Explore the Agenda
8:00 am Registration & Morning Coffee
8:55 am Chair’s Opening Remarks
Advancing Process Development in Oligonucleotide Synthesis to Control Variability from Early Design to Scale
9:00 am Bridging Process Development & CMC to De-Risk Scale-Up of Oligonucleotide Therapeutics Toward Commercial Readiness
- Designing process development strategies that proactively define critical process parameters (CPPs) and critical quality attributes (CQAs) to enable a seamless transition into robust CMC control strategies
- Translating early-phase process understanding into scalable, reproducible manufacturing workflows to minimize variability and avoid late-stage rework during scale-up
- Establishing effective knowledge transfer between process development, analytical, and CMC teams to ensure alignment on impurity control, process consistency, and regulatory expectations
9:30 am Establishing Process & Analytical Readiness for First Oligonucleotide Programs to Enable IND Filing & Seamless Technology Transfer
- Defining the critical analytical, process, and raw material requirements across DS and DP to support IND submission
- Designing scaled-down models and analytical strategies to build process understanding and de-risk scale-up
- Overcoming technical and organizational challenges in early CDMO partnerships and technology transfer to ensure alignment on methods, specifications, and accelerate GMP readiness
10:00 am Speaker Q&A: What Breaks First When You Scale Oligos from IND to Commercial?
- Which early-stage assumptions around CPPs and impurity control most commonly fail at commercial scale?
- How to predict the point where oligo impurity complexity outpaces the existing analytical control strategy?
- What elements of process design, raw material qualification, or method validation are most frequently reworked post-Phase II, and why?
10:30 am Morning Break & Networking
Expanding the Analytical Toolkit for Novel Oligonucleotide Modalities to Address Increased Structural Complexity
11:30 am Expanding the Analytical Toolkit for siRNA Development to Build Phase- Appropriate CMC Control Strategies
- Assessing which analytical tools are most critical for characterizing siRNA quality attributes to support confident progression from early development into the clinic
- Exploring how impurity, conjugation, and structural complexity considerations are shaping analytical strategy as siRNA programs become more advanced and diverse
- Sharing how analytical development, QC, and CMC teams can align on phase appropriate expectations so resources are focused on what matters most at each stage of development
12:00 pm Analytical Strategies for Oligonucleotide Conjugates: Overcoming Heterogeneity to Enable IND Readiness
- Addressing conjugation-driven heterogeneity and complexity in oligonucleotide conjugates to enable robust characterization and control
- Defining and controlling critical quality attributes (CQAs) such as DAR, linkage stability, and payload distribution to support IND readiness
- Designing scalable analytical strategies across internal teams and CDMOs to ensure consistent characterization through development and tech transfer
12:30 pm Lunch Break & Networking
Digitalizing Oligonucleotide Development to Improve Control, Comparability, & Speed
1:30 pm Harnessing Sequencing for Digital QC & Impurity Intelligence to Build Actionable & Reproducible Oligonucleotide Manufacturing Control Strategies
- Identifying sequence-related variants that influence batch consistency and downstream complexity to better inform manufacturing control strategies
- Apply high-resolution variant profiling to anticipate impurity shifts during scale-up and tech transfer, to enable more predictable manufacturing
- Integrating sequencing data into comparability assessments to strengthen regulatory confidence across the product lifecycle
2:00 pm Roundtable Discussion: Data to Decisions: Applying AI to Transform Complex Analytical Datasets into Predictive Insight for Oligonucleotide Process Optimization
- Applying AI to integrate chromatographic, spectrometric, and in-process datasets to uncover relationships between process parameters and product quality
- Developing predictive models to simulate scale-up performance, parameter sensitivity, and process robustness before implementing manufacturing changes
- Embedding AI-driven analytics into change control and lifecycle management workflows to support consistent, data-backed manufacturing decisions across sites and stages