The role of genetic variants in CYP2C8, LPIN1, PPARGC1A and PPARγ on the trough steady-state plasma concentrations of rosiglitazone and on glycosylated haemoglobin A1c in type 2 diabetes

Selected musculoskeletal disorders in patients with thyroid dysfunction, diabetes, and obesity

Many medical conditions affect the skeletal system and constitute independent risk factors for fractures. The action of thyroid hormones is necessary to maintain adequate development, mineralization, and bone strength. Untreated hyperthyroidism can lead to a decrease in bone mineral density (BMD), osteoporosis, and pathological fractures. In hypothyroidism, the changes in the quality of bone structure lead to an increase in the frequency of fractures. Excessive body weight negatively impacts fracture risk, increases the risk of osteoarthritis and accelerates the development of rheumatoid arthritis and osteoporosis. Type 1 and type 2 diabetes are associated with an increased risk of bone fractures despite different etiopathogenesis due to the duration of the disease and the pro-inflammatory state, the incorporation of advanced glycation end products (AGEs) into the bone matrix, and microvascular disorders. This study summarizes the current literature on the influence of thyroid dysfunction, obesity, and diabetes on the skeletal system.

Pharmacogenomic Studies of Current Antidiabetic Agents and Potential New Drug Targets for Precision Medicine of Diabetes

Diabetes is a major threat to people's health and has become a burden worldwide. Current drugs for diabetes have limitations, such as different drug responses among individuals, failure to achieve glycemic control, and adverse effects. Exploring more effective therapeutic strategies for patients with diabetes is crucial. Currently pharmacogenomics has provided potential for individualized drug therapy based on genetic and genomic information of patients, and has made precision medicine possible. Responses and adverse effects to antidiabetic drugs are significantly associated with gene polymorphisms in patients. Many new targets for diabetes also have been discovered and developed, and even entered clinical trial phases. This review summarizes pharmacogenomic evidence of some current antidiabetic agents applied in clinical settings, and highlights potential drugs with new targets for diabetes, which represent a more effective treatment in the future.

Role of Genetic Variations in the Hepatic Handling of Drugs

The liver plays a pivotal role in drug handling due to its contribution to the processes of detoxification (phases 0 to 3). In addition, the liver is also an essential organ for the mechanism of action of many families of drugs, such as cholesterol-lowering, antidiabetic, antiviral, anticoagulant, and anticancer agents. Accordingly, the presence of genetic variants affecting a high number of genes expressed in hepatocytes has a critical clinical impact. The present review is not an exhaustive list but a general overview of the most relevant variants of genes involved in detoxification phases. The available information highlights the importance of defining the genomic profile responsible for the hepatic handling of drugs in many ways, such as (i) impaired uptake, (ii) enhanced export, (iii) altered metabolism due to decreased activation of prodrugs or enhanced inactivation of active compounds, and (iv) altered molecular targets located in the liver due to genetic changes or activation/downregulation of alternative/compensatory pathways. In conclusion, the advance in this field of modern pharmacology, which allows one to predict the outcome of the treatments and to develop more effective and selective agents able to overcome the lack of effect associated with the existence of some genetic variants, is required to step forward toward a more personalized medicine.

Pharmacogenetics of type 2 diabetes mellitus, the route toward tailored medicine

Type 2 diabetes mellitus (T2DM) is a chronic disease that has reached the levels of a global epidemic. In order to achieve optimal glucose control, it is often necessary to rely on combination therapy of multiple drugs or insulin because uncontrolled glucose levels result in T2DM progression and enhanced risk of complications and mortality. Several antihyperglycemic agents have been developed over time, and T2DM pharmacotherapy should be prescribed based on suitability for the individual patient's characteristics. Pharmacogenetics is the branch of genetics that investigates how our genome influences individual responses to drugs, therapeutic outcomes, and incidence of adverse effects. In this review, we evaluated the pharmacogenetic evidences currently available in the literature, and we identified the top informative genetic variants associated with response to the most common anti-diabetic drugs: metformin, DPP-4 inhibitors/GLP1R agonists, thiazolidinediones, and sulfonylureas/meglitinides. Overall, we found 40 polymorphisms for each drug class in a total of 71 loci, and we examined the possibility of encouraging genetic screening of these variants/loci in order to critically implement decision-making about the therapeutic approach through precision medicine strategies. It is possible then to anticipate that when the clinical practice will take advantage of the genetic information of the diabetic patients, this will provide a useful resource for the prevention of T2DM progression, enabling the identification of the precise drug that is most likely to be effective and safe for each patient and the reduction of the economic impact on a global scale.

