Population pharmacodynamic analysis of LDL-cholesterol lowering effects by statins and co-medications based on electronic medical records

Is there a role for earlier use of combination therapy?

As the global population ages and cardiovascular risk factors rise, we can expect a continued increase in atherosclerotic disease. Low-density lipoprotein cholesterol (LDL-C) reduction is a cornerstone of cardiovascular risk reduction with strong, causal evidence indicating that the greatest benefit is derived from early and large decreases in LDL-C. Despite the adoption of statins as the backbone of lipid-therapy regimens, numerous studies and registry analyses reveal our collective inability to achieve LDL-C goals in high-risk patients. Combination therapy with ezetimibe has been shown to result in statistically significant decreases in LDL-C level, atheroma volume, and cardiovascular adverse event rates. A major barrier to implementing an upfront combination therapy approach is the perceived side effects from therapeutic agents although multiple studies show that a therapeutic patient-physician relationship could overcome this issue. Novel agents such as PCSK-9 inhibitors, bempedoic acid, and inclisiran have the potential to achieve similar outcomes although additional research is needed regarding the cost effectiveness of these approaches. Despite these hurdles, there is a role for the newer agents early in the disease course of high-risk patients such as those with markedly elevated LDL-C >190 mg/dL and FH. The implementation of upfront combination therapy, especially in high-risk patients, will decrease clinical inertia while allowing for earlier consideration of newer, effective agents to decrease cardiovascular burden.

Population pharmacokinetic and pharmacokinetic-pharmacodynamic modeling of bempedoic acid and low-density lipoprotein cholesterol in healthy subjects and patients with dyslipidemia

Population pharmacokinetics (popPK) of bempedoic acid and the popPK/pharmacodynamic (popPK/PD) relationship between bempedoic acid concentrations and serum low-density lipoprotein cholesterol (LDL-C) from baseline were characterized. A two-compartment disposition model with a transit absorption compartment and linear elimination best described bempedoic acid oral pharmacokinetics (PK). Multiple covariates, including renal function, sex, and weight, had statistically significant effects on the predicted steady-state area under the curve. Mild (estimated glomerular filtration rate (eGFR) 60 to < 90 mL/min vs. ≥ 90 mL/min) and moderate (eGFR 30 to < 60 mL/min vs. ≥ 90 mL/min) renal impairment, female sex, low (< 70 kg vs. 70-100 kg) and high (> 100 kg vs. 70-100 kg) body weight were predicted to have a 1.36-fold (90% confidence interval (CI) 1.32, 1.41), 1.85-fold (90% CI 1.74, 2.00), 1.39-fold (90% CI 1.34, 1.47), 1.35-fold (90% CI 1.30, 1.41), and 0.75-fold (90% CI 0.72, 0.79) exposure difference relative to their reference populations, respectively. An indirect response model described changes in serum LDL-C with a model-predicted 35% maximal reduction and bempedoic acid IC50 of 3.17 µg/mL. A 28% reduction from LDL-C baseline was predicted for a steady-state average concentration of 12.5 µg/mL after bempedoic acid (180 mg/day) dosing, accounting for approximately 80% of the predicted maximal LDL-C reduction. Concurrent statin therapy, regardless of intensity, reduced the maximal effect of bempedoic acid but resulted in similar steady-state LDL-C levels. While multiple covariates had statistically significant effects on PK and LDL-C lowering, none were predicted to warrant bempedoic acid dose adjustment.

Characterisation of individual ferritin response in patients receiving chelation therapy

Aims

To develop a drug–disease model describing iron overload and its effect on ferritin response in patients affected by transfusion‐dependent haemoglobinopathies and investigate the contribution of interindividual differences in demographic and clinical factors on chelation therapy with deferiprone or deferasirox.

Methods

Individual and mean serum ferritin data were retrieved from 13 published studies in patients affected by haemoglobinopathies receiving deferiprone or deferasirox. A nonlinear mixed effects modelling approach was used to characterise iron homeostasis and serum ferritin production taking into account annual blood consumption, baseline demographic and clinical characteristics. The effect of chelation therapy was parameterised as an increase in the iron elimination rate. Internal and external validation procedures were used to assess model performance across different study populations.

