Longitudinal Patterns of Antidepressant Usage in the Context of CYP2C19 Genotype Among All of Us Participants
Clinical Genetics and Therapeutics
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Primary Categories:
- Genomic Medicine
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Secondary Categories:
- Genomic Medicine
Introduction:
Depression is a common mental health disorder and is a leading cause of disability worldwide. Despite advances in pharmacological interventions for depression, drug response rates for patients with major depressive disorder is only 42-53%, and 30% of patients experience no remission. There is considerable evidence that antidepressant response is influenced by variation in a handful of genes, including CYP2C19. The Clinical Pharmacogenetics Implementation Consortium published evidence-based guidelines recommending patients with certain risk genotypes avoid antidepressants metabolized by CYP2C19. However, there is a lack of data on longitudinal differences in CYP2C19-stratified antidepressant usage derived from EHR prescription data in an unselected population. Understanding whether individuals with CYP2C19 risk genotypes do in fact struggle with drug efficacy would help drive the adoption of preemptive testing in routine clinical practice. Recent work by Haddad et al. used linked whole genome and EHR data from All of Us participants to determine the frequency of several PGx variants, predicted phenotypes, and relevant medication exposures. However, the patterns of medication usage across time, including drug and dose changes, remain unexplored. Using the longitudinal EHR data from All of Us, we sought to determine if individuals with CYP2C19 risk genotypes encounter more difficulty in arriving at an effective therapeutic regimen for depression.
Methods:
Using the All of Us Researcher Workbench, we generated a cohort of participants with prior exposure to an antidepressant metabolized by CYP2C19 (citalopram, escitalopram, sertraline, amitriptyline, clomipramine, doxepin, imipramine, trimipramine). Using participant-level predicted phenotype data as generated by Haddad et al., we assigned individuals to each of the CPIC-recognized metabolizer phenotype groups for CYP2C19: Ultrarapid (n=1,888), Rapid (n=10,431), Normal (n=16,077), Intermediate (n=10,928), Likely Intermediate (n=251), Poor (n=1,116), Likely Poor (n=65), or Indeterminate (n=297). Next, from longitudinal EHR data, we extracted all antidepressant prescription data for these individuals, including the drugs metabolized by CYP2C19 and those that are not. From these data, we identified individuals who had switched between different drugs, as well as the number of such switches per individual.
Results:
Across all phenotype groups, most individuals were exposed to multiple different antidepressants (Ultrarapid, median = 4; all others, median = 2). However, individuals for whom the CPIC guidelines indicate the use of a non-CYP2C19 metabolized drug (Ultrarapid, Rapid, Poor, Likely Poor) were significantly more likely to be exposed to multiple different CYP2C19 drugs (29.7 vs 28.7%, chi sq. p=0.04). This group was also significantly more likely to have switched from a CYP2C19-metabolized drug to a different class of antidepressant (52.1% vs 50.5, p=0.004). However, the proportion of individuals who switched away from CYP2C19 drugs, but later back to a different CYP2C19 drug, did not differ significantly (37.8% vs 37.1%, p=0.16). Furthermore, the proportion of individuals who started on a CYP2C19 drug, and stayed only on CYP2C19 drugs did not significantly differ by predicted phenotype (29.3% vs 30.2%, p=0.08).
Conclusion:
The significant difference in the proportion of individuals prescribed multiple CYP2C19 antidepressants between those with genotypes that contraindicate CYP2C19 drugs and those without suggests that preemptive testing may have helped avoid the serial trialing of multiple antidepressants. However, we also found that the proportion of those taking exclusively CYP2C19-metabolized drugs did not differ between groups, nor did the proportion of those who returned to CYP2C19 drugs after switching to a different class. This suggests that preemptive testing of CYP2C19 alone may not be sufficient in guiding antidepressant choice nor reducing the time to find an effective drug for each patient. Extending this work to analyze dose changes, symptom prevalence, and genotypes of other genes will clarify the potential benefits of preemptive PGx testing.
Depression is a common mental health disorder and is a leading cause of disability worldwide. Despite advances in pharmacological interventions for depression, drug response rates for patients with major depressive disorder is only 42-53%, and 30% of patients experience no remission. There is considerable evidence that antidepressant response is influenced by variation in a handful of genes, including CYP2C19. The Clinical Pharmacogenetics Implementation Consortium published evidence-based guidelines recommending patients with certain risk genotypes avoid antidepressants metabolized by CYP2C19. However, there is a lack of data on longitudinal differences in CYP2C19-stratified antidepressant usage derived from EHR prescription data in an unselected population. Understanding whether individuals with CYP2C19 risk genotypes do in fact struggle with drug efficacy would help drive the adoption of preemptive testing in routine clinical practice. Recent work by Haddad et al. used linked whole genome and EHR data from All of Us participants to determine the frequency of several PGx variants, predicted phenotypes, and relevant medication exposures. However, the patterns of medication usage across time, including drug and dose changes, remain unexplored. Using the longitudinal EHR data from All of Us, we sought to determine if individuals with CYP2C19 risk genotypes encounter more difficulty in arriving at an effective therapeutic regimen for depression.
Methods:
Using the All of Us Researcher Workbench, we generated a cohort of participants with prior exposure to an antidepressant metabolized by CYP2C19 (citalopram, escitalopram, sertraline, amitriptyline, clomipramine, doxepin, imipramine, trimipramine). Using participant-level predicted phenotype data as generated by Haddad et al., we assigned individuals to each of the CPIC-recognized metabolizer phenotype groups for CYP2C19: Ultrarapid (n=1,888), Rapid (n=10,431), Normal (n=16,077), Intermediate (n=10,928), Likely Intermediate (n=251), Poor (n=1,116), Likely Poor (n=65), or Indeterminate (n=297). Next, from longitudinal EHR data, we extracted all antidepressant prescription data for these individuals, including the drugs metabolized by CYP2C19 and those that are not. From these data, we identified individuals who had switched between different drugs, as well as the number of such switches per individual.
Results:
Across all phenotype groups, most individuals were exposed to multiple different antidepressants (Ultrarapid, median = 4; all others, median = 2). However, individuals for whom the CPIC guidelines indicate the use of a non-CYP2C19 metabolized drug (Ultrarapid, Rapid, Poor, Likely Poor) were significantly more likely to be exposed to multiple different CYP2C19 drugs (29.7 vs 28.7%, chi sq. p=0.04). This group was also significantly more likely to have switched from a CYP2C19-metabolized drug to a different class of antidepressant (52.1% vs 50.5, p=0.004). However, the proportion of individuals who switched away from CYP2C19 drugs, but later back to a different CYP2C19 drug, did not differ significantly (37.8% vs 37.1%, p=0.16). Furthermore, the proportion of individuals who started on a CYP2C19 drug, and stayed only on CYP2C19 drugs did not significantly differ by predicted phenotype (29.3% vs 30.2%, p=0.08).
Conclusion:
The significant difference in the proportion of individuals prescribed multiple CYP2C19 antidepressants between those with genotypes that contraindicate CYP2C19 drugs and those without suggests that preemptive testing may have helped avoid the serial trialing of multiple antidepressants. However, we also found that the proportion of those taking exclusively CYP2C19-metabolized drugs did not differ between groups, nor did the proportion of those who returned to CYP2C19 drugs after switching to a different class. This suggests that preemptive testing of CYP2C19 alone may not be sufficient in guiding antidepressant choice nor reducing the time to find an effective drug for each patient. Extending this work to analyze dose changes, symptom prevalence, and genotypes of other genes will clarify the potential benefits of preemptive PGx testing.
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