Analytical innovation in biologics development: Why better decisions start earlier.
by Dr Jane Doe, Ph.D. — 8 minute read — April 27, 2026
In biologics development, speed matters.
But speed without confidence is just risk in disguise.
That was one of the clearest messages from our recent panel discussion on “Analytical innovation as a strategic driver of quality, speed, and success in biologics development” held during the last NextGen Biomed conference in London this March.
Moderated by Ping Zhang, Product Manager at NanoTemper, the panel brought together leaders working across discovery, CMC (Chemistry, Manufacturing ,and Controls), and analytical development:
Beth Wesley, Principal Scientist, LifeArc,
leading protein and analytical sciences in discovery
Salis Rabelo, Associate Director, AstraZeneca,
specializing in physicochemical and biophysical characterization of biologics across development stages
focused on analytical development and bioassays across clinical stages
Agnieszka Lewandowska, Associate Director, Immunocore,
Dr Jane Doe
Senior Formulation Scientist
The Earlier You Characterize Stability, The Fewer Surprises You Face In Manufacturing
Perhaps the most important leadership message is that quality analytics should not be viewed as a late-stage checkpoint. It needs to be embedded across the development continuum, informing decisions in upstream and downstream processing, formulation, and release strategy alike.
For teams working on novel or complex modalities, this means identifying analytical and CMC challenges early, engaging cross-functional stakeholders sooner, and building data integrity into the process from the start.
High-throughput, automation, and AI are changing expectation
Another clear theme was that high-throughput technologies and automation are enabling earlier, more informed candidate evaluation.
As biologics pipelines become more complex, teams need tools and workflows that can keep pace without compromising data quality.
AI also featured prominently in the discussion, but with an important caveat: its value depends entirely on the quality and consistency of the underlying data. The panel emphasized that successful AI in biologics development will likely come not from hype-driven adoption,
but from targeted, practical applications built on strong experimental foundations.
Quality analytics must be embedded, not added later.
A Step-by-Step Workflow for Stability Screening
Ut labore et dolore magna aliqua enim ad minim veniam. The following workflow outlines how a typical formulation screening campaign is structured using Prometheus Panta to generate actionable data in a single day.
Table 1: Thermal stability summary for five formulation candidates measured using Prometheus Panta. Ranked by combined Tm and Tagg scores.
72.1
68.2
7.4
PBS + 5% Sucrose
69.4
PBS + 5% Sucrose
7.4
68.2
Citrate-Phosphate
6.5
63.4
66.7
Acetate + Sorbitol
5.0
61.1
63.2
Tris + NaCl
8.0
58.7
60.3
Condition
PH
Tm (°C)
Tagg (°C)
Rank
Key Parameters Measured in a Single Thermal Ramp Experiment
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium. Multi-parameter stability data collected early in development gives formulation teams the information they need to deprioritize poorly performing candidates before committing to expensive downstream studies.
Video 1: See how Prometheus Panta simultaneously measures Tm and Tagg in a single experiment. Runtime: ~3 min.
Looking ahead.
Key terms referenced in this article
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium. Multi-parameter stability data collected early in development gives formulation teams the information they need to deprioritize poorly performing candidates before committing to expensive downstream studies.
Formulation Screening at Scale
Learn how leading biopharma teams run 48-condition stability screens in a single day using Prometheus Panta.
~45min
Aggregation temperature — the onset of protein self-association, measured via static light scattering (SLS).
Tagg
Melting temperature — the midpoint of the protein's thermal unfolding transition, indicating conformational stability.
Tm
A non-active ingredient (e.g., sucrose, sorbitol) added to a formulation to stabilize the therapeutic molecule.
Excipient
Dynamic Light Scattering — technique to measure hydrodynamic radius and polydispersity of proteins in solution
DLS
The future of biologics development will be shaped by organizations that treat analytical innovation as a strategic capability, not a technical afterthought. The combination of AQbD, automation, high-throughput methods, and fit-for-purpose AI is creating a new model for development: one that is faster, more connected, and more resilient.
For us at NanoTemper, this reflects a broader truth: analytical innovation creates the most value when it helps scientists and biopharma teams move from data to decision faster and with greater confidence.
Conclusion
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integrating multi-parameter stability data into early formulation workflows fundamentally changes how teams allocate resources. Rather than running sequential assays across separate instruments, scientists gain a comprehensive view of molecule behavior in a single, streamlined experiment.
The key takeaway: analytical innovation is only valuable when it is intentional.
The strongest thought running through the discussion was this: more data is not the same as better insight.
Teams are moving away from simply adding more assays and more complexity. Instead, they are asking a more strategic question: What does this method need to answer, at this stage, to move the program forward with confidence? That phase-appropriate mindset, grounded in frameworks like Quality Target Product Profile (QTPP), Quality by Design (QbD), and Analytical Quality by Design (AQbD), is helping organizations make smarter analytical decisions from preclinical development through commercialization.