Obesity-related drug-metabolizing enzyme expression alterations in the human liver

OBJECTIVE

Implications of obesity extend beyond the association with various health conditions, impacting physiological changes that affect the liver and the activity of metabolizing enzymes. Given the prevalence of obesity and the risk for drug-drug interactions owing to the comorbidity burden, the current drug dosage recommendations may need reevaluation for patients with obesity. This study evaluated the implications of obesity on the gene expression of hepatic drug-metabolizing enzymes. As drug clearance is an essential pharmacokinetic parameter for maintaining drug dosing regimens, investigating alterations in metabolizing enzymes expression is a critical step.

METHODS

Human liver samples were collected post-mortem from 32 individuals and classified into the control (18.5 ≤ BMI <25 kg/m2; range 18.9-24.4 kg/m2; median 22.3 kg/m2) and the study group (BMI ≥25 kg/m2; range 25.1-55.5 kg/m2; median 31.2 kg/m2). Real-time quantitative PCR was performed for the analysis of 168 drug-metabolizing enzymes.

RESULTS

Our studies revealed several potential physiologically relevant differences, but the statistical significance was reached only for ALDH3B1, PTGS1, and CEL (all being up-regulated in the study group).

CONCLUSIONS

The study adds to our understanding of the mechanisms of pharmacokinetic changes in overweight and obesity. The findings require further exploration on the protein level, through proteomic and functional studies.

Screening study for genetic polymorphisms affecting pharmacokinetics of talniflumate

Talniflumate is a phthalidyl ester of niflumic acid, which has potent analgesic and anti-inflammatory effects and is widely used to treat inflammatory disorders, such as rheumatoid arthritis. To screen the possible genetic factors affecting the pharmacokinetics (PK) of talniflumate, 23 male Korean volunteers were enrolled from two separate bioequivalence studies. All subjects received 740 mg (two tablets) talniflumate in a standard 2×2 cross-over model in a randomized order. For the genetic study, PK parameters of the reference drug were used. We used Illumina Human610Quad v1.0 DNA Analysis BeadChip for whole genome single nucleotide polymorphism (SNP) analysis and whole genome genotyping data were processed by linear regression analysis for PK parameters. Whole genome analysis revealed 1498 significant SNPs (P < 0.0001) for Cmax, 65 significant SNPs (P < 0.0001) for T max, and 1491 significant SNPs (P < 0.0001) for AUC inf. For clinical pharmacological purposes, we selected SNPs from drug metabolizing enzymes and transporters, and analyzed the PK parameters of various genotypes. Two SNPs (rs11165069 from ABCA4 (p=0.00002); rs17847036 from CYP2C9 (p=0.000001)) showed significant associations with talniflumate C max. In the T max group, two SNPs (rs3787555 from CYP24A1 (p=0.00035); rs2275034 from ABCA4 (p=0.000587)) showed significant associations with talniflumate T max. In the AUC inf group, two SNPs (rs11165069 from ABCA4 (p=0.00002); rs12461006 from SLC1A6 (p=0.00008)) exhibited significant associations with talniflumate absorption. These results show that genetic factors could affect the PK parameters, and provide information that may be used in the development of personalized talniflumate therapy.