Targeting Epigenetics Signaling with Curcumin: A Transformative Drug Lead in Treatment of Schizophrenia?
Bibliographic record
Abstract
Despite advances in pharmacotherapy, schizophrenia continues to carry a high societal disease burden related to positive and negative symptoms, and neurocognitive deficits and severe functional impairment. Findings from epigenetics have yet to translate the epigenetics targets to efficacious drug therapy. We review the repertoire of CNS pharmacology of curcumin derived from Curcuma Longa, commonly known as “turmeric”: the well-known curry extract. We highlight the body of evidence in support of the emerging role of curcumin as a panhistone deacetylase (HDAC) inhibitor regulating the expression of genes involved in inflammation and NMDA N-methyl-aspartic acid (NMDA)-glutamate systems, as related to schizophrenia. Based on the findings from translational studies of curcumin extracts, curcumin C-3 complex formulation (Supercurcumin™) and patented liposome-based curcumin (Lipocurc™), we propose that intravenous infusion of Lipocurc™ holds promise as the novel drug lead and preferred targeted brain delivery mode in reprogramming faculty epigenetics network and in remodeling restrictive chromatin configuration in schizophrenia. Phase II/Phase III epigenetics-biomarker-based randomized placebo-controlled trials in treatment resistant schizophrenia are warranted. Our approach of intravenous infusion of Lipocurc™ behaving as pan HDAC inhibitor represents a new paradigm of drug development in combining targeting epigenetics footprints with brain-specific drug delivery system in schizophrenia. Lipocurc™ can open the Pandora box in unexplored therapeutics vistas in modifying the phenotype of treatment resistant schizophrenia.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".