PROTACs: Current and Future Potential as a Precision Medicine Strategy to Combat Cancer
Bibliographic record
Abstract
Proteolysis targeting chimeras (PROTAC) are an emerging precision medicine strategy, which targets key proteins for proteolytic degradation to ultimately induce cancer cell killing. These hetero-bifunctional molecules hijack the ubiquitin proteasome system to selectively add polyubiquitin chains onto a specific protein target to induce proteolytic degradation. Importantly, PROTACs have the capacity to target virtually any intracellular and transmembrane protein for degradation, including oncoproteins previously considered undruggable, which strategically positions PROTACs at the crossroads of multiple cancer research areas. In this review, we present normal functions of the ubiquitin regulation proteins and describe the application of PROTACs to improve the efficacy of current broad-spectrum therapeutics. We subsequently present the potential for PROTACs to exploit specific cancer vulnerabilities through synthetic genetic approaches, which may expedite the development, translation, and utility of novel synthetic genetic therapies in cancer. Finally, we describe the challenges associated with PROTACs and the ongoing efforts to overcome these issues to streamline clinical translation. Ultimately, these efforts may lead to their routine clinical use, which is expected to revolutionize cancer treatment strategies, delay familial cancer onset, and ultimately improve the lives and outcomes of those living with cancer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".