Interim analyses of a first-in-human phase 1/2 mRNA trial for propionic acidaemia.

Propionic acidaemia is a rare disorder caused by defects in the propionyl-coenzyme A carboxylase α or ß (PCCA or PCCB) subunits that leads to an accumulation of toxic metabolites and to recurrent, life-threatening metabolic decompensation events. Here we report interim analyses of a first-in-human, phase 1/2, open-label, dose-optimization study and an extension study evaluating the safety and efficacy of mRNA-3927, a dual mRNA therapy encoding PCCA and PCCB. As of 31 May 2023, 16 participants were enrolled across 5 dose cohorts. Twelve of the 16 participants completed the dose-optimization study and enrolled in the extension study. A total of 346 intravenous doses of mRNA-3927 were administered over a total of 15.69 person-years of treatment. No dose-limiting toxicities occurred. Treatment-emergent adverse events were reported in 15 out of the 16 (93.8%) participants. Preliminary analysis suggests an increase in the exposure to mRNA-3927 with dose escalation, and a 70% reduction in the risk of metabolic decompensation events among 8 participants who reported them in the 12-month pretreatment period.

CITIES: Clinical trials with intercurrent events simulator.

Although clinical trials are often designed with randomization and well-controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The pharmaceutical industry has been focused on developing new methods that can yield pertinent causal inferences in trials with ICEs. However, it is difficult to compare the properties of different methods developed in this endeavor as real-life clinical trial data cannot be easily shared to provide benchmark data sets. Furthermore, different methods consider distinct assumptions for the underlying data-generating mechanisms, and simulation studies often are customized to specific situations or methods. We develop a novel, general simulation model and corresponding Shiny application in R for clinical trials with ICEs, aptly named the Clinical Trials with Intercurrent Events Simulator (CITIES). It is formulated under the Rubin Causal Model where the considered treatment effects account for ICEs in clinical trials with repeated measures. CITIES facilitates the effective generation of data that resemble real-life clinical trials with respect to their reported summary statistics, without requiring the use of the original trial data. We illustrate the utility of CITIES via two case studies involving real-life clinical trials that demonstrate how CITIES provides a comprehensive tool for practitioners in the pharmaceutical industry to compare methods for the analysis of clinical trials with ICEs on identical, benchmark settings that resemble real-life trials.

Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys.

The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low-/high-rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.

Utilization of treatment effect on a surrogate endpoint for planning a study to evaluate treatment effect on a final endpoint.

To design a phase III study with a final endpoint and calculate the required sample size for the desired probability of success, we need a good estimate of the treatment effect on the endpoint. It is prudent to fully utilize all available information including the historical and phase II information of the treatment as well as external data of the other treatments. It is not uncommon that a phase II study may use a surrogate endpoint as the primary endpoint and has no or limited data for the final endpoint. On the other hand, external information from the other studies for the other treatments on the surrogate and final endpoints may be available to establish a relationship between the treatment effects on the two endpoints. Through this relationship, making full use of the surrogate information may enhance the estimate of the treatment effect on the final endpoint. In this research, we propose a bivariate Bayesian analysis approach to comprehensively deal with the problem. A dynamic borrowing approach is considered to regulate the amount of historical data and surrogate information borrowing based on the level of consistency. A much simpler frequentist method is also discussed. Simulations are conducted to compare the performances of different approaches. An example is used to illustrate the applications of the methods.

Microbial metabolites and heart failure: Friends or enemies?

Heart failure (HF), a global health issue characterized by structural or functional cardiac dysfunction, which was found to be associated with the gut microbiome recently. Although multiple studies suggested that the gut microbiome may have an impact on the development of cardiovascular diseases, the underlying mechanism of the gut microbiome in HF remains unclear. The study of metabolites from gut microbiota influenced by dietary nutrition uptake suggested that gut microbiota may affect the process of HF. However, on the basis of the microbiota's complicated roles and their interactions with metabolites, studies of microbial metabolites in HF had rarely been described so far. In this review, we focused on dietary nutrition-related factors that were involved in the development and progression of HF, such as trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFAs), and bile acids (BAs), to summarize their advances and several potential targets in HF. From a therapeutic standpoint, we discussed microbial metabolites as a potential strategy and their applications in HF as well.

A generalized weighted combination test of treatment effect for clinical trials with a sequential parallel comparison design and binary endpoint.

