Covariate-adjusted value-guided subgroup identification via boosting.

It is widely recognized that treatment effects could differ across subgroups of patients. Subgroup analysis, which assesses such heterogeneity, provides valuable information in developing personalized therapies. There has been extensive research developing novel statistical methods for subgroup identification. The recent contribution is a value-guided subgroup identification method that directly maximizes treatment benefit at the subgroup level for survival outcome, rather than relying on individual treatment effect estimation. In this paper, we first completed this framework by illustrating its application to continuous and binary outcomes. More importantly, we extended the original framework to account for the prognostic effects and named this new method Covariate-Adjusted Value-guided subgroup identification via boosting (CAVboost). The original method directly used the outcome to formulate the value function for subgroup identification. Since the outcome can further be decomposed as prognostic effects and treatment effects, specifying the prognostic effects as the covariates of a model for the outcome can single out the treatment effects and improve the power to detect them across subgroups. Our proposed CAVboost was based on this key idea. It used a covariate-adjusted treatment effect estimator, instead of the outcome itself, to formulate the value function for subgroup identification. CAVboost estimates the treatment effect by using covariates to account for the prognostic effects, which mimics the idea of using covariates in an ANCOVA estimator. We showed that CAVboost could effectively improve the subgroup identification capability for both continuous and binary outcomes.

Estimands for overall survival in clinical trials with treatment switching in oncology.

An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the inferential quantity of interest, that is, the estimand. This definition explicitly accounts for intercurrent events, such as switching to new anticancer therapies for the analysis of overall survival (OS), the gold standard in oncology. Traditionally, OS in confirmatory studies is analyzed using the intention-to-treat (ITT) approach comparing treatment groups as they were initially randomized regardless of whether treatment switching occurred and regardless of any subsequent therapy (treatment-policy strategy). Regulatory authorities and other stakeholders often consider ITT results as most relevant. However, the respective estimand only yields a clinically meaningful comparison of two treatment arms if subsequent therapies are already approved and reflect clinical practice. We illustrate different scenarios where subsequent therapies are not yet approved drugs and thus do not reflect clinical practice. In such situations the hypothetical strategy could be more meaningful from patient's and prescriber's perspective. The cross-industry Oncology Estimand Working Group (www.oncoestimand.org) was initiated to foster a common understanding and consistent implementation of the estimand framework in oncology clinical trials. This paper summarizes the group's recommendations for appropriate estimands in the presence of treatment switching, one of the key intercurrent events in oncology clinical trials. We also discuss how different choices of estimands may impact study design, data collection, trial conduct, analysis, and interpretation.

Principal stratum strategy: Potential role in drug development.

A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post-randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called intercurrent events in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post-hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions.

A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data.

In randomized clinical trials with survival outcome, there has been an increasing interest in subgroup identification based on baseline genomic, proteomic markers, or clinical characteristics. Some of the existing methods identify subgroups that benefit substantially from the experimental treatment by directly modeling outcomes or treatment effect. When the goal is to find an optimal treatment for a given patient rather than finding the right patient for a given treatment, methods under the individualized treatment regime framework estimate an individualized treatment rule that would lead to the best expected clinical outcome as measured by a value function. Connecting the concept of value function to subgroup identification, we propose a nonparametric method that searches for subgroup membership scores by maximizing a value function that directly reflects the subgroup-treatment interaction effect based on restricted mean survival time. A gradient tree boosting algorithm is proposed to search for the individual subgroup membership scores. We conduct simulation studies to evaluate the performance of the proposed method and an application to an AIDS clinical trial is performed for illustration.

Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer.

