August 17, 2023
Life-science companies must stay on top of new requirements, innovations, process improvements, and potential roadblocks to navigate complexities across the development, manufacturing, and regulatory life cycles of their products. Amid the increasing availability of cutting-edge technologies such as artificial intelligence (AI) and blockchain ledgers, organizations must establish best practices to support strategic decision-making, improve information collection and sharing with key stakeholders, and prioritize supply-chain management. Below, I share some insights from our team about emerging digital technologies and their implications for processes, operations, and regulations for the pharmaceutical and biotechnology industries.
AI and Big Data in the Biopharmaceutical Industry
The topic of natural language generation (NLG) has gathered momentum in 2023 with the evolution of tools such as OpenAI’s ChatGPT chatbot. Although such technology remains in an early (and error-prone) state, it could significantly change how we interact with computers, gain knowledge from different sources, and generate technical, scientific, and medical documentation — and it certainly will blur the boundaries between structured and unstructured digital information.
AI, machine learning (ML), NLG, natural language processing (NLP), and “big data” are enabling companies across multiple industries to restructure their business models, reduce human errors, increase efficiency and performance, and bring innovative products to markets much faster than ever before. In the life-sciences industry, such technologies have brought about a “pharmaceutical intelligence” that enables companies to move away from traditional, slow-moving, and costly processes.
AI already is becoming an important tool for drug discovery and development as well as for clinical trials, operational surveillance, pharmacovigilance, and many other areas. Collaborations among information-technology (IT) vendors and pharmaceutical, biotechnology, and medical-device companies are sure to increase applications of AI and big data to development of targeted and personalized medicines. Such work could lead to more effective treatments for cancers, immune deficiencies, and other intractable diseases. AI and big-data technologies also will help companies to create advanced computational models to use in finding effective treatments faster than ever before while reducing drug-development costs.
Blockchain and the Future of Data Transparency
Blockchain distributed electronic ledgers are now recognized as one of the safest and easiest formats to share data across organizations. In the life-science industry, companies are using the technology to provide associated parties with current and accurate data sets about supply chains and other assets. Such records are essential for maintaining robustness in manufacturing processes.
A particularly valuable characteristic of blockchain technology is that recorded transactions are stored permanently. Because all network participants can view the record, it is difficult for fraudulent activities to go unnoticed. Such features help to ensure that sensitive data — e.g., patient information and drug-safety standards — remain secure, confidential, and trustworthy.
Increases in research and development (R&D) activity for personalized medicines are driving demand for technologies that ensure data accuracy and integrity. Blockchain technology is being used more frequently in that context. The same is true for clinical trials. Because trials involve vast amounts of information about both patients and processes, using blockchain technology provides real-time, efficient, robust data management and tracking.
One group that is putting blockchain to the test is TransCelerate BioPharma. By facilitating “global collaboration of biopharmaceutical member companies,” the nonprofit organization seeks “to create a future state for clinical research” in which R&D inefficiencies “are no longer roadblocks to success” (1). TransCelerate shares R&D data on secure platforms to support collaboration on innovative ideas, processes, tools, and technologies, gathering insights from around the globe to enhance clinical research and align the work of the biophamaceutical industry.
Data Analytics and Statistics
As drug-development, regulatory, and manufacturing processes become more complex, advanced data analytics and statistical methods have gained momentum. Many companies are working with Bayesian biostatistics, using probabilities to determine likely outcomes throughout a drug’s product life cycle. For example, during clinical trials, companies want to know as early as possible whether their candidates are safe, effective, and likely to succeed. Bayesian methodology helps by assessing the probability of candidate safety, efficacy, and clinical success. Such evaluation can be performed by leveraging preexisting information, including data from scientific literature.
Bayesian methodology can also help companies make economic decisions, such as whether to build a manufacturing line for a drug candidate in development. Using Bayesian statistics, it is possible to calculate the future probability of success during phase 3 clinical trials, informing economic decisions. Similarly, in portfolio management, Bayesian methodology can help companies to compute each product’s probability of success and thus decide how to invest future resources.
Bringing Innovations to Market
The digital revolution in the pharmaceutical sector is poised to take a huge leap forward. Already, adoption of digital technologies into clinical studies has enabled decentralization, which could provide for more efficient, accessible, and cost-effective trials that span multiple regions while removing many barriers for patient enrollment and retention. At the same time, Industry 5.0 will build upon the principles of Industry 4.0 to bring personalization into production processes. Therapies could be tailored to individual patient needs, and drug companies could develop more effective treatments for specific patient groups. Significant opportunities surround “digital twins” and augmented reality, both of which could elevate simulations in the manufacturing space to new levels.
Other digital advancements include wearable technologies such as fitness trackers, heart-rate monitors, and smartwatches, which allow users to monitor their health more accurately than ever before. Wearables also have potential to revolutionize clinical trials by providing clinicians with more accurate and reliable data than what are gathered by traditional monitoring methods. A related concept is “connected health,” which has multiple potential applications, from increasing patient access to care to improving healthcare providers’ monitoring of patient health. Going forward, technologies that power wearable health devices and support connected health are likely to become increasingly popular as companies continue to explore possibilities for remote health monitoring and clinical-trial support.
