Abstract PO5-27-05: Computational design and validation of a novel peptide-drug conjugate for treatment of triple negative breast cancer
Bibliographic record
Abstract
Abstract Sortilin (SORT1) is a member of the vacuolar protein sorting 10 protein (Vps10p) family that functions as a receptor regulating peptide and protein trafficking between the plasma membrane, lysosomes, and trans-golgi network. As a cell surface receptor, SORT1 is able to mediate efficient endocytosis of extracellular ligands to the lysosomal compartment. Numerous reports have identified enriched SORT1 expression in a variety of tumor types, including triple-negative breast cancer (TNBC), a subtype of breast cancer associated with aggressive clinical behavior and poor disease outcomes. We sought to exploit SORT1-dependent internalization of peptides as a platform for rapid and specific chemotherapy delivery into TNBC cells. Using ProteinStudio (our proprietary computation-enabled design capabilities), we generated high-affinity SORT1 targeting peptides that exhibit efficient receptor dependent internalization and lysosomal localization. Alternative computational approaches such as AlphaFold2 failed to recapitulate the peptide design. Peptide drug conjugates (PDCs) were generated via a linkage strategy that combines our designed peptides to the antimitotic agent monomethyl auristatin E (MMAE). Our PDC molecules exhibit potent tumor regression in a MDA-MB-231 TNBC cell derived xenograft model, thereby highlighting the potential of SORT1-engaging PDCs as an efficacious targeted chemotherapeutic delivery strategy. Citation Format: Francine Liu, Andrew Zhai, Ozge Yoluk, Aron Broom, Tracy Stone, Glenn Butterfoss, Serban Popa, Tianyu Li, Lucas Siow, Christopher Ing, David White. Computational design and validation of a novel peptide-drug conjugate for treatment of triple negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-27-05.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".