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Updated: Aug 2, 2025
Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries
Published on: January 17, 2015
Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico
Tushar Jain1, Todd Boland2, Maximiliano Vásquez1
1Adimab LLC, Palo Alto, CA, USA.
Abstract:
With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.
Insights
In vitro assays for antibody developability, particularly polyspecificity and hydrophobicity, better predict clinical success than computational methods. Reproducibility challenges persist, requiring standardized controls and data sharing for improved antibody drug discovery.
Area of Science:
- Biopharmaceutical development
- Drug discovery and design
- Protein engineering
Background:
- Antibodies are a critical therapeutic class, necessitating early identification of developability risks.
- High-throughput in vitro assays and in silico methods are employed to mitigate these risks during early discovery.
- Assessing the predictive power of these methods for clinical progression is crucial.
Approach:
- Compiled and analyzed published experimental assessments and computational metrics for clinical antibodies.
- Evaluated the performance of existing developability prediction models on external datasets.
- Identified challenges in reproducibility for both in vitro and in silico approaches.
Key Points:
- In vitro measurements of polyspecificity and hydrophobicity show higher predictivity for clinical progression compared to in silico predictions.
- Current computational models struggle with generalization to new molecular data.
- Reproducibility issues stem from variations in homology modeling, assay reagents, and data curation.
Conclusions:
- Standardized controls with disclosed sequences and shared structural models are recommended to enhance assay reproducibility.
- Improved reproducibility is essential for critical assessment and advancement of in silico developability prediction tools.
- This work provides insights into optimizing antibody developability assessments for clinical success.