To address the issue of a large placebo effect in certain therapeutic areas, rather than the application of the traditional gold standard parallel group placebo-controlled design, different versions of the sequential parallel comparison design have been advocated. In general, the design consists of two consecutive stages and three treatment groups. Stage 1 placebo nonresponders potentially form a prespecified patient subgroup for formal between-treatment comparison at the final analysis. In this research, a version of the design is considered for a binary endpoint. To fully utilize all available data, a generalized weighted combination test is proposed in case placebo has a relatively small effect for some of the study endpoints. The weighted combination of the test based on stage 1 data and the test based on stage 2 data of stage 1 placebo nonresponders suggested in the literature uses only a part of the study data and is a special case of this generalized weighted combination test. A multiple imputation approach is outlined for handling missing not at random data. Simulation is conducted to evaluate the performances of the methods and a data example is employed to illustrate the applications of the methods.

Estimating the treatment effect for adherers using multiple imputation.

Randomized controlled trials are considered the gold standard to evaluate the treatment effect (estimand) for efficacy and safety. According to the recent International Council on Harmonization (ICH)-E9 addendum (R1), intercurrent events (ICEs) need to be considered when defining an estimand, and principal stratum is one of the five strategies to handle ICEs. Qu et al. (2020, Statistics in Biopharmaceutical Research 12:1-18) proposed estimators for the adherer average causal effect (AdACE) for estimating the treatment difference for those who adhere to one or both treatments based on the causal-inference framework, and demonstrated the consistency of those estimators; however, this method requires complex custom programming related to high-dimensional numeric integrations. In this article, we implemented the AdACE estimators using multiple imputation (MI) and constructed confidence intervals (CIs) through bootstrapping. A simulation study showed that the MI-based estimators provided consistent estimators with the nominal coverage probabilities of CIs for the treatment difference for the adherent populations of interest. As an illustrative example, the new method was applied to data from a real clinical trial comparing two types of basal insulin for patients with type 1 diabetes.

Implementation of tripartite estimands using adherence causal estimators under the causal inference framework.

Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is relevant to the clinical research question is the first step in analyzing data. The tripartite estimands, which evaluate the treatment differences in the proportion of patients with ICEs due to adverse events, the proportion of patients with ICEs due to lack of efficacy, and the primary efficacy outcome for those who can adhere to study treatment under the causal inference framework, are of interest to many stakeholders in understanding the totality of treatment effects. In this manuscript, we discuss the details of how to estimate tripartite estimands based on a causal inference framework and how to interpret tripartite estimates through a phase 3 clinical study evaluating a basal insulin treatment for patients with type 1 diabetes.

Estimation of group means in generalized linear mixed models.

In this study, we investigate the concept of the mean response for a treatment group mean as well as its estimation and prediction for generalized linear models with a subject-wise random effect. Generalized linear models are commonly used to analyze categorical data. The model-based mean for a treatment group usually estimates the response at the mean covariate. However, the mean response for the treatment group for studied population is at least equally important in the context of clinical trials. New methods were proposed to estimate such a mean response in generalized linear models; however, this has only been done when there are no random effects in the model. We suggest that, in a generalized linear mixed model (GLMM), there are at least two possible definitions of a treatment group mean response that can serve as estimation/prediction targets. The estimation of these treatment group means is important for healthcare professionals to be able to understand the absolute benefit vs risk. For both of these treatment group means, we propose a new set of methods that suggests how to estimate/predict both of them in a GLMMs with a univariate subject-wise random effect. Our methods also suggest an easy way of constructing corresponding confidence and prediction intervals for both possible treatment group means. Simulations show that proposed confidence and prediction intervals provide correct empirical coverage probability under most circumstances. Proposed methods have also been applied to analyze hypoglycemia data from diabetes clinical trials.

The Serpentine Illusion: A Visual Motion Illusion Induced by Phase-Shifted Line Gratings.

