Treatment with sitagliptin or metformin does not increase body weight despite predicted reductions in urinary glucose excretion

Dipeptidyl peptidase IV inhibition delays developmental programming of obesity and metabolic disease in male offspring of obese mothers

Maternal obesity programs the offspring to metabolic diseases later in life; however, the mechanisms of programming are yet unclear, and no strategies exist for addressing its detrimental transgenerational effects. Obesity has been linked to dipeptidyl peptidase IV (DPPIV), an adipokine, and treatment of obese individuals with DPPIV inhibitors has been reported to prevent weight gain and improve metabolism. We hypothesized that DPPIV plays a role in maternal obesity-mediated programming. We measured plasma DPPIV activity in human maternal and cord blood samples from normal-weight and obese mothers at term. We found that maternal obesity increases maternal and cord blood plasma DPPIV activity but only in male offspring. Using two non-human primate models of maternal obesity, we confirmed the activation of DPPIV in the offspring of obese mothers. We then created a mouse model of maternal high-fat diet (HFD)-induced obesity, and found an early-life increase in plasma DPPIV activity in male offspring. Activation of DPPIV preceded the progression of obesity, glucose intolerance and insulin resistance in male offspring of HFD-fed mothers. We then administered sitagliptin, DPPIV inhibitor, to regular diet (RD)- and HFD-fed mothers, starting a week prior to breeding and continuing throughout pregnancy and lactation. We found that sitagliptin treatment of HFD-fed mothers delayed the progression of obesity and metabolic diseases in male offspring and had no effects on females. Our findings reveal that maternal obesity dysregulates plasma DPPIV activity in males and provide evidence that maternal inhibition of DPPIV has potential for addressing the transgenerational effects of maternal obesity.

FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective

The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision‐making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug‐specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study

Recent clinical successes of immune checkpoint modulators have unleashed a wave of enthusiasm associated with cancer immunotherapy. However, this enthusiasm is dampened by persistent translational hurdles associated with cancer immunotherapy that mirror the broader pharmaceutical industry. Specifically, the challenges associated with drug discovery and development stem from an incomplete understanding of the biological mechanisms in humans that are targeted by a potential drug and the financial implications of clinical failures. Sustaining progress in expanding the clinical benefit provided by cancer immunotherapy requires reliably identifying new mechanisms of action. Along these lines, quantitative and systems pharmacology (QSP) has been proposed as a means to invigorate the drug discovery and development process. In this review, I discuss two central themes of QSP as applied in the context of cancer immunotherapy. The first theme focuses on a network-centric view of biology as a contrast to a "one-gene, one-receptor, one-mechanism" paradigm prevalent in contemporary drug discovery and development. This theme has been enabled by the advances in wet-lab capabilities to assay biological systems at increasing breadth and resolution. The second theme focuses on integrating mechanistic modeling and simulation with quantitative wet-lab studies. Drawing from recent QSP examples, large-scale mechanistic models that integrate phenotypic signaling-, cellular-, and tissue-level behaviors have the potential to lower many of the translational hurdles associated with cancer immunotherapy. These include prioritizing immunotherapies, developing mechanistic biomarkers that stratify patient populations and that reflect the underlying strength and dynamics of a protective host immune response, and facilitate explicit sharing of our understanding of the underlying biology using mechanistic models as vehicles for dialogue. However, creating such models require a modular approach that assumes that the biological networks remain similar in health and disease. As oncogenesis is associated with re-wiring of these biological networks, I also describe an approach that combines mechanistic modeling with quantitative wet-lab experiments to identify ways in which malignant cells alter these networks, using Interleukin-12 as an example. Collectively, QSP represents a new holistic approach that may have profound implications for how translational science is performed.

Acute oral metformin enhances satiation and activates brainstem nesfatinergic neurons

OBJECTIVE

The study was designed to determine metformin effects on meal pattern, gastric emptying, energy expenditure, and to identify metformin-sensitive neurons and their phenotype.

METHODS

This study was performed on C57BL/6J and obese/diabetic (db/db) mice. Metformin (300 mg/kg) was administered by oral gavage. Food intake, meal pattern, oxygen consumption (VO2 ), and carbon dioxide production (VCO2 ) were obtained using an Oxylet Physiocage System. Gastric emptying assay and real-time RT-PCR from dorsal vagal complex extracts were also performed. C-Fos expression was used as a marker of neuronal activation. Phenotypic characterization of activated neurons was performed using either proopiomelanocortin (POMC)-Tau-Topaz GFP transgenic mice or NUCB2/nesfatin-1 and tyrosine hydroxylase (TH) labeling.

RESULTS

Acute per os metformin treatment slowed down gastric emptying, reduced meal size, but not meal number in a leptin-independent manner, and transiently decreased energy expenditure in a leptin-dependent manner. Metformin specifically activated central circuitry within the brainstem, independently of vagal afferents. Finally, while POMC neurons seemed sparsely activated, we report that a high proportion of the c-Fos positive cells were nesfatinergic neurons, some of which coexpressing TH.

