A white paper on Tuberculosis, antimalarial dataset analysis, the state of drug discovery and beyond. Prepared for www.collaborativedrug.com.

1. Sean Ekins, November 2010
1
Collaborative Drug Discovery:
The Rising Importance of Rare And Neglected
Diseases
Sean Ekins, Ph.D., D.Sc.
Image generated with www.Tagxedo.com

2. Sean Ekins, November 2010
2
Transformation of the Pharmaceutical Industry
We have seen in a decade a big transformation in the pharmaceutical industry brought about
by massive patent expirations of blockbuster drugs, increased generic penetration and concerns
about rapidly escalating healthcare costs. The industry has had to adapt by acquiring or partnering to
bring in innovation or products, diversify into generics and consumer products, move into emerging
markets and target complimentary businesses such as animal health [1].
Overall big pharma has looked at the key chronic diseases in the western hemisphere. Yet if
we think about healthcare from a global perspective there are still diseases (neglected) common in
the developing world that can in most cases be readily treated with available drugs, while resistance
is occurring and there is a need for new drugs to be developed.
Neglected infectious diseases such as tuberculosis (TB) and malaria kill over two million
people annually [2] while estimates suggest that over 2 billion individuals are infected with
Mycobacterium tuberculosis (Mtb) alone [3]. These statistics represent both enormous economic and
healthcare challenges for the countries and governments affected. Also there are thousands of
diseases that occur in small patient populations and are not addressed by any treatments [4], these
are classed as rare or orphan diseases. Neglected and rare diseases traditionally have not been the
focus of big pharma, while biotech and academia have been primarily involved in their drug discovery.
This situation is changing primarily because pharma’s see these rare or neglected diseases as
a way to bring in more revenue as well as improve public relations. Developing treatments for rare or
orphan diseases brings additional benefits for an industry struggling to bring new treatments to
market for more common diseases compared with the $100’s millions licensing drugs for other
diseases [5]. Within a very short time we have seen GSK make some relatively small investments in
rare diseases [6], as well as Pfizer [7] and several big pharmas and the WHO working together and
investing $150M on neglected diseases [8]. These are likely only the tip of the iceberg and more
substantial deals will follow in future to solidify the trend.
We are also witnessing shifts in how pharmaceutical research can be potentially accelerated or
made more efficient including by decentralizing research, engaging with the external research
communities through crowdsourcing etc. Overall there is a trend towards collaboration [9-13]. In
parallel there is a renewed interest in neglected disease research (on malaria, tuberculosis (TB),
kinetoplastids etc [14]) due to the significant influence of the US National Institutes of Health (NIH),
foundations such as the Bill and Melinda Gates Foundation, The European Commission and
increasing investment from pharmaceutical companies and others [14,15].
The dividing line between diseases that are rare or neglected may be very fuzzy. Traditionally
rare diseases have small patient populations though there is no global agreement on what this size is,
although in the US it is a disease that affects less than 200,000 people. Clearly such a ‘small’ market
size would make these diseases less marketable compared with cancers, cardiovascular disease,
diabetes etc. which number in the millions treated annually. For example treating addictions for
cocaine, methamphetamine and cannabis are major public health issues. Statistics suggest there are
over 1 million users of methamphetamine annually in the US. According to Dr. Phil Skolnick, the
Director, Division of Pharmacotherapies and Medical Consequencies of Drug Abuse, National

