient. Although there is no definite remedy, open
access is an option that offers a promising alternative
to traditional models of drug development.[36,37] This
study investigates the impact of this model by comparison through literature and patent landscape analysis to
compare the development of JQ1, a drug developed
with no IP (Intellectual Property) restrictions until validated and conventionally developed equivalents.
2. Quantitative and qualitative assessment of
open access in drug development
2.1. Research and development stage – academic
impact
Open innovation (OI) is a multifaceted concept [38] that
is difficult to define because it represents a continuum
of states from completely closed systems at one end
and open access at the other.[39] Organizations utilize
the concept to different degrees by varying the permeability of the boundaries [36] where ideas and resources
can be exchanged.[38] Pharmaceutical companies such
as AstraZeneca have adopted OI methods through the
‘licensing in and out’ of resources to reduce risk and
share reward.[40] Open access is a narrow and more
precise aspect of OI. Its distinguishing feature is that it
involves no restriction on the use of information in the
public open domain, meaning no fee, licenses, or conditions for use or access to information (see Figure 1).
Organizations such as the Structural Genomics
Consortium (SGC, University of Oxford, U.K.) and the
Single
Nucleotide
Polymorphisms
Consortium
(National Institute of Health, Bethesda, MD, U.S.A.)
have taken this OI concept of licensing technology platforms further to more controversially adopt a fully open
access drug discovery model.
JQ1, a BRD4 inhibitor, is an example of a drug candidate that was developed through open access methods.
The target BRD4 was initially explored through the open
access work between the SGC and GlaxoSmithKline (GSK,
Brentford, U.K.) in 2009. Just 11 months later this led to
the discovery of JQ1 and its investigation to treat the
rare cancer NUT midline carcinoma [41] at the Dana
Farber Cancer Institute, where it was then made publically available. It is currently in phase I/II clinical trials as
well as investigation for the treatment of several other
malignancies.
To investigate the impact that open access can have
on the process of drug translation, the development of
JQ1, since it was discovered and made publically available, was compared to two conventional drug discovery
candidates: PRI 724 and MK 1775 (control drugs
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324
Z. ARSHAD ET AL.
candidates) that were never made available to the public domain. The control drug candidates were selected
via the following criteria that they must be: (i) first-inclass, (ii) used to treat cancer in the first instance, and
(iii) small molecules (<600 molecular weight). The initial
development of all drugs were compared, this time
being the point of target discovery to drug discovery,
to ensure similarity in terms of transition times up until
JQ1 was discovered and made available on an open
access basis to highlight differences from the point that
JQ1 was made publically available. Two metrics were
employed to evaluate and compare the research and
development (R&D) stages of translation for each drug
molecule: the literature landscapes, and the intellectual
property patent landscapes. To further compare the
impact of an open access drug discovery model with
OI licensing models adopted by pharmaceutical companies, the open access JQ1 probe and the control drug
candidates were then compared to selumetinib, which
is currently being developed in AstraZeneca’s (London,
U.K.) OI pipeline.
Before going onto discussion, here we present a
short overview of the controls and selumetinib:
PRI-724, a beta- catenin inhibitor in the wnt-signaling pathway was originally implicated in colon cancer in
2004. It is currently in phase I/II clinical trials being
investigated in colonic cancer, pancreatic cancer, hepatitis C induced liver cirrhosis as well as a range of
hematological malignancies.
MK-1775 is a WEE1 inhibitor and was implicated in
cancer in 2009 when it was discovered that it would
prevent cancer cells making it across the G1/S cell cycle
checkpoint, and therefore, should be investigated as a
potential cancer treatment. It is currently in phase I/II
clinical trials involving a range of cancers including acute
myeloid leukemia (AML), chronic myeloid leukemia (CML),
gynecological, and gastrointestinal tract malignancies.
Selumetinib, a MEK inhibitor, is a part of
AstraZeneca’s OI pipeline and earlier this year received
orphan drug approval for the treatment of uveal melanoma – a rare intraocular cancer affecting various tissues in the eye. The drug is also being investigated in
clinical trials for thyroid cancer, KRAS mutation positive
lung cancer, and neurofibromatosis type 1.