CONCLUSIONS

Altogether, these results show that metformin modifies satiation by activating brainstem circuitry and suggest that NUCB2/nesfatin-1 could be involved in this metformin effect.

Cardiovascular effects of dipeptidyl peptidase-4 inhibitors: from risk factors to clinical outcomes

Dipeptidyl peptidase-4 (DPP-4) inhibitors (gliptins) are oral incretin-based glucose-lowering agents with proven efficacy and safety in the management of type 2 diabetes mellitus (T2DM). In addition, preclinical data and mechanistic studies suggest a possible additional non-glycemic beneficial action on blood vessels and the heart, via both glucagon-like peptide-1-dependent and glucagon-like peptide-1-independent effects. As a matter of fact, DPP-4 inhibitors improve several cardiovascular risk factors: they improve glucose control (mainly by reducing the risk of postprandial hyperglycemia) and are weight neutral; may lower blood pressure somewhat; improve postprandial (and even fasting) lipemia; reduce inflammatory markers; diminish oxidative stress; improve endothelial function; and reduce platelet aggregation in patients with T2DM. In addition, positive effects on the myocardium have been described in patients with ischemic heart disease. Results of post hoc analyses of phase 2/3 controlled trials suggest a possible cardioprotective effect with a trend (sometimes significant) toward lower incidence of major cardiovascular events with sitagliptin, vildagliptin, saxagliptin, linagliptin, or alogliptin compared with placebo or other active glucose-lowering agents. However, the definite relationship between DPP-4 inhibition and better cardiovascular outcomes remains to be proven. Major prospective clinical trials involving various DPP-4 inhibitors with predefined cardiovascular outcomes are under way in patients with T2DM and a high-risk cardiovascular profile: the Sitagliptin Cardiovascular Outcome Study (TECOS) on sitagliptin, the Saxagliptin Assessment of Vascular Outcomes Recorded in Patients With Diabetes Mellitus-Thrombolysis in Myocardial Infarction (SAVOR-TIMI) 53 trial on saxagliptin, the Cardiovascular Outcomes Study of Alogliptin in Subjects With Type 2 Diabetes and Acute Coronary Syndrome (EXAMINE) trial on alogliptin, and the Cardiovascular Outcome Study of Linagliptin Versus Glimepiride in Patients With Type 2 Diabetes (CAROLINA) on linagliptin. If these trials confirm that a DPP-4 inhibitor can reduce the cardiovascular burden of T2DM, it would be major progress that would dramatically influence the management of the disease.

Cardiovascular effects of gliptins

Dipeptidyl peptidase 4 (DPP-4) inhibitors (commonly referred to as gliptins) are a novel class of oral antihyperglycaemic agents with demonstrated efficacy in the treatment of type 2 diabetes mellitus (T2DM). Preclinical data and mechanistic studies have indicated a possible beneficial action on blood vessels and the heart, via both glucagon-like peptide 1 (GLP-1)-dependent and GLP-1-independent effects. DPP-4 inhibition increases the concentration of many peptides with potential vasoactive and cardioprotective effects. Clinically, DPP-4 inhibitors improve several risk factors in patients with T2DM. They improve blood glucose control (mainly by reducing postprandial glycaemia), are weight neutral (or even induce modest weight loss), lower blood pressure, improve postprandial lipaemia, reduce inflammatory markers, diminish oxidative stress, and improve endothelial function. Some positive effects on the heart have also been described in patients with ischaemic heart disease or congestive heart failure, although their clinical relevance requires further investigation. Post-hoc analyses of phase II-III, controlled trials suggest a possible cardioprotective effect with a trend for a lower incidence of major cardiovascular events with gliptins than with placebo or active agents. However, the actual relationship between DPP-4 inhibition and cardiovascular outcomes remains to be proven. Major prospective clinical trials with predefined cardiovascular outcomes and involving various DPP-4 inhibitors are now underway in patients with T2DM and a high-risk cardiovascular profile.

Mechanistic systems modeling to guide drug discovery and development

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research.

Effects of 6-month sitagliptin treatment on glucose and lipid metabolism, blood pressure, body weight and renal function in type 2 diabetic patients: a chart-based analysis

Background

Sitagliptin is one of the dipeptidyl peptidase-4 (DPP-4) inhibitors which prevent the inactivation of incretins, increasing the endogenous active incretin levels. Incretins stimulate insulin secretion from pancreatic β-cells and inhibit glucagon secretion from pancreatic α-cells, which is favorable for the treatment of diabetes. Sitagliptin is released on December, 2009, in Japan. We retrospectively studied effects of 6-month-treatment with sitagliptin on glucose and lipid metabolism, blood pressure, body weight and renal function in patients with type 2 diabetes by a chart-based analysis.