Validating Computer System Safety and Efficacy
Computer software assurance (CSA) and computer systems validation (CSV) are two distinct areas of regulatory compliance that companies must consider when developing new computer systems for use in drug manufacturing and other related processes. CSA is a set of standards that ensures the safety and efficacy of computer systems used in drug manufacturing, whereas CSV involves validating the computer systems themselves.
One of the biggest changes for computer-system compliance is that CSV and CSA can no longer assume deterministic functionality in tested systems. In the past, system operations A and B always generated result C. With ML-driven approaches, neuronal networks progress continuously. Thus, an ML-based system might return C* instead of C because it has learned over time to use that result. Such a method requires new validation approaches.
Evolving Regulatory Landscapes
The regulatory environment is becoming increasingly interdependent and globalized. Regulatory authorities are establishing interagency programs such as the Access Consortium (Australia, Canada, Singapore, Switzerland, and the United Kingdom) and Project Orbis (United States, Brazil, Canada, Israel, Singapore, Switzerland, and the United Kingdom) to promote discussion among health authorities from different regions. Drug companies are looking to those and other initiatives to perform collaborative reviews of candidate products, increasing regulatory efficiencies while ensuring that quality and safety standards are met. Such standards are especially important as AI, cybersecurity, and data analytics are incorporated into a rapidly growing number of healthcare products. In response, new regulations have been created to evaluate those products’ quality, safety, and efficacy.
Globally, regulators are taking steps to tighten and update requirements in the increasingly scrutinized world of medical devices and in vitro diagnostics. Although the European Parliament approved the proposal to delay the Medical Device Regulation (MDR) transition period, manufacturers will still be required to meet requirements and should work to transition to the MDR and In Vitro Diagnostic Regulation (IVDR) as soon as possible. One obstacle is that the IVDR sets out specific requirements for design, development, and fulfillment of software as a medical device (SaMD) from product concept to market entry.
Companies must also consider new guidance from the Medical Devices Coordination Group (MDCG) and any transitional arrangements. The US Food and Drug Administration (FDA) has introduced an electronic submission template and resource (e-STAR) program to support development and review of new medical devices, providing an interesting opportunity.
New Requirements for Drug Safety and Quality
Continued interest in RNA-based therapies is driving innovation within chemistry, manufacturing, and controls (CMC) for biologics production. CMC defines the characteristics, safety profiles, and specifications for drugs and their manufacturing processes. Ultimately, the goal of CMC in the biologics space is to ensure that pharmaceutical products consistently meet or exceed specifications throughout production.
The revised EudraLex Annex 1, published in August 2022, has brought a much-needed shift in regulatory expectations. Instead of simply listing acceptable and unacceptable specifications, companies must provide risk-based, patient-centered rationales for CMC decisions. The updated guidance creates several difficulties for organizations with immature quality risk management (QRM) systems.
To ensure readiness by the August 2023 deadline, companies doing business in Europe should first evaluate their level of compliance with Annex 1 regulations to identify gaps between their current statuses and the new regulatory expectations. Such assessment should include all areas of sterile manufacturing and involve cross-functional teams to ensure that all parties involved understand how the revised Annex 1 will affect their work.
Early Phase Modeling To Inform Strategic Decisions
Early phase modeling (EPM) is growing in significance as more companies seek to ensure that their products get to patients after regulatory approval. EPM capability is particularly critical for companies that are developing innovative products such as cell and gene therapies, for which there are high price tags and uncertainty about the science and the product.
The EPM process involves gathering key evidence from early stages of product development to support and inform decisions around pricing, reimbursement, and market positioning. Such modeling helps companies to understand their products’ potential before release to the public, increasing the chances of successful reimbursement and pricing. EPM also enables companies to identify gaps that might need attention. The process provides information about how a product might be accepted in the market, helping to predict the likelihood of regulatory or health technology assessment (HTA) concerns. Timely evidence-gathering also helps to demonstrate a product’s potential to investors, encouraging investment in innovative products.
Preparing for the Future
The rise of advanced technologies and strategic approaches to decision-making will revolutionize how life-science companies manage their product portfolios. For example, companies will be able to leverage AI-driven analytics to predict customer demand and adjust supply-chain operations proactively. Digital technologies are becoming critical to clinical trials, enabling more studies to become decentralized and supporting how subject data are gathered and managed. Such advances also will enhance data-sharing to support patients.
Advances in technology, globalization, and regulatory compliance will continue to shape the life-sciences industry. Companies that fail to prepare for such developments will find themselves left behind by nimbler competitors. It is crucial, therefore, that companies find ways to navigate the increasingly complex and fluid life-sciences ecosystem so that they can achieve their overarching objective — to bring life-changing products to patients in need.
Reference
1 Our Strategy. TransCelerate BioPharma: West Conshohocken, PA, 2023; https://www.transceleratebiopharmainc.com/our-mission/strategy.
Jane Lyons is European regional coordinator for QMC VDC and country manager for PharmaLex Ireland, Suite 2, First Floor, Stafford House, Strand Road, Burrow, Portmarnock, Co. Dublin, Ireland; [email protected]; https://www.pharmalex.com/country/ireland.
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