BACKGROUND: Pembrolizumab is a humanized monoclonal antibody against programmed death 1 (PD-1) that has antitumor activity in advanced non-small-cell lung cancer (NSCLC), with increased activity in tumors that express programmed death ligand 1 (PD-L1). METHODS: In this open-label, phase 3 trial, we randomly assigned 305 patients who had previously untreated advanced NSCLC with PD-L1 expression on at least 50% of tumor cells and no sensitizing mutation of the epidermal growth factor receptor gene or translocation of the anaplastic lymphoma kinase gene to receive either pembrolizumab (at a fixed dose of 200 mg every 3 weeks) or the investigator's choice of platinum-based chemotherapy. Crossover from the chemotherapy group to the pembrolizumab group was permitted in the event of disease progression. The primary end point, progression-free survival, was assessed by means of blinded, independent, central radiologic review. Secondary end points were overall survival, objective response rate, and safety. RESULTS: Median progression-free survival was 10.3 months (95% confidence interval [CI], 6.7 to not reached) in the pembrolizumab group versus 6.0 months (95% CI, 4.2 to 6.2) in the chemotherapy group (hazard ratio for disease progression or death, 0.50; 95% CI, 0.37 to 0.68; P<0.001). The estimated rate of overall survival at 6 months was 80.2% in the pembrolizumab group versus 72.4% in the chemotherapy group (hazard ratio for death, 0.60; 95% CI, 0.41 to 0.89; P=0.005). The response rate was higher in the pembrolizumab group than in the chemotherapy group (44.8% vs. 27.8%), the median duration of response was longer (not reached [range, 1.9+ to 14.5+ months] vs. 6.3 months [range, 2.1+ to 12.6+]), and treatment-related adverse events of any grade were less frequent (occurring in 73.4% vs. 90.0% of patients), as were grade 3, 4, or 5 treatment-related adverse events (26.6% vs. 53.3%). CONCLUSIONS: In patients with advanced NSCLC and PD-L1 expression on at least 50% of tumor cells, pembrolizumab was associated with significantly longer progression-free and overall survival and with fewer adverse events than was platinum-based chemotherapy. (Funded by Merck; KEYNOTE-024 ClinicalTrials.gov number, NCT02142738 .).

Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial.

BACKGROUND: Despite recent advances in the treatment of advanced non-small-cell lung cancer, there remains a need for effective treatments for progressive disease. We assessed the efficacy of pembrolizumab for patients with previously treated, PD-L1-positive, advanced non-small-cell lung cancer. METHODS: We did this randomised, open-label, phase 2/3 study at 202 academic medical centres in 24 countries. Patients with previously treated non-small-cell lung cancer with PD-L1 expression on at least 1% of tumour cells were randomly assigned (1:1:1) in blocks of six per stratum with an interactive voice-response system to receive pembrolizumab 2 mg/kg, pembrolizumab 10 mg/kg, or docetaxel 75 mg/m(2) every 3 weeks. The primary endpoints were overall survival and progression-free survival both in the total population and in patients with PD-L1 expression on at least 50% of tumour cells. We used a threshold for significance of p<0.00825 (one-sided) for the analysis of overall survival and a threshold of p<0.001 for progression-free survival. This trial is registered at ClinicalTrials.gov, number NCT01905657. FINDINGS: Between Aug 28, 2013, and Feb 27, 2015, we enrolled 1034 patients: 345 allocated to pembrolizumab 2 mg/kg, 346 allocated to pembrolizumab 10 mg/kg, and 343 allocated to docetaxel. By Sept 30, 2015, 521 patients had died. In the total population, median overall survival was 10.4 months with pembrolizumab 2 mg/kg, 12.7 months with pembrolizumab 10 mg/kg, and 8.5 months with docetaxel. Overall survival was significantly longer for pembrolizumab 2 mg/kg versus docetaxel (hazard ratio [HR] 0.71, 95% CI 0.58-0.88; p=0.0008) and for pembrolizumab 10 mg/kg versus docetaxel (0.61, 0.49-0.75; p<0.0001). Median progression-free survival was 3.9 months with pembrolizumab 2 mg/kg, 4.0 months with pembrolizumab 10 mg/kg, and 4.0 months with docetaxel, with no significant difference for pembrolizumab 2 mg/kg versus docetaxel (0.88, 0.74-1.05; p=0.07) or for pembrolizumab 10 mg/kg versus docetaxel (HR 0.79, 95% CI 0.66-0.94; p=0.004). Among patients with at least 50% of tumour cells expressing PD-L1, overall survival was significantly longer with pembrolizumab 2 mg/kg than with docetaxel (median 14.9 months vs 8.2 months; HR 0.54, 95% CI 0.38-0.77; p=0.0002) and with pembrolizumab 10 mg/kg than with docetaxel (17.3 months vs 8.2 months; 0.50, 0.36-0.70; p<0.0001). Likewise, for this patient population, progression-free survival was significantly longer with pembrolizumab 2 mg/kg than with docetaxel (median 5.0 months vs 4.1 months; HR 0.59, 95% CI 0.44-0.78; p=0.0001) and with pembrolizumab 10 mg/kg than with docetaxel (5.2 months vs 4.1 months; 0.59, 0.45-0.78; p<0.0001). Grade 3-5 treatment-related adverse events were less common with pembrolizumab than with docetaxel (43 [13%] of 339 patients given 2 mg/kg, 55 [16%] of 343 given 10 mg/kg, and 109 [35%] of 309 given docetaxel). INTERPRETATION: Pembrolizumab prolongs overall survival and has a favourable benefit-to-risk profile in patients with previously treated, PD-L1-positive, advanced non-small-cell lung cancer. These data establish pembrolizumab as a new treatment option for this population and validate the use of PD-L1 selection. FUNDING: Merck & Co.