Methods

We retrospectively studied 220 type 2 diabetic patients who have taken sitagliptin for 6 months by a chart-based analysis. Subjects studied include patients treated with sitagliptin monotherapy, sitagliptin add-on therapy, and switching from glinide to sitagliptin. We selected patients who have both data before and after 6-month sitagliptin treatment and compared the data before the sitagliptin treatment with the data at 6 month after the sitagliptin treatment started. Body weight, blood pressure, plasma glucose, hemoglobin A1c (HbA1c), serum lipids, and estimated glomerular filtration rate in type 2 diabetic patients were measured almost at the same time points before and after 6-month-treatment with sitagliptin.

Results

Body weight was significantly reduced after 6-month sitagliptin treatment by 0.8 kg. HbA1c levels were also significantly decreased after the sitagliptin treatment by 0.6%. We found a significant and negative correlation between change in body weight and body mass index at baseline. We also observed a significant and negative correlation between change in HbA1c and HbA1c levels at baseline. The number of patients who showed the absence of urinary glucose was significantly increased after the sitagliptin treatment.

Effects of 6-month sitagliptin treatment on glucose and lipid metabolism, blood pressure, body weight and renal function in type 2 diabetic patients: a chart-based analysis

BACKGROUND

Sitagliptin is one of the dipeptidyl peptidase-4 (DPP-4) inhibitors which prevent the inactivation of incretins, increasing the endogenous active incretin levels. Incretins stimulate insulin secretion from pancreatic β-cells and inhibit glucagon secretion from pancreatic α-cells, which is favorable for the treatment of diabetes. Sitagliptin is released on December, 2009, in Japan. We retrospectively studied effects of 6-month-treatment with sitagliptin on glucose and lipid metabolism, blood pressure, body weight and renal function in patients with type 2 diabetes by a chart-based analysis.

METHODS

We retrospectively studied 220 type 2 diabetic patients who have taken sitagliptin for 6 months by a chart-based analysis. Subjects studied include patients treated with sitagliptin monotherapy, sitagliptin add-on therapy, and switching from glinide to sitagliptin. We selected patients who have both data before and after 6-month sitagliptin treatment and compared the data before the sitagliptin treatment with the data at 6 month after the sitagliptin treatment started. Body weight, blood pressure, plasma glucose, hemoglobin A1c (HbA1c), serum lipids, and estimated glomerular filtration rate in type 2 diabetic patients were measured almost at the same time points before and after 6-month-treatment with sitagliptin.

RESULTS

Body weight was significantly reduced after 6-month sitagliptin treatment by 0.8 kg. HbA1c levels were also significantly decreased after the sitagliptin treatment by 0.6%. We found a significant and negative correlation between change in body weight and body mass index at baseline. We also observed a significant and negative correlation between change in HbA1c and HbA1c levels at baseline. The number of patients who showed the absence of urinary glucose was significantly increased after the sitagliptin treatment.

A review of gliptins in 2011

INTRODUCTION

Dipeptidylpeptidase-4 (DPP-4) inhibitors offer new options for the management of type 2 diabetes (T2DM).

AREAS COVERED

This paper is an updated review, providing an analysis of both the similarities and the differences between the various compounds known as gliptins, currently used in the clinic (sitagliptin, vildagliptin, saxagliptin, alogliptin and linagliptin). This paper discusses the pharmacokinetic and pharmacodynamic characteristics of gliptins; both the efficacy and safety profiles of gliptins in clinical trials (compared with classical glucose-lowering agents), given as monotherapy or in combination, including in special populations; the positioning of DPP-4 inhibitors in the management of T2DM in recent guidelines; and various unanswered questions and perspectives.

EXPERT OPINION

The role of DPP-4 inhibitors in the therapeutic armamentarium of T2DM is evolving, as their potential strengths and weaknesses become better defined. Future critical issues may include the durability of glucose control, resulting from better β-cell protection, positive effects on cardiovascular outcomes and long-term safety issues.

Animal models to explore the effects of CNS drugs on food intake and energy expenditure

Obesity has reached epidemic proportions globally with an increasing incidence not just in Western cultures but also Mexico, Brazil, China and parts of Africa. In terms of pharmacological intervention, the track record of drug treatments for obesity is poor, especially in the case of centrally acting medicines, and there remains an unmet need for the development of safer compounds delivering superior efficacy. Animal models are of importance not only in detecting changes in food intake, energy expenditure and body weight but also providing confidence that these changes are behaviourally specific and not a result of drug-induced side effects. We review animal models of feeding behaviour that are used to aid our understanding of the control of body weight and energy regulation with special reference to CNS-acting drugs. The use of such models in the discovery of new drugs for the treatment of obesity is given particular emphasis. This article is part of a Special Issue entitled 'Central Control of Food Intake'.