Effects of pioglitazone in combination with metformin or a sulfonylurea compared to a fixed-dose combination of metformin and glibenclamide in patients with type 2 diabetes.
ABSTRACT This study was designed to compare the effectiveness of co-administration of pioglitazone with metformin or a sulfonylurea (SU), with a fixed-dose combination of metformin and glibenclamide on glycemic control and beta-cell function in patients with type 2 diabetes.
Patients (n = 250) treated with metformin (<or=3 g/day) or an SU as monotherapy for >3 months and with glycosylated hemoglobin (HbA(1c)) between 7.5% and 11% inclusive were randomized to receive either pioglitazone (15-30 mg/day) as add-on therapy to metformin or an SU or a fixed-dose combination of metformin (400 mg) and glibenclamide (2.5 mg) (up to three tablets per day) for 6 months. HbA(1c) and fasting plasma glucose (FPG) were measured at baseline and 2, 4, and 6 months. C-peptide levels were measured at baseline and 6 months, and post-challenge glucose and insulin responses were measured.
After 6 months, pioglitazone-based and fixed-dose metformin + glibenclamide resulted in similar reductions in HbA(1c) (-1.11% vs. -1.29%, respectively; P = 0.192) and FPG (-2.13 vs. -1.81 mmol/L, respectively; P = 0.370). Patients treated with pioglitazone for 6 months had significantly reduced C-peptide levels compared with baseline (-0.09 nmol/L, P = 0.001), while patients receiving fixed-dose metformin + glibenclamide combination had slightly increased C-peptide levels (+0.04 nmol/L, P = 0.08). Pioglitazone treatment also improved post-challenge insulin responses.
Co-administration of pioglitazone with metformin or an SU is an effective alternative to fixed-dose metformin + glibenclamide combination for patients with type 2 diabetes. The complementary effects of pioglitazone with either metformin or an SU may also have the potential to preserve beta-cell function and delay the progression of type 2 diabetes.
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ABSTRACT: Oral antidiabetics have comparable safety and efficacy when used as fixed-dose combination therapies (FDCT) or loose-pill combination therapies (LPCT) for patients with T2DM. To evaluate alternative outcomes to safety and efficacy with FDCT, a systematic review of literature was conducted. Searches of Medline/Embase databases from 1998 to 2009 used predefined terms: 'fixed-dose combination', 'loose-dose combination' and 'diabetes'. Abstracts were reviewed from ISPOR, ADA, and EASD meetings (1998-2009). T2DM studies reporting adherence, patient-reported outcomes, costs, resource use or cost effectiveness were included. Seventeen studies met the search criteria. Seven studies reported adherence. Adherence was 10-13% higher for FDCT than LPCT in patients starting combination therapy. Adherence decreased 1.5% and 10.0% when switching from monotherapy to combination therapy for FDCT and LPCT respectively (p < 0.001). Switching to FDCT increased adherence 3.5%-12.4%, while remaining on LPCT changed adherence -1.5% to 5.0% (p < 0.005). For patients newly initiating OAD medication, one study found no adherence advantage for FDCT compared with monotherapy or LPCT. Five RCTs reported treatment satisfaction. Four publications reported patients preferred FDCT using the Diabetes Treatment Satisfaction Questionnaire (DTSQ). One publication reported improved satisfaction for one DTSQ subscale. Five abstracts reported economic outcomes. Two abstracts determined patients on FDCT used fewer healthcare resources and had decreased direct monthly healthcare costs versus LPCT. Two cost-effectiveness analyses determined clinical benefits from clinical trials translate into cost savings and increased life expectancy. One budget impact model reported minimal budget impact. (1) There was limited published literature identified in this review. (2) FDCT are oral medications; these findings may only be relevant to those individuals taking an oral antidiabetic therapy. (3) Publication and reporting biases may exist. The published literature suggested that T2DM patients treated with FDCT may have better adherence, improved satisfaction, and lower direct medical costs, compared to those treated with LPCT.Current Medical Research and Opinion 04/2011; 27(6):1157-68. DOI:10.1185/03007995.2011.570745 · 2.37 Impact Factor
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ABSTRACT: Background Biological systems are robust and complex to maintain stable phenotypes under various conditions. In these systems, drugs reported the limited efficacy and unexpected side-effects. To remedy this situation, many pharmaceutical laboratories have begun to research combination drugs and some of them have shown successful clinical results. Complementary action of multiple compounds could increase efficacy as well as reduce side-effects through pharmacological interactions. However, experimental approach requires vast cost of preclinical experiments and tests as the number of possible combinations of compound dosages increases exponentially. Computer model-based experiments have been emerging as one of the most promising solutions to cope with such complexity. Though there have been many efforts to model specific molecular pathways using qualitative and quantitative formalisms, they suffer from unexpected results caused by distant interactions beyond their localized models. Results In this work, we propose a rule-based multi-scale modelling platform. We have tested this platform with Type 2 diabetes (T2D) model, which involves the malfunction of numerous organs such as pancreas, circulation system, liver, and adipocyte. We have extracted T2D-related 190 rules by manual curation from literature, pathway databases and converting from different types of existing models. We have simulated twenty-two T2D drugs. The results of our simulation show drug effect pathways of T2D drugs and whether combination drugs have efficacy or not and how combination drugs work on the multi-scale model. Conclusions We believe that our simulation would help to understand drug mechanism for the drug development and provide a new way to effectively apply existing drugs for new target. It also would give insight for identifying effective combination drugs.BMC Medical Informatics and Decision Making 04/2013; 13(1). DOI:10.1186/1472-6947-13-S1-S4 · 1.50 Impact Factor