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Record W2795073536 · doi:10.1093/schbul/sby018.1009

S222. CLINICAL UTILITY OF PHARMACOGENETIC TESTING IN SCHIZOPHRENIA TREATMENT

2018· article· en· W2795073536 on OpenAlexaff
Daniel J. Müller, Arun K. Tiwari, Clement C. Zai, James L. Kennedy

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsPharmacogeneticsSchizophrenia (object-oriented programming)MedicinePsychiatryDrugAntipsychoticClinical trialMoodGenetic testingPharmacodynamicsDrug responseCYP2C19BioinformaticsPharmacologyPharmacokineticsInternal medicineGeneGeneticsGenotypeBiology

Abstract

fetched live from OpenAlex

Antipsychotics (APs), antidepressants (ADs) and mood stabilizers are essential components in treatment of most psychiatric disorders and in particular in schizophrenia. Unfortunately, among the various compounds which have been developed, lengthy trials are often required before the optimum medication treatment is found, i.e. with most significant symptom alleviation and minimal side effects. Thus, predictive factors would thus be extremely beneficial in clinical practice. The underlying reasons for this large inter-individual variability in terms of treatment response are not fully understood. Important factors that influence drug dose, response and side effects include age, gender, patient compliance, constellation of symptoms, co-morbidity, and to a large extent genetic factors. Methods follow two strategic concepts, i.e. 1) review of the literature and review of the clinical utility of using genetic information preemptively and 2) results of own studies evaluating treatment outcome in psychiatric care after genetic information (e.g., CYP2D6 and CYP2C19) was provided to more than 350 physicians. There is growing consensus among expert that genetic testing to optimize medication treatment in psychiatry meets criteria for clinical utility. However, utility remains restricted to specific gene-drug pairs and multi-gene test require further validation. Our own research has shown that variation in genes involved in the metabolism of psychotropic drugs (pharmacokinetics) and genes encoding drug targets, such as brain receptors (pharmacodynamics) are associated with plasma drug levels, treatment response, and side effects (e.g., antipsychotic-induced weight gain). In addition, our genome-wide analyses have revealed associations with clinical outcome to antipsychotics or antidepressants and markers in neurotrophins, cell-signaling and inflammatory pathways. With respect to our preemptive genetic testing program in more than 10,000 patients, we received supportive responses from physicians who enrolled patients in our study. Notably, while the vast majority of patients reported improvement in patient outcome, only two physicians indicated that their patient’s symptoms has slightly worsened after they had used the pharmacogenetic report to guide treatment. There is emerging evidence that preemptive genetic testing for numerous gene & psychiatric-drug pairs has reached levels for clinical utility which includes validation of analytical and clinical validity. Genetic testing has become readily available but however clinicians and patients are poorly prepared to this new emerging field and proper education is of utmost importance. This presentation will review the level of evidence for ‘actionable’ gene-drug pairs in psychiatry in addition to present novel genomic findings and reports from our ongoing genetic testing experiences.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.375
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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