This study highlights the clinical relevance and benefits of benchmarking innate drug metabolism reserve using the CYP450 combinatory approach described in our index methodologies paper [38
]. We found that lipid measures of LDLc, HDLc and the LDLc:HDLc ratio varied significantly with combined drug metabolism reserve and alteration index values. Notably, these correlations could not be found to the same degree when using a single-gene index. In this MDD cohort, average cholesterol levels were not significantly elevated, which is consistent with earlier studies that found no link between MDD and elevated lipids [9
]. However, dyslipidemia has been associated with psychiatric pharmacotherapy [8
]. Our findings build upon these observations by demonstrating that drug intolerance, as a result of decreased innate liver enzyme capacity, may result in an increased lipid effect in some patients.
Owing to the timing of the cholesterol measurements upon admission it is difficult to associate dyslipidemia with any particular psychotropic. In such an instance, the combinatory drug metabolism indices are particularly useful. By quantifying innate drug metabolism capacity, they allow for analysis of drug response and effect when numerous enzymes and substrates are relevant to the analysis. indicates that a wide variety of psychotropics were administered to this cohort. CYP2D6 serves as a primary metabolic pathway for many of the most prescribed drugs, including fluoxetine, paroxetine, trazodone, venlafaxine and risperidone. However, CYP2C9 and/or CYP2C19 are major metabolizers for citalopram, escitalopram, fluoxetine and sertraline. shows that particularly for LDLc, CYP2D6 is often the strongest contributor to the model, consistent with the CYP2D6 isoenzyme’s primary role in psychotropic metabolism. However, CYP2D6 scores alone never supersede in significance the combinatory analysis.
The flexibility of the CYP450 combinatory drug metabolism indices allow for a rigorous and thorough analytical approach. In we demonstrate how each combinatory index can be broken down into single-gene indices. In the case of HDLc, we were able to note that CYP2C9 and CYP2D6 gene-specific indices were the primary contributors to the relationship, and therefore create a new combined index omitting the less-relevant CYP2C19 and generating a more accurate model. Moreover, we demonstrate in this study how drug-specific indices, which utilize the metabolism pathway coefficients shown in , are useful in predicting the effect of a particular drug. While the lack of a treatment timeframe weakens any conclusive determination of causality, it is notable that the sertraline-specific indices correlated more closely with dyslipidemia phenotypes than the combinatory indices for patients treated with this drug prior to hospitalization. Since lipid tests were performed at the time of hospitalization, it remains possible that other medications administered prior to this may have influenced the lipid values in our dataset. A future study could be specifically designed to investigating the drug-specific indices and their relation to cardiometabolic markers.
Beyond the issues surrounding the unspecific timing of drug prescription prior to lipid level evaluation, this study has further limitations that must be addressed. First, diet and patient compliance were not measured as covariates, and could contribute to drug response and dyslipidemia. Ideally, steady-state plasma concentrations and quantitative lifestyle covariates would be considered. In the case of those patients overdosed to psychotropics by virtue of their low functional metabolic reserve, a concomitant assay of all present drugs would have been informative. In addition, the minor metabolic pathway coefficients in the drug-specific indices have been estimated to have a value of 0.25 for the purposes of this study. Precise pharmacologically assayed values between 0.0 and 1.0 could be determined for each drug and metabolic pathway. Covariates such as drug interactions and drug properties such as self-inhibition or active metabolites should also be modeled into the indices as described in our methodology manuscript [38
]. Indeed, it is known that certain antipsychotics, such as clozapine and olanzapine, have inherent metabolic side effects that could exacerbate dyslipidemias in patients so treated, which could be accounted for by drug covariates [4
]. Finally, our sample size, particularly in the case of the drug-specific analyses, would need to be larger in order to draw conclusions with a higher degree of confidence. Nevertheless, the strength of our statistical associations, the consistency with previously published works, and the congruence with models of pharmacokinetic and biological activity render our discoveries novel and noteworthy.
In conclusion, the results of this study demonstrate the utility and clinical relevance of the CYP450 combinatory drug metabolism indices. Ranking an individual relative to a population, as described by our group [38
], represents a potential tool for assessing risk of dyslipidemia in MDD patients being treated with antidepressants and antipsychotics. In addition, the drug-specific indices appear promising as quantitative measures assisting in prescription by modeling a variable of potential relevance to an individual’s risk of drug-related dyslipidemia. The clinical benefits of CYP450 genotyping in psychiatry are growing increasingly clear, and in this case, considering an array of genes together has proven the most effective in evaluating risk of adverse drug reactions. By nature, the CYP450 combinatory indices are easily generalized, allowing for the addition of further genes, such as CYP1A2
, and CYP3A5
, as well as new alleles such as CYP2C19
*17. Research indicates that the appearance of any one of the many components of metabolic syndrome significantly increases the likelihood of developing metabolic syndrome and eventually dangerous and costly cardiovascular disease [48
]. Any information to assist in predicting which psychotropic medications may result in dyslipidemia for a given patient would represent a substantial step forward in controlling these widespread, debilitating and costly side effects. In this study, the CYP450 combinatory indices and substrate specific indices have proven more effective than any single gene score when determining risk of dyslipidemia in depressed patients treated with psychotropics. We anticipate that this combinatory approach will continue to yield significant clinical pharmacogenetic applications.