Information-based sample size re-estimation in group sequential design for longitudinal trials.

Group sequential design has become more popular in clinical trials because it allows for trials to stop early for futility or efficacy to save time and resources. However, this approach is less well-known for longitudinal analysis. We have observed repeated cases of studies with longitudinal data where there is an interest in early stopping for a lack of treatment effect or in adapting sample size to correct for inappropriate variance assumptions. We propose an information-based group sequential design as a method to deal with both of these issues. Updating the sample size at each interim analysis makes it possible to maintain the target power while controlling the type I error rate. We will illustrate our strategy with examples and simulations and compare the results with those obtained using fixed design and group sequential design without sample size re-estimation.

Rationale and Design of the Phase III KEYLYNK-012 Study of Pembrolizumab and Concurrent Chemoradiotherapy Followed by Pembrolizumab With or Without Olaparib for Stage III Non-Small-Cell Lung Cancer.

BACKGROUND: Concurrent chemoradiotherapy is a standard therapy for patients with stage III non-small-cell lung cancer (NSCLC). Durvalumab is an approved treatment option following concurrent chemoradiotherapy in the absence of disease progression. The multicenter, phase III, randomized, placebo- and active-controlled, double-blind KEYLYNK-012 study evaluates whether initiation of immunotherapy with pembrolizumab concurrently with chemoradiotherapy, followed by post-chemoradiotherapy pembrolizumab with or without olaparib improves outcomes for participants with stage III NSCLC. (ClinicalTrials.gov: NCT04380636) METHODS: Eligible participants are aged ≥18 years with previously untreated, pathologically confirmed, stages IIIA-C, squamous or nonsquamous NSCLC not suitable for surgery with curative intent. Participants will be randomized 1:1:1 to platinum-doublet chemotherapy plus radiotherapy with pembrolizumab (Groups A and B) or concurrent chemoradiotherapy alone (Group C) for 3 cycles. In the absence of disease progression, participants will receive pembrolizumab plus olaparib placebo (Group A), pembrolizumab plus olaparib (Group B), or durvalumab monotherapy (Group C). Dual primary endpoints are progression-free survival per RECIST version 1.1 by independent central review and overall survival. RESULTS: Enrollment began on July 6, 2020, and is ongoing at approximately 190 sites. CONCLUSION: KEYLYNK-012 will provide important information on the efficacy and safety of pembrolizumab combined with concurrent chemoradiotherapy and subsequent pembrolizumab with or without olaparib in participants with unresectable stage III NSCLC.

