antidepressant pgx - bishop

Antidepressant Pharmacogenomics

Presenter Information

  • Name: Jeffrey R. Bishop, PharmD, MS, BCPP, FCCP

  • Position: Professor, College of Pharmacy, University of Minnesota

  • Program/Initiative: Driven to Discover SM

Learning Objectives

  • Sources of Genetic Variability:
      - Describe sources of genetic variability and their potential impact on antidepressant treatment outcomes.

  • Utilization of Evidence-Based Resources:
      - Identify and utilize evidence-based resources to evaluate pharmacogenomic (PGx) information relevant to antidepressants.

  • Consideration of Patient-Specific Factors:
      - Consider patient-specific factors in the application of antidepressant pharmacogenomics in clinical care.

Background Information

  • Effectiveness of Treatments:
      - Effective treatments for depression are available; however, medication selection often relies on trial and error methods.
      - Response Rates: Approximately 40-60% of patients respond to antidepressant medications, demonstrating significant variability in treatment outcomes.

Study of Serotonin Reuptake Inhibitor Dose Relationships

  • Pharmacokinetics and PET Studies:
      - Studies (PK-linked PET studies and fixed-dose randomized controlled trials) indicate minimal antidepressant exposure thresholds for 5-HT transporter occupancy and symptom response.
      - Adverse Drug Reactions (ADRs): Primarily dose/concentration-dependent, including:
        - Sexual dysfunction
        - Sweating
        - QT prolongation
        - Agitation
        - Nausea
        - Dizziness
        - Anxiety
        - Insomnia

  • ADR Dropout Rates:
      - ADR dropout percentages include: <16.7%, 16.7-33.3%, 33.3-41.75%, >41.75%.
      - References:
        - Safer DJ. J Clin Psychopharmacol. 2016 PMID: 27518478
        - Jakubovski E et al. Am J Psychiatry. 2016 PMID: 26552940
        - Grunder et al. Pharmacopsychiatry 2011 PMID: 21959785

Dose Adjustments for Selective Serotonin Reuptake Inhibitors (SRIs) Based on Pharmacokinetic Factors

  • Important Factors: Renal, Hepatic, and Drug-Drug Interactions
      - Citalopram: 50% max dose in case of renal impairment.
      - Desvenlafaxine:
        - CrCl < 30: 50% dose reduction.
        - Severe: Lower max dose.
      - Duloxetine: Avoid in severe renal impairment; CrCl < 30.   - Escitalopram: 50% max dose reduction.   - Fluoxetine: Initiate with “lower” doses.   - Levomilnacipran: 33% max dose reduction if CrCl < 60; 66% if CrCl < 30.   - Milnacipran: Use caution if CrCl < 60; 50% dose reduction if CrCl < 30.   - Paroxetine: Reduced starting and max dose if CrCl < 30.   - Sertraline: Avoid in any renal impairment severity.   - Venlafaxine: 25-50% dose reduction if CrCl < 90; >50% if CrCl < 30.
      - Vilazodone: Adjust based on CYP3A4 inhibitors/inducers.
      - Vortioxetine: Adjust based on CYP2D6 inhibitors.

Sources of Variability in Treatment Response and Tolerability

  • Factors Influencing Variability:
      - Adherence
      - Drug interactions
      - Dosing
      - Age
      - Sex
      - Medical and psychiatric comorbidities
      - Liver and renal function
      - Disease etiology
      - Genetics (target, metabolism, outcomes)

Genetic Variability Influencing Drug Metabolism

Example: CYP2C19 Enzyme Variants

  • Population Statistics:
      - Variation among different population groups:
        - 2.4% ultra-rapid metabolizers
        - 42.3% extensive metabolizers
        - 22.5% intermediate metabolizers
        - 4.2% poor metabolizers
        - 28.5% unknown phenotype
      - Reference: Caudle, KE et al. Genet Med 2017;19:215-223.

Importance of Phenoconversion in Drug Metabolism

  • Definition:
      - Phenocopy: Variation in phenotype caused by environmental exposure mimicking genetically induced phenotypes.
      - Example: Drug inhibition may transiently transform an individual into a poor metabolizer (e.g., Paroxetine, Fluoxetine).

  • Contextual Factors: Genetics should be interpreted alongside the patient's overall condition.

Research on Variation in Pharmacogenes and Antidepressant Treatment Outcomes

  • Psychotropic Pharmacogenomics (PGx):
      - Includes pharmacokinetic (PK) genes (drug metabolism), pharmacodynamic (PD) genes (target receptors, transporters, immune system), etc.
      - Over 70% of commonly prescribed psychotropic medications metabolized by CYP450 and UGT enzymes.

Resources for Evaluating Pharmacogenomics Evidence

  • Clinical Pharmacogenetics Implementation Consortium (CPIC): NIH-supported efforts since 2009 providing standardized evaluations of PGx evidence.

  • Dutch Pharmacogenetics Working Group (DPWG): European consortium for PGx evidence evaluation.

  • FDA Table of Pharmacogenetic Associations: Evaluates PGx data submitted during drug applications.

Genes on Commercial Pharmacogenomic Tests for Psychiatric Medications

Gene

CPIC Level

CYP2C9

A

CYP2C19

A

CYP2D6

A

HLAA

A

HLAB

A

CYP2B6

B

COMT

C

DRD2

C

GRIK4

C

HTR2A

C

MC4R

C

MTHFR

C

SLC6A4

C

OPRM1

C

UGT2B15

C

ABCB1

C

CYP1A2

D

GRIK1

D

UGT1A4

D

UGT2B7

D

CPIC Level Interpretation

  • Levels A & B: Genotype should or could guide prescribing.

