Clinically Relevant Interactions between Newer Antidepressants and Second-Generation Antipsychotics

INTRODUCTION: Combinations of newer antidepressants and second-generation antipsychotics (SGAs) are frequently used by clinicians. Pharmacokinetic drug interaction (PK DI) and poorly understood pharmacodynamic (PD) drug interaction (PD DI) can occur between them. AREAS COVERED: This paper comprehensively reviews PD DI and PK DI studies. EXPERT OPINION: More PK DI studies are needed to better establish dose correction factors after adding fluoxetine and paroxetine to aripiprazole, iloperidone and risperidone. Further PK DI studies and case reports are also needed to better establish the need for dose correction factors after adding i) fluoxetine to clozapine, lurasidone, quetiapine and olanzapine; ii) paroxetine to olanzapine; iii) fluvoxamine to asenapine, aripiprazole, iloperidone, lurasidone, olanzapine, quetiapine and risperidone; iv) high sertraline doses to aripiprazole, clozapine, iloperidone and risperidone: v) bupropion and duloxetine to aripiprazole, clozapine, iloperidone and risperidone; and vi) asenapine to paroxetine and venlafaxine. Possible beneficial PD DI effects occur after adding SGAs to newer antidepressants for treatment-resistant major depressive and obsessive-compulsive disorders. The lack of studies combining newer antidepressants and SGAs in psychotic depression is worrisome. PD DIs between newer antidepressants and SGAs may be more likely for mirtazapine and bupropion. Adding selective serotonin reuptake inhibitors and SGAs may increase QTc interval and may very rarely contribute to torsades de pointes

Identification, Replication, and Functional Fine-Mapping of Expression Quantitative Trait Loci in Primary Human Liver Tissue

The discovery of expression quantitative trait loci (“eQTLs”) can help to unravel genetic contributions to complex traits. We identified genetic determinants of human liver gene expression variation using two independent collections of primary tissue profiled with Agilent (n = 206) and Illumina (n = 60) expression arrays and Illumina SNP genotyping (550K), and we also incorporated data from a published study (n = 266). We found that ∼30% of SNP-expression correlations in one study failed to replicate in either of the others, even at thresholds yielding high reproducibility in simulations, and we quantified numerous factors affecting reproducibility. Our data suggest that drug exposure, clinical descriptors, and unknown factors associated with tissue ascertainment and analysis have substantial effects on gene expression and that controlling for hidden confounding variables significantly increases replication rate. Furthermore, we found that reproducible eQTL SNPs were heavily enriched near gene starts and ends, and subsequently resequenced the promoters and 3′UTRs for 14 genes and tested the identified haplotypes using luciferase assays. For three genes, significant haplotype-specific in vitro functional differences correlated directly with expression levels, suggesting that many bona fide eQTLs result from functional variants that can be mechanistically isolated in a high-throughput fashion. Finally, given our study design, we were able to discover and validate hundreds of liver eQTLs. Many of these relate directly to complex traits for which liver-specific analyses are likely to be relevant, and we identified dozens of potential connections with disease-associated loci. These included previously characterized eQTL contributors to diabetes, drug response, and lipid levels, and they suggest novel candidates such as a role for NOD2 expression in leprosy risk and C2orf43 in prostate cancer. In general, the work presented here will be valuable for future efforts to precisely identify and functionally characterize genetic contributions to a variety of complex traits