Mou-Ze Liu

Pharmacogenomics and Herb-Drug Interactions: Merge of Future and Tradition

The worldwide using of herb products and the increasing potential herb-drug interaction issue has raised enthusiasm on discovering the underlying mechanisms. Previous review indicated that the interactions may be mediated by metabolism enzymes and transporters in pharmacokinetic pathways. On the other hand, an increasing number of studies found that genetic variations showed some influence on herb-drug interaction effects whereas these genetic factors did not draw much attention in history. We highlight that pharmacogenomics may involve the pharmacokinetic or pharmacodynamic pathways to affect herb-drug interaction. We are here to make an updated review focused on some common herb-drug interactions in association with genetic variations, with the aim to help safe use of herbal medicines in different individuals in the clinic.

Epigenetic perspectives on cancer chemotherapy response.

Epigenetic programs are now widely recognized as being critical to the biological processes of cancer genesis. However, it has not been comprehensively understood how and to what degree they can influence anticancer drugs responses. The development of drugs targeting epigenetic regulation has generated great enthusiasm, with a growing number in clinical development. We highlight here that epigenetic modifications can be involved in the regulation of genes responsible for the absorption, distribution, metabolism and excretion of drugs and for the pathological progression of cancer, thereby affecting anticancer drug responses. The major epigenetic regulatory mechanisms are reviewed, including DNA methylation, miRNA regulation and histone modification, with the aim of promoting rational use of anticancer drugs in the clinic and epigenetic drug development.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67–0.76)] and validation cohorts [0.73 (0.63–0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

IL-3 and CTLA4 gene polymorphisms may influence the tacrolimus dose requirement in Chinese kidney transplant recipients

The highly variable pharmacokinetics and narrow therapeutic window of tacrolimus (TAC) has hampered its clinical use. Genetic polymorphisms may contribute to the variable response, but the evidence is not compelling, and the explanation is unclear. In this study we attempted to find previously unknown genetic factors that may influence the TAC dose requirements. The association of 105 pathway-related single nucleotide polymorphisms (SNPs) with TAC dose-adjusted concentrations (C0/D) was examined at 7, 30 and 90 d post-operation in 382 Chinese kidney transplant recipients. In CYP3A5 non-expressers, the patients carrying the IL-3 rs181781 AA genotype showed a significantly higher TAC logC0/D than those with the AG genotype at 30 and 90 d post-operation (AA vs AG, 2.21±0.06 vs 2.01±0.03, P=0.004; and 2.17±0.06 vs 2.03±0.03, P=0.033, respectively), and than those with the GG genotype at 30 d (AA vs GG, 2.21±0.06 vs 2.04±0.03, P =0.011). At 30 d, the TAC logC0/D in the grouped AG+GG genotypes of CTLA4 rs4553808 was significantly lower than that in the AA genotype (P =0.041) in CYP3A5 expressers, but it was higher (P=0.008) in the non-expressers. We further validated the influence of CYP3A5 rs776746, CYP3A4 rs2242480 and rs4646437 on the TAC C0/D; other candidate SNPs were not associated with the differences in TAC C0/D. In conclusion, genetic polymorphisms in the immune genes IL-3 rs181781 and CTLA4 rs4553808 may influence the TAC C0/D. They may, together with CYP3A5 rs776746, CYP3A4 rs2242480 and rs4646437, contribute to the variation in TAC dose requirements. When conducting individualized therapy with tacrolimus, these genetic factors should be taken into account.

SLCO1B1 Variants and Angiotensin Converting Enzyme Inhibitor (Enalapril) -Induced Cough: a Pharmacogenetic Study

Clinical observations suggest that incidence of cough in Chinese taking angiotensin converting enzyme inhibitors is much higher than other racial groups. Cough is the most common adverse reaction of enalapril. We investigate whether SLCO1B1 genetic polymorphisms, previously reported to be important determinants of inter-individual variability in enalapril pharmacokinetics, are associated with the enalapril-induced cough. A cohort of 450 patients with essential hypertension taking 10 mg enalapril maleate were genotyped for the functional SLCO1B1 variants, 388A > G (Asn130Asp, rs2306283) and 521T > C (Val174Ala, rs4149056). The primary endpoint was cough, which was recorded when participants were bothered by cough and respiratory symptoms during enalapril treatment without an identifiable cause. SLCO1B1 521C allele conferred a 2-fold relative risk of enalapril-induced cough (95% confidence interval [CI] = 1.34–3.04, P = 6.2 × 10−4), and haplotype analysis suggested the relative risk of cough was 6.94-fold (95% CI = 1.30–37.07, P = 0.020) in SLCO1B1*15/*15 carriers. Furthermore, there was strong evidence for a gene-dose effect (percent with cough in those with 0, 1, or 2 copy of the 521C allele: 28.2%, 42.5%, and 71.4%, trend P = 6.6 × 10−4). Our study highlights, for the first time, SLCO1B1 variants are strongly associated with an increased risk of enalapril-induced cough. The findings will be useful to provide pharmacogenetic markers for enalapril treatment.

