ArticlemAbs
Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Article
- Review
- Article
- A Review on Applications of Artificial Intelligence (AI) in Monoclonal Antibody (mAb) Manufacturing.Molecules (Basel, Switzerland) · 2026Review
- The effect of syringe design and cannula dimensions on time-force curve in intravitreal injection across different drug viscosities: area under the curve and peak injection force.International journal of retina and vitreous · 2026Article
- Article
- Review
- Article
- Investigating the feasibility of selective anti-VEGF delivery to the retina using a microinvasive approach.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles.Computational and structural biotechnology journal · 2024Article
- DOTAD: A Database of Therapeutic Antibody Developability.Interdisciplinary sciences, computational life sciences · 2024Article
- Stability of Protein Pharmaceuticals: Recent Advances.Pharmaceutical research · 2024Review
- A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data.Sensors (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Solutions of monoclonal antibodies (mAbs) can show increased viscosity at high concentration, which can be a disadvantage during protein purification, filling, and administration. The viscosity is determined by protein-protein-interactions, which are influenced by the antibody's sequence as well as solution conditions, like pH, buffer type, or the presence of salts and other excipients. To predict viscosity, experimental parameters, like the diffusion interaction parameter (kD), or computational tools harnessing information derived from primary sequence, are often used, but a reliable predictive tool is still missing. We present a modeling approach employing artificial neural networks (ANNs) using experimental factors combined with simulation-derived parameters plus viscosity data from 27 highly concentrated (180 mg/mL) mAbs. These ANNs can be used to predict if mAbs exhibit problematic viscosity at distinct concentrations or to model viscosity-concentration-curves.
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What OpenQuestion holds
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.