Dr. Smith — the development lead for her company’s most promising new drug product — has been losing sleep over the past month pulling together the CMC (Chemistry, Manufacturing and Control) section of the regulatory submission. The compound has shown excellent efficacy and safety data, and the entire company is counting on a quick review and approval. Dr. Smith is now faced with a significant headache, though. She is searching for key information related to solubility that was characterized early in development. She had to email, call and even physically visit the medicinal chemistry team. While she has been successful with most of the data, results from one series of tests is proving hard to find. The individual responsible for the tests long ago left the company and now no one knows where to find the missing information. Dr. Smith knows that re-running the tests would be risky — and might even delay the filing — so she is now in a manual process to hunt down the data across multiple databases.
This is an issue commonly heard across pharma companies. While this specific example occurs in CMC, similar problems happen in many of the other functions. As pharma companies are becoming increasingly lean, new processes and technology approaches are needed to move our scientists, engineers and specialists away from documentation management and back to their core skill set.
NEW APPROACHES AND TOOLS FOR A CHALLENGING LANDSCAPE
Recent years have brought tremendous change to the pharma landscape. Significant economic pressures are driving an increased focus on costs within healthcare systems and payers around the world are under increased pressure to deliver incremental outcomes for patients. This, in turn, is forcing pharma companies to reduce costs and development cycle times, while broadening their portfolios to include specialty products, large and small molecule drugs, biosimilars, generics and combination drug/device products.
Pharma companies employ different models to succeed in this demanding new environment. One common challenge across these models — discovery, development and launch of new products — is management of product and process data. As companies “lean out” and speed up their development processes with new technologies, they face increasing challenges with capturing and efficiently managing data as it flows from their labs to their plants around the globe. This is further complicated as companies leverage external development and manufacturing partnerships for speed and cost and they face the same challenges outside the “four walls” of their process and technology landscape. To address this product and process data challenge, pharma companies are beginning to tap into the capabilities of Product Lifecycle Management (PLM) processes and tools. So, for Dr. Smith, a solution is starting to emerge.
SPECIFIC PRODUCT CORE DATA CHALLENGES
Product and process data define a compound, as the compound moves from discovery through development and, hopefully, onto the market. Today, product and process data is created and managed by multiple functions via independent processes throughout the drug development lifecycle (from discovery through to launch). In most pharma companies, information is mostly stored in documents and usually separated among functions. Some companies have dozens of separate databases and/or systems to collect vast amounts of data. As a result, valuable time is lost in collating information that is required to file with regulatory agencies, respond to regulatory questions/investigations, or revisit a technical or management decision. With the above pressures on reducing time to market while improving development and manufacturing efficiency, an accurate and integrated product and process data structure becomes essential. And the existing manual and resource-intensive approaches will not be tolerated.
Based on our work with top pharma companies, we have identified several common challenges with product and process data management. While the ones listed below are more prevalent in chemistry, manufacturing and controls function, there are other product and process data related challenges within clinical, regulatory, quality, manufacturing, supply chain, safety and commercial. Some of the key CMC challenges include: