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Record W2982435673 · doi:10.4088/jcp.19m12910

Impact of Pharmacogenomics on Clinical Outcomes for Patients Taking Medications With Gene-Drug Interactions in a Randomized Controlled Trial

2019· article· en· W2982435673 on OpenAlexfundno aff
Michael E. Thase, Sagar V. Parikh, Anthony J. Rothschild, Boadie W. Dunlop, Charles DeBattista, Charles R. Conway, Brent P. Forester, Francis M. Mondimore, Richard C. Shelton, Matthew Macaluso, James Li, Krystal Brown, Michael Jablonski, John F. Greden

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsnot available
FundersNational Institute of Mental HealthTaylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine in St. LouisAgency for Healthcare Research and QualityNational Institutes of HealthMyriad GeneticsSunovionH. Lundbeck A/SEisaiSpier Family FoundationRogers Family FoundationEli Lilly and CompanyCanadian Network for Mood and Anxiety TreatmentsAssurex HealthCerecorSage TherapeuticsLivaNovaGlaxoSmithKlineUniversity of KansasCanadian Institutes of Health ResearchFoundation for Barnes-Jewish HospitalStanley Medical Research InstitutePfizerBiogenAllerganCancer Research InstituteAstraZenecaBristol-Myers SquibbOntario Brain InstituteSanofi
KeywordsPharmacogenomicsRandomized controlled trialDrugMedicinePharmacogeneticsClinical trialPsychiatryIntensive care medicinePharmacologyInternal medicineGeneGeneticsBiologyGenotype

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of the Genomics Used to Improve DEpression Decisions (GUIDED) trial was to evaluate the utility of pharmacogenomic testing to improve outcomes among patients with major depressive disorder (MDD) who had not responded to at least 1 prior medication trial. The objective of the present analysis was to assess outcomes for the subset of patients expected to benefit from combinatorial pharmacogenomic testing because they were taking medications with predicted gene-drug interactions. METHODS: Participants (enrolled from April 14, 2014, to February 10, 2017) had an inadequate response to at least 1 psychotropic medication in the current episode of MDD. Patients were randomized to treatment as usual (TAU) or the guided-care arm, in which clinicians had access to a combinatorial pharmacogenomic test report to inform medication selection. Patients and raters were blinded to study arm through week 8. The following outcomes were assessed using the 17-item Hamilton Depre​ssion Rating Scale (HDRS-17): symptom improvement (percent change in HDRS-17 score), response (≥ 50% decrease in HDRS-17 score), and remission (HDRS-17 score ≤ 7). In the GUIDED trial, the primary endpoint of symptom improvement did not reach significance in the intent-to-treat cohort (P = .069). Here, a post hoc analysis of patients who were taking medications subject to gene-drug interactions at baseline as predicted by combinatorial pharmacogenomic testing (N = 912) is presented. RESULTS: Among participants taking medications subject to gene-drug interactions at baseline, outcomes at week 8 were significantly improved for those in the guided-care arm compared to TAU (symptom improvement: 27.1% versus 22.1%, P = .029; response: 27.0% versus 19.0%, P = .008; remission: 18.2% versus 10.7%, P = .003). When patients who switched medications were assessed, all outcomes were significantly improved in the guided-care arm compared to TAU (P = .011 for symptom improvement, P = .011 for response, P = .008 for remission). CONCLUSIONS: By identifying and focusing on the patients with predicted gene-drug interactions, use of a combinatorial pharmacogenomic test significantly improved outcomes among patients with MDD who had at least 1 prior medication failure. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02109939​.

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)0.008
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.151
GPT teacher head0.566
Teacher spread0.415 · 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 designRandomized trial
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

Citations71
Published2019
Admission routes1
Has abstractyes

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