Making sense of pharma data


Sophia Ktori goes behind the scenes at the


Swiss company Genedata T


he pharmaceutical R&D industry is leveraging a wide variety of cutting-edge technology platforms and processes for identifying, validating and testing both


small molecule and large molecule or biologic drug candidates. From high throughput screening (HTS)


and high content screening (HCS), to mass spectrometry-based characterisation of biological molecules, and multiple omics platforms for use in translational research, patient stratification and toxicogenomics, these technologies generate huge amounts of complex data. Developing informatics solutions that can manage and automate the collection, management, analysis and interpretation of this data presents an immense challenge.


Enterprise platforms for pharmaceutical R&D Headquartered in Switzerland, Genedata has focused over the last 20 years on developing a portfolio of enterprise platforms that can achieve that task, and manage the experimental and analytical workflows that the pharmaceutical R&D sector is exploiting. ‘Our core focus has always been on drug


discovery and biotechnology processes for big pharma,’ explains Dr. Othmar Pfannes, Genedata CEO. ‘Tat’s what makes us different. Tese processes are contextually very rich, and our aim has been to develop platforms that empower our clients to manage and analyse very large quantities of complex data. We build in the ability to configure each platform to meet the specific needs of the R&D organisation overall, as well as the needs of individual users.’


A portfolio of key platforms Te portfolio comprises a range of soſtware solutions, including turnkey products that work straight out of the box. Tere are six key platforms, Pfannes explains. Genedata Biologics has been designed to


automate biopharmaceutical R&D workflow; Genedata Expressionist supports mass spectrometry-based workflows for characterising biopharmaceuticals; Genedata Screener is the firm’s flagship plate-based screening platform; and Genedata Profiler has been developed to manage genomic profiling and translational research workflows. Genedata Selector facilitates genome


knowledge management, and Genedata Phylosopher – the firm’s first platform – is still widely used for target and disease data management. Additional solutions have been developed to support and automate data management and analysis for R&D processes; and functions such as cell line and bioprocess development, and integrative statistical analysis.


Othmar Pfannes Te world’s top 25 pharmaceutical


companies now all licence one or more of Genedata’s products or services, and the firm has relationships with more than 40 of the top 50 pharma companies, and numerous smaller biotechs, Pfannes adds. ‘Our clients also include five of the top six agro-biotechs, as well as leading industrial biotech companies and major players in the food and beverage sector.’ Genedata in parallel offers a consulting service


for customising soſtware, and this helps to drive the development of new solutions. ‘Customisation projects for clients provide rich opportunities to develop more broadly applicable solutions for the market.’ Te firm similarly offers data analysis and data integration and migration consultancy services, which have grown on the back of scientific domain expertise. ‘We can provide data analysis services for clients who don’t require an enterprise platform, but who may have a one-off project, for example.’


Keeping modest All Genedata soſtware packages are offered through subscription licensing agreements, and this model ‘keeps us modest’ and drives innovation, Pfannes notes. ‘If we want our clients to come back to us, then we have to continue to provide the high level of technology, service and innovation they expect.’ Te overarching aim is to facilitate


Genedata’s focus is on drug discovery and biotech process 18 SCIENTIFIC COMPUTING WORLD


standardisation and automation of complex R&D processes, optimise efficiency, facilitate innovation through the provision of scalable data analytics, and maximise return on investment in data-rich


@scwmagazine l www.scientific-computing.com


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36