The genetic background for interindividual variability of the polymorphic CYP2D6 enzyme activity remains incompletely understood and the role of NFIB genetic polymorphism for this variability was evaluated in this translational study. We investigated the effect of NFIB expression in vitro using 3D liver spheroids, Huh7 cells, and the influence of the NFIB polymorphism on metabolism of risperidone in patients in vivo. We found that NFIB regulates several important pharmacogenes, including CYP2D6. NFIB inhibited CYP2D6 gene expression in Huh7 cells and NFIB expression in livers was predominantly nuclear and reduced at the mRNA and protein level in carriers of the NFIB rs28379954 T>C allele. Based on 604 risperidone treated patients genotyped for CYP2D6 and NFIB, we found that the rate of risperidone hydroxylation was elevated in NFIB rs28379954 T>C carriers among CYP2D6 normal metabolizers, resulting in a similar rate of drug metabolism to what is observed in CYP2D6 ultrarapid metabolizers, with no such effect observed in CYP2D6 poor metabolizers lacking functional enzyme. The results indicate that NFIB constitutes a novel nuclear factor in the regulation of cytochrome P450 genes, and that its polymorphism is a predictor for the rate of CYP2D6 dependent drug metabolism in vivo.

Clinical response of clozapine is closely associated with serum concentration.Although tobacco smoking is the key environmental factor underlying interindividual variability in clozapine metabolism, recent genome-wide studies suggest that CYP1A and NFIB genetic variants may also be of significant importance, but their quantitative impact is unclear. We investigated the effects of the rs2472297 C>T (CYP1A) and rs28379954 T>C (NFIB) polymorphisms on serum concentrations in smokers and nonsmokers. The study retrospectively included 526 patients with known smoking habits (63.7% smokers) from a therapeutic drug monitoring service in Norway. Clozapine dose-adjusted concentrations (C/D) and patient proportions with subtherapeutic levels (<1070 nmol/L) were compared between CYP1A/NFIB variant allele carriers and homozygous wild-type carriers (noncarriers), in both smokers and nonsmokers. Clozapine C/D was reduced in patients carrying CYP1A-T and NFIB-C variants versus noncarriers, both among smokers (−48%; p < 0.0001) and nonsmokers (−35%; p = 0.028). Patients who smoke carrying CYP1A-T and NFIB-C variants had a 66% reduction in clozapine C/D versus nonsmoking noncarriers (p < 0.0001). The patient proportion with subtherapeutic levels was 2.9-fold higher in patients who smoke carrying NFIB-C and CYP1A-T variants versus nonsmoking noncarriers (p < 0.0001). In conclusion, CYP1A and NFIB variants have significant and additive impact on clozapine dose requirements for reaching target serum concentrations. Patients who smoke carrying the studied CYP1A and NFIB variants, comprising 2.5% of the study population, may need threefold higher doses to prevent risk of clozapine undertreatment.The results suggest that pre-emptive genotyping of NFIB and CYP1A may be utilized to guide clozapine dosing and improve clinical outcomes in patients with treatment-resistant schizophrenia.

Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR < 0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.