Note for all drug candidates, the date they were
discovered is not known (apart from JQ1) and as such
the date of first publication regarding each drug candidate was taken as T0 to allow fair comparison between
them.
The first papers regarding the drug candidates in a
therapeutic context, and their citation metrics, were
identified using Web of Science software (Thomson
Reuters, New York, NY, U.S.A.) (see Table 1 for an overview of the search). A brief overview of the search
methodology and analysis is as follows. The first publication for each drug candidate was identified using
the Web of Science Software. The search was then
narrowed to include articles published within 4 years
from but not including the first publication year for
each drug candidate. The results were then manually
reviewed to confirm the relevance of search results and
to remove any duplicates. The cleaned results were
broken down by year, author, source title, and research
area for analysis. The search only included authors that
had more than two publications as the search criteria
had to be narrowed to allow the software to compute
the results for JQ1. It should be noted that one paper
was removed as it was deemed irrelevant in relation to
JQ1 and one duplicate author was also removed
for JQ1.
It is clear that, even when not accounting for the
number of years since publication, the paper regarding
JQ1 has a greater number of citations compared to the
controls, suggesting that open access can increase
Table 1. An overview of citation and publication analysis for each drug candidate that was carried out using Web of Science.
Drug
JQ1
Search terms
‘JQ1’ OR ‘SGCBD01’
‘PRI-724’ OR ‘PRI724’ OR
‘PRI 724’ OR ‘ICG-001’
OR ‘ICG001’ OR ‘ICG 001’
MK-1775
‘MK-1775’ OR ‘MK1775’ OR ‘MK 1775’
OR
‘AZD-1775’ OR ‘AZD 1775’ OR
‘AZD1775’
Selumetanib ‘Selumetinib’ OR ‘AZD6244’ OR
‘AZD-6244’ OR ‘AZD 6244’ OR
‘AZD6244’ OR ‘AZD-6244’ OR
‘AZD 6244’ OR ‘ARRY142886’ OR
‘ARRY-142886’ OR ‘ARRY 142886’
PRI-724
First paper
Selective inhibition of BET
bromodomains
A small molecule inhibitor of catenin/
CREB- binding protein transcription
Discovery of gene expression -based
pharmacodynamic biomarker for a
p53 context-specific anti-tumor drug
Wee1 inhibitor
Targeted agents for the treatment of
advanced renal cell carcinoma
Year of first
publication/years
analyzed
Citing articles
within years
required
Published articles
within years
required
2010/ 2011–2014
294
98
2004/ 2005–2008
29
5
2009/ 2010–2013
14
25
2005/ 2006–2009
18
48
EXPERT OPINION ON DRUG DISCOVERY
160
a.)
JQ1
140
350
325
b.)
300
250
PRI-724
200
Selumetinib
Number of Authors
60
Selumetinib
20
PRI-724
JQ1
PRI-724
JQ1
PRI-724
d.)
JQ1
Number of Citations
Number of Publications
30
Number of Journals
1200
JQ1
40
Number of Research Areas
1400
c.)
50
Number of Countries
Selumetinib
4
MK-1775
3
Selumetinib
2
Number of Years After First Publication
MK-1775
1
Selumetinib
0
0
JQ1
50
20
JQ1
100
40
MK-1775
150
60
PRI-724
80
Selumetinib
100
MK-1775
Number of Citations
MK-1775
120
1000
800
600
400
Selumetinib
MK-1775
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10
200
MK-1775
PRI-724
PRI-724
0
0
1
2
3
4
Number of Years After First Publication
1
2
3
4
Number of Years After First Publication
Figure 2. a.) Figure presents the number of citations first papers regarding each drug candidate in a therapeutic context gained in
the four years after publication b.) Figure presents the first papers regarding the drug candidates in a therapeutic context, and their
citation metrics, in the four years after publication c.) The figure represents the number of articles published with the said drug
candidate as a key word in the four years after its discovery d.) The figure represents the number of citations articles published with
the said drug candidate as a key word gained, in the four years after their discovery. T0 was taken as the beginning of the next
whole year after publication. Figure was created using the Web of Science Software.
dissemination and awareness of a drug candidate’s discovery. The results were then limited to the number of
citations each paper received within 4 years following
initial publication reporting discovery (Figure 2a).
