The Formulation of Lipid-Based Nanotechnologies for the Delivery of Fixed Dose Anticancer Drug Combinations
Bibliographic record
Abstract
The introduction of combination chemotherapeutic regimens for the treatment of childhood leukaemia in the 1960s provided the proof-of-principle that cytotoxic drugs were capable of curing cancer. However, in the four decades since this discovery, the majority of cancers still cannot be cured by chemotherapy. Clinical evidence supports the hypothesis of Goldie and Coldman that treating cancers with all the available effective agents simultaneously provides the greatest chance of eliciting a cure. Unfortunately, for traditional cytotoxic agents with narrow therapeutic indices, life-threatening toxicity precludes combination chemotherapy regimens employing multiple agents. This review discusses the concept of fixed dose combination chemotherapy with emphasis on capturing therapeutic efficacy described as synergistic as a basis for improving the effectiveness of combination chemotherapy. The use of lipid-based nanotechnologies, focusing on liposomes, as an enabling technology to facilitate the delivery of cytotoxic agents to the tumour site at concentrations and/or drug ratios judged to be synergistic will be discussed. It is envisaged that the development of this model system will be supported by cell-based screening technologies, pharmacokinetic and pharmacodynamic parameters and mathematical models describing therapeutic drug:drug interactions (the Median Effect Principle of Chou and Talalay). Experiments using preclinical models are presented to support the benefits of drug delivery systems as a foundation for fixed dose anticancer drug combinations. The ultimate goal of this research is to prepare a 'single vial' fixed dose combination product that encompasses both traditional cytotoxic agents and new molecularly targeted modalities with optimum therapeutic effects and acceptable toxicity.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".