Big Data as the Pharmaceutical R&D Strategy
With rising of big data and data mining technologies, the analytics are providing opportunities to cure of suffering pharma R&D productivity. In this end the pharmaceutical company who embraces the big data technologies and increases the R&D effectiveness can have unfair advantages over other competitors. That's because pharmaceutical companies fond it hard to capitalise on medical advances
Its core problem is lack of productivity in the lab. Several external
factors have arguably exacerbated the industry’s difficulties, but the
inescapable truth is that it now spends far more on research and
development (R&D) and produces far fewer new molecules than it did
20 years ago.
Specifically, by implementing eight technology-enabled measures, pharmaceutical companies can expand the data they collect and improve their approach to managing and analyzing these data. Here are some examples:
1. Integrate all data
Having data that are consistent, reliable, and well linked is one of the biggest challenges facing pharmaceutical R&D. The ability to manage and integrate data generated at all stages of the value chain, from discovery to real-world use after regulatory approval, is a fundamental requirement to allow companies to derive maximum benefit from the technology trends.
2. Collaborate internally and externally
Pharmaceutical R&D has been a secretive activity conducted within the confines of the R&D department, with little internal and external collaboration. By breaking the silos that separate internal functions and enhancing collaboration with external partners, pharmaceutical companies can extend their knowledge and data networks.
3. Employ IT-enabled portfolio-decision support
To ensure the appropriate allocation of scarce R&D funds, it is critical to enable expedited decision making for portfolio and pipeline progression. IT-enabled portfolio management allows data-driven decisions to be made quickly and seamlessly. Smart visual dashboards should be used whenever possible to allow rapid and effective decision making, including for the analysis of current projects, business-development opportunities, forecasting, and competitive information. These visual systems should provide high-level dashboards that permit users to deeply examine the data, including information to bolster managerial decision making as well as detailed tactical information, and that make asset performance and opportunities more transparent.
4. Leverage new discovery technologies
Pharmaceutical R&D must continue to use cutting-edge tools. These include sophisticated modeling techniques such as systems biology and high-throughput data-production technologies—that is, technologies that produce a lot of data quickly, for example, next-generation sequencing, which, within 18 to 24 months, will make it possible to sequence an entire human genome at a cost of roughly $100.
The wealth of new data and improved analytical techniques will enhance future innovation and feed the drug-development pipeline.
Integrating vast amounts of new data will test a pharmaceutical company’s analytical capabilities.
5. Deploy sensors and devices
Advances in instrumentation through miniaturized biosensors and the evolution in smartphones and their apps are resulting in increasingly sophisticated health-measurement devices. Pharmaceutical companies can deploy smart devices to gather large quantities of real-world data not previously available to scientists. Remote monitoring of patients through sensors and devices represents an immense opportunity. This kind of data could be used to facilitate R&D, analyze drug efficacy, enhance future drug sales, and create new economic models that combine the provision of drugs and services.
Remote-monitoring devices can also add value by increasing patients’ adherence to their prescriptions. Examples of devices that are under development include smart pills that can release drugs and relay patient data, as well as smart bottles that help track usage. Technology and mobile providers are offering services such as data feeds, tracking, and analysis to complement medical devices. These devices and services, combined with in-home visits, have the potential to decrease health-care costs through shortened hospital stays and earlier identification of health issues.
6. Raise clinical-trial efficiency
A combination of new, smarter devices and fluid data exchange will enable improvements in clinical-trial design and outcomes as well as greater efficiency. Clinical trials will become increasingly adaptable to react to drug-safety signals seen only in small but identifiable subpopulations of patients.
7. Improve safety and risk management
Pharmaceutical companies can use safety as a competitive advantage in regulatory submissions and after regulatory approval, once the drug is on the market.
8. Sharpen focus on real-world evidence
Real-world outcomes are becoming more important to pharmaceutical companies as payors increasingly impose value-based pricing. These companies should respond to this cost-benefit pressure by pursuing drugs for which they can show differentiation through real-world outcomes, such as therapies targeted at specific patient populations. In addition, the FDA and other government organizations have created incentives for research on health economics and outcomes.
To expand their data beyond clinical trials, some leading pharmaceutical companies are creating proprietary data networks to gather, analyze, share, and respond to real-world outcomes and claims data. Partnerships with payors, providers, and other institutions are critical to these efforts.
Organizational silo is the major challenge. Functions typically have responsibility for their systems and the data they contain. Adopting a data-centric view, with a clear owner for each data type across functional silos and through the data life cycle, will greatly facilitate the ability to use and share data. The expertise gained by the data owner will be invaluable when developing ways to use existing information or to integrate internal and external data. Furthermore, having a single owner will enhance accountability for data quality. These organizational changes will be possible only if a company’s leadership understands the potential long-term value that can be unlocked through better use of internal and external data.
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