DATA MANAGEMENT: GLOBAL PHARMA SOLUTIONS
of building blocks and connections within the molecule. Genedata has developed a Biologics Data Platform (BDP) to support biologics data management and analysis at Bayer Schering Pharma R&D sites worldwide. Biologics drugs include monoclonal antibodies and recombinant proteins, and lead generation for these large molecules is very different to that of small molecules, explains Fischer. This is due, in part, to the process: ‘There are different screening technologies, molecule registration is different, the assays are different, the development and lead optimisation is different, process documentation differs, even toxicology and safety assessments are different. Expanding existing small molecule informatics solutions to make a full biologics system through incrementally modifying functions is, therefore, difficult to achieve.’ There were three major issues that Genedata tried to address with the BDP: data volume, process complexity inherent to the R&D processes, and integration with existing systems. The platform is based on a scalable software architecture, capable of handling huge volumes of data. Business logic was built into the system to address process complexity, and system integration was addressed by Application Programming Interfaces (APIs), which allowed easy integration with existing infrastructure. ‘Pharmaceutical companies are trying to industrialise their discovery pipelines and there is a strict division of labour into specialised groups,’ Fischer says. However, there are many interfaces between these groups: one group, for instance, produces a DNA vector, which needs to be handed over to the expression group. At the same time, this group receives specialised cell lines from another department to produce recombinant cell lines and these are passed on for further downstream processing. ‘This level of process complexity cannot be handled without a sophisticated supporting IT infrastructure,’ states Fischer. Process complexity is not only internal, but also reaches outside the boundaries of the company, as many steps in drug discovery are outsourced for economic and efficiency reasons. Most data is produced by instruments and, therefore, scalability is another dimension. ‘It’s no longer a biologist noting down results in a notebook, but machines writing terabytes of data to disk from which the company needs to make sense,’ Fischer says.
www.scientific-computing.com
Another aspect was integrating the system
with the existing IT infrastructure. Bayer Schering Pharma already had a significant chunk of IT infrastructure available, along with specialised pieces of software that had been developed in-house – all of which had to be integrated with the BDP system. ‘Many companies are very traditional in terms of IT; even big pharma, and many of the biology and biologics groups are still using Excel- based solutions,’ comments Fischer. This leads to highly inconsistent nomenclature, with regards to experimental results, and data is not automatically updated or centrally stored. The BDP is supporting the full drug discovery
process on the biologics lead finding side, from compound library generation to screening to protein production. Genedata plans to release a biologics platform in late 2010/early 2011, using the knowledge gained from a consortium of companies involved in biologics.
‘Many companies are very traditional in terms of IT; even big pharma, and many of the biology and biologics groups are still using Excel-based solutions’
Elsewhere, a major US biotech company,
which prefers to remain anonymous, is in the process of implementing an ELN from IDBS for its bioprocess division. The production version of the software went live on 15 March 2010 and the company currently has around 150 users, with the aim to roll out the ELN to more than 700 users in its bioprocess division by the end of the year. The bioprocess group develops the clinical material and carries out any characterisation of the protein, so aspects such as cell culture, purification, drug formulation and analytics are all part of the division. IDBS’s E-WorkBook suite will firstly act
as a direct replacement for paper laboratory notebooks, with analysts using the software to record experimental data. IDBS’ E-WorkBook for Biology will also be used in areas where large amounts of data are generated, driven in part by the increase in laboratory automation. A spokesman for the company said:
‘There are a lot of robots involved in purification or developing formulations, etc, and that automation generates large amounts of data, which requires more standardised processing methods. That’s an appeal of the E-WorkBook for Biology component and is one of the distinguishing features of IDBS’s ELN compared to other ELNs. ‘The traditional paper lab notebook is akin
to a personal diary in the way it’s used and the idea that experiments might be designed with collaboration in mind from the beginning is a relatively new concept for our company,’ he continues. The ELN will improve how accessible the
data is and make it searchable, which should ease communication between the company’s two sites, as well as make handoffs easier between early-stage (phase II clinical trials) and late-stage pharmaceutical development (phase III clinical trials), and handoffs to manufacturing once the drug is approved. In some instances, large pharma companies
can be running too many software platforms over their various sites and departments. Laboratory automation and data management solutions provider Xyntek is currently mid- way through a three-year implementation programme of an ELN for a major biotech company, which wanted to reduce the total number of software applications operating. Elliot Abreu, vice president at Xyntek, says: ‘Some of these large pharma companies are running up to 20 different laboratory informatics platforms within the enterprise, support and maintenance of which can be very costly. In addition, integrating numerous platforms can be complex.’ One of the objectives of the installation was
to find one solution that would fit most of the operations. However, the recommendation was to install two different products, one covering the discovery/clinical pharma area and the other covering the method execution and analytical testing areas of pharma c euti c a l development . ➤
SCIENTIFIC COMPUTING WORLD JUNE/JULY 2010
13
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 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60