The mean increase in the number of citations for the
paper regarding JQ1 was 73.5 per year compared to the
control average of 5.4 per year, suggesting that open
access leads to an increased rate of acknowledgment of
drug candidate discovery. Selumetenib did not fare
better either suggesting OI is not as effective an oncology drug discovery model, in this respect, as open
access. Further, open access may lead to wider, more
multidisciplinary community acknowledgment of drug
candidate discovery, as citations of JQ1 were from a
greater number of countries, research areas, and by a
greater number of authors in a broader range of journals (Figure 2b).
This broader diaspora of knowledge into the
research community brought by open access methodology has the potential to spark intellectual discussion
and experimentation into novel drug candidates. Lack
of innovation, after all, can be seen as another contributing factor to the pharmaceutical industry’s recent
decline [42], and as such drawing on a broader range
of skills and a greater variety of innovative minds to
investigate drug candidates could meaningfully
improve drug innovation. JQ1 appears to have
improved innovation (Figure 2c and d). Based on the
number of articles published naming the drug candidates in the 4 years after its discovery, open access
seems to have catalyzed further research into the application of JQ1 by comparison to the controls. Although,
selumetinib stimulated later research more than the
controls, JQ1 still outstripped selumetinib. This is one
of the factors that highlight the success of the R&D
stage of the development of JQ1.
JQ1 is not the only a drug candidate that has
evolved from open access models of innovation. The
Biomarkers Consortium (Foundation of the National
Health Institute, Bethesda, MD U.S.A.) is a public–private
research partnership. It aims to develop and gain
approval for biomarkers in the hope of developing
new drugs and diagnostic test paving the path for
personalized medicine by making their work available
on an open access basis. Similar motivations can be
seen in other open access organizations such as the
SGC that was set up in 2004 as a not for profit organization to determine the structures of proteins of medical interest, and then make the work available on an
open access basis with a priority being benefit to
society rather than capital or personal gain. One of
the SGC’s main focuses has been epigenetics, in particular areas that are under-studied. In addition, in this
respect, the findings have been ground breaking:
326
Z. ARSHAD ET AL.
40
Average Number of Citations per Article
BRD4 (JQ1)
35
beta-catenin (PRI724)
30
WEE1 (MK-1775)
25
MEK1/2
(Selumetinib)
20
JMJD3
15
10
5
0
0
1
2
3
4
5
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Year After First Publication
Figure 3. This figure shows data collected showing the number of citations per article published regarding each drug candidate’s
therapeutic target in the four years after each candidate’s discovery. Data was collected using the Web of Science software. The SGC
open access target JMJD3, while still in the very early stages of development, shows promising potential.
determining the structure of the ZMPSTE24, a nuclear
zinc metalloprotease involved in progeria [43] as well as
the discovery of the target JMJD3, a histone demethylase.[44] The small molecule GSK J1, has been shown to
inhibit JMJD3 and, thus stops the production of cytokines such as tumor necrosis factor that are associated
with the inflammatory response, and as a result been
implicated in inflammatory disorders such as rheumatoid arthritis.[44] This has been possible through the
open access work of the SGC and its partners:
GlaxoSmithKline and Cellzome, in this instance. This
success story behind open access highlights another
important part of how it has potential to improve R&D
though improved target validation.
GSK J1 and JQ1 have led to more impactful research
into their therapeutic targets (Figure 3 and Table 2
present an overview of the search with the methodology and analysis being the same as discussed previously). It should be noted that selumetinib did not
fare better than the drugs developed via conventional
drug development strategies in this respect. A better
understating of a drug candidates therapeutic target is
imperative to understanding its mechanism of action
and how it may behave in the human body in terms of
efficacy and toxicity. This understanding is often lacking
within the pharmaceutical industry as illustrated by the
fact that only 7% of approved small molecular drugs
have a known target.[27]
The effects of a thorough understanding of a therapeutic target during drug development can be exemplified by the success of Gleevec (Novartis, Basel,
Switzerland), a first-in-class treatment for chronic myeloid leukemia that was approved by the Federal Drug
Table 2. An overview of citation and publication analysis for
each drug candidate therapeutic target that was carried out
using Web of Science.
Search term
‘BRD4’ OR ‘Bromodomain 4’
OR ‘Bromodomain Four’
‘Beta-catenin’ OR ‘β-catenin’
WEE1
‘MEK1’ OR ‘MAPKK1’ OR
‘MKK1’ OR ‘PRKMK1’ OR
‘ERK activator
kinase 1’ OR ‘MAP kinase
kinase 1’ OR ‘MAPK/ERK
kinase 1’ OR ‘mitogenactivated protein kinase
kinase 1’ OR ‘ME
K2’ OR ‘MAPKK2’ OR
‘MKK2’ OR ‘PRKMK2’ OR
‘ERK activator kinase
2’ OR ‘MAP kinase kinase
2’ OR ‘MAPK/ERK kinase 2’
OR ‘mitogen-activated
protein kinase kinase 2’
JMJD3
Drug
JQ1
Target
BRD4
PRI-724
Beta-catenin
MK-1775
WEE1
Selumetanib MEK1/2
GSKJ1
JMJD3
Publications
within years
required
289
8970
242
1263
117
Association (FDA) in 2001.[45] Unlike other cancer
therapies, the effects of Gleevec are highly specific to
leukemic cancer cells that display the typical cytological
genetic mutation, the Philadelphia chromosome, present in 95% of those who suffer from the once fatal
CML. The drug targets the fusion protein bcr–abl, a
tyrosine kinase, created as a result of reciprocal translocation of chromosomes 9 and 22.[46] This high specificity was achieved through the advent of rational drug
design [47] in which, once a good understanding of the
therapeutic target is achieved, a systematic approach is
taken to identify an inhibitor.[45] The drug has also
EXPERT OPINION ON DRUG DISCOVERY
327
Table 3. An overview of patent search that was carried out using Thomson Innovation Software.
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Drug
Broad target
Dates searched
Search terms
CTB = (bromodomain* OR BRD1 OR BRD2 OR BRD3
OR BRD4 OR BRD5 OR BRD6 OR BRD7 OR BRD8 OR
BRD9 OR BRDT OR JQ1 OR extra terminal OR (extra
NEAR2 terminal) OR extrac OR (extra adj c)) NOT
DC = (C OR S OR T OR U OR V OR W OR X) and AD ≥
(20110101) and AD ≤ (20150615);
CTB = ((wnt NEAR5 pathway NEAR5 inhibitor) OR
(wnt NEAR5 modulator) OR ICG001 OR (ICG adj
001) OR (canonical NEAR5 wnt) OR (CBP NEAR5
catenin) OR ((β adj catenin) NEAR5 (inhibitor OR
antagonist)) OR ((beta adj catenin) NEAR5
(inhibitor OR antagonist)) OR ((wnt NEAR5
pathway) NEAR5 (inhibitor OR antagonist)) OR
(beta adj catenin NEAR5 signal*) OR (wnt NEAR5
(inhibitor OR antagonist)) OR (wnt NEAR5
signaling NEAR5 inhibitor)) not DC = (C OR S OR
T OR U OR V OR W OR X) and AD ≥ (20050101)
and AD ≤ (20090615);
CTB = (wee1 OR (wee adj 1) OR wee2 OR (wee adj
2) OR (wee adj like adj protein*) OR (wee1 adj
(kinase* OR tyrosine* OR inhibitor*)) OR MK1775
OR (MK adj 1775) OR (checkpoint adj kinase*)) NOT
DC = (C OR S OR T OR U OR V OR W OR X) AND
AD≥(20100101) AND AD ≤ (20140615);
JQ1
Bromodomain
01/01/2011–15/06/2015
PRI-724
WNT pathway
01/01/2005–15/06/2009
MK-1775
Checkpoint kinase
01/01/2010–15/06/2014
been approved for treating gastrointestinal tract stromal tumors [48] and many say has paved the way to
other targeted cancer therapies such as Herceptin
(Genentech Inc., San Francisco, CA, U.S.A.) and
Gefitinib (AstraZeneca; London, U.K.). Gleevec’s success,
as further illustrated by the fact that the drug gained
approval within just 3 years of entering clinical trials,
[47] can, at least in part, be attributed to good understanding of the drug’s target. This understanding, however, took years to build with the discovery of the
Philadelphia chromosome occurring decades before
the advent of Gleevec. Open access offers a possible
path to achieve better target understanding at a more
rapid rate by catalyzing research, without the wasteful
costs of duplicative work among industry.
