NTG-101: A Novel Molecular Therapy that Halts the Progression of Degenerative Disc Disease
Bibliographic record
Abstract
The tremendous cost, pain and disability associated with degenerative disc disease (DDD) makes the development of a biological agent that can mitigate the course of DDD, a critical unmet need. We have identified and reported that a single injection of a combination of recombinant human (rh) Transforming growth factor beta 1 (TGF-β1) and Connective tissue growth factor (CTGF) proteins into the injured intervertebral disc (IVD) nucleus pulposus (NP) can mediate DDD in a pre-clinical rodent model. In this study, we developed and evaluated the efficacy of a novel molecular therapy (NTG-101) containing rhTGF-β1 and rhCTGF proteins suspended in an excipient solution using in vivo models of DDD including rat-tail and chondrodystrophic (CD) canines. Needle puncture injury in CD-canine NPs resulted in loss of hydration, disc height and showed radiographic evidence of DDD like humans. However, NTG-101-injected IVDs maintained disc height and demonstrated retention of viscoelastic properties as compared to IVDs injected with phosphate buffer saline (PBS, 1X, pH = 7.2). In addition, a single intra-discal injection of NTG-101 into the injured IVD-NPs resulted in sustained expression of healthy extra-cellular matrix (ECM) proteins (aggrecan, collagen 2A1) and reduced expression of inflammation associated proteins and molecules (IL-1β, IL-6, IL-8, MMP-13, Cox-2 and PGE2) as compared to vehicle controls. In conclusion, we demonstrated that a single intra-discal injection of the novel formulation, NTG-101 confers a robust anti-inflammatory, anti-catabolic and pro-anabolic effects in pre-clinical models of DDD thereby restoring homeostasis. These findings suggest the therapeutic potential of NTG-101 for clinical use.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".