‘Magic bullets’ for bone diseases: progress in rational design of bone-seeking medicinal agents
Bibliographic record
Abstract
An ideal therapeutic agent for bone diseases should act solely on bone tissue with no pharmacological activity at other anatomical sites. Current therapeutic agents, however, do not usually display a preferential affinity to bones and non-specifically distribute throughout the body after administration. Attempts to design bone-specific agents have relied on engineering a desired therapeutic agent with bone-seeking molecules so that the latter delivers the therapeutic agents specifically to bones. In this critical review, we summarize the latest attempts to engineer bone-seeking therapeutic agents based on formulating therapeutic agents with bisphosphonates, a class of compounds with high affinity to biological apatite. We first provide a relevant summary of the structure of bone mineral and bisphosphonates, highlighting the mode of interaction between these two entities. The use of bisphosphonates in the diagnosis of bone diseases is then presented, since this application helps us to understand the bone-carrier properties of bisphosphonates under physiological conditions. A summary of recent attempts to formulate bisphosphonates with traditional therapeutic agents to restrict their activities to bone tissues is then provided, with special emphasis on the structure-function relationships of the engineered compounds. Finally, attempts to use bisphosphonates to deliver macromolecular therapeutics (i.e., proteins) are summarized, based on recent data from the authors' lab. The collective research into bone-seeking medicinal agents is progressively laying the foundation for next-generation 'magic bullets' that display desirable activities at the disease sites with no undesirable activity on other organ systems. (164 references.).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".