Technology Selection and Assessment

The biologics discovery landscape offers a dizzying array of technology options, and the right choice depends on your target biology, desired format, throughput requirements, and downstream development path. Phage display remains the workhorse for antibody and nanobody discovery—it's well-validated, scalable, and supported by decades of IP and know-how. But it has blind spots: certain epitopes are underrepresented in phage libraries due to display bias, and affinity maturation in phage is slower than in yeast or mammalian display systems.

Yeast display offers real-time sorting by affinity via FACS, making it excellent for affinity maturation and multi-parameter selection (e.g., simultaneous binding and expression level). Mammalian display adds native post-translational modifications and correct folding context but at lower throughput and higher cost. Ribosome display avoids transformation bottlenecks entirely and can access library sizes exceeding 10^13, but requires specialized expertise and is less commonly outsourced.

Computational discovery—using tools like RFDiffusion, BoltzGen, and ProteinMPNN—is increasingly competitive with display approaches for certain target classes. It excels when structural data is available and you need epitope-specific binders, but currently lacks the ability to optimize for properties like pharmacokinetics or effector function that display systems handle naturally through functional screens.

The expression system you choose for screening should match your development path. If your lead will ultimately be produced in CHO cells, screening in E. coli may select for variants that express well in bacteria but aggregate or misfold in mammalian systems. This is especially relevant for full-length IgGs with complex glycosylation requirements. VHHs and single-domain antibodies are more forgiving—they express well in E. coli, yeast, and mammalian systems—but even here, early choices about signal peptides, purification tags, and codon optimization can affect downstream manufacturability.

Developability assessment should be built into your technology stack from the start, not bolted on after lead selection. Tools like CamSol for solubility prediction, TAP for hydrophobic interaction chromatography modeling, and aggregation propensity scores can flag problematic molecules early. The goal is to eliminate undevelopable candidates before you invest in characterization, not after.

One of the most common strategic mistakes in early-stage biotech is building your entire discovery engine on a single vendor's proprietary platform. This creates dependencies that are expensive to unwind: proprietary sequence formats, closed-source scoring algorithms, licensing restrictions on generated IP, and single-supplier risk for critical reagents. A sound technology strategy uses open formats, maintains data portability, and diversifies across vendors and in-house capabilities wherever the cost is reasonable.