3. Sean Ekins, November 2010
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Institute on Drug Abuse, the development of treatments has been impacted by big pharma mergers
[16]. These resulted in a loss of research programs which along with other companies dropping CNS
programs, the reduction in enthusiasm for working in this area and low probability of success for
treating CNS disorders, all make the research environment difficult and suggest the need for new
research approaches.
In the neglected diseases space we are seeing academics and companies look at repurposing
compounds that are already approved for other indications, a strategy being applied elsewhere
[17,18]. The benefits of this are working on known druggable targets, availability of materials and
hence making it cheaper and faster. Even from the academic side there is transformation occurring in
which the NIH is requiring more collaborative research and proposals that reward the complete drug
discovery paradigm. Dr. Michael Pollastri at Northeastern University has suggested a distributed
model for neglected disease research in which different groups from other institutions contribute their
specific expertise [19]. Such research networks may not be unique to neglected diseases and could
be applied to more common diseases.
But what will be needed for all of these initiatives will be cost effective secure software for
selective sharing of chemical structures and data between collaborators who are likely to be chemists
and biologists by training [20].
Go forth and screen
Drug Discovery in the pharmaceutical industry has for over 20 years relied on the “brute force”
industrialization of the process rather than the “trial and error” serendipity which produced many
drugs in the past. This has reached a pinnacle in the high throughput screening (HTS) methods that
are in use across the industry both for finding hits against targets and counter screening. Ricardo
Macarrón, PhD, VP of Sample Management Technologies at GlaxoSmithKline has suggested
recently that HTS is now producing drugs and healthy return on investment producing from 20-70% of
leads for targets at GSK [21]. HTS is now a key component of the drug discovery process at GSK and
elsewhere. While there are many drugs recently approved by the FDA (mainly cancer or HIV
treatments) that came out of HTS hits in the early 1990’s. This suggests a sobering lesson for those
working on neglected and rare diseases, even if hits are found by HTS and its many variants today
(or for that matter any technology) a drug may not emerge for over a decade due to the lengthy
clinical trials and regulatory approval process. That is unless something dramatic changes to shorten
this process. Recently GSK released malaria HTS screening data which is hosted in the CDD
database (see later).
Even if a HTS campaign is run for a target or against a disease it is no guarantee of finding a
hit that can be optimized [22] and in vitro screens may not be very predictive due to an incomplete
understanding of disease biology and if it is a microorganism its replication status may be unknown.
Dr. Cifton Barry, Senior Investigator of the Tuberculosis Section of the National Institute of Allergy &
Infection Diseases and collaborators has explored the limits of target vulnerability in Mycobacterium
Tuberculosis (Mtb) using quantitative HTS (qHTS), in which a compound library is screened under
different conditions [23]. These conditions produce pan-active as well as condition selective hits. A
pairwise comparison showed that 90% of the hits could be found with glucose, cholesterol or low pH
screening conditions. Enabling the sharing of such large Mtb HTS screening data between

4. Sean Ekins, November 2010
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collaborators has been facilitated by CDD in a grant funded by the Bill and Melinda Gates
Foundation.
One common observation looking at hits and approved drugs for neglected diseases is that to
the experienced chemist many of the molecules appear ugly. As beauty is in the eye of the beholder it
is hard to define ‘ugly’ but the incorporation of rules for chemical reactivity or structural alerts [24-28]
can help. These filters in particular pick up a range of undesirable chemical substructures such as
thiol traps and redox-active compounds, epoxides, anhydrides, and Michael acceptors. Reactivity can
be defined as the ability to covalently modify a cysteine moiety in a surrogate protein [26-28]. Older
rules such as the Lipinski rule of 5 [29] have been more widely used. For example if you look at the
FDA approved drugs nearly 90% pass this rule (Figure 1). However the more Lipinski violations a
compound has also correlates with the increase in the failure using various pharmaceutical filtering
methods for reactive groups (Figure 2) [30]. So this suggests some undesirable or ugly molecules
may have additional risks such as undesirable promiscuity or toxicity [31].
Dr. Richard Elliott, Senior Program Officer at the Bill & Melinda Gates Foundation thinks that
the types of ugly compounds for neglected diseases may be related to having to cross multiple cell
walls, and have activatable warheads for activity that can act on multiple targets or via non specific
mechanisms [32]. Therefore such compounds may still become effective drugs and will require using
a variety of tools to understand the risk that can be assessed with computational, in vitro and in vivo
methods. He also thinks we need new chemistry to explore more chemical diversity.
75.2
13.5
5.7 5.5
0.1
0
20
40
60
80
100
0 1 2 3 4
Number of Lipinski violations
%ofFDAdrugs
Figure 1. Percent of FDA approved drugs (N = 2804) and Lipinski rule of five violations (≥ 2 = failure)
[30].

