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Record W1985196300 · doi:10.2174/092986709787549299

Polymorphism and Crystallization of Active Pharmaceutical Ingredients (APIs)

2009· review· en· W1985196300 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Medicinal Chemistry · 2009
Typereview
Languageen
FieldMaterials Science
TopicCrystallization and Solubility Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsActive ingredientCrystallizationPolymorphism (computer science)Pharmaceutical technologyChemistryComputational biologyCombinatorial chemistryPharmacologyMedicineChromatographyBiologyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Active pharmaceutical ingredients (APIs), frequently delivered to the patient in the solid-state as part of an approved dosage form, can exist in such diverse solid forms as polymorphs, pseudopolymorphs, salts, co-crystals and amorphous solids. Various solid forms often display different mechanical, thermal, physical and chemical properties that can remarkably influence the bioavailability, hygroscopicity, stability and other performance characteristics of the drug. Hence, a thorough understanding of the relationship between the particular solid form of an active pharmaceutical ingredient (API) and its functional properties is important in selecting the most suitable form of the API for development into a drug product. In past decades, there have been significant efforts on the discovery, selection and control of the solid forms of APIs and bulk drugs. This contribution discusses the thermodynamics and kinetics of polymorphic systems, the characterization of polymorphs, and the transformation between polymorphs. The major techniques for polymorph discovery and control developed in the past years are discussed as well.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.081
GPT teacher head0.397
Teacher spread0.316 · 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