Results

An indirect response model was identified, including baseline ferritin concentrations and annual blood consumption as covariates. The effect of chelation on iron elimination rate was characterised by a linear function, with different slopes for each drug (0.0109 [90% CI: 0.0079–0.0131] vs. 0.0013 [90% CI: 0.0008–0.0018] L/mg mo). In addition to drug‐specific differences in the magnitude of the ferritin response, simulation scenarios indicate that ferritin elimination rates depend on ferritin concentrations at baseline.

Conclusion

Modelling of serum ferritin following chronic blood transfusion enabled the evaluation of drug‐induced changes in iron elimination rate and ferritin production. The use of a semi‐mechanistic parameterisation allowed us to disentangle disease‐specific factors from drug‐specific properties. Despite comparable chelation mechanisms, deferiprone appears to have a significantly larger effect on the iron elimination rate than deferasirox.

Effects of Evolocumab Added to Moderate-Intensity Statin Therapy in Chinese Patients With Acute Coronary Syndrome: The EMSIACS Trial Study Protocol

Background: Several studies have demonstrated that using a higher dose of statin can easily induce liver injury and myopathy. Low-density lipoprotein cholesterol (LDL-C) is a well-established modifiable risk factor for cardiovascular disease; however, the large majority of Chinese patients cannot meet the target level of LDL-C recommended by the Chinese expert consensus. Evolocumab has been demonstrated to reduce LDL-C by approximately 60% in many studies. Nevertheless, whether combined evolocumab and moderate-intensity statin is as effective in lowering LDL-C and decreasing incidence of MACE in Chinese patients presenting with the acute phase of acute coronary syndrome (ACS) remains unknown. Therefore, the "Evolocumab added to Moderate-Intensity Statin therapy on LDL-C lowering and cardiovascular adverse events in patients with Acute Coronary Syndrome" (EMSIACS) is conducted. Methods: The EMSIACS is a prospective, randomized, open-label, parallel-group, multicenter study involving analyzing the feasibility and efficacy of evolocumab added to moderate-intensity statin therapy on lowering LDL-C levels in adult Chinese patients hospitalized for acute phase ACS. The sample size calculation is based on the primary outcome, and 500 patients will be planned to recruit. Patients are randomized in evolocumab arm (evolocumab 140mg every 2weeks plus rosuvastatin 10mg/day or atorvastatin 20mg/day) and statin-only arm (rosuvastatin 10mg/day or atorvastatin 20mg/day). The primary outcome is the percentage change in LDL-C in weeks 4 and week 12 after treatment. The secondary outcome is the occurrence of MACE after 12weeks and 1year of treatment. Discussion: If the EMSIACS trial endpoints prove statistically significant, the evolocumab added to moderate-intensity statin therapy will have the potential to effectively lower subjects' LDL-C levels, especially for the Chinese patients with acute phase ACS. However, if the risk of MACE is not significantly different between the two groups, we may extend follow-up time for secondary outcome when the clinical trial is over. Clinical trial registration: The study is registered to ClinicalTrials.gov (NCT04100434), which retrospectively registered on November 24, 2020.

Model-Based Approach to Improve Clinical Outcomes in Neonates With Opioid Withdrawal Syndrome Using Real-World Data

At least 60% of the neonates with opioid withdrawal syndrome (NOWS) require morphine to control withdrawal symptoms. Currently, the morphine dosing strategies are empiric, not optimal and associated with longer hospital stay. The aim of the study was to develop a quantitative, model-based, real-world data-driven approach to morphine dosing to improve clinical outcomes, such as reducing time on treatment. Longitudinal morphine dose, clinical response (Modified Finnegan Score (MFS)), and baseline risk factors were collected using a retrospective cohort design from the electronic medical records of neonates with NOWS (N = 177) admitted to the University of Maryland Medical Center. A dynamic linear mixed effects model was developed to describe the relationship between MFS and morphine dose adjusting for baseline risk factors using a split-sample data approach (70% training: 30% test). The training model was evaluated in the test dataset using a simulation based approach. Maternal methadone and benzodiazepine use, and race were significant predictors of the MFS response. Positive autocorrelations of 0.56 and 0.12 were estimated between consecutive MFS responses. On an average, for a 1,000 μg increase in the morphine dose, the MFS decreased by 0.3 units. The model evaluation showed that observed and predicted median time on treatment were similar (13.0 vs. 13.8 days). A model-based framework was developed to describe the MFS-morphine dose relationship using real-world data that could potentially be used to develop an adaptive, individualized morphine dosing strategy to improve clinical outcomes in infants with NOWS.

