This page contains a Flash digital edition of a book.
Pharma’s forward motion


Screens showing the LabVantage dashboards


The pharmaceutical industry needs new informatics tools if it is to master the age of personalised medicine,


Sophia Ktori discovers


P


atent cliffs, consolidation, the end of the blockbuster era, dwindling R&D budgets and personalised medicine. All are phrases associated with any


discussion on the pharma industry. But if there’s one single word that underpins all of pharma’s activities and decision making, and that drives pharma’s direction moving forwards, it is ‘risk’,


4 SCIENTIFIC COMPUTING WORLD


suggests Adel Laoui, managing director of life sciences at Accelrys. ‘It’s all about making sure every piece of


relevant data that you have generated, or have access to from third party or public sources, is usable and relevant. Historically there has been a great emphasis on making those risk-based decisions at the early discovery stage, where compounds have already been generated and it’s time to select those to move forward. Pharma is now increasingly tapping into the huge volumes of data emerging from external research, including patient studies, genomics (particularly genome sequencing), and omics in general (DNA, RNA, proteins and endogenous small molecules level) to really understand the biological pathways that cause and impact on disease, in a patient-relevant setting, not just in test tubes.’


Commodity to cutting-edge Know as much as you can about a disease before you start to look at small or biological molecules that might impact on that disease, Laoui stresses. Much of what pharma ‘does’ in-house, in terms of drug discovery and development, particularly when it comes to early-discovery processes such as high- throughput screening (HTS), is largely commoditised, he believes. ‘From an informatics perspective, the automation and data management/analytics capabilities are all in place to make this a push-button exercise. Pharma is using cutting edge modelling and simulation systems, combined with business intelligence tools, to validate known biological pathways and targets and make downstream decisions on promising compounds identified through discovery workflows such as HTS.’


@scwmagazine l www.scientific-computing.com


LabVantage


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