Claire Hill, Head of Strategic Marketing at IDBS, explores ways in which the pharmaceutical sector can reap the benefits of an increasingly digital world.
The Covid-19 pandemic pushed the process of drug development into the spotlight in a new way. The challenge for biopharmaceutical companies—to create, test, and manufacture a vaccine in months rather than years—appears to have been successful, with vaccine rollout already making a measurable difference in the communities that have received it1. This has highlighted both the potential benefits of getting it right2 (Pfizer/BioNTech) and the high cost of mistakes3 (Sanofi-GSK). In the space of a year, the biopharmaceutical industry was pushed to accelerate movement in directions it was already exploring, as well as embrace completely new ways of working. In the way of successes, Pfizer developed an effective method to recycle special filters needed in vaccine production that were in short supply and Moderna reduced the time involved in inspection and packaging of the vaccine. In the end, the pandemic taught us a lot about which old systems can be abandoned and which new ones should become the norm.
There are numerous reasons for optimism as we move further into this decade of industrialised biology and biopharma 4.0—but there are still challenges in research and development (R&D) that need to be addressed before the biopharmaceutical industry can reap the benefits of an increasingly digital world. Much of the opportunity lies in addressing the massive, but still-fragmented data framework that underlies almost every step of R&D.
This is because as therapeutic possibilities grow and become realities, so do the complexity and volume of data—and while it should flow continuously between departments, the reality is that data is extremely siloed and only a small amount flows from one stage to the next. To take future-facing steps, R&D organisations will need to embrace new, end-to-end technologies for data management that solve the fundamental issues in workflow, process quality, collaboration, and data analysis. As capable as they’ve been, current electronic solutions, like Laboratory Information System (LIMS) and Electronic Laboratory Notebook (ELN), aren’t sufficient for the coming decade. Overhauling and streamlining the process with management systems that span the entire development lifecycle is key.
The current challenges in R&D
The latest research suggests the average cost to develop a new drug sits at $1.3 billion, and can take eight to 16 years, with attrition rates as high as 88%. These numbers underline the fact that the time and cost of delivering new therapies and vaccines remain one of the industry’s biggest barriers to the timely and cost-effective treatment of disease. Our team at IDBS, a leading provider of cloud-based R&D technology and solutions, has calculated that, given the many variables that can slow development, if an organisation lacks an effective data management system across the lifecycle, the development timeline of a biologic can be prolonged by up to three years. Delay on this scale has obvious implications for the company’s success, but also for the health of the patients who will benefit from the therapeutic in question.
As touched on earlier, a staggering contributor is poor data management and its current siloed nature. Many people outside of the industry may be surprised to learn that the data backbone that ties together all the activities from research and discovery through process development and manufacturing is still mainly Microsoft Office documents. In a recent Aspen survey, 50% of the participants were using legacy applications such as electronic lab notebooks (ELNs) to record process development work and the other 50% were using a mix of paper, Excel, and standalone instrument software. In addition, 62% of participants reported spending at least five hours a week on data administration and in some cases more than 20 hours a week. Finally, up to 30% of work is subject to rework because of lost data on process execution and outcomes. So why, when clones can now be grown and screened on a chip4 and digital twins5 can apply the power of AI to predict and increase process performance, is this still the industry’s Achilles heel?
Adding to the costs is the risky nature of biologics development itself—variation in conditions is a major source of unpredictability, from environment to contaminants to equipment. Given this unpredictability, operational burdens and the lack of traceable data make it extremely difficult to understand root causes of unanticipated outcomes and to make quick, informed decisions as a drug candidate progresses. The fallout is that progress may be hampered, affecting time to market, attrition rates, and time to trials, to the tune of 6-to-18-month delays. Finally, the resources involved in demonstrating integrity and compliance in manual data management also present stumbling blocks. Without a well-structured and efficient system, regulatory filings and tech transfer take much longer and are more difficult than they need to be.