5. Sean Ekins, November 2010
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0
20
40
60
80
100
0 1 2 3 4
Number of Lipinski violations
%SMARTsfilterfailuresinFDAapproved
drugs
% Abbott Alarm
% Pfizer Blake
% Glaxo filter
% Accelrys
Figure 2. A plot of the percentage of SMARTs filter failures for compounds with different numbers of
Lipinski violations [30].
When so many research groups are screening similar or overlapping chemical libraries using
HTS methods there has to be a balance between accepting less desirable looking molecules and
problematic molecules. Dr. Jonathan Baell from the Walter and Eliza Hall Institute, Melbourne,
showed that many classes of compounds can be active against many targets [33]. Such frequent
hitters can interfere with assays due to color, being redox-active, chelating and protein reactive [34].
This can be a major problem for many academic screening groups that are not experienced in these
frequent hitters and they subsequently may publish hits which are actually frequent hitters. The
research community needs ways to alert them to such frequent hitter compounds [34].
Filtering
We have recently seen several large HTS datasets of compounds for TB and malaria become
available publically. For example GSK released >13,500 in vitro screening hits against Malaria using
Plasmodium falciparum along with their associated cytotoxicity (in HepG2 cells) data from an initial
screen of over 2 million compounds [35]. Three data bases initially all hosted the data (European
Bioinformatics Institute-European Molecular Biology Laboratory (EBI-EMBL, ChEMBL
http://www.ebi.ac.uk/chembl/), PubChem (http://pubchem.ncbi.nlm.nih.gov/) and CDD [20], while
others also followed suit including ChemSpider from the Royal Society of Chemistry
(www.chemspider.com).
We have also undertaken an evaluation of this and other datasets using a simple descriptor
analysis as well as readily available substructure alerts or “filters” [36-38]. For example (~57-76%,

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respectively) of the GSK malaria screening hit molecules fail the Pfizer and Abbott filters [26] (Figure
3). We have also recently used the same rules to filter sets of compounds with activity against
tuberculosis [39,40], with 81-92% failing the Abbott filters [38] (Figure 4) which may be related to
mechanism of action. A detailed analysis of our calculated molecular descriptors for the GSK malaria
hits [35] shows that most are normally distributed apart from the skewed Lipinski violations data and
the bimodal molecular weight. Interestingly 3,269 (24.3%) of the compounds fail more than one of the
Lipinski rules of 5 (MW ≤ 500, logP ≤ 5, HBD ≤ 5, HBA ≤ 10) [29] using the descriptors calculated in
the CDD database. The GSK screening hits are generally large and very hydrophobic as is also
suggested in their publication [35], and although they suggested this may be important to reach
intracellular targets, there is no discussion of the limitations of such compounds. We have also
suggested these compounds may not be ‘lead-like’ [41,42] and are closest to ‘natural product lead-
like’ [43]. These antimalarial hits as a group are also vastly different to the mean molecular properties
of compounds that have shown activity against TB, which are generally of lower molecular weight,
less hydrophobic and with lower pKa and fewer RBN [44].
Figure 3. Percent failure of SMARTS filters (http://pasilla.health.unm.edu/tomcat/biocomp/smartsfilter)
for different antimalarial datasets.
Figure 4. Percent failure of SMARTS filters (http://pasilla.health.unm.edu/tomcat/biocomp/smartsfilter)
for different TB datasets.

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The GSK antimalarial hits dataset [35] also stood out from the other antimalarial screening
datasets in terms of physicochemical properties as the mean molecular weight, logP and number of
rotatable bonds were much higher than in the St. Jude [45] and Novartis datasets of antimalarial
compounds [46]. The GSK, St Jude and Novartis datasets also have very high failure rates with the
Abbott Alerts [26,28] (75- 85%) and Pfizer Lint filters (40-57%) (Figure 3). A set of 14 FDA approved
widely used antimalarial drugs has properties much closer to the St Jude and Novartis hits. These
compounds had fewer failures with the Abbott filters when compared to the GSK, Novartis and St.
Jude antimalarial datasets.
Many companies avoid compounds that have reactive groups prior to screening and the
availability and use of such computational filters is common. This is not however the case in
academia. Our analysis suggests that hits from some of these HTS datasets may represent a more
difficult starting point for lead optimization.
By creating a collaborative database CDD TB, we have been able to compare on a very large
scale, actives and inactives against Mtb in a dataset containing over 200,000 compounds [44]. The
mean molecular weight (357 ± 85), logP (3.6 ±1.4) and rule of 5 alerts (0.2 ± 0.5) were statistically
significantly (based on t-test) higher in the most active compounds, while the mean PSA (83.5 ± 34.3)
was slightly lower compared to the inactive compounds for the single point screening data [44]. Our
most recent analysis for TB used a dataset consisting of another 102,633 molecules screened by the
same laboratory against Mtb [38]. We were able to analyze the molecular properties, differentiate the
actives from the inactives and show that the actives had statistically significantly (based on t-test)
higher values for the mean logP (4.0 ± 1.0) and rule of 5 alerts (0.2 ± 0.4), while also having lower
HBD count (1.0 ± 0.8), atom count (41.9 ± 9.4) and lower PSA (70.3 ± 29.5) than the inactives [38].
Overall, comparing these two datasets the mean values are remarkably similar.
Figure 5. Integrating the CDD TB database into various TB screening paradigms [30].