2.2. Product discovery stage – intellectual property
Intellectual property is a key concern in models of OI.
[49] In many respects, IP is necessary to allow organizations to benefit financially from inventions and thus
spur R&D into new products that can benefit society
as a whole, while creating capital for organizations to
further innovate.[50] This is especially true within the
pharmaceutical industry. With multibillion dollar investments in molecules that can easily be recreated by
competitors, investors require proof of protection of
their assets. It is therefore natural to assume that
open access may jeopardize this, as it is a widely held
belief that an exclusivity period is required for an organization to profit from a new drug.[51]
Initial number of
DWPI families
Final
number of DWPI families
241
222
149
125
144
125
To investigate the impact that open access can have
on intellectual property practices, the IP landscapes of
both control targets and bromododomains were compared. In respect of bromodomains, these patents refer
to the patents filed after JQ1 was discovered and made
available on an open access basis in relation to its
therapeutic target. Patent families were harvested
regarding the broad target of each drug using
Thomson Innovation software (Thomson Reuters, New
York, NY, U.S.A.) in the four and half years after their
respective discoveries (Table 3 presents an overview of
the search).
The search includes both granted patents and published patent applications. Keyword searches were performed of a patent’s title, abstract and claims sections
across a complete global dataset of 50 patent authorities. The search time period was restricted to 4.5 years
from the year of discovery, as determined by the year of
first publication. Table 3 lists the patent search results
collapsed by Derwent World Patents Index (DWPI)
families, meaning one invention per family of member
patents. This enables a clearer assessment of the number of inventions that has resulted since discovery of
the drug molecule rather than the total number of
patent publications that has resulted, which does not
account for duplication when the same invention is
patented in multiple jurisdictions. All initial collections
of search results were cleaned by manual review and
irrelevant patent documents were removed.
The initial open access availability of JQ1 positively
impacted the number of patents filed regarding
Z. ARSHAD ET AL.
inventions involving bromodomains (Figure 4a).
Although the number of patents filed in the first year
after creation is similar for both JQ1 and controls, by
the fourth year a difference is apparent, with 105
patents regarding bromodomains and a mean of just
29 for MK 1775 and PRI 724 – highlighting the effect
that open access can have on downstream patenting.
Although concerns regarding open access and IP are
understandable, JQ1 is premise against this issue as the
availability of the drug has catalyzed the generation of
new inventions, while retaining the inventors’ rights to
benefit from them. Since patents hold value in themselves it could be concluded that open access has the
potential to create commercial value: increasing a company’s worth, while also encouraging investment.
Similar phenomena have occurred in other fields [52]
such as the software industry. Initially, the industry
thought that the value was in the hardware and then
software. However, when the computer-programming
licenses were opened to users, it actually encouraged
more innovation and better quality programming. This
was eventually more lucrative to the industry than if it
had clung to protecting proprietary ownership.[36]
The patterns highlighted in the R&D stage of JQ1’s
development seem to be reiterated in the product discovery stage (Figure 4b). The wider diaspora of knowledge created in the R&D stage of development has
continued along the process of translation, leading to
greater patent diversity in the product discovery stage.
This is testament to the fact that open access has the
potential to spark innovation. Greater patent diversity
100
3. Conclusion
In conclusion, the output of new drugs by pharmaceutical companies has been declining. Open access is an
emerging model of OI with the potential to be an
economically viable model that could be used by drug
developers to help combat this problem. To investigate
the effects, open access can have on drug development
100
a.)
b.)