Assessment of Human Tribbles Homolog 3 Genetic Variation (rs2295490) Effects on Type 2 Diabetes Patients with Glucose Control and Blood Pressure Lowering Treatment

Effects of human tribbles homolog 3 (TRIB3) genetic variation (c.251 A > G, Gln84Arg, rs2295490) on the clinical outcomes of vascular events has not been evaluated in patients with type 2 diabetes after blood pressure lowering and glucose controlling treatment. We did an analysis of a 2 × 2 factorial (glucose control axis and blood pressure lowering axis) randomized controlled clinical trial at 61 centers in China, with a follow-up period of 5 years. The major vascular endpoints were the composites of death from cardio-cerebral vascular diseases, non-fatal stroke and myocardial infraction, new or worsening renal and diabetic eye disease. A total of 1884 participants were included in our research with a 4.8 years median follow-up. For glucose lowering axis, patients with TRIB3 (rs2295490) AA (n = 609) genotype exhibited significantly reduced risk of major vascular events compared with AG + GG (n = 335) genotype carriers (Hazard ratio 0.72, 95% CI 0.55–0.94, p = 0.016), Paradoxically, the risk of vascular events were significantly increased in patients with AA (n = 621) compared to AG + GG (n = 319) genotype for intensive glucose control (Hazard ratio 1.46, 95% CI, 1.06–2.17, 35 p = 0.018). For blood pressure lowering axis, marginally significant difference was found between TRIB3 variant and coronary events. Our findings suggest that good glucose and blood pressure control exhibited greater benefits on vascular outcomes in patients with TRIB3 (rs2295490) G allele.

Corrigendum: Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

This corrects the article DOI: 10.1038/srep42192.

MIR4532 gene variant rs60432575 influences the expression of KCNJ11 and the sulfonylureas-stimulated insulin secretion

PurposeDiabetes mellitus is a major chronic disease and causes over one million deaths. KCNJ11 genetic polymorphisms influence the response of first-line oral antidiabetic agent sulfonylureas. Hsa-miR-4532 correlates with diabetic nephropathy and has a high abundance in urine. MIR4532 rs60452575 G>A variant changes the mature sequence of hsa-miR-4532. We studied whether the genetic polymorphisms of MIR4532 rs60452575 would influence KCNJ11 expression and sulfonylurea-stimulated insulin secretion or not.MethodsTo estimate the influence that rs60452575 G>A variant has on the interaction of hsa-miR-4532 and KCNJ11, we constructed a pmirGLO vector containing 3′ UTR of KCNJ11 and co-transfected it with wild-type and mutant hsa-miR-4532 mimics into HEK293 cells; and we overexpressed wild-type and mutant hsa-miR-4532 mimics into HEK293 cells and MIN6 cells to access its effects on KCNJ11 expression and response of sulfonylureas.ResultsMIR4532 rs60452575 G>A variant appeared to disrupt the repression of KCNJ11 expression in both cell lines, and reduce the sulfonylurea-stimulated insulin secretion by breaking the binding of the hsa-miR-4532 to 3′ UTR of KCNJ11 in MIN6 cells.ConclusionsOur study indicates that MIR4532 rs60452575 variant influences KCNJ11 expression and sulfonylurea response. It might be a potential predictive factor of sulfonylureas therapy.

Drug-induced hyperglycaemia and diabetes: pharmacogenomics perspectives

Drug-induced diabetes is widely reported in clinical conditions, and it is becoming a global issue because of its potential to increase the risk of severe cardiovascular complications. However, which drug mechanisms exert their diabetogenic effects and why the effects present significant inter-individual differences remain largely unknown. Pharmacogenomics, which is the study of how genomic variation influences drug responses, provides an explanation for individual differences in drug-induced diabetes. We highlight that pharmacogenomics can be involved in regulating the expression of genes in signaling pathways related to the pharmacokinetics or pharmacodynamics of drugs or the pathogenesis of diabetes, contributing to the differences in drug-induced glucose impairment. The pharmacogenomics studies of the major diabetogenic drugs are reviewed, including calcineurin inhibitors, antipsychotics, hormones, and antihypertensive drugs. We intend to elucidate the genetic basis of drug-induced diabetes and pave the way for the precise use of these drugs in the clinic.

Corrigendum to “Assessment of Human Tribbles Homolog 3 Genetic Variation (rs2295490) Effects on Type 2 Diabetes Patients with Glucose Control and Blood Pressure Lowering Treatment” [EBioMedicine 13 (2016) 181–189]

The author wishes to point out that there is an error in the Abstract of this article, where (Hazard ratio 1.46, 95% CI, 1.06–2.17, 35 p = 0.018) should read as (Hazard ratio 1.46, 95% CI, 1.06–2.17, p = 0.018). The correct correspondence address is Prof. Wei Zhang, Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiangya Rode, Kaifu district, Changsha, Hunan 410008, P.R. China. Phone: 0731-8480-5380, Fax: 0731-8235-4476, Email address: yjsd2003@163. com.