Analyses of repeated failures in cancer therapy for solid tumors: poor tumor‐selective drug delivery, low therapeutic efficacy and unsustainable costs
Bibliographic record
Abstract
For over six decades reductionist approaches to cancer chemotherapies including recent immunotherapy for solid tumors produced outcome failure-rates of 90% (±5) according to governmental agencies and industry. Despite tremendous public and private funding and initial enthusiasm about missile-therapy for site-specific cancers, molecular targeting drugs for specific enzymes such as kinases or inhibitors of growth factor receptors, the outcomes are very bleak and disappointing. Major scientific reasons for repeated failures of such therapeutic approaches are attributed to reductionist approaches to research and infinite numbers of genetic mutations in chaotic molecular environment of solid tumors that are bases of drug development. Safety and efficacy of candidate drugs tested in test tubes or experimental tumor models of rats or mice are usually evaluated and approved by FDA. Cost-benefit ratios of such 'targeted' therapies are also far from ideal as compared with antibiotics half a century ago. Such alarming records of failure of clinical outcomes, the increased publicity for specific vaccines (e.g., HPV or flu) targeting young and old populations, along with increasing rise of cancer incidence and death created huge and unsustainable cost to the public around the globe. This article discusses a closer scientific assessment of current cancer therapeutics and vaccines. We also present future logical approaches to cancer research and therapy and vaccines.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".