A note on dichotomization of continuous response variable in the presence of contamination and model misspecification.

The purpose of this note is to raise awareness of the complexity of the practice involving dichotomization. It is well known that the regular regression models are effective tools for analyzing Gaussian-type response variables, and researchers are often told that it is a 'bad idea' to practice dichotomization if continuous measurements are available. We demonstrate through special cases, however, that there is another side of the story if the response variable is contaminated. Although dichotomization causes loss of information, it can also reduce input of contamination. If the reduction of contamination input outweighs the loss of information, analysis based on dichotomization can sometimes provide better results. We derive formulas of bias and variance for binary regression estimators under a contamination model of unknown additive errors, and compare them with both the least squares and robust M-estimators from the corresponding linear regression analysis using continuous responses. As a case study, we study extensively the case in which the observed response is contaminated by an error with a mean and a variance proportional to the mean and the variance of the uncontaminated true response. Conditions under which dichotomization is preferred are obtained. A simulation study based on a real data setting is provided, which supports the theoretical developments.

Independent drug action and its statistical implications for development of combination therapies.

Researchers have long sought to find combinations of cancer drugs that might achieve synergy. However, while observed in some preclinical tumor models, synergistic effects are rarely seen in clinical trials. In fact, growing evidence in clinical trial data shows that the treatment effect of most approved combination therapies can be largely explained by the independent drug action model at the patient level. Previous statistical research on drug combinations mainly centered on experimental designs for dose-finding followed by measure of combination efficacy. In this paper, we introduce the independent drug action model to those working in late stage clinical development, propose a new approach to predict the progression-free survival of combination therapies, and discuss its statistical implications for trial design and monitoring. The discussion is enriched with real data examples.

Assessing the Impact of COVID-19 on the Clinical Trial Objective and Analysis of Oncology Clinical Trials-Application of the Estimand Framework.

Abstract-Coronavirus disease 2019 (COVID-19) outbreak has rapidly evolved into a global pandemic. The impact of COVID-19 on patient journeys in oncology represents a new risk to interpretation of trial results and its broad applicability for future clinical practice. We identify key intercurrent events (ICEs) that may occur due to COVID-19 in oncology clinical trials with a focus on time-to-event endpoints and discuss considerations pertaining to the other estimand attributes introduced in the ICH E9 addendum. We propose strategies to handle COVID-19 related ICEs, depending on their relationship with malignancy and treatment and the interpretability of data after them. We argue that the clinical trial objective from a world without COVID-19 pandemic remains valid. The estimand framework provides a common language to discuss the impact of COVID-19 in a structured and transparent manner. This demonstrates that the applicability of the framework may even go beyond what it was initially intended for.

Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic.

Abstract-The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials. Its effects on trial data create multiple potential statistical issues. The scale of impact is unprecedented, but when viewed individually, many of the issues are well defined and feasible to address. A number of strategies and recommendations are put forward to assess and address issues related to estimands, missing data, validity and modifications of statistical analysis methods, need for additional analyses, ability to meet objectives and overall trial interpretability.

A randomized clinical trial evaluating the safety and efficacy of sitagliptin added to the combination of sulfonylurea and metformin in patients with type 2 diabetes mellitus and inadequate glycemic control.

