A novel model-based meta-analysis to indirectly estimate the comparative efficacy of two medications: an example using DPP-4 inhibitors, sitagliptin and linagliptin, in treatment of type 2 diabetes mellitus

Applications of Model-Based Meta-Analysis in Drug Development

Model-based meta-analysis (MBMA) is a quantitative approach that leverages published summary data along with internal data and can be applied to inform key drug development decisions, including the benefit-risk assessment of a treatment under investigation. These risk-benefit assessments may involve determining an optimal dose compared against historic external comparators of a particular disease indication. MBMA can provide a flexible framework for interpreting aggregated data from historic reference studies and therefore should be a standard tool for the model-informed drug development (MIDD) framework.In addition to pairwise and network meta-analyses, MBMA provides further contributions in the quantitative approaches with its ability to incorporate longitudinal data and the pharmacologic concept of dose-response relationship, as well as to combine individual- and summary-level data and routinely incorporate covariates in the analysis.A common application of MBMA is the selection of optimal dose and dosing regimen of the internal investigational molecule to evaluate external benchmarking and to support comparator selection. Two case studies provided examples in applications of MBMA in biologics (durvalumab + tremelimumab for safety) and small molecule (fenebrutinib for efficacy) to support drug development decision-making in two different but well-studied disease areas, i.e., oncology and rheumatoid arthritis, respectively.Important to the future directions of MBMA include additional recognition and engagement from drug development stakeholders for the MBMA approach, stronger collaboration between pharmacometrics and statistics, expanded data access, and the use of machine learning for database building. Timely, cost-effective, and successful application of MBMA should be part of providing an integrated view of MIDD.

Effect of linagliptin plus insulin in comparison to insulin alone on metabolic control and prognosis in hospitalized patients with SARS-CoV-2 infection

To evaluate the effect of the combination of linagliptin and insulin on metabolic control and prognosis in hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and hyperglycemia. A parallel double-blind randomized clinical trial including hospitalized patients with SARS-CoV-2 infection and hyperglycemia, randomized to receive 5 mg linagliptin + insulin (LI group) or insulin alone (I group) was performed. The main outcomes were the need for assisted mechanical ventilation and glucose levels during hospitalization. Subjects were screened for eligibility at hospital admission if they were not with assisted mechanical ventilation and presented hyperglycemia, and a total of 73 patients with SARS-CoV-2 infection and hyperglycemia were randomized to the LI group (n = 35) or I group (n = 38). The average hospital stay was 12 ± 1 vs 10 ± 1 days for the I and LI groups, respectively (p = 0.343). There were no baseline clinical differences between the study groups, but the percentage of males was higher in the LI group (26 vs 18, p = 0.030). The improvements in fasting and postprandial glucose levels were better in the LI group that the I group (122 ± 7 vs 149 ± 10, p = 0.033; and 137 ± 7 vs 173 ± 12, p = 0.017, respectively), and insulin requirements tended to be lower in the LI group than the I group. Three patients in the LI group and 12 in the I group required assisted mechanical ventilation (HR 0.258, CI 95% 0.092–0.719, p = 0.009); 2 patients in the LI group and 6 in the I group died after a follow-up of 30 days (p = 0.139). No major side effects were observed. The combination of linagliptin and insulin in hospitalized patients with SARS-CoV-2 infection and hyperglycemia reduced the relative risk of assisted mechanical ventilation by 74% and improved better pre and postprandial glucose levels with lower insulin requirements, and no higher risk of hypoglycemia.

This study is registered at clinicaltrials.gov, number NCT04542213 on 09/03/2020.

Characterizing Effects of Antidiabetic Drugs on Heart Rate, Systolic and Diastolic Blood Pressure