In a pattern of horizontal lines containing ± 45° zigzagging phase-shifted strips, vivid illusory motion is perceived when the pattern is translated up or down at a moderate speed. Two forms of illusory motion are seen: [i] a motion "racing" along the diagonal interface between the strips and [ii] lateral (sideways) motion of the strip sections. We found the relative salience of these two illusory motions to be strongly influenced by the vertical spacing and length of the line gratings, and the period length of the zigzag strips. Both illusory motions are abolished when the abutting strips are interleaved, separated by a gap or when a real line is superimposed at the interface. Illusory motion is also severely weakened when equiluminant colored grating lines are used. Illusory motion perception is fully restored at < 20% luminance contrast. Using adaptation, we find that line-ends alone are insufficient for illusory motion perception, and that both physical carrier motion and line orientation are required. We finally test a classical spatiotemporal energy model of V1 cells that exhibit direction tuning changes that are consistent with the direction of illusory motion. Taking this data together, we constructed a new visual illusion and surmise its origin to interactions of spatial and temporal energy of the lines and line-ends preferentially driving the magnocellular pathway.

Going with the Flow: The Neural Mechanisms Underlying Illusions of Complex-Flow Motion.

Studying the mismatch between perception and reality helps us better understand the constructive nature of the visual brain. The Pinna-Brelstaff motion illusion is a compelling example illustrating how a complex moving pattern can generate an illusory motion perception. When an observer moves toward (expansion) or away (contraction) from the Pinna-Brelstaff figure, the figure appears to rotate. The neural mechanisms underlying the illusory complex-flow motion of rotation, expansion, and contraction remain unknown. We studied this question at both perceptual and neuronal levels in behaving male macaques by using carefully parametrized Pinna-Brelstaff figures that induce the above motion illusions. We first demonstrate that macaques perceive illusory motion in a manner similar to that of human observers. Neurophysiological recordings were subsequently performed in the middle temporal area (MT) and the dorsal portion of the medial superior temporal area (MSTd). We find that subgroups of MSTd neurons encoding a particular global pattern of real complex-flow motion (rotation, expansion, contraction) also represent illusory motion patterns of the same class. They require an extra 15 ms to reliably discriminate the illusion. In contrast, MT neurons encode both real and illusory local motions with similar temporal delays. These findings reveal that illusory complex-flow motion is first represented in MSTd by the same neurons that normally encode real complex-flow motion. However, the extraction of global illusory motion in MSTd from other classes of real complex-flow motion requires extra processing time. Our study illustrates a cascaded integration mechanism from MT to MSTd underlying the transformation from external physical to internal nonveridical flow-motion perception.SIGNIFICANCE STATEMENT The neural basis of the transformation from objective reality to illusory percepts of rotation, expansion, and contraction remains unknown. We demonstrate psychophysically that macaques perceive these illusory complex-flow motions in a manner similar to that of human observers. At the neural level, we show that medial superior temporal (MSTd) neurons represent illusory flow motions as if they were real by globally integrating middle temporal area (MT) local motion signals. Furthermore, while MT neurons reliably encode real and illusory local motions with similar temporal delays, MSTd neurons take a significantly longer time to process the signals associated with illusory percepts. Our work extends previous complex-flow motion studies by providing the first detailed analysis of the neuron-specific mechanisms underlying complex forms of illusory motion integration from MT to MSTd.

Dose Unit Establishment for a New Basal Insulin Using Joint Modeling of Insulin Dose and Glycemic Response.

BACKGROUND: For new insulin analogs with properties that vary from human insulin, defining activity in units of human insulin based on glycemic lowering efficacy may be challenging. Here we present a new method that can be used to quantify a unit dose of an experimental insulin when the traditional euglycemic clamp method is not adequate. METHODS: Joint modeling of insulin dose and the glycemic outcome variable hemoglobin A1c (HbA1c), where both were response variables, was used to evaluate insulin unit potency for basal insulin peglispro (BIL). The data were from the Phase 3 program for BIL, which included greater than 5500 patients with type 1 or type 2 diabetes who were treated for 26 or 52 weeks with BIL or a comparator insulin. Both basal-bolus and basal insulin only studies were included, and some type 2 diabetes patients were insulin-naïve. RESULTS: The analysis showed that 1 unit of BIL, composed of 9 nmol of active ingredient, had similar or slightly greater potency compared to 1 unit insulin glargine or NPH insulin for all populations. CONCLUSIONS: Despite some limitations, the joint modeling of HbA1c and insulin dose provides a reasonable approach to estimate the relative potency of a new basal insulin versus an established basal insulin.

Relationship of Glucose Variability With Glycated Hemoglobin and Daily Mean Glucose: A Post Hoc Analysis of Data From 5 Phase 3 Studies.