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A more recent analysis of TB screening data (<300 compounds) from Novartis available in
CDD suggests that we can also differentiate aerobic and anaerobic compounds based on their
statistically different mean molecular properties [30]. These analyses may help scientists to focus on
compounds with properties that may lead to increased probability of bioactivity against this or other
neglected diseases. In addition these large datasets can be used to create computational machine
learning models that can identify active molecules against infectious diseases [30,38,44] such as TB
and databases like CDD may have a role for both target-based and phenotypic screening (Figure 5)
[30].
Engaging Big Pharma and Helping the Community
We have recently asked the question “are there technologies that we could bring together in
pharmaceutical research that may seem rather simplistic yet if combined could lead to new insights?”
From a cheminformatics perspective we suggested secure sharing of chemical information [47] and
collaborations between groups as one such technology for the future. As computational chemistry
software companies have generally catered to the computational modeling community and have not
done well in translating their tools to bench biologists and chemists it will be important that tools such
as CDD can cross scientific boundaries and do not require an expert user.
We think the future of drug discovery will be different to what it is now, collaborative networks
will be key and software tools for sharing data and analysis that are frequently used should have a
low barrier to entry similar to using Google, Facebook and Twitter etc. Mobile computing devices also
present a new frontier (and business opportunity) with constraints in how much can be shown on very
small screen real estate, which might drive cheminformatics software developers to consider how
they expose their tools to new users [48] in the pharmaceutical industry or academia. Uses of such
tools may also be driven by the academic scientific community if they are found to be of value.
Biomedical research is moving quickly towards a collaborative network of chemists and
biologists but they commonly find themselves overwhelmed by the availability of information
(especially if they are in industry). Today we find a major limitation in the availability of biological
information related to the understanding of absorption, distribution, metabolism, excretion and toxicity
(ADME/Tox) data [49-51] for drugs and molecules evaluated as drug candidates. We would argue
that ADME/Tox data is also precompetitive data and should be made freely available on the web as a
resource for all scientists. Generating this data is also very costly and in many cases data is
reproduced by different groups when comparing their own proprietary compounds with a competitor
compound. Why not share this data? It would certainly enable the industry to quickly understand
ADME/Tox liabilities with different classes of compounds targeting a specific indication and enable
the generation of computer models for these properties. We have proposed that the scientific
community should tackle the lack of public databases that contain preclinical ADME/Tox or
pharmacokinetic data [52]. This would naturally greatly assist those in the neglected disease space
were such data is rarely generated. For example scientists could expose their ADME/Tox data in
CDD.
In parallel to this, pharmaceutical companies increasingly evaluate lead compounds for drug-
like properties (such as ADME/Tox) very early on in the discovery process using computational
prediction methods utilizing experimental data from in vitro or physicochemical property assays [53].

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Well validated ligand-based in silico approaches are important and exist in the large pharmaceutical
companies because these organizations have large diverse proprietary data sets, the financial
resources for expensive commercial software and access to in-house computational, medicinal
chemistry and high-throughput screening expertise. All these enablers are generally or in part lacking
in academia, small biotechnology companies and non-profit neglected disease foundations. In
collaboration with Pfizer we have demonstrated how ligand-based computational models could be
more readily shared between researchers and organizations if they were generated with open source
molecular descriptors (e.g. chemistry development kit, CDK) and modeling algorithms, as this would
negate the requirement for proprietary commercial software [54]. We initially evaluated open source
descriptors and model building algorithms using a training set of approximately 50,000 molecules and
a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A
C5.0 decision tree model demonstrated that CDK descriptors together with a set of SMARTS keys
had good statistics (Kappa = 0.43, sensitivity = 0.57, specificity 0.91, positive predicted value (PPV) =
0.64) equivalent to models built with commercial MOE2D and the same set of SMARTS keys (Kappa
= 0.43, sensitivity = 0.58, specificity 0.91, PPV = 0.63). Extending the dataset to ~193,000 molecules
and generating a continuous model using Cubist software with a combination of CDK and SMARTS
keys or MOE2D and SMARTS keys confirmed this observation. The same combination of descriptor
set and modeling method was applied to other ADME datasets with similar model testing statistics.
In summary, open source tools demonstrated comparable predictive results to commercial
software with attendant cost savings (Figure 6). The results of this study may provide an important
starting point for a validated universal framework for enabling the sharing of ADME/Tox models and
facilitating their use for making predictions by third parties, without the requirement of sharing
sensitive molecule structure data.
Figure 6. Generating and sharing computational models.