A Genetic Score Associates With Pioglitazone Response in Patients With Non-alcoholic Steatohepatitis

Pioglitazone is used effectively to treat non-alcoholic steatohepatitis (NASH), but there is marked variability in response. This study examined whether genetic variation contributes to pioglitazone response variability in patients with NASH. This genetic substudy includes 55 participants of a randomized controlled trial designed to determine the efficacy of long-term pioglitazone treatment in patients with NASH. The primary outcome of the clinical trial was defined as ≥2-point reduction in the non-alcoholic fatty liver disease activity score (NAS). In this substudy, single nucleotide polymorphisms (SNPs) in putative candidate genes were tested for association with primary and secondary outcomes. A genetic response score was constructed based on the sum of response alleles for selected genes. The genetic response score was significantly associated with achievement of the primary outcome (odds ratio 1.74; 95% CI 1.27–2.54; p = 0.0015). ADORA1 rs903361 associated with resolution of NASH (p = 0.0005) and change in the ballooning score among Caucasian and Hispanic patients (p = 0.0005). LPL rs10099160 was significantly associated with change in ALT (p = 0.0005). The CYP2C8^∗3 allele, which confers faster pioglitazone clearance in allele carriers, was associated with change in fibrosis score (p = 0.026). This study identified key genetic factors that explain some of the inter-individual variability in response to pioglitazone among patients with NASH.

Effects of metformin, rosiglitazone and insulin on bone metabolism in patients with type 2 diabetes

BACKGROUND

Fracture risk is increased in individuals with type 2 diabetes (T2D). The pathophysiological mechanisms accentuating fracture risk in T2D are convoluted, incorporating factors such as hyperglycaemia, insulinopenia, and antidiabetic drugs. The objectives of this study were to assess whether different insulin regimens, metformin and rosiglitazone influence bone metabolism. We explored if the concentration of metformin and rosiglitazone in blood or improved glycaemic control altered bone turnover.

METHODS

Two-year clinical trial designed to investigate effects of antidiabetic treatment in 371 T2D patients. Participants were randomized to short or long-acting human insulin (non-blinded) and then further randomized to metformin + placebo, rosiglitazone + placebo, metformin + rosiglitazone or placebo + placebo (blinded). Fasting bone turnover markers (BTM) representing bone resorption (CTX) and formation (PINP) including HbA1c were measured at baseline and after 3, 12 and 24 months. Trough steady-state plasma concentrations of metformin and rosiglitazone were measured after 3, 6 and 9 months of treatment. Associations between treatments and BTMs during the follow-up of the trial were analysed in mixed-effects models that included adjustments for age, gender, BMI, renal function and repeated measures of HbA1c.

RESULTS

BTMs increased from baseline to month 12 and remained higher at month 24, with CTX and PINP increasing 28.5% and 23.0% (all: p < 0.001), respectively. Allocation of insulin regimens was not associated with different levels of BTMs. Metformin and metformin + rosiglitazone but not rosiglitazone alone were associated with lower bone formation (PINP). Neither metformin nor rosiglitazone plasma concentrations was associated with BTMs. HbA1c was inversely associated with CTX but not P1NP.

CONCLUSIONS

The choice of insulin treatment is not influencing BTMs, metformin treatment may decrease BTMs, and improvement of glycaemic control may influence bone resorption activity.