BACKGROUND: Type 2 diabetes mellitus (T2DM) treatment generally requires multiple antihyperglycemic agents. When diet, exercise, and treatment with sulfonylurea and metformin do not achieve glycemic goals, several options are available. The present study evaluated the efficacy and tolerability of sitagliptin 100 mg/day added to therapy with sulfonylurea and metformin. METHODS: Patients with HbA1c ≥7.5% and ≤10.5% while on a sulfonylurea and metformin were randomized 1: 1 to sitagliptin 100 mg/day or placebo for 24 weeks. At Week 24, patients in the placebo group switched to pioglitazone 30 mg/day and both groups continued treatment for another 30 weeks. RESULTS: Of 427 patients randomized, 339 (79.4%) completed the study. At Week 24, significantly greater (P < 0.001) mean reductions from baseline were seen in the sitagliptin versus placebo group for HbA1c (-0.84% vs -0.16%, respectively), 2-h post-meal glucose (-2.0 vs -0.2 mmol/L, respectively) and fasting plasma glucose (-0.7 vs 0.3 mmol/L, respectively). At Week 54, improvements in glycemic control continued. At Week 24, the incidence of adverse events (AEs) was numerically greater with sitagliptin than placebo, primarily because of a higher incidence of hypoglycemia. At Week 54, the incidence of AEs was similar in both groups, primarily because of a higher incidence of hypoglycemia and edema in the placebo/pioglitazone group after Week 24. The only meaningful change in body weight was an increase in the placebo/pioglitazone group at Week 54. CONCLUSIONS: In this study, sitagliptin 100 mg/day was generally well tolerated and provided improvement in glycemic control when added to the combination of sulfonylurea and metformin in patients with T2DM.

Early Clinical Development Planning via Biomarkers, Clinical Endpoints, and Simulation: A Case Study to Optimize for Phase 3 Dose Selection.

BACKGROUND: This study investigated a framework that leverages the relationship between biomarkers and a target clinical endpoint to optimize an early development plan. METHODS: Different biomarker designs were assessed for proof of concept (PoC) and dose finding (DF) to improve phase 2b (Ph2b) design as well as phase 3 (Ph3) dose choice. A case study using a Bayesian trivariate normal distribution model for 2 biomarkers and a clinically relevant endpoint was utilized with simulation to assess performance characteristics. RESULTS: We found the following: (1) at typical sample sizes for early development trials, biomarkers appear useful for PoC but not for clinical endpoint DF; and (2) even with large amounts of prior information and near perfect correlation between biomarkers and clinical endpoints, Ph2b variability is only overcome by increased Ph2b sample sizes to improve Ph3 dose choice. CONCLUSIONS: For highly variable clinical endpoints, the fastest path should be to demonstrate PoC by biomarkers and then go directly to Ph2b to measure the target clinical endpoint.

Efficacy and Tolerability of Sitagliptin Compared with Glimepiride in Elderly Patients with Type 2 Diabetes Mellitus and Inadequate Glycemic Control: A Randomized, Double-Blind, Non-Inferiority Trial.

OBJECTIVE: The aim of this study was to evaluate the efficacy and tolerability of sitagliptin compared with glimepiride in elderly patients with type 2 diabetes mellitus (T2DM) and inadequate glycemic control with diet and exercise alone. METHODS: This was a randomized, parallel-group, multinational, non-inferiority clinical trial with an active-controlled, double-blind treatment period in which patients ≥ 65 and ≤ 85 years of age with T2DM were screened at 85 sites. Patients were randomized to once-daily sitagliptin (100 or 50 mg, depending on renal function) or glimepiride (in titrated doses) for 30 weeks. The main outcome measures were change from baseline in glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), and body weight; and the incidence of symptomatic hypoglycemia. RESULTS: The mean baseline HbA1c was 7.8% in both the sitagliptin group (n = 197) and the glimepiride group (n = 191). After 30 weeks, the least squares (LS) mean change in HbA1c baseline was -0.32% with sitagliptin and -0.51 % with glimepiride, for a between-group difference (95% CI) of 0.19% (0.03-0.34). This result met the pre-specified criterion for declaring non-inferiority, which required that the upper 95% confidence limit lie below 0.4%. The LS mean change in FPG from baseline was -14.5 mg/dL with sitagliptin and -21.2 mg/dL with glimepiride, for a between-group difference (95% CI) of 6.7 mg/dL (0.7-12.7). The percentages of patients with adverse events of symptomatic hypoglycemia were 0.8% in the sitagliptin group and 4.7% in the glimepiride group (between-treatment difference = -3.9%, p = 0.009). The LS mean change in body weight from baseline was 0.4 kg with sitagliptin and 1.1 kg with glimepiride, for a between-group difference of -0.7 kg (p = 0.011). CONCLUSION: In elderly patients with T2DM and inadequate glycemic control with diet and exercise alone, sitagliptin provided non-inferior glycemic control after 30 weeks of treatment compared with glimepiride. Compared with glimepiride, sitagliptin had a lower risk of hypoglycemia. Sitagliptin was weight-neutral; while the between-group difference in change from baseline in body weight was statistically significant, the modest difference may not be clinically meaningful. STUDY IDENTIFIER: ClinicalTrials.gov NCT01189890.

