BioPharm October eBook: Best Practices 2018

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Page 26 of 27 October 2018 BioPharm International eBook 27 certain areas as it helps reduce overheads and potentially speeds up the process. One major indus- t r y d r iver forc i ng engagement of CROs is the movement of the pharmaceutical industry toward biologics and biosimilars. T he sh i f t to la rge molec u le/ biologics as drug candidates has been occurring for the past 10 –15 years (1)—the trend often attrib- uted to the success of Herceptin, G enentech's blockbuster t reat- ment for certain types of breast cancer. Many pharma and bio - tech companies have looked to mimic this success, as evidenced by the increase in new drug appli- cations for biologic-based drugs, ye a r- on -ye a r for 10 ye a r s . A s companies rapidly change their foc us, t hey may lack t he labo - rator y space, i nst r u ment at ion, and often, the scientific domain k nowledge to suppor t biolog ics research without a huge resource investment. INFORMATIC STRATEGIES Commonly, the focus of a spon- sor organization when collaborat- ing with a CRO is on data. Data's integrity, traceability, and qual- it y are the foundations of dr ug discovery and development—and now w ith the sw itch to biolog- ics—there is even more data to worry about. This is forcing orga- nizations to adapt and modern- ize their informatics strategies, in parallel to changing their science. T he d r ive for data i nteg r it y, traceability, and quality is born from the requirement to support FDA and regulatory submissions, including new drug applications (NDA) and biologics licence appli- cations (BLA). Data used to make dec isions du r i ng t he resea rc h, development, and manufacturing processes must be available and reviewable, when required. Although dedicated software and data management solutions exist to support every aspect of drug discovery, research, and develop- ment—f rom simple, sec ure f ile sharing to comprehensive collabo- ration portals—most do not cater or readily support the externalized nature of R&D that is emerging. Instead they are based on every- one and everything being inter- nal. Hence, there is a need for the entirety of the research and devel- opment data to be brought together in an easy, robust, and meaning- ful way, but also with significant data coming from external CROs and third parties. Given the reg- ulatory 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 importance of the integrity, quality, and traceability should not be underestimated or considered trivial. Scientific data are complex in many ways; scien- tific data use specific language and formats, and their interpretation is affected by many factors. So, for a biopharma company to share key pieces of data as part of a collab- oration, 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 t y pe 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 underes- timated is when the "language" or "context" is ignored. For data to be comparable with other data, they must use a common lexicon 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 har monized data catalog ues 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 according to a specific sponsor's catalogue. These two things make exchange of data cumbersome and fraught with potential quality and effi- ciency problems. T he s e c o nd c h a l le n ge w it h exchange of data between enter- prises is the integrity aspect. Are the data secure? Who has seen the data? Who has edited the data? W hat compa ny d id t he work? When was the work done? Where was t he work per for med?— t he who, what, when, and where ques- tions. 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 required by 21 Code of Federal Regulations Part 11 and aspects of good laboratory practice (GLP) all drive at traceability and security of data—integrity. Data integrity is a Biopharma Laboratory Best Practices Outsourcing The drive for data integrity, traceability, and quality is born from the requirement to support FDA and regulatory submissions.

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