A major engineering opportunity is the cell’s secretory pathway, which governs protein folding, post-translational modification, and export. Although most biologics depend on this pathway, it remains one of the least predictable and least engineered stages of bioprocessing.

As biologics become more structurally complex, this pathway is moving from background biology to a core determinant of whether promising therapies ever reach patients.

The Hidden Constraint in Modern Biologics

The secretory pathway, and the key role of signal peptides in modulating its efficiency, is often invisible in early R&D discussions. However, the choice of a signal peptide for a given protein can govern critical outcomes: yield, quality, stability, and ultimately cost of goods.

This challenge is becoming more acute as pipelines shift toward harder-to-manufacture formats such as bispecific antibodies, fusion proteins, engineered cytokines, next-generation vaccines, and AI-designed proteins with novel architectures.

Greater therapeutic sophistication often comes with greater manufacturing complexity.

From Analyzing a Handful to Engineering Thousands

The industry has historically addressed expression problems through familiar levers: media optimization, vector design, clone selection, process tuning, and incremental sequence changes. These methods remain valuable, but they do not always solve constraints rooted in intracellular trafficking and protein biogenesis.

The gamechanger for complex molecules is signal peptide engineering. Signal peptides are short amino acid sequences that direct nascent proteins into the secretory pathway. Although removed from the final mature protein, they can strongly influence how efficiently a protein enters cellular processing routes and how successfully it is produced.

In many organizations, secretory pathway engineering has been underexplored because available tools were limited. In fact, the current industry standard is analyzing a handful of signal peptides – even though thousands could be analyzed and engineered, resulting in significant increases in protein production.

The industry typically relies on a narrow set of conventional signal peptides or trial-and-error approaches. This approach was workable when pipelines were dominated by relatively standardized monoclonal antibodies. It is less sufficient in an era where each new modality may interact differently with the host cell’s folding and secretion systems.

High-throughput screening and machine learning now make it possible to test thousands of naturally occurring and engineered signal peptide variants against specific protein targets, replacing guesswork with data-driven optimization.

·         Higher expression can reduce manufacturing footprint and lower costs

·         Better intracellular processing can improve consistency and reduce downstream purification burden

·         Earlier identification of manufacturability issues can prevent expensive late-stage setbacks

·         Improve manufacturability is achieved without altering the therapeutic mechanism of action or final target structure

The molecule still matters. But increasingly, so does the machinery that makes it.

This article is written by Avenue Biosciences