A model-based meta-analysis was performed with reported data from obese subjects and patients with type 2 diabetes (T2DM) to characterize the effects of dipeptidyl peptidase 4 (DPP4) inhibitors, gastric inhibitory polypeptides (GIPs), glucagon-like peptide-1 (GLP1), and dual GIP/GLP1 agonists, or a combination of these antidiabetic drugs (ADs) on heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A systematic literature search and review after the Cochrane method identified sources for investigational and approved ADs resulted in a comprehensive database with data from 178 clinical studies in obese subjects and patients with T2DM. Results indicated that there were AD class-dependent effects on HR and SBP, whereas no clear AD-related effects on DBP were found. All AD classes, except for DPP4 inhibitors, increased HR. The largest increase of 12 bpm was seen with GLP1 receptor agonists. All AD classes appeared to decrease SBP. DPP4 inhibitors were associated with a marginal decrease of ~ 1 mmHg, whereas GLP1 and GIP/GLP1 dual agonists exhibited the largest decrease of ~ 3 mmHg in SBP. AD-related effects were similar in obese subjects and patients with T2DM. In conclusion, there are clinically relevant AD-related effects on both HR and SBP, but not on DBP. DPP4 inhibitors are associated with the smallest (if at all) effects on HR and SBP, whereas GLP1 inhibitors exhibited the largest effects on these two cardiovascular end points. Additional studies are warranted to further investigate how AD-related SBP decreases combined with HR increases affect long-term cardiovascular mortality.

Quantitative prediction of bone mineral density by using bone turnover markers in response to antiresorptive agents in postmenopausal osteoporosis: A model-based meta-analysis

AIMS

This study aimed to predict time course of bone mineral density (BMD) by using corresponding response of bone turnover markers (BTMs) in women with postmenopausal osteoporosis under antiresorptive treatments.

METHODS

Data were extracted from literature searches in accessible public database. Time courses of percent change from baseline in serum C-telopeptide of type 1 collagen (sCTX) and N-telopeptide of type 1 collagen were described by complex exponential onset models. The relationship between BTM changes and BMD changes at lumbar spine and total hip was described using a multiscale indirect response model.

RESULTS

The dataset included 41 eligible published trials of 5 US-approved antiresorptive agents (alendronate, ibandronate, risedronate, zoledronic acid and denosumab), containing over 28 800 women with postmenopausal osteoporosis. The time courses of BTM changes for different drugs were differentiated by maximal effect and onset rate in developed model, while sCTX responses to zoledronic acid and denosumab were captured by another model formation. Furthermore, asynchronous relationship between BTMs and BMD was described by a bone remodelling-based semimechanistic model, including zero-order production and first-order elimination induced by N-telopeptide of type 1 collagen and sCTX, separately. After external and informative validations, the developed models were able to predict BMD increase using 1-year data.

CONCLUSION

This exploratory analysis built a quantitative framework linking BTMs and BMD among antiresorptive agents, as well as a modelling approach to enhance comprehension of dynamic relationship between early and later endpoints among agents in a certain mechanism of action. Moreover, the developed models can offer predictions of BMD from BTMs supporting early drug development.

Evaluation of non-linear-mixed-effect modeling to reduce the sample sizes of pediatric trials in type 2 diabetes mellitus

Recruitment for pediatric trials in Type II Diabetes Mellitus (T2DM) is very challenging, necessitating the exploration of new approaches for reducing the sample sizes of pediatric trials. This work aimed at assessing if a longitudinal Non-Linear-Mixed-Effect (NLME) analysis of T2DM trial could be more powerful and thus require fewer patients than two standard statistical analyses commonly used as primary or sensitivity efficacy analysis: Last-Observation-Carried-Forward (LOCF) followed by (co)variance (AN(C)OVA) analysis at the evaluation time-point, and Mixed-effects Model Repeated Measures (MMRM) analysis. Standard T2DM efficacy studies were simulated, with glycated hemoglobin (HbA1c) as the main endpoint, 24 weeks' study duration, 2 arms, assuming a placebo and a treatment effect, exploring three different scenarios for the evolution of HbA1c, and accounting for a dropout phenomenon. 1000 trials were simulated, then analyzed using the 3 analyses, whose powers were compared. As expected, the longitudinal modeling MMRM analysis was found to be more powerful than the LOCF + ANOVA analysis at week 24. The NLME analysis gave slightly more accurate drug-effect estimations than the two other methods, however it tended to slightly overestimate the magnitude of the drug effect, and it was more powerful than the MMRM analysis only in some scenarios of slow HbA1c decrease. The gain in power afforded by NLME was more apparent when two additional assessments enriched the design; however, the gain was not systematic for all scenarios. Finally, this work showed that NLME analyses may help to reduce significantly the required sample sizes in T2DM pediatric studies, but only for enriched designs and slow HbA1c decrease.