Cytochrome P450 in Pharmacogenetics: An Update

Interindividual variability in drug disposition is a major cause of lack of efficacy and adverse effects of drug therapies. The majority of hepatically cleared drugs are metabolized by cytochrome P450 (CYP) enzymes, mainly in families CYP1, CYP2, and CYP3. Genes encoding these enzymes are highly variable with allele distribution showing considerable differences between populations. Genetic variability of especially CYP2C9, CYP2C19, CYP2D6, and CYP3A5 is known to have clear clinical impact on drugs that are metabolized by these enzymes. CYP1A2, CYP2A6, CYP2B6, CYP2C8, and CYP3A4 all show variability that affects pharmacokinetics of drugs as well, but so far the evidence regarding their clinical implications is not as conclusive. In this review, we provide an up-to-date summary of the pharmacogenetics of the major human drug-metabolizing CYP enzymes, focusing on clinically significant examples.

CYP2C8 and SLCO1B1 Variants and Therapeutic Response to Thiazolidinediones in Patients With Type 2 Diabetes

OBJECTIVE

Thiazolidinediones (TZDs) are putatively transported into the liver by OATP1B1 (encoded by SLCO1B1) and metabolized by CYP450 2C8 enzyme (encoded by CYP2C8). While CYP2C8*3 has been shown to alter TZD pharmacokinetics, it has not been shown to alter efficacy.

RESEARCH DESIGN AND METHODS

We genotyped 833 Scottish patients with type 2 diabetes treated with pioglitazone or rosiglitazone and jointly investigated association of variants in these two genes with therapeutic outcome.

RESULTS

The CYP2C8*3 variant was associated with reduced glycemic response to rosiglitazone (P = 0.01) and less weight gain (P = 0.02). The SLCO1B1 521T>C variant was associated with enhanced glycemic response to rosiglitazone (P = 0.04). The super responders defined by combined genotypes at CYP2C8 and SLCO1B1 had a 0.39% (4 mmol/mol) greater HbA_1c reduction (P = 0.006) than the poor responders. Neither of the variants had a significant impact on pioglitazone response.

CONCLUSIONS

These results show that variants in CYP2C8 and SLCO1B1 have a large clinical impact on the therapeutic response to rosiglitazone and highlight the importance of studying transporter and metabolizing genes together in pharmacogenetics.

Precision medicine: The future in diabetes care?

Personalized medicine aims at better targeting therapeutic intervention to the individual to maximize benefit and minimize harm. Type 2 diabetes (T2D) is a heterogeneous disease from a genetic, pathophysiological and clinical point of view. Thus, the response to any antidiabetic medication may considerably vary between individuals. Numerous glucose-lowering agents, with different mechanisms of action, have been developed, a diversified armamentarium that offers the possibility of a patient-centred therapeutic approach. In the current clinical practice, a personalized approach is only based upon phenotype, taking into account patient and disease individual characteristics. If this approach may help increase both efficacy and safety outcomes, there remains considerable room for improvement. In recent years, many efforts were taken to identify genetic and genotype SNP's (Single Nucleotide Polymorphism's) variants that influence the pharmacokinetics, pharmacodynamics, and ultimately the therapeutic response of oral glucose-lowering drugs. This approach mainly concerns metformin, sulphonylureas, meglitinides and thiazolidinediones, with only scarce data concerning gliptins and gliflozins yet. However, the contribution of pharmacogenetics and pharmacogenomics to personalized therapy still needs to mature greatly before routine clinical implementation is possible. This review discusses both opportunities and challenges of precision medicine and how this new paradigm may lead to a better individualized treatment of T2D.

