Paul Elizondo, Chief Innovation Officer, Intelliguard
The pharmaceutical industry is in the midst of a digital transformation, fueled by technologies that enhance visibility, efficiency, and patient safety. Among the most impactful is RAIN RFID, an ISO (18000-6C) and GS1 (EPC Gen2) standard that operates in the UHF (915 MHz) band. Widely adopted in the retail sector, tracking over 45 billion items annually, RAIN RFID is also the foundation of Intelliguard’s platform, bringing real-time intelligence to hospital inventory management.
In healthcare, RAIN RFID enables precise tracking of high-value medications, reduces waste, and eliminates manual processes that burden clinical teams. By embedding automation and data into medication workflows, hospitals and health systems can optimize their supply chains, ensure regulatory compliance, and improve care delivery.
However, challenges remain for broader adoption. Legacy IT systems, data integration complexities, and upfront investment costs can slow implementation. Equally important is aligning new technologies with clinical workflows to ensure acceptance by frontline staff.
At Intelliguard, we’ve seen how marrying clinical operations with smart technologies drives measurable improvements in both cost containment and patient outcomes. As digital health becomes more embedded in care, strong collaboration among technology leaders, providers, and health systems will be critical to scaling innovation industry-wide.
Nicolas Descamps, Ph.D., Global Technology Liaison Manager, Pharma, Roquette Health & Pharma Solutions
In a word: efficiency. The integration of artificial intelligence (AI) is transforming the way formulation scientists approach drug development, particularly by optimizing excipient preselection and process conditions. This significantly enhances resource efficiency, although it doesn’t necessarily accelerate processes due to stringent regulatory constraints.
Despite AI's promising capabilities, many of the tools currently available to scientists aren’t well suited to pharma’s specific requirements. Fully embracing the potential of AI will mean addressing this challenge, along with managing diverse experimental conditions and avoiding overfitting (where models ‘memorize’ the training data instead of identifying the underlying patterns).
A cornerstone of accurate AI predictions is the availability of extensive, high-quality information. Much of the historical data at formulators’ disposal falls short of this standard, posing challenges for effective modeling. Companies can address this by automating laboratory experiments, thereby generating more comprehensive data. But AI is not the only option when it comes to modeling. Existing mechanistic models are not only simpler and highly effective for unit operations, but they also require far less extensive data sets.
Effective AI integration ultimately depends on collaboration. This can involve everything from data sharing to fostering a collaborative culture across various functions. Working together, we can leverage AI's full potential for formulators, manufacturers and — most importantly — patients.
Jinhan Kim, Ph.D., Vice President, Head of Artificial Intelligence Lab, Samsung Biologics
The rise of digital–drug combinations and the emergence of digital therapeutics (DTx) as standalone treatments pose both opportunities and challenges for contract development and manufacturing organizations (CDMOs).
Demand is growing for the manufacturing and packaging of pharmaceuticals with digital devices or software interfaces, such as smart injectors. The industry’s growing appetite for digital–drug integrations is encouraging CDMOs to augment the competitiveness of their end-to-end services by adapting cross-functional product designs that combine drug, device, and mobile applications. Device prototyping and digital connectivity are two prominent examples of such integrations. However, these digital advancements bring new challenges. The absence of clear regulatory guidance regarding their implementation creates uncertainty for biopharmaceutical companies. Additionally, the complexity of managing hardware/software providers alongside traditional API/raw material suppliers adds logistical and operational challenges. CDMOs must also navigate uncharted areas — such as data interoperability and post-market device monitoring — by strengthening their digital infrastructure through strategic investments and targeted talent acquisition.
Also, the emergence of DTx as standalone treatments is dismantling the boundary of conventional therapeutics. Riding on this trend, CDMOs can now explore options for hybrid pipeline development, like drug–DTx trials, through partnerships with DTx startup ventures. The application of DTx into CDMO operations offers several competitive advantages, including platform diversification, robust data analytics, software-powered quality assurance, and digitalized infrastructure support. This DTx-led transformation would position CDMOs to become integrated service providers, extending beyond their conventional business model. However, the rise of DTx also poses challenges. The business model for CDMO services in a DTx-centric value chain has not been clearly defined. Many CDMOs lack digital product development capabilities and regulatory expertise in software validation. Furthermore, cultural and operational differences between biopharmaceutical companies and DTx/software startups can lead to friction.
The convergence of digital health and traditional drug development is reshaping the biopharmaceutical industry and the role of CDMOs. While the opportunities are substantial, from end-to-end services to integrated partnerships, CDMOs must navigate challenges — such as regulatory uncertainty, supply chain complexity, and the need for digital expertise — by proactively engaging in cross-industry collaboration and fostering digital expertise. Preemptive adoption and gradual integration of digital innovations in operations will position CDMOs as indispensable partners in developing next-generation therapies, bridging the gap between evolving molecules and advanced software.
Jason Housley, Senior Director, Customer Strategy and Success, eClinical Solutions
Bringing new drugs to market requires efficiency in harnessing, organizing, and analyzing the increasing abundance of clinical data. New digital health solutions, including wearable devices, electronic health records (EHRs), apps, sensors, and numerous others, offer promising advancements and personalization for patients and sites, but are simultaneously adding complexity to clinical data management. (We must, however, ask ourselves, if this volume and diversity of digital data is actually adding the value anticipated.) Stakeholders cannot access the full benefit of digital data breakthroughs, unless that data can be integrated and utilized for meaningful insights. As cycle times continue to pressure drug development, the industry is experiencing challenges in connecting diverse and ubiquitous data efficiently.
Clinical data is a powerful asset, but modernizing our approach is necessary to accelerate drug development. To overcome this, implementing holistic data strategies and thoughtfully adopting technology, such as AI, is necessary. Innovating infrastructure and processes is paramount to widespread digital health adoption. Leaders should focus on the reexamination of existing workflows, embedding technology to connect people, attitudes to risk, data, and applications so that data insights can drive decision-making. Doing so may enable the long-coveted nirvana of leveraging the power of digital health solutions to optimize NCE breakthroughs at pace.