Towards medicinal mechanochemistry: evolution of milling from pharmaceutical solid form screening to the synthesis of active pharmaceutical ingredients (APIs)
Bibliographic record
Abstract
This overview highlights the emergent area of mechanochemical reactions for making active pharmaceutical ingredients (APIs), and covers the latest advances in the recently established area of mechanochemical screening and synthesis of pharmaceutical solid forms, specifically polymorphs, cocrystals, salts and salt cocrystals. We also provide an overview of the most recent developments in pharmaceutical uses of mechanochemistry, including real-time reaction monitoring, techniques for polymorph control and approaches for continuous manufacture using twin screw extrusion, and more. Most importantly, we show how the overlap of previously unrelated areas of mechanochemical screening for API solid forms, organic synthesis by milling, and mechanochemical screening for molecular recognition, enables the emergence of a new research discipline in which different aspects of pharmaceutical and medicinal chemistry are addressed through mechanochemistry rather than through conventional solution-based routes. The emergence of such medicinal mechanochemistry is likely to have a strong impact on future pharmaceutical and medicinal chemistry, as it offers not only access to materials and reactivity that are sometimes difficult or even impossible to access from solution, but can also provide a general answer to the demands of the pharmaceutical industry for cleaner, safer and efficient synthetic solutions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".