BACKGROUND: The association of glucose variability (GV) with other glycemic measures is emerging as a topic of interest. The aim of this analysis is to study the correlation between GV and measures of glycemic control, such as glycated hemoglobin (HbA1c) and daily mean glucose (DMG). METHODS: Data from 5 phase 3 trials were pooled into 3 analysis groups: type 2 diabetes (T2D) treated with basal insulin only, T2D treated with basal-bolus therapy, and type 1 diabetes (T1D). A generalized boosted model was used post hoc to assess the relationship of the following variables with glycemic control parameters (HbA1c and DMG): within-day GV, between-day GV (calculated using self-monitored blood glucose and fasting blood glucose [FBG]), hypoglycemia rate, and certain baseline characteristics. RESULTS: Within-day GV (calculated using standard deviation [SD]) was found to have a significant influence on endpoints HbA1c and DMG in all 3 patient groups. Between-day GV from FBG (calculated using SD), within-day GV (calculated using coefficient of variation), and hypoglycemia rate were found to significantly influence the endpoint HbA1c in the T2D basal-only group. CONCLUSIONS: Lower within-day GV was significantly associated with improvement in DMG and HbA1c. This finding suggests that GV could be a marker in the early phases of new antihyperglycemic therapy development for predicting clinical outcomes in terms of HbA1c and DMG.

Lipid changes during basal insulin peglispro, insulin glargine, or NPH treatment in six IMAGINE trials.

Basal insulin peglispro (BIL) is a novel basal insulin with hepato-preferential action resulting from reduced peripheral effects. This report provides an integrated summary of lipid changes at 26 weeks with BIL and comparator insulins (glargine, NPH) from phase III studies in type 1 diabetes (T1D), insulin-naïve patients with type 2 diabetes (T2D), patients with T2D on basal insulin only and patients with T2D on basal-bolus therapy. BIL treatment had little effect on HDL cholesterol and LDL cholesterol in all patients. The effect of both BIL and glargine treatment on triglycerides (TG) depended on whether patients had been previously treated with insulin. When BIL replaced conventional insulin glargine or NPH treatments, increases in TG levels were observed. When BIL or comparator insulins were given for 26 weeks to insulin-naïve patients with T2D, TG levels were unchanged from baseline with BIL but decreased with either glargine or NPH. The decreased peripheral action of BIL may reduce suppression of lipolysis in peripheral adipose tissue resulting in increased free fatty acid delivery to the liver and, hence, increased hepatic TG synthesis and secretion.

Randomized Clinical Trial Comparing Basal Insulin Peglispro and Insulin Glargine in Patients With Type 2 Diabetes Previously Treated With Basal Insulin: IMAGINE 5.

OBJECTIVE: To evaluate the efficacy and safety of basal insulin peglispro (BIL) versus insulin glargine in patients with type 2 diabetes (hemoglobin A1c [HbA1c] ≤9% [75 mmol/mol]) treated with basal insulin alone or with three or fewer oral antihyperglycemic medications. RESEARCH DESIGN AND METHODS: This 52-week, open-label, treat-to-target study randomized patients (mean HbA1c 7.42% [57.6 mmol/mol]) to BIL (n = 307) or glargine (n = 159). The primary end point was change from baseline HbA1c to 26 weeks (0.4% [4.4 mmol/mol] noninferiority margin). RESULTS: At 26 weeks, reduction in HbA1c was superior with BIL versus glargine (-0.82% [-8.9 mmol/mol] vs. -0.29% [-3.2 mmol/mol]; least squares mean difference -0.52%, 95% CI -0.67 to -0.38 [-5.7 mmol/mol, 95% CI -7.3 to -4.2; P < 0.001); greater reduction in HbA1c with BIL was maintained at 52 weeks. More BIL patients achieved HbA1c <7% (53 mmol/mol) at weeks 26 and 52 (P < 0.001). With BIL versus glargine, nocturnal hypoglycemia rate was 60% lower, more patients achieved HbA1c <7% (53 mmol/mol) without nocturnal hypoglycemia at 26 and 52 weeks (P < 0.001), and total hypoglycemia rates were lower at 52 weeks (P = 0.03). At weeks 26 and 52, glucose variability was lower (P < 0.01), basal insulin dose was higher (P < 0.001), and triglycerides and aminotransferases were higher with BIL versus glargine (P < 0.05). Liver fat content (LFC), assessed in a subset of patients (n = 162), increased from baseline with BIL versus glargine (P < 0.001), with stable levels between 26 and 52 weeks. CONCLUSIONS: BIL provided superior glycemic control versus glargine, with reduced nocturnal and total hypoglycemia, lower glucose variability, and increased triglycerides, aminotransferases, and LFC.

