Literature DB >> 34105965

Ultradilute Measurements of Self-Association for the Identification of Antibodies with Favorable High-Concentration Solution Properties.

Charles G Starr1, Emily K Makowski, Lina Wu, Brendan Berg, Jonathan S Kingsbury1, Yatin R Gokarn1, Peter M Tessier.   

Abstract

There is significant interest in formulating antibody therapeutics as concentrated liquid solutions, but early identification of developable antibodies with optimal manufacturability, stability, and delivery attributes remains challenging. Traditional methods of identifying developable mAbs with low self-association in common antibody formulations require relatively concentrated protein solutions (>1 mg/mL), and this single challenge has frustrated early-stage and large-scale identification of antibody candidates with drug-like colloidal properties. Here, we describe charge-stabilized self-interaction nanoparticle spectroscopy (CS-SINS), an affinity-capture nanoparticle assay that measures colloidal self-interactions at ultradilute antibody concentrations (0.01 mg/mL), and is predictive of antibody developability issues of high viscosity and opalescence that manifest at four orders of magnitude higher concentrations (>100 mg/mL). CS-SINS enables large-scale, high-throughput selection of developable antibodies during early discovery.

Entities:  

Keywords:  aggregation; developability; formulation; mAb; opalescence; viscosity

Mesh:

Substances:

Year:  2021        PMID: 34105965     DOI: 10.1021/acs.molpharmaceut.1c00280

Source DB:  PubMed          Journal:  Mol Pharm        ISSN: 1543-8384            Impact factor:   4.939


  6 in total

1.  High-throughput profiling of antibody self-association in multiple formulation conditions by PEG stabilized self-interaction nanoparticle spectroscopy.

Authors:  Samantha Phan; Auralee Walmer; Eudean W Shaw; Qing Chai
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 6.440

Review 2.  Improving antibody drug development using bionanotechnology.

Authors:  Emily K Makowski; John S Schardt; Peter M Tessier
Journal:  Curr Opin Biotechnol       Date:  2021-12-07       Impact factor: 10.279

3.  DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity.

Authors:  Pin-Kuang Lai
Journal:  Comput Struct Biotechnol J       Date:  2022-04-29       Impact factor: 6.155

4.  Antibodies with Weakly Basic Isoelectric Points Minimize Trade-offs between Formulation and Physiological Colloidal Properties.

Authors:  Priyanka Gupta; Emily K Makowski; Sandeep Kumar; Yulei Zhang; Justin M Scheer; Peter M Tessier
Journal:  Mol Pharm       Date:  2022-02-02       Impact factor: 5.364

5.  Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.

Authors:  Emily K Makowski; Patrick C Kinnunen; Jie Huang; Lina Wu; Matthew D Smith; Tiexin Wang; Alec A Desai; Craig N Streu; Yulei Zhang; Jennifer M Zupancic; John S Schardt; Jennifer J Linderman; Peter M Tessier
Journal:  Nat Commun       Date:  2022-07-01       Impact factor: 17.694

6.  Antibody apparent solubility prediction from sequence by transfer learning.

Authors:  Jiangyan Feng; Min Jiang; James Shih; Qing Chai
Journal:  iScience       Date:  2022-09-22
  6 in total

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