Download Spinout Equinox Pharma speeds up and reduces the cost of drug

Spinout Equinox Pharma speeds up and reduces the cost of drug discovery
S
pinout Equinox Pharma1 provides a service to the pharma and biopharma industries that helps in the
development of new drugs. The company, established by researchers from Imperial College London in
2008, is using its own proprietary software to help speed up and reduce the cost of discovery processes.
Equinox is based on BBSRC-funded collaborative research
conducted by Professors Stephen Muggleton2, Royal
Academy Chair in Machine Learning, Mike Sternberg3, Chair
of Structural Bioinformatics, and Paul Freemont4, Chair of
Protein Crystallography, at Imperial College London.
The technology developed by the company takes a logicbased approach to discover novel drugs, using computers to
learn from a set of biologically-active molecules. It combines
knowledge about traditional drug discovery methods and
computer-based analyses, and focuses on the key stages in
drug discovery of target identification, lead validation, and
lead optimisation.
The software can screen tens of millions of potential
molecules in only a few hours, and is more accurate than
other software-based approaches to identifying promising
leads for drug discovery.
The technology also has applications in the agrochemical
sector, and is being used by global agri-tech company
Syngenta.
“The power of a logic-based approach is that it can
propose chemically-novel molecules that can have
enhanced properties and are able to be the subject of novel
‘composition of matter’ patents,” says Muggleton.
Proving the technology
A three-year BBSRC grant in 2003 funded the work of
Muggleton and Sternberg in their initial development of the
technology. BBSRC follow-on funding was also provided in
2006 for a further year.
BBSRC support enabled Dr Ata Amini, a researcher at
Imperial College London, to prove the technology and for
details to be published5. The researchers developed a unique
machine learning method known as SVILP (Support Vector
Inductive Logic Programming) and filed patents for it. These
were granted in Japan and USA and remain pending in
Europe6.
Imperial Innovations, part of the university, and technology
commercialisation company NetScientific Ltd subsequently
both made investments of £250,000 in Equinox to in-license
(an agreement to collaborate between companies with
different strengths) drug discovery technology and develop
the business7.
Scientists examine a computer model of a biologically-active
molecular structure.
1
Dr Amini was subsequently employed by Equinox, where
he used the concepts of the technology to develop the
IMPACT SUMMARY
Spinout Equinox Pharma provides services to the agritech and pharmaceuticals industries to accelerate the
discovery of molecules that could form the basis of new
products such as drugs or herbicides.
The company arose from BBSRC bioinformatics and
structural biology research at Imperial College London.
Technology commercialisation company NetScientific
Ltd has invested £250K in Equinox.
Equinox customers include major pharmaceutical
companies such as Japanese pharmaceutical company
Astellas and agro chemical companies such as
Syngenta, as well as SMEs based in the UK, Europe,
Japan and the USA.
The company is also using their software in-house, to
develop new antibiotics.
company’s proprietary software for drug discovery. This was
called INDDex (Investigational Novel Drug Discovery by
Example)8.
More recently, further development work has been
undertaken by chemoinformatics PhD student Chris
Reynolds. His work, supported by a BBSRC CASE studentship
between Equinox and Imperial College London, involves
integrating chemical synthesis into in silico (performed by
computer simulation) ‘hit’ discovery of drugs.
Logic-based drug discovery
In recent years, computer-based virtual screening has
become an integral part of the drug discovery process. As
part of this development, the INDDEx software uses its
logic-based rules to search databases of millions of small
molecules for those that could potentially form the basis of
Spinout Equinox Pharma speeds up and reduces the cost of drug discovery
new drugs. In addition, the software can perform ‘scaffold
hopping’ – a process that enables novel compounds to be
found while maintaining the known activity of an original
substance. By identifying novel molecular structures that
have little similarity to the original substances, the software
increases the chance of discovering patentable molecules.