Does evidence matter? Comparative effectiveness research and prescribing of Type 2 diabetes mellitus drugs

Aim: Comparative effectiveness research (CER) can help ascertain value of new drugs; however, limited research assesses the translation of CER into clinical practice. The objective of this study was to analyze the association between CER evidence and prescribing trends within two markets of Type 2 diabetes mellitus. Methods: A retrospective analysis to determine the prescribing trends from 2006 to 2016 and an electronic literature search to identify CER comparing different drugs was conducted. Results: In glucagon-like peptide-1 (GLP-1) agonists market, CER showed superiority of Liraglutide. Prescribing of Exenatide twice daily dropped by 50% points as Liraglutide entered the market. In dipeptidyl peptidase-4 (DPP4) inhibitors market, CER did not suggest conclusive superiority. Nevertheless, Sitagliptin, the first entrant, continued to dominate throughout. Conclusion: CER evidence appeared to be associated with prescribing trends in GLP-1 agonists market; however, no associations were found in DPP4 inhibitors market. The translation of evidence into practice can be limited by the availability of superiority trials and timing of their availability.

A model-based comparative meta-analysis of the efficacy of dolutegravir-based and efavirenz-based regimens in HIV-infected patients

Currently, combinations of typical types of antiretroviral agents have been adopted as chemotherapy for human immunodeficiency virus (HIV) infection, comprising two nucleoside analogue reverse transcriptase inhibitors plus one of a non-nucleoside reverse transcriptase inhibitor, an integrase strand-transfer inhibitor, and a protease inhibitor. Although several meta-analyses have been conducted to determine first-line combination antiretroviral therapy, this has yet to be confirmed due to the technical limitation associated. In the present study, we applied a model-based meta-analysis (MBMA) approach, because it allows integration of information from clinical trials with varying dosing, duration, and sampling time points, resulting in enlargement of available data sources. We performed a bibliographic search to identify clinical trials involving dolutegravir (DTG)-based and efavirenz (EFV)-based regimens in HIV-infected, antiretroviral therapy-naïve adults, and then identified 30 independent trial data. The time course of drug effect was described by a consecutive first-order kinetic model and analyzed using the nonlinear mixed effect modeling approach. The developed model suggests that the DTG-based regimen provides a faster-acting and more sustainable drug effect than the EFV-based regimen. Moreover, the drug effect tends to appear more slowly and decay faster in severe patients having higher viral load or smaller baseline CD4 count.

A model of fracture risk used to examine the link between bone mineral density and the impact of different therapeutic mechanisms on fracture outcomes in patients with osteoporosis

A hazard model of fracture was developed using individual patient data (IPD) from the NHANES (2005-2008) database and summary-level data from an aggregate dataset (AD). The AD was built by performing a comprehensive and systematic literature search of clinical studies published from 1995 to 2015, recording fracture rate and bone mineral density (BMD) for both treatment and placebo arms. The search resulted in a metadata set comprised of 21 studies investigating the effects of various bisphosphonates, teriparatide, denosumab, and raloxifene in 65,254 patients over a cumulative 56.75 years of study. The IPD was used to augment an AD in a model-based meta-analysis (MBMA) hierarchical modeling approach. The resulting model predicts the probability of fracture events in patients with osteoporosis. The object of model building using this approach was to promote understanding of the impact of therapeutic drug effects on the probability of fracture together with, or independent of their effects on BMD. Candidate models were evaluated by deviance information criteria and posterior predictive check. The model with covariates for lumbar spine BMD with interaction with a drug effect on BMD, and patient body mass index, years post-menopause, fracture measure method (clinical or radiological) and an additional drug effect outperformed those models without interaction and without additional drug effects. The model quantitatively supports the widely held notion that changes in bone microarchitecture, which cannot be measured by areal BMD elicited by therapy contribute in a significant way to a reduction in fracture. Furthermore, this model can be used to simulate fracture risk in a clinical cohort similar to those contained in the MBMA.

Linagliptin versus sitagliptin in patients with type 2 diabetes mellitus: a network meta-analysis of randomized clinical trials

Background

Diabetes is one of the most common chronic and costly diseases worldwide and type 2 diabetes is the most common type which accounts for about 90% of cases with diabetes. New medication-therapy regimens such as those containing linagliptin alone or in combination with other medications (within the category of DDP-4 inhibitors) must be evaluated in terms of efficacy and compared with other currently used drugs and then enter the medication list of the country. Hence, this study aimed to compare the clinical efficacy of the two drugs, i.e. linagliptin and sitagliptin, in patients with type 2 diabetes.

