mAbs (Dec 2023)
Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies
- Marissa Mock,
- Alex W. Jacobitz,
- Christopher James Langmead,
- Athena Sudom,
- Daniel Yoo,
- Sara C. Humphreys,
- Mai Alday,
- Larysa Alekseychyk,
- Nicolas Angell,
- Vivian Bi,
- Hannah Catterall,
- Chen-Chun Chen,
- Hui-Ting Chou,
- Kip P. Conner,
- Kevin D. Cook,
- Ana R. Correia,
- Andrew Dykstra,
- Sudipa Ghimire-Rijal,
- Kevin Graham,
- Peter Grandsard,
- Joon Huh,
- John O. Hui,
- Mani Jain,
- Victoria Jann,
- Lei Jia,
- Sheree Johnstone,
- Neelam Khanal,
- Carl Kolvenbach,
- Linda Narhi,
- Rupa Padaki,
- Emma M. Pelegri-O’Day,
- Wei Qi,
- Vladimir Razinkov,
- Austin J. Rice,
- Richard Smith,
- Christopher Spahr,
- Jennitte Stevens,
- Yax Sun,
- Veena A. Thomas,
- Sarah van Driesche,
- Robert Vernon,
- Victoria Wagner,
- Kenneth W. Walker,
- Yangjie Wei,
- Dwight Winters,
- Melissa Yang,
- Iain D. G. Campuzano
Affiliations
- Marissa Mock
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Alex W. Jacobitz
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Christopher James Langmead
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Athena Sudom
- Structural Biology, Amgen Research, South San Francisco, CA, USA
- Daniel Yoo
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Sara C. Humphreys
- Pharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA
- Mai Alday
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Larysa Alekseychyk
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Nicolas Angell
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Vivian Bi
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Hannah Catterall
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Chen-Chun Chen
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Hui-Ting Chou
- Structural Biology, Amgen Research, South San Francisco, CA, USA
- Kip P. Conner
- Pharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA
- Kevin D. Cook
- Pharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA
- Ana R. Correia
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Andrew Dykstra
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Sudipa Ghimire-Rijal
- Structural Biology, Amgen Research, Thousand Oaks, CA, USA
- Kevin Graham
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Peter Grandsard
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Joon Huh
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- John O. Hui
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Mani Jain
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Victoria Jann
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Lei Jia
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Sheree Johnstone
- Structural Biology, Amgen Research, South San Francisco, CA, USA
- Neelam Khanal
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Carl Kolvenbach
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Linda Narhi
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Rupa Padaki
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Emma M. Pelegri-O’Day
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Wei Qi
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Vladimir Razinkov
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Austin J. Rice
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Richard Smith
- Pharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA
- Christopher Spahr
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Jennitte Stevens
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Yax Sun
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Veena A. Thomas
- Pharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA
- Sarah van Driesche
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Robert Vernon
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Victoria Wagner
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Kenneth W. Walker
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Yangjie Wei
- Process Development, Amgen Operations, Thousand Oaks, CA, USA
- Dwight Winters
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Melissa Yang
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- Iain D. G. Campuzano
- Biologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA
- DOI
- https://doi.org/10.1080/19420862.2023.2256745
- Journal volume & issue
-
Vol. 15,
no. 1
Abstract
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.
Keywords
