15th PBP World Meeting Conference Recap

Key themes from 15th PBP World Meeting

• AI & digitalization: A transformative shift toward using AI and robotics to optimize drug formulation and patient selection.
• Advanced mRNA delivery: Focus on cytokine cocktails and in vivo CAR-T therapies to overcome immunosuppression in cancer.
• Targeting breakthroughs: Development of pH-sensitive and ligand-specific LNPs to reach tissues outside the liver.

Selected presentation highlights

Here are the insights we’re excited to share from the talks that made an impact for us.

mRNA cancer vaccines and cytokine cocktails (Raymond Schifflers, UMC Utrecht)

Introduction

This talk explored the rapidly evolving field of nucleic acid (NA) nanomedicine for cancer therapy, with a particular focus on mRNA therapeutics, lipid nanoparticle (LNP) delivery systems, and next-generation immunotherapies. The presentation highlighted how advances in RNA technologies and nanomedicine are enabling both personalized and off-the-shelf cancer treatments. The speaker discussed the advantages of nucleic acids as therapeutic platforms, current developments in mRNA cancer vaccines, and the growing interest in in vivo cell engineering approaches such as CAR-T therapies. A major theme throughout the presentation was the importance of targeted delivery systems capable of directing nucleic acid therapeutics to the correct tissues and immune cell populations while minimizing off-target effects. [1]

General considerations for nucleic acid delivery

• Several strategies exist to deliver nucleic acids across biological membranes, including:
  • Viral vectors
  • Nanoparticles (NPs)
  • Electroporation
• One major advantage of nucleic acid therapeutics is that the core molecule remains fundamentally similar across applications. As a result:
  • The delivery platform and formulation strategy can often be reused or adapted
  • Development does not require a complete reformulation for every new therapeutic sequence

mRNA cancer vaccines

The presentation described the general workflow for personalized mRNA cancer vaccines:

1. Tumor biopsy collection
2. Tumor sequencing
3. mRNA design and synthesis
4. LNP formulation and administration

Examples of mRNA cancer vaccine programs

• BNT111:
  • Off-the-shelf (non-personalized) mRNA melanoma vaccine
  • Based on a lipoplex delivery strategy
• Autogene Cevumeran (BNT122):
  • Personalized mRNA vaccine approach for pancreatic cancer
• The speaker emphasized the existence of a rapidly expanding pipeline including:
  • Personalized cancer vaccines
  • Non-personalized (“off-the-shelf”) cancer vaccines

Challenges in targeted delivery

• Intratumoral targeting remains a critical challenge.
• Efficient delivery systems are required to ensure that LNPs selectively transfect the desired cells while avoiding off-target tissues.
• Targeting specificity was presented as a key requirement for future clinical success.

Cytokine-boosting mRNA immunotherapy

The presentation also explored the transition from ex vivo to in vivo CAR-T approaches:

• Traditional ex vivo CAR-T manufacturing remains highly complex and expensive.
• Reported manufacturing costs can reach approximately $500,000 per patient.
• In vivo approaches aim to directly engineer immune cells within the patient, simplifying production and reducing costs.

Examples of in vivo CAR-T developments

• MagicRNA:
  • Reported as the first human mRNA-LNP-based in vivo CAR-T approach
• Kelonia:
  • Developing targeted lentiviral vector-based in vivo CAR-T therapies

Targeted lipid nanoparticles (tLNPs)

• The speaker highlighted the development of targeted LNPs (tLNPs) designed to specifically target T cells.
• These systems could enable:
  • More efficient in vivo immune cell engineering
  • Improved therapeutic specificity
  • Reduced systemic side effects

AI & robotics for intelligent drug formulation (Christine Allen, University of Toronto)