Role of Cytochrome P450 2C8 in Drug Metabolism and Interactions

During the last 10-15 years, cytochrome P450 (CYP) 2C8 has emerged as an important drug-metabolizing enzyme. CYP2C8 is highly expressed in human liver and is known to metabolize more than 100 drugs. CYP2C8 substrate drugs include amodiaquine, cerivastatin, dasabuvir, enzalutamide, imatinib, loperamide, montelukast, paclitaxel, pioglitazone, repaglinide, and rosiglitazone, and the number is increasing. Similarly, many drugs have been identified as CYP2C8 inhibitors or inducers. In vivo, already a small dose of gemfibrozil, i.e., 10% of its therapeutic dose, is a strong, irreversible inhibitor of CYP2C8. Interestingly, recent findings indicate that the acyl-β-glucuronides of gemfibrozil and clopidogrel cause metabolism-dependent inactivation of CYP2C8, leading to a strong potential for drug interactions. Also several other glucuronide metabolites interact with CYP2C8 as substrates or inhibitors, suggesting that an interplay between CYP2C8 and glucuronides is common. Lack of fully selective and safe probe substrates, inhibitors, and inducers challenges execution and interpretation of drug-drug interaction studies in humans. Apart from drug-drug interactions, some CYP2C8 genetic variants are associated with altered CYP2C8 activity and exhibit significant interethnic frequency differences. Herein, we review the current knowledge on substrates, inhibitors, inducers, and pharmacogenetics of CYP2C8, as well as its role in clinically relevant drug interactions. In addition, implications for selection of CYP2C8 marker and perpetrator drugs to investigate CYP2C8-mediated drug metabolism and interactions in preclinical and clinical studies are discussed.

Effect of insulin and metformin on methylation and glycolipid metabolism of peroxisome proliferator-activated receptor γ coactivator-1A of rat offspring with gestational diabetes mellitus

OBJECTIVE

To discuss the effect of insulin and metformin on a methylation and glycolipid metabolism of peroxisome proliferator-activated receptor γ coactivator-1A (PPARGC1A) of rat offspring with gestational diabetes mellitus (GDM).

METHODS

A total of 45 pregnant rats received the intraperitoneal injection of streptozotocin to establish the pregnant rat model of GDM. A total of 21 pregnant rats with GDM were randomly divided into three groups, with 7 rats in each group, namely the insulin group, metformin group and control group. Rats in the insulin group received the abdominal subcutaneous injection of 1 mL/kg recombinant insulin glargine at 18:00 every day. Rats in the metformin group received the intragastric infusion of metformin hydrochloride at 18:00 every day, with the first dose of 300 mg/kg. The doses of two groups were adjusted every 3 d to maintain the blood glucose level at 2.65-7.62 mmol/L. Rats in the control group received the intragastric infusion of 1 mL normal saline at 18:00 every day. After the natural delivery of pregnant rats, 10 offspring rats were randomly selected from each group. At birth, 4 wk and 8 wk after the birth of offspring rats, the weight of offspring rats was measured. The blood glucose level of offspring rats was measured at 4 wk and 8 wk, while the level of serum insulin, triglyceride and leptin was measured at 8 wk.

RESULTS

The weight of offspring rats at birth in the insulin group and metformin group was significantly lower than the one in the control group (P < 0.05), and there was no significant difference at 4 wk and 8 wk among three groups (P > 0.05). The fasting blood glucose and random blood glucose in the insulin group and metformin group at 4 wk and 8 wk were all significantly lower than ones in the control group (P < 0.05); there was no significant difference between the insulin group and metformin group (P > 0.05). The expression of PPARGC1A mRNA in the insulin group and metformin group was significantly higher and the methylation level of PPARGC1A was significantly lower than the one in the control group (P < 0.05); but there was no significant difference between the insulin group and metformin group (P > 0.05). Insulin and leptin at 8 wk in the insulin group and metformin group were significantly higher, while triglyceride was significantly lower than the one in the control group (P < 0.05); triglyceride level in the insulin group was significantly higher than the one in the metformin group (P < 0.05). There was no significant difference in insulin and leptin level between the insulin group and metformin group (P > 0.05).

CONCLUSIONS

GDM can induce the methylation of PPARGC1A of offspring rats to reduce the expression of PPARGC1A mRNA and then cause the disorder of glycolipid metabolism when the offspring rats grow up; the insulin or metformin in the treatment of pregnant rats with GDM can reduce the methylation level of PPARGC1A and thus improve the abnormal glycolipid metabolism of offspring rats.

