Cardiovascular Safety Assessment in Early‐Phase Clinical Studies: A Meta‐Analytical Comparison of Exposure‐Response Models

Conrado, DJ; Chen, D; Denney, William S.

doi:10.1002/psp4.12086

Cited by 5 publications

(7 citation statements)

References 42 publications

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“…To our knowledge, comprehensive baseline heart rate models using circadian rhythm in adults are not currently available, although a 24-hour circadian rhythm model for heart rate in pediatrics has been reported. 21 The time course of systolic and diastolic BP in the current analyses was described using a single 24-hour circadian rhythm, consistent with that described by Conrado et al 22 Other time-related effects were also observed in the baseline measures, which reached a steady state within 2-3 days. These time-related drifts cannot be clearly explained and could be attributed to the interday and intraday performances of the electrocardiogram tracings.…”

Section: Placebo Models For Cardiovascular Safety Variablessupporting

confidence: 87%

Models of Variability and Circadian Rhythm in Heart Rate, Blood Pressure, and QT Interval for Healthy Subjects Who Received Placebo in Phase I Trials

Minocha

Chiu

et al. 2019

Clinical Translational Sci

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This work characterized the time‐course, circadian rhythm, and inherent variability in key cardiovascular variables (heart rate, corrected QT interval, and systolic and diastolic blood pressure) that are routinely collected as part of safety monitoring in phase I trials. Longitudinal data from 1,035 healthy volunteers who received placebo in 65 single‐dose and multiple‐dose phase I trials conducted by AbbVie were compiled and analyzed using nonlinear mixed‐effects modeling. An independent nonlinear mixed‐effects model was developed for each variable, and combinations of cosine functions were used to capture circadian oscillations. Gender, race, age, and body weight were significant covariates for variability in baseline measures, and the contributions of these covariates were quantitatively characterized. Based on the extensive data set analyzed, the developed models represent valuable tools to help contextualize and differentiate inherent variability that can be expected in a typical phase I setting from true drug‐related cardiovascular safety signals. In addition, these placebo models can be used to support exposure–response analyses that estimate treatment‐related effects on the evaluated cardiovascular measures.

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Section: Placebo Models For Cardiovascular Safety Variablessupporting

confidence: 87%

Models of Variability and Circadian Rhythm in Heart Rate, Blood Pressure, and QT Interval for Healthy Subjects Who Received Placebo in Phase I Trials

Minocha

Chiu

et al. 2019

Clinical Translational Sci

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“…Relevant subject characteristics were also tested for association with dropout. Model selection was guided by a modified version of the Akaike information criterion (AIC mod ) with a per‐parameter penalty of 3.841 to be equivalent to a one‐parameter, nested‐model χ 2 test with a P value of 0.05.…”

Section: Methodsmentioning

confidence: 99%

Dopamine Transporter Neuroimaging as an Enrichment Biomarker in Early Parkinson's Disease Clinical Trials: A Disease Progression Modeling Analysis

Conrado

Nicholas

Tsai

et al. 2017

Clinical Translational Sci

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Given the recognition that disease‐modifying therapies should focus on earlier Parkinson's disease stages, trial enrollment based purely on clinical criteria poses significant challenges. The goal herein was to determine the utility of dopamine transporter neuroimaging as an enrichment biomarker in early motor Parkinson's disease clinical trials. Patient‐level longitudinal data of 672 subjects with early‐stage Parkinson's disease in the Parkinson's Progression Markers Initiative (PPMI) observational study and the Parkinson Research Examination of CEP‐1347 Trial (PRECEPT) clinical trial were utilized in a linear mixed‐effects model analysis. The rate of worsening in the motor scores between subjects with or without a scan without evidence of dopamine transporter deficit was different both statistically and clinically. The average difference in the change from baseline of motor scores at 24 months between biomarker statuses was –3.16 (90% confidence interval [CI] = –0.96 to –5.42) points. Dopamine transporter imaging could identify subjects with a steeper worsening of the motor scores, allowing trial enrichment and 24% reduction of sample size.

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“…Interestingly, the simpler model performed well by showing minimal bias on the final estimate of QTc prolongation and had the non-negligible advantage of not having convergence issues that would limit its use. Conrado et al 9 have performed a meta-analysis of ECG data from 39 SAD or multiple-ascending dose (MAD) studies comparing different models. Among other factors, five unique baseline models were tested and it appears from these results, that an unstructured baseline model (i.e., with a baseline value for each time-point) was selected most often for QT/QTc.…”

Section: Applying Exposure Response Modeling To Determine Proarrhythmmentioning

confidence: 99%

“… 28 Another method to assess hysteresis using the derivative of drug concentration versus time and standardized residuals from the fitted model has been reported. 9 This prior assessment may avoid the need for testing models with delayed effects, keeping in mind that reducing the number of models tested reduces both analysis time and review time.…”

Section: Applying Exposure Response Modeling To Determine Proarrhythmmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating cardiac risk: exposure response analysis in early clinical drug development

Grenier

Paglialunga

Morimoto

et al. 2018

DHPS

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The assessment of a drug’s cardiac liability has undergone considerable metamorphosis by regulators since International Council for Harmonization of Technical Requirement for Pharmaceuticals for Human Use E14 guideline was introduced in 2005. Drug developers now have a choice in how proarrhythmia risk can be evaluated; the options include a dedicated thorough QT (TQT) study or exposure response (ER) modeling of intensive electrocardiogram (ECG) captured in early clinical development. The alternative approach of ER modeling was incorporated into a guidance document in 2015 as a primary analysis tool which could be utilized in early phase dose escalation studies as an option to perform a dedicated TQT trial. This review will describe the current state of ER modeling of intensive ECG data collected during early clinical drug development; the requirements with regard to the use of a positive control; and address the challenges and opportunities of this alternative approach to assessing QT liability.

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Cardiovascular Safety Assessment in Early‐Phase Clinical Studies: A Meta‐Analytical Comparison of Exposure‐Response Models

Cited by 5 publications

References 42 publications

Models of Variability and Circadian Rhythm in Heart Rate, Blood Pressure, and QT Interval for Healthy Subjects Who Received Placebo in Phase I Trials

Models of Variability and Circadian Rhythm in Heart Rate, Blood Pressure, and QT Interval for Healthy Subjects Who Received Placebo in Phase I Trials

Dopamine Transporter Neuroimaging as an Enrichment Biomarker in Early Parkinson's Disease Clinical Trials: A Disease Progression Modeling Analysis

Evaluating cardiac risk: exposure response analysis in early clinical drug development

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