Computer‐aided solvent selection and design for the efficient extraction of a pharmaceutical molecule
Bibliographic record
Abstract
Abstract We have employed a computer‐aided approach to select and design task‐specific organic solvents for the liquid‐liquid extraction of ephedrine from its aqueous solution. We have identified three solvent performance indicators (SPIs) as the shortlisting criteria for solvents with desirable properties: high ephedrine solubility; low solvent loss; and high partition coefficient. Other properties that were considered include octanol‐water partition coefficient and toxicity, which give a measure of the safety/health/environmental (SHE) impacts, and liquid viscosity, which is an important process design parameter. We have first analyzed the trends in these SPIs for a range of common organic solvents. Toluene (currently employed for the extraction of R‐phenylacetylcarbinol, the precursor to ephedrine) has low solvent loss but relatively poor values of the other SPIs for ephedrine extraction, though with a relatively benign SHE impact. We are unable to identify a solvent (from the list of common organic solvents) that satisfies all the shortlisting criteria for use in the pharmaceutical industry and this provided the motivation for the design of task‐specific solvents. We have designed organic solvents (acyclic aliphatic, aromatic with one side chain attachment, and aromatic with two side chain attachments) with superior values of SPIs than the reference solvent, toluene. We have employed limiting values on the melting and boiling points to ensure the designed solvents are liquids at the operating conditions. Designed aliphatic compounds contain the chloro‐group(s), whereas there are aromatic solvents without the chloro‐groups with better SPIs than toluene. Designed solvents without chloro‐groups may be considered as the starting point for further screening experiments.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".