90
Bromodomain (JQ1)
80
60
40
in terms of inventors and assignees can be seen as a
positive: preventing a single organization from dominating the market and encouraging other, often-smaller, organizations, to get a piece of the action. On the
other hand, it could also lead to patent thickets that
hinder development [53], although this does not yet
seem to be the case.
Comparing the maps (Figure 5) for all three drugs,
the field of bromodomains has had patents filed in a
broader range of therapeutic areas – ranging from male
contraception to kidney disease as well as a range of
different cancers. The themescape maps for both controls also have white peaks in certain therapeutic indications highlighting areas of high patent activity. This is
in contrast to the map concerning bromodomains, indicating that patent activity is not occurring in a single
area and as such, lowering the chances of patent thickets forming. This will allow the greater number of
patent holders (Figure 4b.) to take their ideas in different directions without having to navigate each other’s
patents – offering clear potential for more capital gain
as well as patient benefit.
WNT Pathway (PRI724)
Number of Patent Families
120
Number of Patent Families
80
70
60
50
40
30
20
10
Countries
Inventors
WNT Pathway
Checkpoint Kinase
Bromodomain
WNT Pathway
Checkpoint Kinase
1
2
3
4
Number of Years After First Publication
Bromodomain
0
WNT Pathway
Checkpoint Kinase
(MK-1775)
Checkpoint Kinase
0
20
Bromodomain
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328
Assignees
Figure 4. a.) Figure represents the number of patent families regarding each broad drug target in the four years after each drug
candidate discovery. Patent families were harvested using the Thomson Innovation software using key search terms for each target.
T0 was taken to be the date of first publication regarding the drug candidates. Datasets were manually sorted to exclude irrelevant
patent families based on the title, abstract and claims of each patent document. b.) This figure shows number of countries patents
have been filed in as well as the number of assignees and inventors of those patents for each drug class. The figure was produced
using the Thomson Innovation software.
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EXPERT OPINION ON DRUG DISCOVERY
329
Figure 5. Figure 5 is map representing the different therapeutic areas patents have been filed under for each respective drug class.
It aims to give a very broad overview of the therapeutic areas each drug target is implicated in, and can assist in visualizing clusters
of patents in particular therapeutic areas. Thomson Innovation software was used to search through the uses section of each patent
document and cluster therapeutic areas together that came up often. Each dot represents a patent and the contours proportionally
represent the degree of patent activity.
this study compared the literature and intellectual
property landscapes of open access drug candidate
JQ1 and conventionally developed equivalents: PRI
725 and MK 1775. Our results found that openly sharing
the JQ1 molecule led to a greater uptake by a wider
and more multidisciplinary research community contributing to greater and more impactful research into the
molecule itself as well as its therapeutic target. This has
potential to improve understanding regarding the
drugs full repertoire of effects and uses within the
body leading to improved translation times and
reduced attrition rates within clinical trials. This
improved R&D accelerated innovation, evidenced by
the broader range of therapeutic areas patents were
filed in regarding JQ1 and its target. More interestingly,
our study found that contrary to popular belief, the
initial free availability of the JQ1 molecule actually led
to greater downstream patenting something that offers
clear evidence that open access is a commercially viable
model of drug discovery that has the potential to lead
to improved commercial gain for drug developers in
the long run.
4. Expert opinion
It is apparent that certain therapeutic areas such as
neurodegenerative disease are facing a critical lack of
innovation in current drug development models. Open
access, although in its infancy, offers a promising
approach to tackle the problems facing drug developers in these areas. Although OI in industry improves
aspects of translation, as exemplified by AstraZeneca’s
OI candidate selumetinib, the benefits appear to be
reduced in comparison to open access. Improved R &
D leading to a greater understanding of therapeutic
target has potential, as exemplified by the success
story of Gleevec, to lead to shorter transition times in
the later stages of translation. Increased understanding
regarding drug candidate through more widespread
and impactful research has also the potential to reduce
attrition rates. Although open access may lead to
greater innovation and therefore potential drug candidates, hopefully by sharing knowledge the ones that do
not have potential to reach market will be weeded out
as early as possible, even as early as the preclinical
stages of development. In addition, it has been argued
that making early stage drug research available to
others, may spur additional investment and partnership
opportunities through avocation of an organizations
work.[54] All these have potential to reduce the exuberant costs associated with drug development: the benefits of which will be felt by all stakeholders in the
pharmaceutical industry.
