Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies
ABSTRACTBiologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computat...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Book |
| Published: |
Taylor & Francis Group,
2023-12-01T00:00:00Z.
|
| Subjects: | |
| Online Access: | Connect to this object online. |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
MARC
| LEADER | 00000 am a22000003u 4500 | ||
|---|---|---|---|
| 001 | doaj_c7e86c82a6684b888888da7d561e8891 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Marissa Mock |e author |
| 700 | 1 | 0 | |a Alex W. Jacobitz |e author |
| 700 | 1 | 0 | |a Christopher James Langmead |e author |
| 700 | 1 | 0 | |a Athena Sudom |e author |
| 700 | 1 | 0 | |a Daniel Yoo |e author |
| 700 | 1 | 0 | |a Sara C. Humphreys |e author |
| 700 | 1 | 0 | |a Mai Alday |e author |
| 700 | 1 | 0 | |a Larysa Alekseychyk |e author |
| 700 | 1 | 0 | |a Nicolas Angell |e author |
| 700 | 1 | 0 | |a Vivian Bi |e author |
| 700 | 1 | 0 | |a Hannah Catterall |e author |
| 700 | 1 | 0 | |a Chen-Chun Chen |e author |
| 700 | 1 | 0 | |a Hui-Ting Chou |e author |
| 700 | 1 | 0 | |a Kip P. Conner |e author |
| 700 | 1 | 0 | |a Kevin D. Cook |e author |
| 700 | 1 | 0 | |a Ana R. Correia |e author |
| 700 | 1 | 0 | |a Andrew Dykstra |e author |
| 700 | 1 | 0 | |a Sudipa Ghimire-Rijal |e author |
| 700 | 1 | 0 | |a Kevin Graham |e author |
| 700 | 1 | 0 | |a Peter Grandsard |e author |
| 700 | 1 | 0 | |a Joon Huh |e author |
| 700 | 1 | 0 | |a John O. Hui |e author |
| 700 | 1 | 0 | |a Mani Jain |e author |
| 700 | 1 | 0 | |a Victoria Jann |e author |
| 700 | 1 | 0 | |a Lei Jia |e author |
| 700 | 1 | 0 | |a Sheree Johnstone |e author |
| 700 | 1 | 0 | |a Neelam Khanal |e author |
| 700 | 1 | 0 | |a Carl Kolvenbach |e author |
| 700 | 1 | 0 | |a Linda Narhi |e author |
| 700 | 1 | 0 | |a Rupa Padaki |e author |
| 700 | 1 | 0 | |a Emma M. Pelegri-O'Day |e author |
| 700 | 1 | 0 | |a Wei Qi |e author |
| 700 | 1 | 0 | |a Vladimir Razinkov |e author |
| 700 | 1 | 0 | |a Austin J. Rice |e author |
| 700 | 1 | 0 | |a Richard Smith |e author |
| 700 | 1 | 0 | |a Christopher Spahr |e author |
| 700 | 1 | 0 | |a Jennitte Stevens |e author |
| 700 | 1 | 0 | |a Yax Sun |e author |
| 700 | 1 | 0 | |a Veena A. Thomas |e author |
| 700 | 1 | 0 | |a Sarah van Driesche |e author |
| 700 | 1 | 0 | |a Robert Vernon |e author |
| 700 | 1 | 0 | |a Victoria Wagner |e author |
| 700 | 1 | 0 | |a Kenneth W. Walker |e author |
| 700 | 1 | 0 | |a Yangjie Wei |e author |
| 700 | 1 | 0 | |a Dwight Winters |e author |
| 700 | 1 | 0 | |a Melissa Yang |e author |
| 700 | 1 | 0 | |a Iain D. G. Campuzano |e author |
| 245 | 0 | 0 | |a Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies |
| 260 | |b Taylor & Francis Group, |c 2023-12-01T00:00:00Z. | ||
| 500 | |a 10.1080/19420862.2023.2256745 | ||
| 500 | |a 1942-0870 | ||
| 500 | |a 1942-0862 | ||
| 520 | |a ABSTRACTBiologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL. | ||
| 546 | |a EN | ||
| 690 | |a Developability | ||
| 690 | |a high throughput | ||
| 690 | |a in vitro assays | ||
| 690 | |a mab | ||
| 690 | |a machine learning | ||
| 690 | |a pharmacokinetics | ||
| 690 | |a Therapeutics. Pharmacology | ||
| 690 | |a RM1-950 | ||
| 690 | |a Immunologic diseases. Allergy | ||
| 690 | |a RC581-607 | ||
| 655 | 7 | |a article |2 local | |
| 786 | 0 | |n mAbs, Vol 15, Iss 1 (2023) | |
| 787 | 0 | |n https://www.tandfonline.com/doi/10.1080/19420862.2023.2256745 | |
| 787 | 0 | |n https://doaj.org/toc/1942-0862 | |
| 787 | 0 | |n https://doaj.org/toc/1942-0870 | |
| 856 | 4 | 1 | |u https://doaj.org/article/c7e86c82a6684b888888da7d561e8891 |z Connect to this object online. |