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Record W4393102388 · doi:10.1158/1538-7445.am2024-2052

Abstract 2052: Screening novel format antibodies to design bispecific ADCs that address target heterogeneity

2024· article· en· W4393102388 on OpenAlexaff
Stuart D. Barnscher, Dunja Urosev, Kevin Yin, Andrea Hernández Rojas, Sam Lawn, Vincent Fung, Jodi Wong, Araba Sagoe-Wagner, Lemlem Degefie, Ali Livernois, Catrina Kim, Paul A. Moore, Jamie R. Rich

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsZymeworks (Canada)
Fundersnot available
KeywordsBispecific antibodyMedicineAntibodyOncologyCancer researchImmunologyMonoclonal antibody

Abstract

fetched live from OpenAlex

Abstract Addressing inter-patient and intra-tumoral target heterogeneity is a challenge for antibody drug conjugates (ADCs). The most common approach to mitigate intra-tumoral ADC target heterogeneity is to employ a bystander active payload. Once the ADC is internalized and metabolized, the payload can diffuse into tumor cells, independent of target expression. This strategy has proven effective, as evidenced by all but one of the eleven FDA-approved ADCs incorporating a bystander active payload. Nevertheless, it is important to note that in most cases there is clear evidence of an expression-response relationship, and the bystander approach does not specifically address inter-patient heterogeneity in target expression. Bispecific ADCs that can target two different tumor associated antigens (TAAs) are a promising approach to overcoming challenges associated with spatial and temporal target heterogeneity. A traditional bispecific ADC design employs a bivalent IgG where one paratope interacts with target A, and the other paratope interacts with target B. This design ensures that the molecule is maximally active when both targets A and B are present. However, this enhanced specificity approach has limitations, as it requires cellular co-expression of both targets to be effective. In contrast, a novel-format bispecific ADC targeting two different TAAs independently could increase the addressable patient population relative to a monospecific ADC. For example, a bispecific ADC with the potential for both independent and dual targeting of folate receptor alpha (FRα) and NaPi2b, established targets in ovarian cancers, could significantly expand the number of patients who could benefit relative to an ADC against either target alone. Here we describe a novel approach to the design and screening of a FRα x NaPi2b bispecific ADC library with the aim of targeting tumors that express either FRα, NaPi2b, or both targets. A library of 48 bispecific molecules was designed, employing multiple paratopes and variable antibody formats. The library comprised three valency bins, 1+1, 2+1, and 2+2, corresponding to the number of binding arms to each target. Bispecific antibodies were generated from 31 half antibodies containing Azymetric™ Het_Fc mutations. Complementary half-antibodies were combined and oxidized to generate interchain disulfide bonds, with bispecific antibody purity in the range of 90%. Antibodies and their corresponding ADCs, prepared through direct conjugation to a cytotoxic payload, were evaluated on a panel of FRα-expressing and Napi2b-expressing cancer cell lines. Key ADC parameters, including binding, internalization, and in vitro cytotoxicity, highlighted paratope combinations, valencies, and geometric formats that offer the unique potential to overcome target heterogeneity in ovarian cancer. Citation Format: Stuart D. Barnscher, Dunja Urosev, Kevin Yin, Andrea Hernández Rojas, Sam Lawn, Vincent Fung, Jodi Wong, Araba Sagoe-Wagner, Lemlem Degefie, Ali Livernois, Catrina Kim, Paul A. Moore, Jamie R. Rich. Screening novel format antibodies to design bispecific ADCs that address target heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2052.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.347
GPT teacher head0.477
Teacher spread0.130 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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