First-in-class and best-in-class dendrimer nanoplatforms from concept to clinic: Lessons learned moving forward
Bibliographic record
Abstract
Research to develop active dendrimers by themselves or as nanocarriers represents a promising approach to discover new biologically active entities that can be used to tackle unmet medical needs including difficult diseases. These developments are possible due to the exceptional physicochemical properties of dendrimers, including their biocompatibility, as well as their therapeutic activity as nanocarriers and drugs themselves. Despite a large number of academic studies, very few dendrimers have crossed the 'valley of death' between. Only a few number of pharmaceutical companies have succeeded in this way. In fact, only Starpharma (Australia) and Orpheris, Inc. (USA), an Ashvattha Therapeutics subsidiary, can fill all the clinic requirements to have in the market dendrimers based drugs/nancocarriers. After evaluating the main physicochemical properties related to the respective biological activity of dendrimers classified as first-in-class or best-in-class in nanomedicine, this original review analyzes the advantages and disavantages of these two strategies as well the concerns to step in clinical phases. Various solutions are proposed to advance the use of dendrimers in human health.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".