Evaluation of the Efficacy and Safety of Bempedoic Acid (ETC-1002) in Patients With Hyperlipidemia and Statin Intolerant

Esperion Therapeutics, Inc.345 enrolled

Overview

The purpose of this study is to determine if bempedoic acid (ETC-1002) is effective and safe versus placebo in patients with elevated LDL cholesterol and who are statin-intolerant.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

TRIPLE

Enrollment

345

Conditions

Hypercholesterolemia Atherosclerotic Cardiovascular Disease Statin Adverse Reaction

Interventions

bempedoic acidDRUG

bempedoic acid 180 mg tablet

placeboOTHER

Matching placebo tablet

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Require lipid-modifying therapy for primary or secondary prevention of cardiovascular disease * Fasting LDL-C ≥130 mg/dL for primary prevention or LDL-C ≥100 mg/dL for secondary prevention (history of HeFH and/or ASCVD) * Be statin-intolerant (unable to tolerate 2 or more statins) Exclusion Criteria: * Total fasting triglyceride ≥500 mg/dL * Renal dysfunction or nephrotic syndrome or history of nephritis * Body Mass Index (BMI) ≥50 kg/m2 * Significant cardiovascular disease or cardiovascular event in the past 3 months

Locations (67)

Outcomes

Primary Outcomes

Percent Change From Baseline (PCFB) in Low-density Lipoprotein Cholesterol (LDL-C) at Week 12

PCFB was calculated as the (\[post-Baseline (BL) value minus the BL value\] divided by the BL value ) x 100. BL was defined as the mean of the last two non-missing values on or prior to Day 1. If only one value was available, that single value was used as BL. PCFB in LDL-C was analyzed using analysis of covariance (ANCOVA), with treatment group and stratification factor (primary prevention; secondary prevention) as fixed effects and BL as a covariate. For participants with missing lipid data at Week 12 who were no longer taking study treatment, missing values were imputed using multiple imputation via a regression-based model including stratification and BL data from placebo participants only. In this imputation model, treatment group was not included. For participants with missing lipid data at Week 12 who were still taking study treatment, missing values were imputed using multiple imputation via a regression-based model including treatment, stratification and BL value.

Time frame: Baseline; Week 12

Secondary Outcomes

Percent Change From Baseline in LDL-C at Week 24

PCFB was calculated as the (\[post-BL value minus the BL value\] divided by the BL value ) x 100. BL was defined as the mean of the last two non-missing values on or prior to Day 1. If only one value was available then that single value was used as BL. PCFB in LDL-C was analyzed using ANCOVA, with treatment group and stratification factor (primary prevention; secondary prevention) as fixed effects and BL as a covariate. For participants with missing lipid data at Week 12 who were no longer taking study treatment, missing values were imputed using multiple imputation via a regression-based model including stratification and BL data from placebo participants only. In this imputation model, treatment group was not included. For participants with missing lipid data at Week 12 who were still taking study treatment, missing values were imputed using multiple imputation via a regression-based model including treatment, stratification and BL value.

Time frame: Baseline; Week 24

Percent Change From Baseline in the Lipid Profile at Week 12

Data from ClinicalTrials.gov

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Gilbert, Arizona, United States

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Anaheim, California, United States

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Long Beach, California, United States

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Sacramento, California, United States

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Santa Ana, California, United States

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Torrance, California, United States

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Ventura, California, United States

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Hamden, Connecticut, United States

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Daytona Beach, Florida, United States

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Fort Lauderdale, Florida, United States

...and 57 more locations

PCFB was calculated as: (\[post-BL value minus the BL value\] divided by the BL value) x 100. BL was defined as the mean of the last two non-missing values on or prior to Day 1. If only one value was available, that single value was used as BL. apoB and TC BL were defined as the last non-missing value on/prior to Day 1. PCFB was analyzed using ANCOVA, with treatment and group stratification factor (primary prevention; secondary prevention) as fixed effects and BL as a covariate. For participants with missing data at Week 12 who were no longer taking study treatment (ST), missing values were imputed using multiple imputation via a regression-based model including stratification and BL data from placebo participants only. In this imputation model, treatment group was not included. For participants with missing lipid data at Week 12 who were still taking ST, missing values were imputed using multiple imputation via a regression-based model including treatment, stratification and BL value.