Impact of CYP2C8*3 polymorphism on in vitro metabolism of imatinib to N-desmethyl imatinib

1. Imatinib is metabolized to N-desmethyl imatinib by CYPs 3A4 and 2C8. The effect of CYP2C8*3 genotype on N-desmethyl imatinib formation was unknown. 2. We examined imatinib N-demethylation in human liver microsomes (HLMs) genotyped for CYP2C8*3, in CYP2C8*3/*3 pooled HLMs and in recombinant CYP2C8 and CYP3A4 enzymes. Effects of CYP-selective inhibitors on N-demethylation were also determined. 3. A single-enzyme Michaelis-Menten model with autoinhibition best fitted CYP2C8*1/*1 HLM (n = 5) and recombinant CYP2C8 kinetic data (median ± SD Ki = 139 ± 61 µM and 149 µM, respectively). Recombinant CYP3A4 showed two-site enzyme kinetics with no autoinhibition. Three of four CYP2C8*1/*3 HLMs showed single-enzyme kinetics with no autoinhibition. Binding affinity was higher in CYP2C8*1/*3 than CYP2C8*1/*1 HLM (median ± SD Km = 6 ± 2 versus 11 ± 2 µM, P=0.04). CYP2C8*3/*3 (pooled HLM) also showed high binding affinity (Km = 4 µM) and single-enzyme weak autoinhibition (Ki = 449 µM) kinetics. CYP2C8 inhibitors reduced HLM N-demethylation by 47-75%, compared to 0-30% for CYP3A4 inhibitors. 4. In conclusion, CYP2C8*3 is a gain-of-function polymorphism for imatinib N-demethylation, which appears to be mainly mediated by CYP2C8 and not CYP3A4 in vitro in HLM.

Stratified medicine for the use of antidiabetic medication in treatment of type II diabetes and cancer: where do we go from here?

At present, the global diabetes epidemic is affecting 347 million individuals, 90% of whom are diagnosed with type II diabetes mellitus (T2DM). T2DM is commonly treated with more than one type of therapy, including oral antidiabetic drugs (OADs) and agents used in the treatment of diabetic complications. Several pharmacological classes of OADs are currently available for the treatment of T2DM, of which insulin secretagogues (i.e. sulphonylureas and meglitinides), insulin sensitizers [thiazolidinediones (TZDs)] and biguanides are the most commonly prescribed. Although many of these OADs have been used for more than half a century in the treatment of T2DM, the pharmacogenomic characteristics of these compounds have only recently been investigated, primarily in retrospective studies. Recent advances in pharmacogenomics have led to the identification of polymorphisms that affect the expression and function of drug-metabolizing enzymes and drug transporters, as well as drug targets and receptors. These polymorphisms have been shown to affect the therapeutic response to and side effects associated with OADs. The aim of this review was to provide an up-to-date summary of some of the pharmacogenomic data obtained from studies of T2DM treatment, with a focus on polymorphisms in genes affecting pharmacokinetics, pharmacodynamics and treatment outcome of the most commonly prescribed OADs. In addition, the implications of pharmacogenomics in the use of the OAD metformin in cancer will be briefly discussed. Finally, we will focus on recent advances in novel 'omics' technologies and discuss how these might aid in the personalized management of T2DM.

Pharmacogenetics of oral antidiabetic drugs

Oral antidiabetic drugs (OADs) are used for more than a half-century in the treatment of type 2 diabetes. Only in the last five years, intensive research has been conducted in the pharmacogenetics of these drugs based mainly on the retrospective register studies, but only a handful of associations detected in these studies were replicated. The gene variants in CYP2C9, ABCC8/KCNJ11, and TCF7L2 were associated with the effect of sulfonylureas. CYP2C9 encodes sulfonylurea metabolizing cytochrome P450 isoenzyme 2C9, ABCC8 and KCNJ11 genes encode proteins constituting ATP-sensitive K(+) channel which is a therapeutic target for sulfonylureas, and TCF7L2 is a gene with the strongest association with type 2 diabetes. SLC22A1, SLC47A1, and ATM gene variants were repeatedly associated with the response to metformin. SLC22A1 and SLC47A1 encode metformin transporters OCT1 and MATE1, respectively. The function of a gene variant near ATM gene identified by a genome-wide association study is not elucidated so far. The first variant associated with the response to gliptins is a polymorphism in the proximity of CTRB1/2 gene which encodes chymotrypsinogen. Establishment of diabetes pharmacogenetics consortia and reduction in costs of genomics might lead to some significant clinical breakthroughs in this field in a near future.