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.

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.

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.

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.

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.

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.

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.

Analysis of hypoglycemic events using negative binomial models.

Negative binomial regression is a standard model to analyze hypoglycemic events in diabetes clinical trials. Adjusting for baseline covariates could potentially increase the estimation efficiency of negative binomial regression. However, adjusting for covariates raises concerns about model misspecification, in which the negative binomial regression is not robust because of its requirement for strong model assumptions. In some literature, it was suggested to correct the standard error of the maximum likelihood estimator through introducing overdispersion, which can be estimated by the Deviance or Pearson Chi-square. We proposed to conduct the negative binomial regression using Sandwich estimation to calculate the covariance matrix of the parameter estimates together with Pearson overdispersion correction (denoted by NBSP). In this research, we compared several commonly used negative binomial model options with our proposed NBSP. Simulations and real data analyses showed that NBSP is the most robust to model misspecification, and the estimation efficiency will be improved by adjusting for baseline hypoglycemia.

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.

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 comparison of prasugrel at the time of percutaneous coronary intervention or as pretreatment at the time of diagnosis in patients with non-ST-segment elevation myocardial infarction: design and rationale for the ACCOAST study.

BACKGROUND: The precise risk/benefit of thienopyridine pretreatment and the optimal dosage and timing of a thienopyridine loading dose (LD) for patients presenting with non-ST-segment elevation (NSTE) acute coronary syndromes are still being debated. Prasugrel, a novel thienopyridine, is an appropriate drug to address this issue as it provides predictably high and rapid inhibition of platelet aggregation. STUDY DESIGN: ACCOAST is a phase 3, multicenter, parallel-group, double-blind, and event-driven study designed to compare 2 prasugrel LD schedules in patients with NSTE myocardial infarction who are scheduled for coronary angiography/percutaneous coronary intervention (PCI). Approximately 4,100 patients will be randomly assigned to an initial LD of 30 mg of prasugrel after the diagnosis followed by coronary angiography with an additional dose of 30 mg of prasugrel given at the time of PCI (pretreatment) or an LD of 60 mg of prasugrel given to patients undergoing PCI at the time of the procedure (non-pretreatment). All patients undergoing PCI will receive 5 or 10 mg of prasugrel daily. The primary objective is to test the hypothesis that prasugrel pretreatment is superior to prasugrel non-pretreatment as measured by a reduction in the composite end point of cardiovascular death, myocardial infarction, stroke, urgent revascularization, or glycoprotein IIb/IIIa inhibitor bailout through 7 days from randomization. Key safety end points include TIMI (Thrombolysis In Myocardial Infarction) major and minor bleeding risks. CONCLUSIONS: The ACCOAST study will provide important evidence with regard to the benefits and risks of prasugrel pretreatment compared with administration of prasugrel at the time of PCI in patients with NSTE myocardial infarction.

The onset of inhibition of platelet aggregation with prasugrel compared with clopidogrel loading doses using gatekeeping analysis of integrated clinical pharmacology data.

The purpose of this analysis was to determine the time by which a prasugrel 60-mg loading dose (LD) achieved significantly greater inhibition of platelet aggregation (IPA) than the peak IPA after a clopidogrel 300-mg LD or 600-mg LD. Data were pooled from nine studies representing 587 individuals: 274 healthy subjects and 313 patients with stable coronary artery disease. The primary pharmacodynamic measure was IPA using 20 [mu]M adenosine-5'-diphosphate as the agonist. Gatekeeping analysis compared the peak IPA at 4, 6, and 24 hours after a clopidogrel 300-mg or 600-mg LD with IPA at various prior time points backwards after a prasugrel LD until a statistically nonsignificant difference was reached. Prasugrel 60-mg LD produced greater IPA by 30 minutes than the peak IPA after a clopidogrel 300-mg LD (P < 0.0001). Significantly greater IPA was achieved at 1 hour after prasugrel 60-mg LD compared with the peak IPA after 600-mg clopidogrel LD (P < 0.0001), regardless of sex, body weight, or age and as early as 30 minutes in the diabetic subgroup. A prasugrel 60-mg LD produces significantly faster onset and greater IPA compared with a clopidogrel 300-mg LD or 600-mg LD.

A comparison of the VerifyNow P2Y12 point-of-care device and light transmission aggregometry to monitor platelet function with prasugrel and clopidogrel: an integrated analysis.

We compared platelet function results obtained with the VerifyNow P2Y12 (VN-P2Y12) point-of-care device and the light transmission aggregometry (5 and 20 microM adenosine diphosphate) method using an integrated database of eight clinical trials with a total of 591 subjects. The study was performed in healthy subjects, patients with coronary artery disease, patients with end-stage renal disease, and patients with acute coronary syndrome after treatment with prasugrel or clopidogrel. Analyses focused on loading doses of 60 mg prasugrel or 600 mg clopidogrel and daily maintenance doses of 10 mg prasugrel or 75 or 150 mg clopidogrel. Similar patterns of platelet inhibition were observed for light transmission aggregometry versus VN-P2Y12 and assay results were well correlated (r approximately 0.7), although a sigmoidal model may more accurately represent the relationship between light transmission aggregometry and VN-P2Y12, because VN-P2Y12 was relatively less sensitive to low and high levels of inhibition. The percentage of poor responders was less with prasugrel compared with clopidogrel by both assays, but the percentages tended to differ between the assays. The VN-P2Y12 "BASE" channel appeared to be susceptible to high levels of P2Y12 blockade, which would underestimate the VN-P2Y12-reported percent inhibition in individuals who respond well to loading doses of thienopyridines. This integrated analysis supports the findings of earlier individual studies comparing these methodologies that assess platelet function.

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.