IP practices are considered essential in allowing inventors to benefit from their discoveries – however over
patenting can be a hindrance to drug developers. By
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330
Z. ARSHAD ET AL.
allowing the initial stages of drug development to be
carried out in an open access environment everyone can
benefit from the availability of information regarding
drug candidates during a time of high risk and attrition.
The open access environment would allow this high risk
to be distributed among different stakeholders all the
while facilitating downstream patenting to allow inventors to benefit from their inventions at a later and more
commercially viable stage of drug translation. This could
in fact lead to greater profit for an industry that has been
suffering from declining reimbursement in the past few
decades. The success of organizations such as the SGC
and the Single Nucleotide Polymorphisms Consortium
shows that open access does in fact have power to
drive forward drug development and is therefore a concept that should be applied more by the pharmaceutical
industry.
Although open access is used within the pharmaceutical industry [31], it makes an appearance in less commercially viable areas such as neglected diseases. An
example of this is the open source malaria project. It
has been estimated that malaria killed 435,000 children
under the age of five in Africa in 2010 alone.[55] With
growing concern that the most fatal form of the disease
caused by the strain Plasmodium Falciparum is growing
resistant to current therapies in parts of the world [56],
the open source malaria project aims to test promising
new compounds in the treatment of malaria [57], and in
doing so, improve these molecules’ capabilities so they
can be translated into new therapies for suffering
patients. The success story of JQ1 suggests that other
areas that are more commercially viable may equally
benefit from truly opening up early stage candidate
validation as well.
To summarize, open access offers a promising path to
improve translation in drug development. If open access
is adopted more widely it offers potential in improving
the R&D stage of translation through catalysis of research
into drug candidate and target. In addition, there is
evidence to suggest that making early stage drug tools
available for free initially may ultimately lead to greater
commercial gain later on through greater downstream
patenting. This could lead to improvement in the number of novel therapies being approved that would benefit not only industry and investors, but also patients in
dire need of new therapy.
Financial and Competing Interests Disclosure
The authors have been funded by the SENS Research Foundation,
the Centre for the Advancement of Sustainable Medical
Innovation (CASMI) Translational Stem Cell Consortium, the
Said Foundation and the Oxford National Institute of Health
Research Musculoskeletal Biomedical Research Unit. In particular,
Z Arshad is funded by the SENS Research Foundation
Scholarship, CASMI Translational Stem Cell Consortium. DA
Brindley is a Said Fellow in Healthcare Translation at Said
Business School, the University of Oxford, Oxford, UK and
Managing Partner at IP Asset Ventures. The content outlined
herein represents the individual opinions of the authors and
may not necessarily represent the viewpoints of their employers.
DA Brindley is a stockholder in Translation Ventures Ltd.
(Charlbury, Oxfordshire, UK), a company that amongst other
services provides cell therapy biomanufacturing, regulatory, and
financial advice to pharmaceutical clients. DA Brindley is subject
to the CFA Institute’s Codes, Standards, and Guidelines, and as
such, this author must stress that this piece is provided for
academic interest only and must not be construed in any way
as an investment recommendation. Additionally, at time of publication, DA Brindley and the organizations with which he is
affiliated may or may not have agreed and/or pending funding
commitments from the organizations named herein. DA Brindley
declares funding from the SAID foundation and the National
Institute for Health Research, Oxford Musculoskeletal Biomedical
Research Unit. B Davies is a Senior Associate at IP Asset Ventures
Ltd. K Bure is a Global Director of Regenerative Medicine at
Sartorius Stedim Biotech. C Bountra is a Structural Genomics
Consortium Oxford Lead Scientist. The authors have no other
relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with
the subject matter or materials discussed in the manuscript apart
from those disclosed.
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