Next-generation human challenge models are revolutionizing respiratory virus research by delivering controlled, accelerated proof-of-concept data that traditional field trials cannot match.
Biologics development lives and dies on the quality of its manufacturing process. A molecule that looks promising in discovery can behave very differently once it moves into a fermenter or bioreactor, and the transition between scales is often where programmes encounter their first major technical hurdles. Scale-up and scale-down aren’t simply engineering exercises — they’re critical scientific decisions that shape product quality, comparability, and ultimately the credibility of the entire development programme.
For early phase biotechs, understanding how to approach these transitions can prevent costly surprises later. The principles are well established, but their application requires judgement, experience, and a clear view of what regulators will expect as the programme matures.
Even in early development, the way a process behaves at small scale influences everything that follows. Parameters such as oxygen transfer, agitation, shear forces, nutrient availability, and reactor geometry all affect cell growth and product expression. If these factors aren’t understood early, teams often discover too late that their “successful” small scale process doesn’t translate to the volumes needed for clinical supply.
The goal isn’t to lock in a commercial process from day one — it’s to understand which parameters truly drive product quality and which can be adapted as the programme grows. That insight is what allows scale-up to be predictable rather than reactive.
While scale-up gets most of the attention, scale-down is equally critical. Small scale models are used to troubleshoot issues, test process changes, and generate comparability data. If the scale-down model doesn’t accurately reflect the behaviour of the larger system, the conclusions drawn from it can be misleading.
A good scale-down model captures the essential physics and biology of the full-scale process — not by mimicking every detail, but by reproducing the conditions that matter most for product quality. When done well, it becomes a powerful tool for development, optimisation, and regulatory justification.
One of the clearest illustrations comes from influenza. Many sequencing vendors still compare new flu genomes against reference databases built from strains circulating decades ago. The result is predictable: hundreds of differences, most of them irrelevant.
When developers try to interpret this, they’re left with noise instead of clarity.
By contrast, using a curated, season‑specific database of representative flu strains transforms the analysis. Suddenly, the question isn’t “what changed?” but “what changed that matters?” — especially when assessing antiviral resistance or vaccine performance.
This is the difference between sequencing as a commodity and sequencing as insight.
The next frontier: multi‑pathogen assays and host response
Once sequencing is embedded in the early‑phase environment, its applications expand quickly:
RSV, hepatitis viruses, and other respiratory pathogens
Bacterial threats such as Bordetella pertussis
Host transcriptomics to understand immune response
Biomarkers that stratify participants or predict treatment effect
These aren’t speculative ideas — they’re the natural evolution of a platform that can already move from sample to sequence within a working week.
NGS has always had the potential to transform early‑phase infectious disease research. What held it back wasn’t the technology — it was the lack of an environment where sequencing, clinical science, and infectious disease expertise could operate together.
That environment now exists. And as sequencing becomes a force multiplier for early‑phase development, sponsors gain something they’ve never truly had before: a molecular‑level understanding of infection and treatment response at the exact moment those insights matter most.
This is the beginning of a new chapter for early‑phase infectious disease research — one where sequencing isn’t an afterthought, but a core part of how smarter, faster decisions get made.