Consistency assessment with global and bridging development strategies in emerging markets.

Global trial strategy with the participation of all major regions including countries from emerging markets surely increases new drug development efficiency. Nevertheless, there are circumstances in which some countries in emerging markets cannot join the original global trial. To evaluate the extrapolability of the original trial results to a new country, a bridging trial in the country has to be conducted. In this paper, we first evaluate the efficiency loss of the bridging trial strategy compared to that of the global trial strategy as a function of between-study variability from consistency assessment perspective. The provided evidence should encourage countries in emerging markets to make a greater effort to participate in the original global trial. We then discuss sample size requirement for desired assurance probability for consistency assessment based on various approaches for both global and bridging trial strategies. Examples are presented for numerical demonstration and comparisons.

Efficacy and safety of sitagliptin added to ongoing metformin and pioglitazone combination therapy in a randomized, placebo-controlled, 26-week trial in patients with type 2 diabetes.

AIMS: To assess efficacy and safety of sitagliptin, a dipeptidyl peptidase-4 inhibitor, in combination therapy with metformin (≥1500 mg/day) and pioglitazone (≥30 mg/day) in patients with type 2 diabetes (T2DM) with inadequate glycemic control (hemoglobin A1c [HbA1c] ≥7.5% and ≤11%). METHODS: This placebo-controlled, double-blind study included 313 patients, mean baseline HbA1c=8.7%, who were randomized to receive sitagliptin 100 mg/day or placebo for 26 weeks. RESULTS: The addition of sitagliptin led to significant (P<.001) mean changes from baseline relative to placebo in HbA1c (-0.7%), fasting plasma glucose (-1.0 mmol/L), and 2-h post-meal glucose (-2.2 mmol/L). In patients with baseline HbA1c ≥9.0%, mean changes from baseline in HbA1c were -1.6% and -0.8% for the sitagliptin and placebo groups, respectively (between-group difference -0.8%; P<.001). The incidences of reported adverse events were generally similar between the treatment groups. Incidences of symptomatic hypoglycemia were 7/157 [4.5%] and 6/156 [3.8%] in the sitagliptin and placebo groups, respectively (P=.786). Two patients, both in the placebo group, experienced an episode of hypoglycemia that required non-medical assistance. CONCLUSIONS: In this 26-week study, addition of sitagliptin to combination therapy with metformin and pioglitazone improved glycemic control and was generally well tolerated.

The addition of sitagliptin to ongoing metformin therapy significantly improves glycemic control in Chinese patients with type 2 diabetes.