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The beneficiaries of such open ADME/Tox models would be those in academia, foundations
e.g. in particular those working on rare or neglected diseases. In addition, pharmaceutical companies
could avoid duplicative testing and cover more chemical space. This open models approach could
certainly result in improved predictions and greater applicability of such models for use by groups with
compounds of interest, but with no idea of their ADME properties and ultimately predict likely issues
before they become major hurdles to a project. Our work suggests a new approach to sharing
ADME/Tox models built using widely available open descriptors and algorithms. CDD will certainly be
at the forefront of model sharing in the future in order to benefit all groups doing drug discovery
research.
Why collaboration matters
In the long history of human kind (and animal kind, too) those who have learned to collaborate and
improvise most effectively have prevailed. - - Charles Darwin
It is also clear that the “new drug discovery” will put a renewed emphasis on collaboration and
that research on neglected and rare diseases will require this for success to connect disparate
researchers around the globe and create virtual drug discovery teams. Currently available
computational database tools for drug discovery, and chemistry in particular are not collaborative and
are of limited application for drug development [55]. Therefore at CDD we emphasize collaboration
as what differentiates us from other companies and technologies currently available.
We recently asked people through an online forum what collaborations meant to them? We
had responses like “collaboration, to me, means that folks from disparate disciplines or skills work
together towards the same end-goal. … A collaboration means free and open data sharing,
transparent goals and intentions, and a relationship that allows open (frank) and constructive
discussion” and “the internet is the perfect place to share (certain) data and many of the new
technologies and format available at the Web (REST, SOAP etc.) are perfect to use data
collaboratively”. In recent months CDD has been putting the finishing touches to “Projects”, soon-to-
be released functionality that will enhance the capability to share research data securely using CDD
(Figure 7).
This will enable users of the CDD Vault to organize their data within a vault into projects, and
invite individual vault members to be able to access specific projects, allowing for more flexible data
sharing and management both within a group as well as across groups. Users will be able to share
data more selectively, allowing users to view only the data relevant to their projects without
compromising the security of data meant to be hidden. This results in no more balancing several
systems for managing data between different groups, no more inviting collaborators into private
networks and compromising other data.
Imagine a future in which your molecules, data and computational models could all be
selectively shared in a single database – this is just a glimpse of some of our long range projects
which could be of immense value to the rare and neglected communities, but also may have wider
implications for more common diseases research productivity in the pharmaceutical industry.

11. Sean Ekins, November 2010
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Figure 7. An example of how “projects” could be used for a community project to efficiently manage
many projects and recreate a “virtual pharma” environment.
If you would like to hear more about these new features or any of the other exciting projects
and collaborative science happening at CDD please contact us (Tel: 215-687-1320; E-mail:
sekins@collaborativedrug.com).
Acknowledgments.
I would like to sincerely thank all the speakers at the 4th
Annual CDD Community Meeting, who
made their slides available for this analysis (Slides available here -
http://collaborativedrug.com/blog/blog/2010/10/26/cdd-hosts-inspiring-4th-annual-ucsf-community-
meeting/). I would also like to acknowledge Dr. Antony Williams and Dr Joel Freundlich for their
valuable discussions and collaborations.
Also for more information, contact:
Barry A. Bunin, Ph.D.,
President,
Collaborative Drug Discovery, Inc.
bbunin@collaborativedrug.com
Administrator
Can load data
for any project
and see shared
data
User project 1
Can read shared and
own data, cannot share
User project 2
Can share own data
but no read access
User project 3
Not sharing currently, read access
User project 4
Can share and read data

12. Sean Ekins, November 2010
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