Rosuvastatin/Ezetimibe: A Review in Hypercholesterolemia

Rosuvastatin/ezetimibe combines two lipid-lowering agents: rosuvastatin, an HMG-CoA reductase inhibitor (i.e. statin) with particularly strong inhibitory effects on hepatic cholesterol synthesis, and ezetimibe, which inhibits the intestinal absorption of cholesterol. A fixed-dose combination (FDC) of rosuvastatin/ezetimibe is indicated as an adjunctive therapy to diet for the management of primary hypercholesterolemia in adults in numerous countries worldwide. In well-designed clinical trials evaluating the therapeutic efficacy of rosuvastatin/ezetimibe administered as either separate agents or as an FDC, rosuvastatin/ezetimibe was significantly more effective than rosuvastatin monotherapy (including at double the dose of rosuvastatin) or simvastatin/ezetimibe in reducing low-density lipoprotein cholesterol (LDL-C) and total cholesterol in adults with hypercholesterolemia. Furthermore, rosuvastatin/ezetimibe enabled significantly higher proportions of patients to achieve recommended LDL-C levels than rosuvastatin monotherapy or simvastatin/ezetimibe. Rosuvastatin/ezetimibe did not significantly differ from rosuvastatin monotherapy with respect to incidences of treatment-related or serious adverse events in these short-term trials and displayed a similar safety profile to simvastatin/ezetimibe. While additional cardiovascular outcomes data and head-to-head comparisons with atorvastatin/ezetimibe would be of interest, rosuvastatin/ezetimibe is a potent and generally well-tolerated drug combination that extends the range of options available for the pharmacological management of primary hypercholesterolemia in adults.

Development of a System for Postmarketing Population Pharmacokinetic and Pharmacodynamic Studies Using Real-World Data From Electronic Health Records

Postmarketing population pharmacokinetic (PK) and pharmacodynamic (PD) studies can be useful to capture patient characteristics affecting PK or PD in real-world settings. These studies require longitudinally measured dose, outcomes, and covariates in large numbers of patients; however, prospective data collection is cost-prohibitive. Electronic health records (EHRs) can be an excellent source for such data, but there are challenges, including accurate ascertainment of drug dose. We developed a standardized system to prepare datasets from EHRs for population PK/PD studies. Our system handles a variety of tasks involving data extraction from clinical text using a natural language processing algorithm, data processing, and data building. Applying this system, we performed a fentanyl population PK analysis, resulting in comparable parameter estimates to a prior study. This new system makes the EHR data extraction and preparation process more efficient and accurate and provides a powerful tool to facilitate postmarketing population PK/PD studies using information available in EHRs.

The Adherence Rate Threshold is Drug Specific

Introduction

Patient adherence to a medication regimen is usually expressed as an adherence rate, defined as the proportion of prescribed doses actually taken. An adherence rate threshold, above which the therapeutic effect is maintained, is typically assigned an arbitrary value, commonly 0.8.

Objective

Here, we determined the value of the adherence rate threshold objectively in different drugs of the same class, using statins as an example.

Methods

We used pharmacokinetic/pharmacodynamic (PK/PD) modeling to predict serum levels of low-density lipoprotein cholesterol (LDL-C) in patients taking simvastatin 20 mg or atorvastatin 5 mg once daily for 30 days. LDL-C reduction was modeled for adherence rates of 1.0, 0.8, 0.6, 0.4, and 0.2. The results were expressed as the percentage of time spent at the LDL-C goal (< 70 mg/dL). The adherence rate threshold was defined as the minimum adherence rate that resulted in the same amount of time at goal as perfect adherence (i.e., a rate of 1.0).

