A 15 Year Evolution of Dichloroacetate-Based Metabolic Cancer Therapy: A Review with Case Reports
Bibliographic record
Abstract
Despite Otto Warburg’s discovery of aerobic glycolysis in cancer cells in the 1920’s, the potential for developing therapeutics that targeted cancer cell metabolism was essentially ignored until 2007 when a groundbreaking publication was released from a group of Canadian researchers. Bonnet et al. (who paradoxically were not specialized in oncology) discovered that the generic drug dichloroacetate sodium (“DCA”) could reverse the Warburg phenotype in cancer cells in vitro and in vivo resulting in natural cancer cell suicide and tumour shrinkage in rats. This phenomenon was previously thought to be impossible as it was believed that mitochondria in malignant cells were permanently altered and unable to trigger apoptosis. Despite the fact that no large clinical trial of DCA as a cancer therapy was ever completed, a small number of doctors in North America and Europe rapidly translated this new knowledge into clinical cancer protocols through independent observational research and creative thinking. Since off-label drug use is permitted in most jurisdictions, clinicians initially began to use DCA in patients who had failed all conventional therapies. Over the years, further novel anti-cancer mechanisms of DCA were discovered such as angiogenesis inhibition, immune activation and cancer stem cell targeting. Around 2011, the work of Seyfried (USA) began to illuminate the importance of glutamine inhibition and suggested that a multi-energetic targeted approach was superior to glycolysis inhibition alone. A collaborative effort of the authors incorporating Seyfried’s concepts resulted in the creation of a new metabolic protocol named “MOMENTUM” (Metabolic, Oncologic, Multi-ENergetic Targeted, Universal, Modified). In this protocol, glucose and glutamine metabolism were targeted simultaneously with a combination of multiple natural and pharmacologic agents administered intravenously. Surprising preliminary clinical results in several difficult cancer cases confirmed that metabolic multi-targeted methods are extremely promising, and more so than metabolic monotherapy. Life threatening side effects of this approach to cancer management are virtually non-existent and therapy costs are manageable. A disappointing absence of industry funding for large clinical trials has not curtailed the development of the metabolic approach as a clinically viable methodology, proving that unadulterated medical science can conquer the ongoing push for multibillion-dollar economic reward.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".