This talk focused on the emerging role of artificial intelligence (AI) and robotics in pharmaceutical research and drug formulation development. While AI has already demonstrated significant value in areas such as preclinical research and clinical trial optimization, the speaker emphasized that formulation development remains one of the largest untapped opportunities for AI integration. The presentation explored how AI-driven approaches, combined with automated robotic experimentation, could transform traditional formulation workflows by accelerating development timelines, reducing experimental burden, and improving decision-making efficiency. Particular attention was given to the importance of high-quality experimental datasets, the need for cross-functional integration within pharmaceutical organizations, and the long-term vision of autonomous “AI Labs” capable of continuously learning and optimizing formulations with minimal human intervention. [2]

Key figures and current impact of AI in pharma

• Global pharmaceutical R&D spending is estimated at approximately $100 billion per year.
• AI adoption in pharma is progressing, although results remain uneven depending on the application area.
• The most notable successes are currently observed in:
  • Preclinical research
  • Drug discovery and candidate selection
• Reported benefits of AI integration include:
  • 25–30% cost savings in preclinical research
  • Higher success rates for compounds entering Phase I clinical trials compared with conventional approaches
  • Potential savings of up to $300 million annually across Phase I–III clinical studies
  • Identification of 99% of eligible patients within 15 days
  • Up to 90% reduction in required sample sizes

Regulatory evolution

• The presentation referenced the 2025 FDA draft guidance regarding the use of AI to support regulatory decision-making.
• This guidance reflects increasing regulatory interest and acceptance of AI-driven approaches in pharmaceutical development.

Current barriers to AI adoption

Several factors continue to slow AI implementation in the pharmaceutical industry:

• Strong separation between functions:
  • Drug discovery
  • Formulation
  • Regulatory affairs
• Reliance on established and validated processes
• Continued dependence on physical experimental work

Why formulation is a major opportunity for AI?

• Drug formulation involves millions of possible parameter combinations, making exhaustive experimentation impossible.
• Experimental work is often limited by:
  • Availability of APIs
  • Time and resource constraints
• AI can help prioritize the most promising experiments and navigate the large formulation design space more efficiently.

Data challenges and machine learning requirements

• Effective AI models require large, high-quality, and unbiased datasets.
• The speaker emphasized the importance of:
  • Open datasets
  • Sharing negative experimental results
• Publishing unsuccessful experiments was presented as essential to improve machine learning performance and reduce bias.

Vision of an autonomous “AI Lab”

The presentation described a closed-loop AI-driven laboratory workflow:

1. Experimental data are fed into AI models.
2. AI analyzes results and identifies knowledge gaps.
3. AI programs robotic systems to perform additional experiments.
4. New data are generated and reintegrated into the system.
5. The cycle continues until an optimized formulation is identified.

Reported benefits of the AI Lab approach

• Significant acceleration of formulation development timelines
• Reduction of experimental blind spots
• Improved optimization efficiency
• One example presented achieved complete in vitro formulation development within only 5 days.

Advanced nanoparticle targeting (Stephano Salmaso, University of Padova)

• Market status: While 25 nucleic acid products have been approved since 1998, most lack active targeting capabilities.
• Liver bypass: New strategies involve specialized PEG coatings and pH-sensitive elements to ensure selective interaction with tumor microenvironments rather than liver accumulation. [3]
• Manufacturing precision: Use of microfluidics for stable lipoplex production at specific N/P ratios.

Industry direction & future outlook

The ResearchPharm® exhibition showcased the hardware driving these scientific leaps. Key highlights included:

• Formulation optimization: Quotient Sciences demonstrated a 25-30% reduction in required formulations through advanced screening.
• Global access: AI is being leveraged to improve drug accessibility and significantly lower the environmental impact of manufacturing.

References

[1]       Raymond Schifflers, “mRNA Cytokine Cocktails for Cancer Immunotherapy,” presented at the 15th PBP World Meeting, Prague, 2026.

[2]       Christine Allen, “AI and Robotics for Intelligent Drug Formulation,” presented at the 15th PBP World Meeting, Prague, 2026.

[3]       Stephano Salmaso, “Advanced Targeting Strategies for Nucleic Acid Delivery,” presented at the 15th PBP World Meeting, Prague, 2026.