BACKGROUND: The present study was conducted to evaluate the efficacy, safety and tolerability of sitagliptin added to ongoing metformin therapy in Chinese patients with type 2 diabetes (T2DM) who failed to achieve adequate glycemic control with metformin monotherapy. METHODS: After a metformin titration/stabilization period and a 2-week, single-blind, placebo run-in period, 395 Chinese patients with T2DM aged 25-77 years (baseline HbA1c 8.5%) were randomized (1:1) to double-blind placebo or sitagliptin 100 mg q.d. added to ongoing open-label metformin (1000 or 1700 mg/day) for 24 weeks. RESULTS: Significant (P < 0.001) changes from baseline in HbA1c (-0.9%), fasting plasma glucose (-1.2 mmol/L), and 2-h post-meal plasma glucose (-1.9 mmol/L) were seen with sitagliptin compared with placebo. There were no significant differences between sitagliptin and placebo in the incidence of hypoglycemia or gastrointestinal adverse events. A small decrease from baseline body weight was observed in the placebo group compared with no change in the sitagliptin group (between-group difference 0.5kg; P=0.018). CONCLUSIONS: The addition of sitagliptin 100 mg to ongoing metformin therapy significantly improved glycemic control and was generally well tolerated in Chinese patients with T2DM who had inadequate glycemic control on metformin alone.

Effects of MK-0941, a novel glucokinase activator, on glycemic control in insulin-treated patients with type 2 diabetes.

OBJECTIVE: To assess the efficacy and safety of MK-0941, a glucokinase activator (GKA), when added to stable-dose insulin glargine in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: In this double-blind study, 587 patients taking stable-dose insulin glargine (±metformin ≥1,500 mg/day) were randomized (1:1:1:1:1) to MK-0941 10, 20, 30, or 40 mg or matching placebo t.i.d. before meals (a.c.). This study included an initial 14-week, dose-ranging phase followed by a 40-week treatment phase during which patients were to be uptitrated as tolerated to 40 mg (or placebo) t.i.d. a.c. The primary efficacy end point was change from baseline in A1C at Week 14. RESULTS: At Week 14, A1C and 2-h postmeal glucose (PMG) improved significantly versus placebo with all MK-0941 doses. Maximal placebo-adjusted least squares mean changes from baseline in A1C (baseline A1C 9.0%) and 2-h PMG were -0.8% and -37 mg/dL (-2 mmol/L), respectively. No significant effects on fasting plasma glucose were observed at any dose versus placebo. By 30 weeks, the initial glycemic responses noted at 14 weeks were not sustained. MK-0941 at one or more doses was associated with significant increases in the incidence of hypoglycemia, triglycerides, systolic blood pressure, and proportion of patients meeting criteria for predefined limits of change for increased diastolic blood pressure. CONCLUSIONS: In patients receiving stable-dose insulin glargine, the GKA MK-0941 led to improvements in glycemic control that were not sustained. MK-0941 was associated with an increased incidence of hypoglycemia and elevations in triglycerides and blood pressure.

Genetic structures of copy number variants revealed by genotyping single sperm.

BACKGROUND: Copy number variants (CNVs) occupy a significant portion of the human genome and may have important roles in meiotic recombination, human genome evolution and gene expression. Many genetic diseases may be underlain by CNVs. However, because of the presence of their multiple copies, variability in copy numbers and the diploidy of the human genome, detailed genetic structure of CNVs cannot be readily studied by available techniques. METHODOLOGY/PRINCIPAL FINDINGS: Single sperm samples were used as the primary subjects for the study so that CNV haplotypes in the sperm donors could be studied individually. Forty-eight CNVs characterized in a previous study were analyzed using a microarray-based high-throughput genotyping method after multiplex amplification. Seventeen single nucleotide polymorphisms (SNPs) were also included as controls. Two single-base variants, either allelic or paralogous, could be discriminated for all markers. Microarray data were used to resolve SNP alleles and CNV haplotypes, to quantitatively assess the numbers and compositions of the paralogous segments in each CNV haplotype. CONCLUSIONS/SIGNIFICANCE: This is the first study of the genetic structure of CNVs on a large scale. Resulting information may help understand evolution of the human genome, gain insight into many genetic processes, and discriminate between CNVs and SNPs. The highly sensitive high-throughput experimental system with haploid sperm samples as subjects may be used to facilitate detailed large-scale CNV analysis.