INDDEx uses artificial intelligence to create rules that
identify the chemical properties of molecules which are
responsible for drug activity. In-house information, and
information provided by clients, act as ‘training’ datasets
of substances that have already been screened against
a target molecule. From the training dataset, INDDEx
generates a set of logic-based rules about the relative
position of parts of a molecule that display desirable
characteristics.
On completion of screening and searches of small molecules
by the software, medicinal chemists are presented with rules
and promising lead molecules, which can then be tested
in the laboratory. The results are in a form that is readily
understood, helping direct the next step of drug discovery.
“It’s incredibly exciting to transfer the results of academic
research into a company and have a commercial framework
to apply the approach to drug discovery,” says Professor
Sternberg. “We’ve proved the technology with commercial
and academic partners and our next project will be to apply
INDDEx to search for novel antibiotics, which will address
one of the global challenges in healthcare.”
2
50% prediction rate
Existing high-throughput conventional screening is
expensive and limited to a few compounds at a time.
Computer-based virtual screening does not have these
limitations, but not all in silico approaches are as effective
as the one developed by Equinox. Some only achieve a
15% (or lower) hit rate, whereas INDDEx is able to achieve
up to a 50% prediction rate. In view of this substantial
advantage, INDDEx can screen large numbers of
compounds rapidly and cost effectively. In some instances,
up to 40 million compounds have been screened by Equinox
in a matter of hours – with important implications for
speeding up drug discovery.
Equinox is following a mixed-business model of ‘fee-forservice’ and in-house drug discovery. Currently, Equinox
is applying its technology to develop novel antibiotics. In
addition, INDDEx has been used at Imperial College London
to progress the development of inhibitors of SIRT2 – a
potential target for the treatment of Parkinson’s disease9.
Customers for the services provided by Equinox include
major international pharmaceutical and agrochemical
companies and SMEs based in the UK, Europe, Japan
and the USA. One of its customers at Syngenta10, an
agrochemical company with 29,000 employees in over
90 countries, says, ‘We were impressed with the ability of
INDDEx to produce good structure-activity relationship
models for activity and selectivity in a series with very
difficult molecules to analyse.’
REFERENCES
1
Equinox Pharma Limited: http://www.equinoxpharma.com/index.html
2
Professor Stephen Muggleton: http://www.imperial.ac.uk/people/s.
muggleton
3
Professor Michael Sternberg: http://www.imperial.ac.uk/people/m.sternberg
4
Professor Paul Freemont: http://www.imperial.ac.uk/people/p.freemont
5
Amini, A., Muggleton, S. H., Lodhi, H. & Sternberg, M. J. (2007). A novel
logic-based approach for quantitative toxicology prediction. J Chem Inf
Model 47, 998-1006.
6
Patent: Muggleton, S.H., Lodhi, H. M., Sternberg, M.J.E. & Amini, A. (2006)
Support Vector Inductive Logic Programming –PCT /GB2006/003320/
Granted USA and Japan, Pending in EU.
7
Imperial Innovations: Investment in Equinox Pharma
8
Reynolds, C. R., Amini, A. C., Muggleton, S. H. & Sternberg, M. J. E. (2012).
Assessment of a Rule-Based Virtual Screening Technology (INDDEx) on a
Benchmark Data Set. Journal of Physical Chemistry B 116, 6732-6739.
9
Di Fruscia, P., Zacharioudakis, E., Liu, C., Moniot, S., Laohasinnarong, S.,
Khongkow, M., Harrison, I. F., Koltsida, K., Reynolds, C. R., Schmidtkunz, K.,
Jung, M., Chapman, K. L., Steegborn, C., Dexter, D. T., Sternberg, M. J. E.,
Lam, E. W. F. & Fuchter, M. J. (2015). The Discovery of a Highly Selective
5,6,7,8-Tetrahydrobenzo[4,5]thieno[2,3-d]pyrimidin-4(3H)-one SIRT2
Inhibitor that is Neuroprotective in an in vitro Parkinson’s Disease Model.
ChemMedChem 10, 69-82.
10Syngenta: http://www3.syngenta.com/country/uk/en/about/businesses/
Pages/Our_UK_businesses.aspx