Big Data for Life Sciences
The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition and provide much needed visibility into Research & Development for Life Sciences Companies. Pharmaceuticals, Biotech, and Medical Device Companies are experimenting with big data to ascertain its potential value in clinical trails and personalized medicine. Some examples of successes in using big data:
- The National Institute for Health and Clinical Excellence, part of the United Kingdom’s National Health Service, has pioneered the use of large clinical datasets to investigate the clinical and cost effectiveness of new drugs and expensive existing treatments. The agency issues appropriate guidelines on such costs for the National Health Service and often negotiates prices and market-access conditions with PMP industries.
- The California-based integrated managed-care consortium Kaiser Permanente connected clinical and cost data early on, thus providing the crucial dataset that led to the discovery of Vioxx’s adverse drug effects and the subsequent withdrawal of the drug from the market.
Big Data Components at Life Science Companies
Life Science Companies have four main pools of data:
Life Science Companies have four main pools of data:
iGATE Big Data Solution Offerings
Rational Drug Design: This involves using simulations and modeling based on preclinical or early clinical datasets along the R&D value chain to predict clinical outcomes as promptly as possible. The evaluation factors can include product safety, efficacy, potential side effects, and overall trial outcomes. This predictive modeling can reduce costs by suspending research and expensive clinical trials on suboptimal compounds earlier in the research cycle.
Rational Drug Design: This involves using simulations and modeling based on preclinical or early clinical datasets along the R&D value chain to predict clinical outcomes as promptly as possible. The evaluation factors can include product safety, efficacy, potential side effects, and overall trial outcomes. This predictive modeling can reduce costs by suspending research and expensive clinical trials on suboptimal compounds earlier in the research cycle.
If planned and deployed in the right sequence then the Rational Drug Design process can shave 3-5 years off the approximately 13 years it can take to bring a new drug to market.
Clinical Trials Data Analysis: By analyzing clinical trials data and patient records, Life Sciences Companies can identify additional indications and discover adverse effects. Drug repositioning, or marketing for additional indications, may be possible after the statistical analysis of large outcome datasets to detect signals of additional benefits. Analyzing the near real-time collection of adverse case reports enables pharmacovigilance, surfacing safety signals too rare to appear in a typical clinical trial or, in some cases, identifying events that were hinted at in the clinical trials but that did not have sufficient statistical power.
Disease Pattern Analysis: By analyzing disease patterns and trends, Life Sciences Companies can model future demand and costs and make strategic R&D investment decisions. This analysis can help the companies optimize the focus of their R&D as well as the allocation of resources including equipment and staff.
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