Approaching a Significant Transformation
While there have been promising advances in terms of technologies to help the development and manufacture of next-gen therapies, limitations remain; but, it is likely that AI will be revolutionary for development in the near future.
Demand for next-generation therapies has increased over recent years as a result of their ability to treat conditions that are rare and difficult-to-treat with more traditional therapies. However, the development and manufacture of these therapies is challenging and complex. Looking at the specific hurdles facing the development and manufacture of these therapies and the potential uses of advanced technologies to overcome these difficulties in greater detail, The Pharma Navigator spoke with Max Baumann, co-founder, partner, Treehill Partners.
Exact Path Forward Unclear
TPN: Could you give an overview of the specific challenges pertaining to the development and manufacture of next-generation therapies?
Baumann: The concept of ‘next-generation’ therapies has evolved significantly over time. A decade ago, companies were grappling with highly complex cell therapies and developing autologous approaches that faced fundamental challenges — they weren't scalable, were extremely cost-intensive, and often encountered regulatory uncertainty. Today's next-generation therapies necessarily aim to overcome these hurdles, though the exact path forward remains unclear and several specific challenges continue to impact development and manufacturing:
Biological targeting complexity. Human-specific targeting complicates nonclinical testing, often necessitating ethically fraught nonhuman primate models or costly human-centric methodologies.
Manufacturing infrastructure requirements. Cell and gene therapies demand specialized infrastructure, with autologous processes facing particular scalability issues due to their inherently personalized production needs.
Delivery system innovations. Large molecules (e.g., mRNA, proteins) face bioavailability challenges, especially for oral administration, requiring innovative delivery systems like nanoparticles or lipid carriers.
Regulatory framework gaps. Uncertainty persists for novel modalities, particularly regarding analytical standards and approaches to decentralized manufacturing for personalized therapies.
Economic barriers. High costs stemming from small batch sizes, labor-intensive processes, and stringent quality control requirements continue limiting accessibility, especially for rare disease treatments.
Helping to Address Challenges
TPN: How can advanced technologies help to overcome these development and manufacturing challenges?
Baumann: Several technological advances are helping address these significant challenges, and we see significant transformation in the sector about how the development of drugs is thought-through — as well as differences between players large and small in that regard:
Artificial intelligence (AI) and machine learning (ML) are revolutionizing drug development by predicting drug-target interactions, optimizing manufacturing schedules, and detecting defects in real time, substantially reducing trial-and-error approaches in R&D. In many instances, we see that AI is not yet being used with maximum effect, and whilst the technology keeps developing seemingly at the speed of light, humans do require time to adapt their workflows and embrace change
Organ-on-a-chip platforms now enable human-relevant disease modeling, effectively bypassing the limitations of inter-species differences in preclinical safety and efficacy testing. Regulation is only very slowly following these approaches, and true ‘wins’ yet need to be reported
Advanced automation and robotics systems (such as AUTOSTEM) are standardizing cell therapy production, minimizing contamination risks, and enabling closed-system manufacturing for greater consistency
Digital twin technology and real-time analytics simulate processes, predict equipment failures before they occur, and accelerate technology transfers between development and production phases
Flexible manufacturing approaches through modular systems allow rapid scaling between small batches (essential for orphan drugs) and large-scale production using adaptable bioreactor technologies
Technological Limitations Remain
TPN: Are there any potential areas where advanced technologies are not yet suitable for use or where further development is needed?
Baumann: Despite promising advances, significant technological limitations remain. Over the past decades, we saw many founding teams with strong science and mechanisms facing insurmountable CMC [chemistry, manufacturing, and controls] challenges, often opting for inferior drug product configurations as a result that compromise commercial viability. Real-world examples include impractical approaches like intratumoral injections for inaccessible tumor sites or magnetic particle treatments requiring prohibitively expensive specialized equipment.
Specific technological limitations include:
GMP compatibility gaps that delay implementation of cutting-edge analytics (including AI-driven quality control) due to regulatory validation requirements
Personalized therapy bottlenecks that persist because autologous processes inherently resist automation and require complex patient-specific logistics
Data integration challenges from interoperability issues that hinder coordination across decentralized digital platforms, complicating end-to-end process monitoring
AI prediction limitations for novel modalities due to sparse training data, particularly regarding long-term safety profiles or rare adverse events
Efficacy disparities where allogeneic therapy approaches, despite their scalability advantages, often remain inferior to autologous approaches for certain indications.
Increasing Criticality of Flexibility
TPN: Why is flexibility particularly important for the increasingly complex development pipelines? How can advanced technologies help with this aspect?
Baumann: Flexibility has become increasingly critical in pharmaceutical development for several reasons:
Modality diversification. The range of therapeutic approaches (mRNA, CAR-T, ADCs, etc.) requires adaptable facilities capable of switching between production of viral vectors, lipid nanoparticles, and live cells.
Market demand variability. The unpredictable nature of demand for both orphan drugs and potential blockbusters necessitates hybrid manufacturing approaches combining batch and continuous processing with real-time output adjustments.
Regulatory landscape evolution. Expedited pathways (especially evident during pandemic responses) demand agile CMC strategies and capabilities for rapid process revalidation.
Supply chain considerations. While large players enhance resilience through on-demand production and digital inventory management, biotech companies face mounting pressure from geopolitical realities to establish onshore manufacturing capacity for U.S. development and commercialization — a requirement that inevitably creates efficiency losses.
Patient-centric innovation. The shift toward self-administered biologics requires flexible formulation technologies such as wearable injectors or temperature-stable lyophilized products.
A Likely AI Revolution
TPN: Are CDMOs utilizing advanced technologies to drive forward innovation in next-gen therapies?
Baumann: CDMOs typically aren't at the vanguard of true manufacturing innovation. Their primary role involves developing and deploying technologies that achieve defined objectives more quickly, effectively, or with fewer resources, rather than pioneering entirely new approaches. While a small component of their work involves process engineering that advances technology, there's an inherent ‘chicken and egg’ problem: CDMOs are contracted for development work that contains only minimal innovation, whereas true research (which drives major innovation) rarely falls within their contracted scope.
Current technological utilization includes:
AI-driven optimization platforms for cell culture media formulation, critical quality attribute prediction, and automated deviation investigations.
Digital integration systems connecting clinical trial data with manufacturing execution systems (MES) to accelerate technology transfers.
Flexible production facilities with modular cleanrooms and single-use bioreactors enabling concurrent production of viral vectors, mRNA vaccines, and cell therapies.
Sophisticated analytical methods like mass cytometry that characterize complex products such as CAR-T cells while meeting stringent release criteria.
We're likely approaching a significant transformation. Within the next few years, AI will likely revolutionize many aspects of development processes. Whether this transformation occurs within established players or through market disruption by new entrants remains uncertain, but AI adoption continues growing across engineering domains and will inevitably transform the CDMO sector.
Areas requiring further development include standardizing AI validation protocols, expanding allogeneic therapy platforms, and harmonizing global regulatory frameworks for decentralized manufacturing approaches.
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