Results

For simvastatin, an adherence rate of 0.8 resulted in a significant decrease in time at the LDL-C goal compared to perfect adherence (54.8% versus 85.1%; P < 0.001), and rates < 0.8 resulted in progressively less time at goal. For atorvastatin, the rates of 0.8 and 0.6 resulted in essentially the same amount of time at goal as perfect adherence (87.8% and 87.7%, respectively, versus 88.1%; P > 0.05 for both), with less time at goal only occurring at rates ≤ 0.4 (P < 0.001). Thus, the adherence rate thresholds are > 0.8 for simvastatin and between 0.4 and 0.6 for atorvastatin.

Conclusion

These results indicate that a value of 0.8 cannot be applied universally.

Pharmacokinetic interactions and tolerability of rosuvastatin and ezetimibe: an open-label, randomized, multiple-dose, crossover study in healthy male volunteers

Purpose

Rosuvastatin is a synthetic 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor that effectively reduces low-density lipoprotein cholesterol levels. However, statin monotherapy does not always achieve acceptable low-density lipoprotein cholesterol levels in patients with severe hypercholesterolemia. Ezetimibe, a selective cholesterol-absorption inhibitor, is approved for use as a monotherapy or combination therapy with 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors for patients with hypercholesterolemia. The aim of this study was to examine the pharmacokinetics (PKs) of drug interactions between rosuvastatin and ezetimibe, and the tolerability of combined administration in healthy Korean male volunteers.

Subjects and methods

Healthy subjects (n=24) were randomly allocated to 3 treatment groups: rosuvastatin (20 mg) alone, ezetimibe (10 mg) alone, and rosuvastatin (20 mg) plus ezetimibe (10 mg). The drugs were taken once every 24 hours over a period of 10 days. Blood samples were collected to analyze steady-state PKs.

Results

All adverse events observed during the study were mild, and the frequency was no higher for combined administration than for mono administration. For rosuvastatin, the steady-state mean ratios (90% CI) of the combined over the single dose were 1.076 (1.019–1.136) for AUC_τ,ss and 1.099 (1.003–1.204) for concentration at steady-state, respectively. In the case of free and total ezetimibe, the steady-state ratios of AUC_τ,ss and concentration at steady-state were 1.131 (1.051–1.218) and 1.182 (1.038–1.346), and 1.055 (0.969–1.148) and 0.996 (0.873–1.135), respectively.

Conclusion

Combined administration of rosuvastatin and ezetimibe was well tolerated. No clinically significant PK interactions between rosuvastatin and ezetimibe were observed when the 2 drugs were administered concomitantly.

Population Pharmacodynamic Analysis of Uric Acid-Lowering Effects of Febuxostat Based on Electronic Medical Records in Two Hospitals

The aim of this study was to develop a population pharmacodynamic (PPD) model to describe uric acid (UA)-lowering effects in patients treated with febuxostat based on electronic medical records in 2 independent hospitals (university and city hospitals). Interhospital differences in the PPD model were also evaluated. We conducted the following 2 approaches to build the PPD models. A PPD model was developed separately using individual hospital data, and structural models and covariates between the two hospitals were compared (approach A). Another PPD model was developed using all available data from both hospitals, and differences between the 2 hospitals were evaluated by performing a covariate analysis on all PPD parameters (approach B). PPD analyses were performed by NONMEM using data from 358 patients. In both approaches, one indirect response model was established. In approach A, 2 diuretics (loops and thiazides) and renal function tests (Scr or BUN) were selected as covariates for the UA baseline level (serum UA levels just before the febuxostat treatment), whereas 2 diuretics and BUN were selected in approach B. A covariate analysis indicated that loops and thiazides increased UA baseline levels by 7%-14% and 6%-11%, respectively. In approach B, "hospital" was identified as a significant covariate for the UA baseline level; the baseline level was 7% higher in the city hospital. A PPD analysis may provide a precise description of the time course of the UA-lowering effects of febuxostat and quantitatively detect an interhospital difference in the UA baseline level.

