As with nearly any industry today, organizations in the biotechnology and pharmaceutical sectors are currently feeling the pressure of digital transformation, shifting regulations and calls for dramatic innovations to support advancement and compliance. In addition to these challenges impacting biotech and pharma firms themselves, the partners of these organizations further up and down the supply chain are also feeling the strain.
At the same time, biotech and pharma organizations in particular must work to reduce the time to market for new products, without shortchanging the necessary research and clinical trial steps along the way.
In this type of industry environment, it's certainly an uphill battle to not only address these current struggles, but to position the firm for success in the future as well. One critical element that can make all the difference in the life sciences field is accurate data, as well as the necessary tools to support and analyze this information.
Let's take a closer examination of the current biotech and pharma industries, and the ways in which access to critical data sources and the ability to analyze and leverage this information can improve operations for biotechnology and pharmaceutical businesses.
What it takes to bring a product to market
"It takes about 17 years for research evidence to reach and be used within clinical practice."
Industry research shows it can take 15 or more years to bring a new biotech or pharma product to market, and much of this has to do with the current gap between research evidence and clinical practice. According to a study in the Journal of the Royal Society of Medicine, report authors from the University of Cambridge found that it takes about 17 years for research evidence to reach and be used within clinical practice.
"Such a convergence around an 'average' time lag of 17 years hides complexities that are relevant to policy and practice which would benefit from greater understanding," the researchers wrote.
Improved efficiency here through advanced data and analytical modeling would result in these complexities being identified earlier in the clinical trial process, helping to ensure that biotech and pharma firms' investments and developments are well-placed. What's more, considering the fact that approved drugs and treatment products are usually only granted market exclusivity for a specific window of time, it's imperative to support efficiency in research and clinical trial processes in order to generate the best return on investment.
What accurate data and analysis can bring
Streamlining the research and clinical trial process to enable firms to bring products to market faster isn't the only advantage to leveraging data and analytics in the life sciences field. Advanced insights made possible by accurate data, analytics algorithms and assisted by artificial intelligence tools like those from Grandview and IBM can support use cases like:
- Identifying new potential candidate molecules through the use of diverse molecular and clinical data, supported by predictive modeling. Backed by data and analytical research, these identified molecules would also have a higher probability of being successfully developed into approved drug and treatment products.
- Expanding patient identification for clinical trials through sources other than physician office referrals. Researchers could extrapolate their patient clinical trial criteria to include a larger number of potential candidates. This would also result in smaller trials that take place over a shorter period and are less costly to execute.
- Real-time monitoring of clinical trials for more efficient pinpointing of any safety or operational issues that require intervention. This level of real-time monitoring could also support the prevention of any clinical trial issues which may contribute to cost or time-to-market delays.
- Real-time forecasting and analysis of costs and personnel needs through the life of any project. This includes Lead Indication, the decision to enter clinical trials, as well as commercialization. Existing tools allow for the monitoring of the projects pipeline into the future utilizing past projects' data through the management of project decision dates and risk level assumptions.
- Streamlined and timely planning and analysis of manufacturing costs that takes into account multiple production lines, plant shutdowns, excess capacity and complex cost allocations.
What's needed to make this possible?
While the above-described use cases aren't being practiced on a widespread scale in the biotech, pharma and life sciences sectors just yet, the data and tools required to make these processes possible are currently available.
Today, pharma and biotech firms have access to a vast number of data sources, but users need the necessary tools and support to gather, format and analyze this information - all the while maintaining compliance with strict industry regulations for privacy and data security. This requires high levels of business intelligence and augmented intelligence that give way to the types of insights that enable biotech and pharma firms to make data-backed decisions about treatment and drug development.
While this type of analytical expertise is difficult to build up internally, with partners like Grandview and IBM on the side of the business, these capabilities certainly aren't out of reach. Grandview, an IBM Gold Business Partner, helps life sciences organizations integrate and manage their data in a secure, compliant way. Our expert Grandview consultants also leverage the most advanced IBM-compatible tools to mine and analyze data sources, and unlock insights that can improve decision-making and enhance efficiency.
To find out more about the possibilities and benefits that a partnership with Grandview offer your organization, connect with us today.