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Use of Anticancer Platinum Compounds in Combination Therapies and Challenges in Drug Delivery

2018· review· en· W2899846936 on OpenAlexaff
Xiao Xiao, James Trevor Oswald, Ting Wang, Weina Zhang, Wenliang Li

Bibliographic record

VenueCurrent Medicinal Chemistry · 2018
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Waterloo
FundersScience and Technology Department of Henan ProvinceProgram for Jilin University Science and Technology Innovative Research TeamDepartment of Science and Technology of Jilin ProvinceEducation Department of Jilin Province
KeywordsDrug deliveryDrugPharmacologyDendrimerDrug resistanceLiposomePlatinumNanotechnologyAnticancer drugChemistryMedicineMaterials scienceBiologyBiochemistry

Abstract

fetched live from OpenAlex

As one of the leading and most important metal-based drugs, platinum-based pharmaceuticals are widely used in the treatment of solid malignancies. Despite significant side effects and acquired drug resistance have limited their clinical applications, platinum has shown strong inhibitory effects for a wide assortment of tumors. Drug delivery systems using emerging technologies such as liposomes, dendrimers, polymers, nanotubes and other nanocompositions, all show promise for the safe delivery of platinum-based compounds. Due to the specificity of nano-formulations; unwanted side-effects and drug resistance can be largely averted. In addition, combinational therapy has been shown to be an effective way to improve the efficacy of platinum based anti-tumor drugs. This review first introduces drug delivery systems used for platinum and combinational therapeutic delivery. Then we highlight some of the recent advances in the field of drug delivery for combinational therapy; specifically progress in leveraging the cytotoxic nature of platinum-based drugs, the combinational effect of other drugs with platinum, while evaluating the drug targeting, side effect reducing and sitespecific nature of nanotechnology-based delivery platforms.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.121
GPT teacher head0.330
Teacher spread0.209 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations13
Published2018
Admission routes1
Has abstractyes

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