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Towards sustainable practices in pharmaceutical research and development: A life cycle approach

2025· article· en· W4415069032 on OpenAlexafffund
Evans A. Monyoncho, Mathew J. Eckelman, Azadeh Kermanshahi‐pour

Bibliographic record

VenueResources Conservation and Recycling · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsTechnical University of Nova ScotiaDalhousie University
FundersMitacs
KeywordsIncinerationLife-cycle assessmentRaw materialEnvironmental impact assessmentInvestment (military)Life cycle inventoryProduction (economics)Fossil fuel

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.344
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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