Evolution of Theranostic Nanoparticles Through the Lens of Patents
Bibliographic record
Abstract
Theranostic nanoparticles integrate diagnostic and therapeutic potential, representing a promising approach in precision medicine. Accordingly, numerous inventions have been patented to protect novel formulations and methods. This review examines the evolution of patented theranostic nanoparticles, focusing on organic nanosystems, particularly polymeric and lipid nanoparticles, to assess their development, technological advances, and patentability. A scoping review approach was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines in the World Intellectual Property Organization (WIPO) and European Patent Office (EPO) database. The search included patents filed within the last ten years (2014–2024) that specifically claimed organic and/or hybrid theranostic nanoparticles. Data extraction focused on nanoparticle composition, synthesis methods, functionalization strategies, and theranostic applications. The search identified 130 patents, of which 13 met the inclusion criteria. These patents were primarily filed by inventors from the United States, Canada, Great Britain, Italy, and China. Polymeric nanoparticles were frequently engineered for targeted drug delivery and imaging, utilizing hyperbranched polyesters, sulfated polymers, or chitosan-based formulations. Lipid nanoparticles were often hybridized with inorganic nanomaterials or magnetic nanostructures to enhance their theranostic potential. While most patents detailed synthesis methods and physicochemical characterizations, only a few provided comprehensive preclinical validation, limiting their demonstrated efficacy. The analysis of recent patents highlights significant advances in the design and application of theranostic nanoparticles. However, a notable gap remains in validating these nanosystems for clinical translation. Future efforts should emphasize robust preclinical data, including in vitro and in vivo assessments, to enhance patent quality and applicability to substantiate the claimed theranostic capabilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".