MétaCan
Menu
Back to cohort
Record W2154017798 · doi:10.1351/pac200476020263

Photodegradation and photosensitization in pharmaceutical products: Assessing drug phototoxicity

2004· article· en· W2154017798 on OpenAlexfundno aff
Gonzalo Cosa

Bibliographic record

VenuePure and Applied Chemistry · 2004
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsnot available
FundersUniversity of Ottawa
KeywordsPhototoxicityChemistryPhotoexcitationPhotodegradationPhotochemistryKetoprofenDrugExcited stateCombinatorial chemistryOrganic chemistryPhotocatalysisPharmacologyChromatographyCatalysisBiochemistry

Abstract

fetched live from OpenAlex

Abstract Toxic reactants are a common result of the interaction of sunlight with pharmaceutical agents transported in the blood system or applied topically. Over the past decade there has been a considerable amount of research toward understanding both the unimolecular deactivation pathway of photoexcited pharmaceutical products and their photosensitizing capability in the presence of biological substrates. This work summarizes recent developments in the study of the photodegradation mechanism of ketoprofen, fenofibric acid, and tiaprofenic acid. An analysis of excited-state electronic energy levels, the type of intermediates formed following excitation, and transient intermediate lifetimes is presented. The analysis involves both parent drugs and their major photoproducts. Phototoxicity, usually the result of adverse photochemical reactions following direct photoexcitation of the drugs, is shown to be strongly related to the photoexcitation of photoproducts when high radiation dose conditions prevail. The photoproducts are the species directly involved in photosensitizing reactions.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.302
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations84
Published2004
Admission routes1
Has abstractyes

Explore more

Same venuePure and Applied ChemistrySame topicPhotodynamic Therapy Research StudiesFrench-language works237,207