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Next-Generation Multimodality of Nanomedicine Therapy: Folic Acid Conjugated Copolymer and Folate Receptor Interactions Disrupt Receptor Functionality Resulting in Dual Therapeutic Anti-Cancer Potential in Triple-Negative Breast Cancer

2020· preprint· en· W3049714867 on OpenAlexafffund
Alexandria DeCarlo, Cecile Malardier‐Jugroot, Myron R. Szewczuk

Bibliographic record

VenuePreprints.org · 2020
Typepreprint
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsTriple-negative breast cancerFolate receptorCancer researchChemistryCancer cellPancreatic cancerCancerBreast cancerTargeted therapyMedicineInternal medicine

Abstract

fetched live from OpenAlex

The development of a highly specific drug delivery system (DDS) for anti-cancer therapeutics is an area of intense research focus. Chemical engineering of a “smart” DDS to specifically target tumor cells has gained interest, designed for safer, more efficient, and effective use of chemotherapeutics for the treatment of cancer. However, the selective targeting and choosing the critical cancer surface biomarker are essential for targeted treatments to work. The folic acid receptor alpha (FRalpha) has gained popularity as a potential target in triple-negative breast cancer (TNBC). We have previously reported on a functionalized folic acid (FA)-conjugated amphiphilic alternating copolymer poly(styrene-alt-maleic anhydride) (FA-DABA-SMA) via a biodegradable linker 2,4-diaminobutyric acid (DABA) that has the essential features for efficient “smart” DDS. This biocompatible DDS self-assembles in a pH-dependent manner, providing stimuli-responsive, active targeting, extended-release of hydrophobic chemotherapeutic agents, and can effectively penetrate the inner core of 3-dimensional cancer spheroid models. The empty FA-DABA-SMA decreased spheroid volume, revealing a previously unknown mechanism of action. Upon further investigation, a size- and shape-dependent interaction FA-DABA-SMA with FR reduced the expression of p53, the product of the highly mutated TP53 gene, and additional oncogenic c-Myc and STAT3 proteins. Here, we investigated how this copolymer influences FR behavior and disrupting the receptor’s functions. Results indicate that FA-DABA-SMA increases FR expression levels in breast MDA MB-231 cancer cells and disrupting FR signaling by the reduction in HES1 and NOTCH1 protein expression levels. Also, FA-DABA-SMA induces apoptosis and further causes a change in the morphology of the MDA MB-231 cells, as well as significantly reduces their ability to migrate in a Scratch wound assay. Collectively, these findings provide a novel insight into the functionalized FA-DABA-SMA copolymer. The 350 kDa and 20 kDa copolymers actively target FRα to initialize internationalization. However, only the large size and sheet-shaped 350 kDa copolymers disrupt FRα signaling. The significance of these novel findings reveals the intracellular activity of the copolymer that is critically dependent on the size and structural shape. This report offers novel therapeutic insight into a dual mechanism of FA-DABA-SMA copolymer for its therapeutic potential for the treatment of cancer.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.132
GPT teacher head0.355
Teacher spread0.224 · 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
Published2020
Admission routes2
Has abstractyes

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