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Clinical validation of combinatorial pharmacogenomic testing and single-gene guidelines in predicting psychotropic medication blood levels and clinical outcomes in patients with depression

2020· article· en· W3111048839 on OpenAlexfundno aff
Anthony J. Rothschild, Sagar V. Parikh, Daniel Hain, Rebecca Law, Michael E. Thase, Boadie W. Dunlop, Charles DeBattista, Charles R. Conway, Brent P. Forester, Richard C. Shelton, Matthew Macaluso, Krystal Brown, David Lewis, Alexander Gutin, Michael Jablonski, John F. Greden

Bibliographic record

VenuePsychiatry Research · 2020
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsnot available
FundersGreenwich BiosciencesTaylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine in St. LouisAgency for Healthcare Research and QualityNational Institutes of HealthSpier Family FoundationRogers Family FoundationEli Lilly and CompanyCanadian Network for Mood and Anxiety TreatmentsAssurex HealthCerecorMyriad GeneticsSunovionH. Lundbeck A/SNational Institute of Mental HealthAvanir PharmaceuticalsEisaiACADIA PharmaceuticalsSage TherapeuticsUniversity of KansasCanadian Institutes of Health ResearchFoundation for Barnes-Jewish HospitalStanley Medical Research InstitutePfizerBiogenAllerganCancer Research InstituteAstraZenecaBristol-Myers SquibbLivaNovaOntario Brain Institute
KeywordsMedicinePharmacogenomicsMajor depressive disorderPharmacogeneticsDepression (economics)Hamilton Rating Scale for DepressionClinical trialInternal medicinePsychiatryPharmacologyMoodGene

Abstract

fetched live from OpenAlex

We evaluated the clinical validity of a combinatorial pharmacogenomic test and single-gene Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines against patient outcomes and medication blood levels to assess their ability to inform prescribing in major depressive disorder (MDD). This is a secondary analysis of the Genomics Used to Improve DEpression Decisions (GUIDED) randomized-controlled trial, which included patients with a diagnosis of MDD, and ≥1 prior medication failure. The ability to predict increased/decreased medication metabolism was validated against blood levels at screening (adjusted for age, sex, smoking status). The ability of predicted gene-drug interactions (pharmacogenomic test) or therapeutic recommendations (single-gene guidelines) to predict patient outcomes was validated against week 8 outcomes (17-item Hamilton Depression Rating Scale; symptom improvement, response, remission). Analyses were performed for patients taking any eligible medication (outcomes N=1,022, blood levels N=1,034) and the subset taking medications with single-gene guidelines (outcomes N=584, blood levels N=372). The combinatorial pharmacogenomic test was the only significant predictor of patient outcomes. Both the combinatorial pharmacogenomic test and single-gene guidelines were significant predictors of blood levels for all medications when evaluated separately; however, only the combinatorial pharmacogenomic test remained significant when both were included in the multivariate model. There were no substantial differences when all medications were evaluated or for the subset with single-gene guidelines. Overall, this evaluation of clinical validity demonstrates that the combinatorial pharmacogenomic test was a superior predictor of patient outcomes and medication blood levels when compared with guidelines based on individual genes.

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)0.009
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.382
GPT teacher head0.549
Teacher spread0.167 · 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 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

Citations19
Published2020
Admission routes1
Has abstractyes

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