Population pharmacodynamic analysis of hemoglobin A1c-lowering effects by adding treatment of DPP-4 inhibitors (sitagliptin) in type 2 diabetes mellitus patients based on electronic medical records

AIM

To develop a population pharmacodynamic (PPD) model describing the time course for the hemoglobin A1c (HbA1c)-lowering effects of adding treatment of DPP-4 inhibitors and to assess the efficacy of combination therapy in type 2 diabetes mellitus patients based on electronic medical records.

METHODS

Information on patients was collected retrospectively from electronic medical records. Of the 4 DPP-4 inhibitors used, we focused on sitagliptin as it had the best time-response relationships. A physiological indirect response model was developed to describe changes in HbA1c levels.

RESULTS

An indirect response model, based on the 1300 HbA1c levels of 160 patients, described the time course for the HbA1c-lowering effects of adding sitagliptin. The combination with pioglitazone decreased the HbA1c synthesis rate by 7.74% relative to without pioglitazone. Bayesian forecasting based on the final PDD model using the first two HbA1c observations, before and within 30days after the addition of sitagliptin, gave a precise prediction of HbA1c-lowering effects individually.

CONCLUSIONS

Our PPD model quantitatively described the beneficial effects of combination therapy with pioglitazone and sitagliptin. The proposal methodology is also expected to be applicable to other medicines based on electronic medical records in clinical practice.

Drug-drug interactions that interfere with statin metabolism

INTRODUCTION

Lipid-lowering drugs, especially hydroxymethylglutaryl-CoA reductase inhibitors (statins), are widely used in the treatment and prevention of atherosclerotic diseases. The benefits of statins are well documented. However, myotoxic side effects, which can sometimes be severe, including myopathy or rhabdomyolysis, have been associated with the use of statins. In some cases, this toxicity is associated with pharmacokinetic alterations. Potent inhibitors of CYP 3A4 significantly increase plasma concentrations of the active forms of simvastatin, lovastatin and atorvastatin. Fluvastatin is metabolized by CYP2C9, while pravastatin, rosuvastatin and pitavastatin are not susceptible to inhibition by any CYP.

AREAS COVERED

This review discusses the pharmacokinetic aspects of the drug-drug interaction with statins and genetic polymorphisms in CYPs, which are involved in the metabolism of statins, and highlights the importance of establishing a system utilizing electronic medical information practically to avoid adverse drug reactions.

EXPERT OPINION

An understanding of the mechanisms underlying statin interactions will help to minimize drug interactions and develop statins that are less prone to adverse interactions. Quantitatively analyzed information for the low-density lipoprotein cholesterol lowering effects of statin based on electronic medical records may be useful for avoiding the adverse effect of statins.

Pharmacokinetic non-interaction analysis in a fixed-dose formulation in combination of atorvastatin and ezetimibe

Recent clinical research has shown that atorvastatin (ATO) in combination with cholesterol absorption inhibitor ezetimibe (EZE) significantly reduces LDL-C level in patients with hypercholesterolemia, showing a superior lipid-lowering efficacy compared to statin alone. With no information currently available on the interaction between the two drugs, a pharmacokinetic study was conducted to investigate the influence of EZE on ATO and conversely when the two drugs were coadministered. The purpose of this study was to investigate the presence of differences in the pharmacokinetic profiles of capsules containing ATO 80 mg, EZE 10 mg or the combination of both 80/10 mg administered to healthy Mexican volunteers. This was a randomized, three-period, six-sequences crossover study. 36 eligible subjects aged between 20 to 50 years were included. Blood samples were collected up to 96 h after dosing, and pharmacokinetic parameters were obtained by non-compartmental analysis. Adverse events were evaluated based on subject interviews and physical examinations. Area under the concentration-time curve (AUC) and maximum plasma drug concentration (C_max) were measured for each drug alone or together and tested for bioequivalence-based hypothesis. The estimation computed (90% confidence intervals) for AUC and C_max, were 96.04% (85.88–107.42%) and 97.04% (82.36–114.35%), respectively for ATO–EZE combination versus ATO alone, while 84.42% (77.19–92.32%) and 95.60% (82.43–110.88%), respectively, for ATO–EZE combination versus EZE alone were estimated. These results suggest that ATO and EZE have no relevant pharmacokinetic drug–drug interaction.