Evidence map›Paper›PMID 37698932›Full record

ArticlemAbs

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 and 37 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 25 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

47 authors at 1 institution in 1 country.

Marissa MockBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.ORCID 0009-0009-0659-988X
Alex W JacobitzProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Christopher James LangmeadBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Athena SudomStructural Biology, Amgen Research, South San Francisco, CA, USA.
Daniel YooBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Sara C HumphreysPharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA.
Mai AldayBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Larysa AlekseychykProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Nicolas AngellProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Vivian BiBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Hannah CatterallBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Chen-Chun ChenBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Hui-Ting ChouStructural Biology, Amgen Research, South San Francisco, CA, USA.
Kip P ConnerPharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA.
Kevin D CookPharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA.
Ana R CorreiaBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Andrew DykstraProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Sudipa Ghimire-RijalStructural Biology, Amgen Research, Thousand Oaks, CA, USA.
Kevin GrahamBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Peter GrandsardBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Joon HuhProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
John O HuiBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Mani JainBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Victoria JannBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Lei JiaBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Sheree JohnstoneStructural Biology, Amgen Research, South San Francisco, CA, USA.
Neelam KhanalProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Carl KolvenbachBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Linda NarhiProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Rupa PadakiProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Emma M Pelegri-O'DayBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Wei QiProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Vladimir RazinkovProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Austin J RiceBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Richard SmithPharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA.
Christopher SpahrBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Jennitte StevensProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Yax SunBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Veena A ThomasPharmacokinetics & Drug Metabolism, Amgen Research, South San Francisco, CA, USA.
Sarah van DriescheBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Robert VernonBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Victoria WagnerBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Kenneth W WalkerBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Yangjie WeiProcess Development, Amgen Operations, Thousand Oaks, CA, USA.
Dwight WintersBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Melissa YangBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Iain D G CampuzanoBiologic Therapeutic Discovery, Amgen Research, Thousand Oaks, CA, USA.
Amgen (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biologic 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 (AUC

Indexed as

Antibodies, MonoclonalDrug DiscoveryAnimalsComputer SimulationMiceRecombinant ProteinsViscosityAntibodies, MonoclonalRecombinant ProteinsDevelopabilityhigh throughputin vitro assaysmabmachine learningpharmacokineticspredictive modeltherapeutic antibodyviscosity

Identifiers

PMID37698932
PMCPMC10498806
OpenAlexW4386691948

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.