Methods

A systematic review was conducted to identify all clinical trials published by 2015 which compared the two drugs in patients with type 2 diabetes. Using keywords such as “linagliptin”, “type 2 diabetes mellitus”, “sitagliptin” and related combinations, we searched databases including Scopus, PubMed, and Web of Science. The quality of the selected studies was evaluated using the Jadad score. Considering primary and secondary outcomes extracted from the reviewed studies, a network meta-analysis was used to conduct a systematic comparison between the two studied drugs.

Results

This network meta-analysis included 32 studies (Linagliptin vs PLB: n = 8, Sitagliptin vs PLB: n = 13, Linagliptin + MET vs PLB + MET: n = 4, and Sitagliptin + MET vs PLB + MET: n = 7) and a total of 13,747 patients. The results showed no significant difference between linagliptin and sitagliptin in terms of key efficacy and safety outcomes such as HbA1c changes from baseline, body weight change from baseline, percentage of patients achieving HbA1c <7, and percentage of patients experiencing hypoglycemic events (p > 0.05). The results showed that the efficacy of the two drug regimens was the same.

Conclusions

Based on the results, there was no significant difference between the two drugs, i.e. linagliptin and sitagliptin, in terms of efficacy; in other words, the efficacy of the two drugs was the same. Therefore, the use of these two drugs depends on their availability and cost.

Graphical abstract

Graphical abstract of the network meta-analysis performed to evaluate the alternatives under the study.

Electronic supplementary material

The online version of this article (10.1186/s40199-017-0189-6) contains supplementary material, which is available to authorized users.

Model-based Meta-Analysis on the Efficacy of Pharmacological Treatments for Idiopathic Pulmonary Fibrosis

Recently, the US Food and Drug Administration (FDA) approved the first two drugs (pirfenidone and nintedanib) indicated for the treatment of idiopathic pulmonary fibrosis (IPF). The purpose of this analysis was to leverage publicly available data to quantify comparative efficacy of compounds that are approved or in development. An analysis-ready database was developed, and the analysis dataset is composed of summary-level data from 43 arms in 20 trials, with treatment durations ranging from 8-104 weeks. A hierarchical multivariable regression model with nonparametric placebo estimation was used to fit the longitudinal profile of change from baseline of percent predicted forced vital capacity (%predicted FVC) data. Pirfenidone and nintedanib were the only drugs identified to have significant estimated positive treatment effects. Model simulations were performed to further evaluate the covariate and time course of treatment effects on longitudinal change from baseline %predicted FVC to inform future trial designs and support decision making.

Pharmacokinetic, pharmacodynamic and clinical evaluation of saxagliptin in type 2 diabetes

INTRODUCTION

Dipeptide peptidase-4 (DPP-4) inhibitors such as saxagliptin are established and efficacious oral therapies in the management of type 2 diabetes. These agents have the potential to confer significant benefits in glycemic control without the risk of weight gain and hypoglycemia, which may be associated with other medications used to treat type 2 diabetes.

AREAS COVERED

This review examines the pharmacokinetics, efficacy and tolerability of saxagliptin for the management of type 2 diabetes.

EXPERT OPINION

Saxagliptin is routinely used in the management of type 2 diabetes as monotherapy, and in combination with other oral agents and insulin. Robust clinical trials have shown consistent improvements in glycated hemoglobin, fasting and postprandial glucose levels, with few adverse effects. The agent is well tolerated with low rates of hypoglycemia in the absence of insulin or sulphonylurea therapy.

A model-based meta-analysis of sofosbuvir-based treatments in chronic hepatitis C patients

The objective of this study was to compare the efficacy of sofosbuvir-based treatments in patients with chronic hepatitis C virus (HCV) infection using a model-based meta-analysis (MBMA). A bibliographic search was performed to identify clinical trials involving sofosbuvir as a unique direct-acting antiviral (DAA) agent or together with daclatasvir, ledipasvir or simeprevir for the treatment of diagnosed HCV infection. The time course of the virological response (VR) was modelled to estimate the effect of treatment and the influence of population characteristics on the longitudinal efficacy profile. The model was validated and simulations of 10 different treatment schedules were performed. Data from 19 clinical trials were included in the analysis. According to the developed model, therapy with sofosbuvir+ledipasvir is the most effective therapy in all scenarios, but it does not differ greatly in terms of sustained VR with respect to other combinations of DAA treatments. In conclusion, this MBMA generates knowledge regarding hypothetical head-to-head trials that have not been conducted previously. Therapies with sofosbuvir+ledipasvir are probably the most effective sofosbuvir-based treatments.

