Pharmacogenomics for Psychiatry: Focusing on Drug Metabolizing Enzymes and Transporters, with Validated Methodology for CYP2D6 and CYP2C19 Including for a Novel Sub-Haplotype
Bibliographic record
Abstract
Pharmacogenomics (PGx) is interested in the impact individual genetic makeup has on a patient’s response to pharmacological agents. In clinical practice, PGx has the potential of reducing adverse drug reactions (ADRs), which cost Canada $65 million dollars in the year 2018, as well as enhancing treatment outcomes. Implementation of PGx in the clinic depends on, among other factors, a) knowledge of enzymes/transporters responsible for absorption, distribution, metabolism and excretion (ADME) and their genetic variation; b) the possible gene-drug pairs and drug interaction effects based on the genes that encode such enzymes/transporters; as well as c) robust methodology that can be applied in the genotyping efforts in order to generate individual data. The aim of my thesis is to address the aforementioned for advancement of PGx in psychiatric practice. With these aims in mind, in the first part of Chapter 2, I review the main enzymes involved in phase I and II metabolism, as well as the transporters involved in phase III (excretion). The second part of the review presents pharmacogenetic associations important to psychiatry, that is, different examples of antipsychotics and antidepressants, as well as atomoxetine, are reviewed in relation to their metabolic pathway, introducing the gene-drug pairs that are of interest for devising pharmacogenetic guidelines. On this topic, existing guidelines by both the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG) are also presented. In Chapter 3, innovative methodology is introduced for the clinical genotyping of the genes CYP2D6 and CYP2C19 in a subset (N=95) of samples from the Genome-based therapeutic drugs for depression (GENDEP) clinical trial designed to investigate pharmacogenomic predictors of response to antidepressants. In it, the technologies used were: TaqMan copy number variant (CNV) and single nucleotide variant (SNV) assays, xTAGv3 Luminex CYP2D6 and CYP2C19, PharmacoScan, the Ion AmpliSeq Pharmacogenomics Panel and the Agena MassARRAY. Through the employment of these different technologies, which were cross- validated, we were able to resolve samples that had been previously genotyped, but for which no data had resulted. This was enabled through the use of the above technologies and long-range polymerase chain reaction (L-PCR) with Sanger sequencing. An important contribution of the methodology described in the chapter is a validated methodology for a comprehensive range of CYP2D6 haplotypes, including a larger range of hybrids and hybrid tandems compared to previous reports in the field. Building on the work described above, Chapter 4 describes the genotyping of an individual sample that was initially detected from the genotyping work in Chapter 3. The sample of interest here was not concordant across the technologies used in terms of the genotypic automated call, which put into question the haplotypes present in the sample. Similar to Chapter 3, through the application of L-PCR and sequencing work, we were able to interrogate the SNVs present in the sample and, from the data generated, present a previously unreported sub-haplotype of CYP2D6*41.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".