Towards sustainable practices in pharmaceutical research and development: A life cycle approach
Bibliographic record
Abstract
A comprehensive Life Cycle Inventory (LCI) has been compiled for cooling, antisolvent, evaporative, and reactive crystallization processes during the active pharmaceutical ingredients (API) purification, covering aspects from raw material extraction to waste disposal, providing a cradle-to-grave evaluation for the solvents. Relevant environmental impact categories such as global warming potential, fossil fuel potential, and human toxicity potential were considered to quantify environmental burdens across the life cycle stages. A comparison between solvent recycling and incineration scenarios has been made to evaluate environmental impacts. Environmental impact assessment indicated that solvent production was the primary contributor in the incineration option. However, implementing solvent recovery systems reduced these impacts, making the recovery option more environmentally favorable compared with incineraion. Economic analysis showed that operating costs are predominantly associated with solvent procurement. Further, solvent recovery systems showed high returns on investment at larger production scales, suggesting their viability for cost savings and profitability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".