MétaCan
Menu
Back to cohort
Record W2910682218 · doi:10.2967/jnumed.118.220095

Preclinical Evaluation of<sup>111</sup>In-Labeled PEGylated Maytansine Nimotuzumab Drug Conjugates in EGFR-Positive Cancer Models

2019· article· en· W2910682218 on OpenAlexaff
Siddesh V. Hartimath, Elahe Alizadeh, V. Raja Solomon, Rufael Chekol, Wendy Bernhard, Wayne Hill, Ángel Casacó Parada, Kris Barreto, C. Ronald Geyer, Humphrey Fonge

Bibliographic record

VenueJournal of Nuclear Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsRoyal University HospitalUniversity of Saskatchewan
Fundersnot available
KeywordsNimotuzumabBiodistributionIn vivoChemistryPEG ratioEx vivoPharmacokineticsAntibody-drug conjugateDOTAMonoclonal antibodyEpidermal growth factor receptorIn vitroPharmacologyAntibodyMedicineReceptorBiochemistryImmunology

Abstract

fetched live from OpenAlex

Epidermal growth factor receptor I (EGFR) is overexpressed in most cancers of epithelial origin. Antibody drug conjugates (ADCs) with PEGylated-maytansine (PEG-DM1) show promise in vitro and in vivo. However, in vivo biodistribution data for ADCs with PEG-DM1 have not been reported. Development of methods to understand the real-time in vivo behavior of these ADCs is needed to move these compounds to the clinic. Methods: Here we have used noninvasive small-animal SPECT/CT imaging and ex vivo biodistribution to understand the in vivo behavior of PEG6-DM1 ADCs. We developed nimotuzumab ADCs conjugated to PEG6-DM1. We generated immunoconjugates with low (nimotuzumab-PEG6-DM1-Low) and high (nimotuzumab-PEG6-DM1-High) drug-to-antibody ratios. The drug-to-antibody of nimotuzumab-PEG6-DM1-Low and nimotuzumab-PEG6-DM1-High was 3.5 and 7.3, respectively. Quality control was performed using ultraviolet spectrophotometry, size-exclusion high-performance liquid chromatography, bioanalyzer, biolayer interferometry, and flow cytometry in EGFR-positive DLD-1 cells. These immunoconjugates were conjugated with DOTA and radiolabeled with 111In. The in vitro binding and internalization rates of 111In-nimotuzumab, 111In-nimotuzumab-PEG6-DM1-Low, and 111In-nimotuzumab-PEG6-DM1-High were characterized. Furthermore, the pharmacokinetics, biodistribution, and imaging characteristics were evaluated in normal and DLD-1 tumor–bearing mice. Results: Flow cytometry and biolayer interferometry showed a trend toward decreasing EGFR affinity with increasing number of PEG6-DM1 on the antibody. Despite the lower overall cellular binding of the PEG6-DM1 radioimmunoconjugates, internalization was higher for PEG6-DM1 ADCs than for the non-PEGylated ADC in the following order: 111In-nimotuzumab-PEG6-DM1-High > 111In-nimotuzumab-PEG6-DM1-Low > 111In-nimotuzumab. Nuclear uptake of 111In-nimotuzumab-PEG6-DM1-High was 4.4-fold higher than 111In-nimotuzumab. Pharmacokinetics and biodistribution showed that 111In-nimotuzumab-PEG6-DM1-High had the slowest blood and whole-body clearance rate. Uptake in DLD-1 tumors of 111In-nimotuzumab was similar to 111In-nimotuzumab-PEG6-DM1-Low but was significantly higher than for 111In-nimotuzumab-PEG6-DM1-High. Tumor-to-background ratios for 111In-nimotuzumab and 111In-nimotuzumab-PEG6-DM1-Low were higher than for 111In-nimotuzumab-PEG6-DM1-High. Conclusion: The results show that conjugation of multiple PEG6-DM1 reduces the affinity for EGFR in vitro. However, the reduced affinity is counteracted by the high internalization rate of constructs with PEG6-DM1 ADCs in vitro. The decreased affinity resulted in low tumor uptake of 111In-nimotuzumab-PEG6-DM1-High, with a slow overall whole-body clearance rate. These data provide insights for evaluating the pharmacokinetics and normal -tissue toxicity and in determining dosing rate of PEGylated ADCs.

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)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.428
Teacher spread0.337 · 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

Citations32
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Nuclear MedicineSame topicHER2/EGFR in Cancer ResearchFrench-language works237,207