Hypoglycemic events analysis via recurrent time-to-event (HEART) models.

Diabetes affects an estimated 25.8 million people in the United States and is one of the leading causes of death. A major safety concern in treating diabetes is the occurrence of hypoglycemic events. Despite this concern, the current methods of analyzing hypoglycemic events, including the Wilcoxon rank sum test and negative binomial regression, are not satisfactory. The aim of this article is to propose a new model to analyze hypoglycemic events with the goal of making this model a standard method in industry. Our method is based on a gamma frailty recurrent event model. To make this method broadly accessible to practitioners, this article provides many details of how this method works and discusses practical issues with supporting theoretical proofs. In particular, we make efforts to translate conditions and theorems from abstract counting process and martingale theories to intuitive and clinical meaningful explanations. For example, we provide a simple proof and illustration of the coarsening at random condition so that the practitioner can easily verify this condition. Connections and differences with traditional methods are discussed, and we demonstrate that under certain scenarios the widely used Wilcoxon rank sum test and negative binomial regression cannot control type 1 error rates while our proposed method is robust in all these situations. The usefulness of our method is demonstrated through a diabetes dataset which provides new clinical insights on the hypoglycemic data.

Estimation of group means when adjusting for covariates in generalized linear models.

Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes.

Asymmetrical color filling-in from the nasal to the temporal side of the blind spot.

The physiological blind spot, corresponding to the optic disk in the retina, is a relatively large (6 × 8°) area in the visual field that receives no retinal input. However, we rarely notice the existence of it in daily life. This is because the blind spot fills in with the brightness, color, texture, and motion of the surround. The study of filling-in enables us to better understand the creative nature of the visual system, which generates perceptual information where there is none. Is there any retinotopic rule in the color filling-in of the blind spot? To find out, we used mono-colored and bi-colored annuli hugging the boundary of the blind spot. We found that mono-colored annuli filled in the blind spot uniformly. By contrast, bi-colored annuli, where one half had a given color, while the other half had a different one, filled in the blind spot asymmetrically. Specifically, the color surrounding the nasal half typically filled in about 75% of the blind spot area, whereas the color surrounding the temporal half filled in only about 25%. This asymmetry was dependent on the relative size of the half rings, but not the two colors used, and was absent when the bi-colored annulus was rotated by 90°. Here, the two colors on the upper and lower sides of the blind spot filled in the enclosed area equally. These results suggest that the strength of filling-in decreases with distance from the fovea consistent with the decrease of the cortical magnification factor.

A clinical utility index for selecting an optimal insulin dosing algorithm for LY2605541 in patients with type 2 diabetes pretreated with basal insulin.

BACKGROUND: Because insulin dosing requires optimization of glycemic control, it is important to use a single metric of benefit and risk to determine best insulin dosing practices. We retrospectively applied a multiplicative clinical utility index (CUI) to a study of LY2605541 (Eli Lilly and Company, Indianapolis, IN), a novel, long-acting basal insulin. MATERIALS AND METHODS: A CUI was developed to transform the multidimensional problem of assessing benefit/risk of multiple dosing algorithms into a single decision-making metric to evaluate two LY2605541 dosing algorithms relative to the insulin glargine (GL) dosing algorithm. The CUI was applied to data in a 12-week, open-label, Phase 2 trial in patients with type 2 diabetes mellitus who were randomized to one of two dosing algorithms for LY2605541 (LY1 or LY2) or GL (algorithm similar to LY1). The CUI was created (via expert input) by weighing the relative benefit/risk of four components (glycosylated hemoglobin [HbA1c], percentage of patients with HbA1c ≤ 7%, hypoglycemia rate, and time to steady-state dose); individual utility values were multiplied to compute CUI values for LY1 and LY2 relative to GL, and bootstrap samples were used to determine variability. RESULTS: The mean CUI was 0.82 for LY1 and 1.35 for LY2. Based on 3,000 bootstrap samples, there was a 48% probability of LY2 performing better than LY1 and a 28% probability of LY1 performing better than LY2. CONCLUSIONS: CUI methodology, and in particular this CUI, is a useful tool for comparing dosing algorithms. Based on this CUI, LY2 is likely to be a better dosing algorithm than LY1.