Novel Techniques for Small Molecule-Based Drug Delivery in Cancerous Tissue: A Literature Review
Bibliographic record
Abstract
Introduction: Small molecule cancer drugs target rapidly growing cancerous and healthy cells, leading to negative side effects. Due to the broad effects of small molecule drugs (SMDs), particularly on healthy cells, researchers have established methods such as altering lipophilicity, introducing drug conjugates, and nano-based drug delivery methods to diminish side effects. Methods: Relevant biomedical literature between 1991 to 2021 was obtained using Medline PubMed. Search terms were “physicochemical properties”, “nanotubes”, “liposomes”, and “small molecule drug conjugates”. Literature was selected based on pertinency after assessing the abstracts. Results: Properties such as a drug’s lipophilicity influence most SMD’s promiscuity. Controlling the hydrophobic features of lipophilic drugs within optimal ranges increases their specificity, half-life, and aqueous solubility. However, the narrow optimal range of lipophilicity makes it challenging to observe noticeable effects without reducing therapeutic effects. SMD conjugates improve drug delivery using a targeting ligand, and a therapeutic payload. The targeting ligand ensures greater binding to receptors on target tissues, such that a lower dose of a drug is required, thereby decreasing toxicity. SMD conjugates are non-immunogenic and have lower molecular weights, allowing for greater entry into solid tumours. Several nanomedical approaches have been developed to improve drug delivery. Carbon nanotubes, which exploit the enhanced permeability and retention effect, increase the accumulation of the drug at the cancerous tissue. Another method involves the use of liposomes, which exhibit high biocompatibility with cell membranes, low toxicity, and capability to carry hydrophobic as well as hydrophilic molecules. Discussion: While said novel therapies show increased targeting effects and decreased toxicity, notable limitations exist. Some SMD chemotherapeutics with high lipophilicity has been linked to adverse side effects. Conversely, lower lipophilicity ranges may reduce the permeability and potency of the drug. Alternatively, select SMD conjugates display poorer bioavailability, quick clearance, and multidrug resistance. Moreover, the toxicity of carbon nanotubes is not entirely deduced which may be associated with negative side effects. Conclusion: This systematic review emphasizes the importance of novel drug delivery systems to mitigate the toxic effects of small molecule cancer drugs through changes in lipophilicity, the introduction of drug conjugates, and nano-based drug delivery methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".