There is a plethora of software applications designed to tackle different aspects of product and process development, from design of experiments (DoE) through instrument integration, automation, data aggregation, and visualisation. Yet the problem with these point solutions is that they don’t address the core challenge: capturing context-rich data at the point of entry, with the right metadata and structure to enable searching, reporting, and analytics across the development lifecycle. The promise of digital transformation and biopharma 4.0 is exciting, but even the most advanced ML/AI applications are useless if the source data hasn’t been properly curated. Data curation after the fact is time consuming, costly, and error prone; contextualised data capture should be an integral part of scientists’ and technicians’ daily work, rather than an afterthought.
A platform to support the entire development lifecycle
The solution is an entirely new category of software: Biopharmaceutical Lifecycle Management (BPLM), an operational foundation for drug development that allows complete integration into the development ecosystem, all the way from early development to clinical and commercial supply.
The key to BPLM is a highly contextualised data backbone, which is created when process and analytical data are brought to together right where the process is executed. This is a gamechanger because valuable data is automatically captured in context at the point of execution, eliminating the challenges associated with linking together disparate data sources across different timepoints after the fact. Not only does this enable advanced search capabilities to help users find exactly what they are looking for, it also supports all the benefits of standardisation —that is, it becomes easier to compare results, identity trends, and have a more comprehensive view of the entire process.
The other core elements of BPLM are workflow, integration, and insight. Standardised workflows that accommodate both the flexibility needed for early development and the additional checks and controls needed for clinical supply are key to ensuring comparability of data while also significantly improving operational efficiency. Plug-and-play instrument connectivity eliminates the problems of having key data locked away in proprietary formats and ensures data integrity. Advanced insight capabilities enable an efficient and effective feedback loop to apply learnings gained at different scales to both maximise the usefulness of process modeling and simulation and focus development efforts on the areas of greatest impact to product quality and yield, or the percentage of usable product retained at the end of the manufacturing process, is a critical factor for biologics and particularly viral vector vaccines.
Our team at IDBS have developed Polar, a cloud-based BPLM platform that allows you to efficiently execute your processes while curating the data you need to accelerate time to market by tackling the biggest challenges in process design, optimisation, scale-up and technology transfer. Importantly, it reduces manual data processing, which leads to fewer human errors and improved accuracy, and includes comprehensive search functions that help users locate their data, which reduces unnecessary duplication and drives innovation.
Additionally, IDBS have partnered with Scitara to integrate their exciting Digital Lab Exchange (DLX) platform with Polar. Scitara is a pioneer in laboratory Internet of things (IoT), and the partnership creates a powerful out-of-the-box solution that significantly boosts Polar’s laboratory data connectivity and instrument integration capabilities. An important element of digital transformation is universal access to laboratory data—the Scitara DLX6 platform combines the benefits of cloud-based architecture with laboratory-specific functionality that is vendor-agnostic, regulatory-compliant, and secure. It allows for true multi-destination data flow and event-driven data exchange between multiple connections, including legacy devices, instruments, and applications.
The way forward
Once this is all in place, biological products can reach the market at least three years faster than the current average. And it doesn’t stop there: the ability to easily share data with partners such as CROs and CDMOs and regulatory agencies can change the game in ways that haven’t even been estimated yet. Greater insight into all aspects of manufacturing including supply chain, forecasting, environmental impact, and cost of goods can encourage more sustainable production practices and improve the availability of cost-effective therapies across the globe. Without true digital transformation, existing problems will continue to cost companies time, money, and rework and could even transition from frustrating hurdles to insurmountable roadblocks for new products. At IDBS we believe the future of biologics is bright and now is the time to address the gap that’s been holding back their potential.
Claire Hill, Head of Strategic Marketing at IBDS, leads IDBS’ strategic market engagement for BioPharma Lifecycle Management. With over 10 years’ business analysis and strategic consulting experience, Claire has in-depth knowledge of the data management challenges in the biopharmaceutical industry. Claire has an MSc in Biochemical Engineering and an MBA and previously worked as a financial analyst for IBM and as a bioprocess consultant for BioPharm Services.