Benefits of Cloud Computing for the Pharma and Life Sciences Industry
The Pharma and Life sciences industry is an information processing business first and a product manufacturing and distribution business second. With the advent of cloud computing infrastructure as a service, “Big Data” technologies and analytic methodologies, scientific research is being transformed from the days of high cost, time and labor intensive biomolecular candidate screening techniques, to targeted computer driven molecular candidate identification, thereby saving many millions of dollars and years of time off the early pre-clinical research landscape. As promising candidates progress to human trials, the benefits of cloud computing continue as well.
“It is true that across the industry and even within individual companies, data is often held in information silos”
Much of the human trial clinical data capture, document management and statistical analysis application space offerings are now available as software as a service solution (SaaS) deployed through cloud infrastructures. A Pharma or biotech startup today would find it difficult to justify building out and staffing a wholly owned and operated computing and software infrastructure, with all the subsequent and required validation and change control documentation needed in a regulated industry. It is far more cost and time efficient to simply “rent” a secure, validated and regulatory compliant infrastructure when necessary to meet a research goal or objective. There is really no good business justification to purchase hardware and software that would sit idle the vast majority of time. Nor does it make sense for a Biotech or pharma company to employ an army of IT specialists. While IT is strategic to life sciences, IT in itself is not the focus. Moving life-improving and life-saving biopharmaceutical products through the phases of research and regulatory approval “IS” the business.
In order to offer the most efficient high quality services, ACI Clinical has embraced a cloud computing strategy. As demand for our clinical endpoint adjudication software and services grow, our move to the cloud was imperative, so we can easily “dial up” the scale and performance. An elastic cloud infrastructure can expand or contract with demand and as a company; we only pay for the infrastructure we are actually using, which in turn allows us to pass the benefits along to our customers.
It is true that across the industry and even within individual companies, data is often held in information silos. The re-use of clinical trial data to answer questions not envisioned in the original protocol or study design is still rare. This is partially due to the regulatory approval driven process of pharma research, competitive intellectual property protection, as well as technical issues such as the use of non-standardized research data formats and specifications.
As clinical data integration is expensive, ACI Clinical recommends to its clients that integration should be driven by the potential questions that such integration may answer. We have found that data integration often requires the highest expertise of our clinical and statistical experts to thoroughly understand the study designs, patient populations, endpoints and interventions. If proper care is not taken in regard to the integration, you may end up with a technically well integrated clinical database that can lead to false or misleading conclusions. Even if the clinical data collected from multiple trials were standardized using published clinical data standards, such as those from the CDISC organization, you need to have a core content understanding prior to the integration.
The value and benefits of data integration and clinical data sharing are to the entire research community, not necessarily or specifically to an individual Life sciences company. For example, a company who did not collect original data on a compound, may derive great value from the availability of shared and well integrated clinical data to prevent them from going down a blind alley or research dead end, saving valuable capital and scientific resources. In another example, shared data could allow a company to evaluate and possibly model the ‘potential’ effects of a known substance on a new patient population, in order to design a prospective study to minimize the potential safety risks of the exposure of that substance to a new patient population, as well as to better measure its efficacy effects. ACI Clinical is pleased and proud to participate in efforts within the academic, commercial and regulatory sectors to promote and enable clinical data sharing by individual companies or via independent “clinical data clearing houses” that evaluate the scientific and ethical merits of a proposed use case of shared clinical data.