Rapid, High-Yielding Solid-Phase Synthesis of Cathepsin-B Cleavable Linkers for Targeted Cancer Therapeutics
Bibliographic record
Abstract
Antibody-drug conjugates (ADCs) constitute an emerging class of anticancer agents that deliver potent payloads selectively to tumors while avoiding systemic toxicity associated with conventional chemotherapeutics. Critical to ADC development is a serum-stable linker designed to decompose inside targeted cells thereby releasing the toxic payload. A protease-cleavable linker comprising a valine-citrulline (Val-Cit) motif has been successfully incorporated into three FDA-approved ADCs and is found in numerous preclinical candidates. Herein, we present a high-yielding and facile synthetic strategy for a Val-Cit linker that avoids extensive protecting group manipulation and laborious chromatography associated with previous syntheses and provides yields that are up to 10-fold higher than by standard methods. This method is easily scalable and takes advantage of cost-effective coupling reagents and high loading 2-chlorotrityl chloride (2-CTC) resin. Modularity allows for introduction of various conjugation handles in final stages of the synthesis. Facile access to such analogues serves to expand the repertoire of available enzymatically cleavable linkers for ADC generation. This methodology empowers a robust and facile library generation and future exploration into linker analogues containing unnatural amino acids as a selectivity tuning tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".