Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies
Open accessAbstract
Understanding the pharmacokinetic (PK) properties of a drug, such as clearance, is a crucial step for evaluating efficacy. The PK of therapeutic antibodies can be complex and is influenced by interactions with the target, Fc-receptors, anti-drug antibodies, and antibody intrinsic factors. A growing body of literature has linked biophysical properties of antibodies, particularly nonspecific-binding propensity, hydrophobicity and charged regions to rapid clearance in preclinical species and selected human PK studies. A clear understanding of the connection between biophysical properties and their impact on PK would allow for early selection and optimization of antibodies and reduce costly attrition during clinical trials due to sub-optimal human clearance. Due to the difficulty in obtaining large and unbiased human PK data, previous studies have focused mostly on preclinical PK. For this study, we obtained and curated the most comprehensive clinical PK dataset to date and calculated accurate estimates of linear clearance for 64 monoclonal antibodies ranging from investigational candidates in Phase 2 trials to marketed products. This allows for the first time a deep analysis of the influence of biophysical and sequence-based in silico properties directly on human clearance. We use statistical analysis and a Random Forest classifier to identify properties that have the greatest influence in our dataset. Our findings indicate that in vitro poly-specificity assay and in silico estimated isoelectric point can discriminate fast and slow clearing antibodies, extending previous observations on preclinical clearance. This provides a simple yet powerful approach to select antibodies with desirable PK during early-stage screening.
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Grinshpun et al. found that pI is the most essential feature to discriminate between fast and slow clearing antibodies.
“They found the pI is the most essential feature to discriminate between fast and slow clearing antibodies.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [35]
Grinshpun et al. collected 64 mAbs that have been approved or are under Phase 2 and 3 clinical trials, and their published clearance data, and trained a random forest classifier to distinguish antibodies with different clearance levels.
“To predict clearance rate through comprehensive evaluation of these factors, Grinshpun et al. [35] collected 64 mAbs that have been approved or are under Phase 2 and 3 clinical trials, and their published clearance data, and trained a random forest classifier to distinguish antibodies with different clearance levels.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [35]
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Computational and artificial intelligence-based methods for antibody development
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Self-citation
- Authors
- Grinshpun, B., Thorsteinson, N., Pereira, J. N., Rippmann, F., Nannemann, D., Sood, V. D., & Fomekong Nanfack, Y.
- Affiliations
- Discovery & Development Technologies, Drug Disposition & Design, EMD Serono Research & Development Institute, Inc, Billerica, MA, USA
- Department of Scientific Services, Chemical Computing Group ULC, Montreal, Quebec, Canada
- Merck KGaA, Darmstadt (Germany) (DE)
- Corresponding author
- Sood, Vanita D.
- ORCID
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2021-01-01
- Volume
- 13
- Issue
- 1
- Pages
- 1932230-1932230
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- PMC
- PMC8204999
- OpenAlex
- W3167428358
- Cited by
- 52
- References
- 49
- Citations by year
- 2026: 12 · 2025: 4 · 2024: 12 · 2023: 14 · 2022: 8 · 2021: 2
- FWCI
- 4.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- mAbs (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Immunology and Microbiology › Immunology › Biosimilars and Bioanalytical Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
- Keywords
- therapeutic antibodies, antibody clearance, pharmacokinetics, biophysical properties, non-specific binding, hydrophobicity, isoelectric point, antibody selection, random forest classification
- MeSH terms
- Machine Learning, Antibodies, Monoclonal, Blood Chemical Analysis, Humans
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