Issue link: https://www.e-digitaleditions.com/i/1052795
26 Pharmaceutical Technology LABORATORY BEST PRACTICES 2018 P h a r mTe c h . c o m Outsourcing Informatic strategies Commonly, the focus of a sponsor organization when collaborating with a CRO is on data. Data's integrity, traceability, and quality are the foundations of drug discovery and development—and now with the switch to biologics—there is even more data to worry about. This is forcing organizations to adapt and modern- ize their informatics strategies, in parallel to changing their science. The drive for data integrity, traceability, and quality is born from the requirement to support FDA and reg- ulatory submissions, including new drug applications (NDA) and biologics licence applications (BLA). Data used to make decisions during the research, develop- ment, and manufacturing processes must be available and reviewable, when required. Although dedicated software and data manage- ment solutions exist to support every aspect of drug discovery, research, and development—from simple, secure file sharing to comprehensive col- laboration portals—most do not cater or readily support the externalized nature of R&D that is emerging. Instead they are based on everyone and everything being internal. Hence, there is a need for the entirety of the research and development data to be brought together in an easy, robust, and mean- ingful way, but also with significant data coming from external CROs and third parties. Given the regulatory requirements, supporting collaboration and external data is paramount; getting the right data landscape in place is now an even more critical business objective. Sharing data The intricacies of the data being shared, and the im- portance of the integrity, quality, and traceability should not be underestimated or considered trivial. Scientific data are complex in many ways; scientific data use specific language and formats, and their in- terpretation is affected by many factors. So, for a bio- pharma company to share key pieces of data as part of a collaboration, it must first assess if the partner can read, understand, and use the data to conduct its work. Many would say, "That's easy, just type it into the other system!" This is one possible route, but it brings into play one of the critical factors—data quality—and the risk of transcription errors. The other factor often underestimated is when the "language" or "context" is ignored. For data to be com- parable with other data, they must use a common lexi- con or catalogue of terms. This is to ensure that when someone puts temp = 37 ºC, everyone and every other system knows that this is 37, and the scale is centigrade. This is perhaps the simplest of things, but it causes the most issues in peer-to-peer data exchange. Many enterprises do not have harmonized data cata- logues or master data management systems to manage them. CROs must work with many different partners, so they must also have a way of using everyone else's data catalogues, or at least giving data back accord- ing to a specific sponsor's catalogue. These two things make exchange of data cumbersome and fraught with potential quality and efficiency problems. The second challenge with exchange of data be- tween enterprises is the integrity aspect. Are the data secure? Who has seen the data? Who has edited the data? What company did the work? When was the work done? Where was the work performed?—the who, what, when, and where questions. Tracking this type of "audit" information is possible when the data and systems reside internally and are used by internal scientists. System and data change audit trails as re- quired by 21 Code of Federal Regulations Part 11 and aspects of good laboratory practice (GLP) all drive at