Ming-Dauh Wang

Potent and Selective PPAR-α Agonist LY518674 Upregulates Both ApoA-I Production and Catabolism in Human Subjects With the Metabolic Syndrome

Objective—The study of PPAR-α activation on apoA-I production in humans has been limited to fibrates, relatively weak PPAR-α agonists that may have other molecular effects. We sought to determine the effect of a potent and highly specific PPAR-α agonist, LY518674, on apoA-I, apoA-II, and apoB-100 kinetics in humans with metabolic syndrome and low levels of HDL cholesterol (C). Methods and Results—Subjects were randomized to receive LY518674 (100 &mgr;g) once daily (n=13) or placebo (n=15) for 8 weeks. Subjects underwent a kinetic study using a deuterated leucine tracer to measure apolipoprotein production and fractional catabolic rates (FCR) at baseline and after treatment. LY518674 significantly reduced VLDL-C (−38%, P=0.002) and triglyceride (−23%, P=0.002) levels whereas LDL-C and HDL-C levels were unchanged. LY518674 significantly reduced VLDL apoB-100 (−12%, P=0.01) levels, attributable to an increased VLDL apoB-100 FCR with no change in VLDL apoB-100 production. IDL and LDL apoB-100 kinetics were unchanged. LY518674 significantly increased the apoA-I production rate by 31% (P<0.0001), but this was accompanied by a 33% increase in the apoA-I FCR (P=0.002), resulting in no change in plasma apoA-I. There was a 71% increase in the apoA-II production rate (P<0.0001) accompanied by a 25% increase in the FCR (P<0.0001), resulting in a significant increase in plasma apoA-II. Conclusions—Activation of PPAR-α with LY518674 (100 &mgr;g) in subjects with metabolic syndrome and low HDL-C increased the VLDL apoB-100 FCR consistent with enhanced lipolysis of plasma triglyceride. Significant increases in the apoA-I and apoA-II production rates were accompanied by increased FCRs resulting in no change in HDL-C levels. These data indicate a major effect of LY518674 on the production and clearance of apoA-I and HDL despite no change in the plasma concentration. The effect of these changes on reverse cholesterol transport remains to be determined.

Effects of the cholesteryl ester transfer protein inhibitor evacetrapib on lipoproteins, apolipoproteins and 24-h ambulatory blood pressure in healthy adults.

We investigated the safety, tolerability, pharmacokinetics and pharmacodynamics of evacetrapib.

SAFETY AND EFFICACY OF LY3015014, A NEW MONOCLONAL ANTIBODY TO PROPROTEIN CONVERTASE SUBTILISIN/KEXIN TYPE 9 (PCSK9) WITH AN INHERENTLY LONGER DURATION OF ACTION, IN PATIENTS WITH PRIMARY HYPERCHOLESTEROLEMIA: A RANDOMIZED, PLACEBO-CONTROLLED, DOSE-RANGING, PHASE 2 STUDY

PCSK9 antibodies under development require dosing every (Q) 2 weeks (W) or large monthly doses to maintain sustained LDL-C reductions. LY3015014 (LY) is a humanized monoclonal antibody with an inherently longer duration of action. This phase 2 study assessed the LDL-C lowering effect of LY given

Evacetrapib: in vitro and clinical disposition, metabolism, excretion, and assessment of drug interaction potential with strong CYP3A and CYP2C8 inhibitors.

Evacetrapib is an investigational cholesteryl ester transfer protein inhibitor (CETPi) for reduction of risk of major adverse cardiovascular events in patients with high‐risk vascular disease. Understanding evacetrapib disposition, metabolism, and the potential for drug–drug interactions (DDI) may help guide prescribing recommendations. In vitro, evacetrapib metabolism was investigated with a panel of human recombinant cytochromes P450 (CYP). The disposition, metabolism, and excretion of evacetrapib following a single 100‐mg oral dose of 14C‐evacetrapib were determined in healthy subjects, and the pharmacokinetics of evacetrapib were evaluated in the presence of strong CYP3A or CYP2C8 inhibitors. In vitro, CYP3A was responsible for about 90% of evacetrapibs CYP‐associated clearance, while CYP2C8 accounted for about 10%. In the clinical disposition study, only evacetrapib and two minor metabolites circulated in plasma. Evacetrapib metabolism was extensive. A mean of 93.1% and 2.30% of the dose was excreted in feces and urine, respectively. In clinical DDI studies, the ratios of geometric least squares means for evacetrapib with/without the CYP3A inhibitor ketoconazole were 2.37 for area under the curve (AUC)(0–∞) and 1.94 for Cmax. There was no significant difference in evacetrapib AUC(0–τ) or Cmax with/without the CYP2C8 inhibitor gemfibrozil, with ratios of 0.996 and 1.02, respectively. Although in vitro results indicated that both CYP3A and CYP2C8 metabolized evacetrapib, clinical studies confirmed that evacetrapib is primarily metabolized by CYP3A. However, given the modest increase in evacetrapib exposure and robust clinical safety profile to date, there is a low likelihood of clinically relevant DDI with concomitant use of strong CYP3A or CYP2C8 inhibitors.

