San Diego, CA – The complex landscape of cell and gene therapy manufacturing, a field consistently challenged by the transition to commercial-scale production, is undergoing a significant transformation. Innovations in artificial intelligence (AI) and sophisticated data management are emerging as critical enablers, promising to streamline operations, accelerate development, and ultimately enhance the success rates of these groundbreaking treatments. This technological evolution was a central theme at the BIO International Convention (BIO 2026) held in San Diego, California, on June 22, 2026, during a pivotal session titled "Manufacturing the Future." Industry leaders from Kriya Therapeutics, Opus Genetics, and Epicrispr Biotechnologies shared their insights and early successes in leveraging these advanced technologies.
AI as a Catalyst for Innovation in Vector Design and Process Optimization
Fraser Wright, Scientific Co-Founder and Chief Gene Therapy Officer at Kriya Therapeutics, a company building on the legacy of Spark Therapeutics’ Luxturna, the first FDA-approved gene therapy, highlighted the tangible benefits Kriya is experiencing through its strategic integration of AI. Kriya’s approach centers on two key pillars: the intelligent design of therapeutic vectors and the creation of a comprehensive in-house data lake.
"In vector design, while these techniques have been in existence before, they’re much easier to implement now that we have such a powerful tool in AI," Wright explained to the BIO 2026 audience. He elaborated on a specific program where Kriya utilized AI to evaluate approximately 80 different vector constructs for their manufacturability. "It’s [still] a little bit manual, but using AI helped us identify the best constructs," he noted, underscoring the efficiency gains even in tasks that retain a degree of human oversight. This capability allows Kriya to navigate the intricate process of selecting optimal vectors with greater speed and precision, a critical factor in accelerating the development pipeline for novel gene therapies.
Beyond design, Kriya has made a substantial investment in a fully integrated manufacturing platform, encompassing the entire process from cell handling to the final viable product. This comprehensive system has naturally led to the accumulation of an enormous dataset. "Based on many engineering and GMP-like runs, as well as GMP runs, we’ve accumulated a very, very large data lake," Wright stated. "I think it’s approaching 100 million data points. These numbers will build, of course, and we feel this is a very powerful tool."
The depth of this data allows for granular analysis, identifying correlations between various process parameters and critical outcomes like yield. "We have hundreds and hundreds of points at a single process step. We can understand what correlates with better yield, for example, and so those are very, very important considerations for us," Wright emphasized. Kriya is actively deploying advanced enterprise-level AI to analyze this data, feeding insights back into its manufacturing processes to optimize efficiency and yield. This continuous feedback loop is fundamental to Kriya’s strategy of establishing robust and scalable manufacturing capabilities.
Streamlining Operations and Maximizing Efficiency in Small-Batch Manufacturing
Opus Genetics, a clinical-stage biopharmaceutical company focused on developing first-in-class gene therapies for inherited retinal diseases, faces a unique manufacturing challenge: the requirement for very small batch sizes. This inherently limits the available historical data for process analysis and validation. However, Opus is also turning to AI to overcome this hurdle.
"For us, the challenges always are analysing your data in a meaningful way when you have such limited data available," said the company’s representative at the convention. Opus is employing AI to analyze existing data and to prepare for Process Performance Qualification (PPQ) studies, specifically by consolidating Critical Quality Attributes (CQA) data more efficiently.
The impact of AI on a smaller, agile team like Opus is particularly pronounced. With a current team size of approximately 38-39 individuals, and only five dedicated to CMC (Chemistry, Manufacturing, and Controls), AI serves as a force multiplier. "For me, AI is a way to streamline my operation and make the staff more efficient, and then you get a better data output," the Opus representative explained.
This efficiency translates directly into cost savings. "We look at it from a cost savings perspective," they added. "I mean, if we can streamline the PPQ process and come away with meaningful risk assessments that allow us to do reduced testing for some part of our platform, then we can save costs." This strategic application of AI allows smaller companies to navigate the rigorous regulatory pathways for novel therapeutics without disproportionately high expenditure on process development and validation.
Quality by Design and Predictive Analytics for Startups
Epicrispr Biotechnologies, a 35-person clinical-stage company specializing in gene-modulating therapies, also shared its experience at BIO 2026. Their lead candidate, EPI-321, is currently in Phase I/II trials for facioscapulohumeral muscular dystrophy (FSHD). As a startup, Epicrispr relies on Contract Development and Manufacturing Organizations (CDMOs) for its bulk manufacturing. Consequently, their internal application of AI is focused on optimizing their own proprietary processes and ensuring seamless collaboration with CDMO partners.
Dipali Patel, Vice President of Technical Operations at Epicrispr, outlined their approach, which is rooted in Quality by Design (QbD) principles. "We implemented QbD principles and [using] advanced tools like AI really helped us generate smarter experiments, smarter [experiment] designs," Patel stated. This AI-driven experimental design capability allows Epicrispr to iterate more rapidly and efficiently.
"We’re able to loop that and feed that data back to really predict the next layer of studies to do in that same time and really cut the number of experiments needed to get there," Patel elaborated. This predictive power has enabled Epicrispr to achieve several key objectives: "one: identify our critical process parameters, and two: understand synergistic relationships between the key process parameters; and then three: help set thresholds." Crucially, she noted that these achievements were realized "in a much more concise time frame than I’m used to in my past life," highlighting a significant acceleration in process understanding and control.
However, Patel also issued a word of caution regarding the application of AI in the highly regulated pharmaceutical sector. "The stakes matter," she stressed, particularly urging prudence when using AI for generating regulatory documentation. The integrity and accuracy of submissions to regulatory bodies like the FDA and EMA are paramount, and while AI can assist in data analysis and report generation, human oversight and validation remain indispensable.
The Broader Context: Navigating a Challenging Regulatory and Commercial Environment
The discussions at BIO 2026 took place against a backdrop of evolving trends in the pharmaceutical industry, particularly concerning the pace of new drug approvals. GlobalData’s latest annual "New Drug Approvals and Their Contract Manufacture" trend report indicated a slowdown in US approvals in the preceding year, with four approvals compared to eight in 2024, following four consecutive years of record-breaking numbers. This trend underscores the intensifying challenges faced by companies aiming to bring novel therapies, especially complex cell and gene therapies, to market.
In this environment, the strategic and judicious application of AI and advanced data analytics presents a compelling opportunity for companies to enhance their operational efficiency, reduce development timelines, and mitigate risks. By freeing up valuable time and resources, these technologies can allow scientific teams to concentrate more intensely on the core mission: improving product efficacy and patient outcomes.
The insights shared by Kriya Therapeutics, Opus Genetics, and Epicrispr Biotechnologies at BIO 2026 demonstrate a clear industry trajectory. Companies are moving beyond the conceptualization of AI’s potential and are actively implementing these tools to achieve concrete operational improvements. From optimizing vector design and building massive data lakes to enabling efficient analysis of limited datasets and driving smarter experimental design, AI is proving to be a transformative force. As the cell and gene therapy sector continues to mature, its ability to harness the power of data and artificial intelligence will be a defining factor in its success, paving the way for a more streamlined, efficient, and ultimately, more impactful future for these life-changing treatments. The careful understanding and application of both the risks and benefits associated with these technologies will be key to unlocking their full potential in this dynamic and critical field.
