ArticlemAbs
Assessment and incorporation of in vitro correlates to pharmacokinetic outcomes in antibody developability workflows.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Prediction of antibody non-specificity using protein language models and biophysical parameters.mAbs · 2026Article
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- Efficient inference of non-polyreactive antibody variants dependent on local fine-tuning.mAbs · 2026Article
- Predicting non-specific binding of VHHs using machine learning models with cluster-aware validation.mAbs · 2026Article
- Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties.bioRxiv : the preprint server for biology · 2025Article
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- Article
- Article
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- Overcoming antigen tolerance to develop GB22-45-2, an anti-DKK1 antibody for gastric cancer.Antibody therapeutics · 2025Article
- Biophysical Analysis of Therapeutic Antibodies in the Early Development Pipeline.Biologics : targets & therapy · 2024Review
Corrections and comments
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Authors and funding
15 authors.
Funding
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Abstract
In vitro assessments for the prediction of pharmacokinetic (PK) behavior of biotherapeutics can help identify corresponding liabilities significantly earlier in the discovery timeline. This can minimize the need for extensive early in vivo PK characterization, thereby reducing animal usage and optimizing resources. In this study, we recommend bolstering classical developability workflows with in vitro measures correlated with PK. In agreement with current literature, in vitro measures assessing nonspecific interactions, self-interaction, and FcRn interaction are demonstrated to have the highest correlations to clearance in hFcRn Tg32 mice. Crucially, the dataset used in this study has broad sequence diversity and a range of physicochemical properties, adding robustness to our recommendations. Finally, we demonstrate a computational approach that combines multiple in vitro measurements with a multivariate regression model to improve the correlation to PK compared to any individual assessment. Our work demonstrates that a judicious choice of high throughput in vitro measurements and computational predictions enables the prioritization of candidate molecules with desired PK properties.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.