CYP‐mediated drug–drug interactions with evacetrapib, an investigational CETP inhibitor: in vitro prediction and clinical outcome

Aims Evacetrapib is a cholesteryl ester transfer protein (CETP) inhibitor under development for reducing cardiovascular events in patients with high risk vascular disease. CETP inhibitors are likely to be utilized as ‘add‐on’ therapy to statins in patients receiving concomitant medications, so the potential for evacetrapib to cause clinically important drug–drug interactions (DDIs) with cytochromes P450 (CYP) was evaluated. Methods The DDI potential of evacetrapib was investigated in vitro, followed by predictions to determine clinical relevance. Potential DDIs with possible clinical implications were then investigated in the clinic. Results In vitro, evacetrapib inhibited all of the major CYPs, with inhibition constants (K i) ranging from 0.57 µm (CYP2C9) to 7.6 µm (CYP2C19). Evacetrapib was a time‐dependent inhibitor and inducer of CYP3A. The effects of evacetrapib on CYP3A and CYP2C9 were assessed in a phase 1 study using midazolam and tolbutamide as probe substrates, respectively. After 14 days of daily dosing with evacetrapib (100 or 300 mg), midazolam exposures (AUC) changed by factors (95% CI) of 1.19 (1.06, 1.33) and 1.44 (1.28, 1.62), respectively. Tolbutamide exposures (AUC) changed by factors of 0.85 (0.77, 0.94) and 1.06 (0.95, 1.18), respectively. In a phase 2 study, evacetrapib 100 mg had minimal impact on AUC of co‐administered simvastatin vs. simvastatin alone with a ratio of 1.25 (1.03, 1.53) at steady‐state, with no differences in reported hepatic or muscular adverse events. Conclusions Taken together, the extent of CYP‐mediated DDI with the potential clinical dose of evacetrapib is weak and clinically important DDIs are not expected to occur in patients taking concomitant medications.

Applications of Probability of Study Success in Clinical Drug Development

The dominant approach to sample size determination for a clinical trial in regulatory review-driven pharmaceutical research has long been by assuming fixed values of parameters under competing hypotheses, i.e., null versus alternative representing futility and desired efficaciousness of a tested drug. A sample size is then determined to ensure sufficient statistical power for differentiating between the null and alternative hypotheses, while controlling the probability of wrongly rejecting the null. This approach bears the criticism of ignoring the variability inherent with the unknown parameters. To improve sample size determination, accounting for variability of parameters has recently been gaining application in pharmaceutical-conducted clinical trials. The common intent of this increased interest is to better predict the probability of a successful trial, which is often termed probability of study success (PrSS) or probability of success (POS). We discuss the important role that PrSS can play in clinical trial design and decision making throughout medical product development. A few examples are given for illustration.

Optimal dichotomization for repeated screening tests

Repeated use of a screening test for ascertaining a characteristic often can improve screening performance. We propose a Bayesian predictive decision-theoretic approach to choosing an optimal dichotomizer for the screening test variable in the repeat-test setting.

Impact of Increased Gastric pH on the Pharmacokinetics of Evacetrapib in Healthy Subjects

To examine the effect of increased gastric pH on exposure to evacetrapib, a cholesteryl ester transfer protein inhibitor evaluated for the treatment of atherosclerotic heart disease.

Bayesian Interim Inference of Probability of Clinical Trial Success

Understanding of the efficacy of an investigated compound in early drug development often relies on assessment of a biomarker or multiple biomarkers that are believed to be correlated with the intended clinical outcome. The biomarker of interest may require enough duration of time to show its satisfactory response to drug effect. Meanwhile, many drug candidates in the portfolio of a pharmaceutical company may compete for the limited resources available. Thus decisions based on assessment of the biomarker after a prolonged duration may be inefficient. One solution is that longitudinal measurements of the biomarker be measured during the expected duration, and analysis be conducted in the middle of the trial, so that the interim measurements may help estimate the measurement at the intended time for interim decision making. Considering the small trial size nature of early drug development and convenience in facilitating interim decisions, we applied Bayesian inference to interim analysis of biomarkers.

An evaluation of the trimmed mean approach in clinical trials with dropout

The trimmed mean is a method of dealing with patient dropout in clinical trials that considers early discontinuation of treatment a bad outcome rather than leading to missing data. The present investigation is the first comprehensive assessment of the approach across a broad set of simulated clinical trial scenarios. In the trimmed mean approach, all patients who discontinue treatment prior to the primary endpoint are excluded from analysis by trimming an equal percentage of bad outcomes from each treatment arm. The untrimmed values are used to calculated means or mean changes. An explicit intent of trimming is to favor the group with lower dropout because having more completers is a beneficial effect of the drug, or conversely, higher dropout is a bad effect. In the simulation study, difference between treatments estimated from trimmed means was greater than the corresponding effects estimated from untrimmed means when dropout favored the experimental group, and vice versa. The trimmed mean estimates a unique estimand. Therefore, comparisons with other methods are difficult to interpret and the utility of the trimmed mean hinges on the reasonableness of its assumptions: dropout is an equally bad outcome in all patients, and adherence decisions in the trial are sufficiently similar to clinical practice in order to generalize the results. Trimming might be applicable to other inter-current events such as switching to or adding rescue medicine. Given the well-known biases in some methods that estimate effectiveness, such as baseline observation carried forward and non-responder imputation, the trimmed mean may be a useful alternative when its assumptions are justifiable.