Cardiometabolic Effects of a New Class of Antidiabetic Agents

PURPOSE

Within the past decade, many new classes of drugs have received approval from the US Food and Drug Administration for treatment of type 2 diabetes mellitus, including glucagon-like peptide-1agonists, dipeptidyl peptidase-4 inhibitors, and the sodium-glucose cotransporter-2 inhibitors. Many trials have been performed, and several more are currently ongoing to evaluate these drugs. This review addresses the broad therapeutic and pleiotropic effects of these drugs. The review also discusses the role of these drugs in the treatment paradigm for type 2 diabetes and identifies patients who would be suitable candidates for treatment with these drugs.

METHODS

In this comprehensive evidence-based review, the following databases were searched from 1990 to the present: PubMed/MEDLINE, Scopus, CINAHL, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Portal, and the American Diabetes Association and European Association for the Study of Diabetes abstract databases. Randomized clinical trials (RCTs) were only included for the main therapeutic and cardiovascular (CV) effects of these drug classes. For pleiotropic effects, RCTs were included unless no RCTs exist, in which case other studies as specified in the detailed Methods section were included.

FINDINGS

All 3 drug classes are effective in lowering hemoglobin A1c between 0.4% and 1.4%, depending on the drug class and population selected. These drug classes have beneficial effects on CV risk factors, such as weight, lipids, and blood pressure, in addition to lowering blood glucose levels. The CV tolerability of some drugs has been evaluated and found to be neutral; however, most trials are currently ongoing to assess CV tolerability. There are no concrete guidelines to determine where these drugs fit in the diabetes management paradigm, and there are ongoing trials to determine the best combination drug with metformin.

IMPLICATIONS

These 3 drug classes will potentially increase the armamentarium against hyperglycemia. However, the specific combinations with other antidiabetic drugs and populations that will best benefit from these drugs are still being tested. Future research is also being conducted on the use of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors in patients with type 1 diabetes.

Utilization of model-based meta-analysis to delineate the net efficacy of taspoglutide from the response of placebo in clinical trials

The objective of this study was to develop quantitative models to delineate the net efficacy of taspoglutide on fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c) from the response of placebo in type 2 diabetes patients, and further find pharmacodynamic potency of taspoglutide and FPG for half of maximum reduction responses of FPG and HbA1c, respectively. Several PD data about taspoglutide treatments for type 2 diabetes patients were digitalized from the published papers related with the clinical development of taspoglutide. The model based meta-analysis (MBMA) studies for FPG and HbA1c were performed with Monolix 4.2 software. The MBMA successfully described the effects of placebo and taspoglutide on pharmacological indexes of FPG and HbA1c through mono and multiple combination therapies in clinical trials. The pharmacodynamic potency (25.3 pmol/l) produced 50% of maximum responses of FPG (−2.39 mmol/l) from the responses of placebo for FPG (−0.371 mmol/l); the response change of FPG (−1.81 mmol/l) affected 50% of maximum response change (−1.74%) for HbA1c from the response of placebo (−0.253%). The leveraging prior knowledge from the longitudinal MBMA will be utilized to guide clinical development of taspoglutide and further support study designs including optimization of dose and duration of therapy.

Dipeptidyl peptidase IV and its inhibitors: therapeutics for type 2 diabetes and what else?

The proline-specific dipeptidyl aminopeptidase IV (DPP IV, DPP-4, CD26), widely expressed in mammalians, releases X-Pro/Ala dipeptides from the N-terminus of peptides. DPP IV is responsible of the degradation of the incretin peptide hormones regulating blood glucose levels. Several families of DPP IV inhibitors have been synthesized and evaluated. Their positive effects on the degradation of the incretins and the control of blood glucose levels have been demonstrated in biological models and in clinical trials. Presently, several DPP IV inhibitors, the "gliptins", are approved for type 2 diabetes or are under clinical evaluation. However, the gliptins may also be of therapeutic interest for other diseases beyond the inhibition of incretin degradation. In this Perspective, the biological functions and potential substrates of DPP IV enzymes are reviewed and the characteristics of the DPP IV inhibitors are discussed in view of type 2 diabetes and further therapeutic interest.

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.