  • Levels C & D: No specific prescribing actions recommended.

Tricyclic Antidepressants (TCAs)

  • Development Timeline: Approved between the 1950s-1970s for depression, also used for pain.

  • Mechanism of Action: Primarily, they function as Serotonin-Norepinephrine Reuptake Inhibitors (SNRIs).

  • Pharmacodynamic Characteristics: Include histamine, alpha-1/2, muscarinic antagonism and 5-HT antagonism.

  • Monitoring: Therapeutic drug monitoring is crucial for symptom optimization and toxicity minimization, as high serum levels can cause severe cardiovascular and neurological effects.

Pharmacokinetics of Tricyclic Antidepressants

  • Metabolism:
      - CYP1A2, CYP2D6, CYP2C19, CYP3A4 involvement in hepatic metabolism.
      - Secondary Amines: Metabolism via CYP2D6 (e.g., Desipramine, Maprotiline).
      - Tertiary Amines: Metabolism via CYP2C19 and/or CYP2D6 (e.g., Amitriptyline).

Tricyclic Antidepressant Metabolism and CYP2D6 Influence

  • Increased concentrations of TCA and plasma concentration influenced significantly by CYP2D6 copy number status.
      - References:
        - Meyer Nat Genetics 2004 PMID: 15372089
        - Ingleman-Sundberg TiPS 1999 PMID: 10431214

CPIC Guidelines for TCA Pharmacogenomics

  • Therapeutic Concentration Risk: Poor or ultrarapid CYP2D6 and CYP2C19 metabolizers may experience significant side effects or treatment failure due to concentration variation.

  • CPIC Guidelines Recommendations:
      - For Poor Metabolizers (PM): Avoid using TCAs or reduce starting dose by 50%.
      - For Ultrarapid Metabolizers (UM): Consider alternative antidepressants or higher titration doses.

  • References: Hicks et al., Clin Pharmacol Ther 2017 PMID: 27997040.

Clinical Considerations in TCA Pharmacogenomics

  • Genotype impacts TCA concentrations and their relationship to treatment outcomes.

  • TCA usage has expanded to pain treatments where dosing differs from antidepressant protocols.

  • Established therapeutic drug monitoring assists in minimizing toxicity for PMs.

  • Role of Genotyping: Could enhance management and avoid underdosing in UM early on.

Selective Serotonin Reuptake Inhibitors (SRIs)

  • Recognized as first-line treatments for depression with genetic variation affecting drug metabolism.

  • CPIC guidelines adapt based on drug-gene interactions, varying from TCAs.

  • Example: Escitalopram CYP2C19 interactions.

Translating Research into Recommendations (2023 CPIC Guidance)

  • UM: Alternative drug or increase dose.

  • RM: Standard starting dose, titrate higher as needed.

  • NM: Normal dosing.

  • IM: Standard starting dose, lower target dose with slower titration, consider monitoring.

  • PM: Recommend alternate drug with possibly reduced maintenance dosage.

CYP2D6 and CYP2C19 Recommendations

CYP2D6 Recommendations

Phenotype

Therapeutic Recommendation

Ultrarapid

Consider Alternative

Normal

Standard Dose

Intermediate

Lower Starting Dose

Poor

Consider Alternative

CYP2C19 Recommendations

| Phenotype | Therapeutic Recommendation |
| Ultrarapid | Consider Alternative |
| Normal | Standard Dose |
| Intermediate | Slower Titration |
| Poor | Consider Alternative or reduced dose. |

Clinical Utility of Randomized Controlled Trials

  • Overview: As of the current date, there have been 10 published PGx RCTs for depression.
      - Most enrolled patients had prior antidepressant treatment failures.
      - Majority of trials were designed as double-blind studies focusing on depression and some included anxiety diagnoses.

Meta-Analysis of PGx Testing

  • Outcome: PGx-tested patients were 1.46 times more likely to achieve remission compared to those receiving treatment as usual.
      - Reference: Brown et al. Clin Pharmacol Ther. 2022 PMID: 36111494.

Economic Benefits of PGx Testing

  • ER visits: PGx-tested patients had 40% fewer all-cause ER visits (p < 0.0001) and 58% fewer hospitalizations compared to controls.

  • Total cost savings: Estimated $1,228 reduction in total medical expenses over 6 months.
      - Reference: Perlis RH et al. 2018 Depress Anxiety. PMID: 29734486.

Systematic Review of Phamrmacogenomics Testing

  • Results: Out of 18 studies, 39% showed cost savings while 50% showed cost-effectiveness. A total of 89% supported PGx testing in psychiatric care.
      - Reference: Karamperis et al. 2021. The Pharmacogenomics Journal. PMID: 34215853.

Commercially Available Tests for Psychiatry

  • Many companies provide color-coded results indicating pharmacogenomic interactions.

  • Recognizing Patterns: Results synthesized from multiple genes may differ based on commercial entities, impacting treatment recommendations.

Process for Integrating PGx into Psychiatric Care Plans

  • Reference study: Ramsey LB et al. 2021. J Am Acad Child Adolescent Psychiatry. PMID: 32860906.

Conclusions

  • Pharmacogenomics represents an evolving precision medicine application specifically tailored for depression treatment, relying on established gene-drug relationships as per CPIC guidelines, Dutch Guidelines, and FDA recommendations.

  • Continued research and clinical trials are necessary to optimize therapeutic decisions, utilizing pharmacogenomics as a complementary tool alongside standard clinical practices for patient care.