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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