Natural and Synthetic Compounds Against Colorectal Cancer: An Update of Preclinical Studies in Saudi Arabia
Bibliographic record
Abstract
Colorectal cancer (CRC) remains a major contributor to global cancer-related mortality, with rising incidence observed in several regions, including Saudi Arabia. This review compiles and critically analyzes recent preclinical research from Saudi-based institutions that investigates the anti-CRC potential of natural and synthetic compounds. Numerous natural products such as Nigella sativa, Moringa oleifera, Curcuma longa, and marine-derived metabolites have demonstrated cytotoxic effects through pathways involving apoptosis induction, reactive oxygen species (ROS) generation, and inhibition of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and cyclooxygenase-2 (COX-2). In parallel, synthetic and semi-synthetic agents, including C4–G4 (semi-synthetic hybrids designed from flavonoids and benzoxazole scaffolds that act as dual epidermal growth factor receptor (EGFR)/COX-2 inhibitors)), oxazole derivatives, and camptothecin-based nanocarriers, exhibit promising anti-tumor activity via molecular targeting of cyclin-dependent kinase 8 (CDK8), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), and β-catenin pathways. Selected in vivo studies primarily utilizing xenograft and chemically induced rodent models have shown reductions in tumor volume and modulation of apoptotic and inflammatory biomarkers. Additionally, green-synthesized metallic nanoparticles (NPs) and polyethylene glycol (PEG)-modified carriers have been investigated to improve bioavailability and tumor targeting of lead compounds. While these findings are encouraging, the majority remain in preclinical phases. Limitations such as poor solubility, lack of pharmacokinetic data, and absence of clinical trials impede translational progress. This review highlights the need for standardized evaluation protocols, mechanistic validation, and region-specific clinical studies to assess efficacy and safety. Given Saudi Arabia’s rich biodiversity and growing research capacity under national strategies like Vision 2030, the country is well-positioned to contribute meaningfully to CRC drug discovery. By integrating bioactive natural products, rationally designed synthetics, and advanced delivery platforms, a pipeline of innovative CRC therapeutics tailored to local and global contexts may be realized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".