A comprehensive assessment of statin discontinuation among patients who concurrently initiate statins and CYP3A4-inhibitor drugs; a multistate transition model

Aims: To describe the 1-year direct and indirect transition probabilities to premature discontinuation of statin therapy after concurrently initiating statins and CYP3A4-inhibitor drugs. Methods and Results: A retrospective new-user cohort study design was used to identify (N=160828) patients who concurrently initiated CYP3A4-inhibitors (diltiazem, ketoconazole, clarithromycin, others) and CYP3A4-metabolized statins (statin DDI exposed, n = 104774) vs. other statins (unexposed, n = 56054) from the MarketScan Commercial claims database (2012 to 2017). These groups were matched (2:1) through propensity score-matching techniques. We applied a multistate transition model to compare the 1-year transition probabilities involving four distinct states (start, adverse drug events [ADEs], discontinuation of CYP3A4-inhibitor drugs, and discontinuation of statin therapy) between those exposed to statin DDIs, vs. unexposed. Statistically significant differences were assessed by comparing the 95% confidence intervals (CIs) of probabilities. Patients exposed to statin DDIs, vs. unexposed, were significantly less likely to discontinue statin therapy (71.4 [95% CI: 71.1, 71.6] vs. 73.3 [95% CI: 72.9, 73.6]) but more likely to experience an ADE (3.4 [95% CI: 3.3, 3.5] vs. 3.2 [95% CI: 3.1, 3.3]) and discontinue with CYP3A4-inhibitor therapy (21.0 [95% CI: 20.8, 21.3] vs. 19.5 [95% CI: 19.2, 19.8]) directly after concurrently starting stains and CYP3A. Subsequent to experiencing an ADE, those exposed to statin DDIs were still less likely to discontinue statin therapy but were significantly more likely to discontinue CYP3A4-inhibitor therapy. Conclusion: While statin DDI exposure was associated with higher likelihood of ADEs, this did not increase the risk of premature statin discontinuation among patients exposed to statin DDIs, versus unexposed.

HCIP: An Online database for prediction CYP450 Enzyme Inhibition potential of bioactive compounds

Background: Concomitant administration of herbal medicine and conventional may lead to severe metabolism-oriented herb-drug interactions. However, detecting herb-drug interaction is expensive and higher time-consuming. Several computer-aided techniques have been proposed in recent years to predict drug interactions. However, most of the methods cannot predict herb-drug interactions effectively. Methods: Canonical SMILES of bioactive compounds was gathered from the PubChem online database, and its inhibition details were gathered PKCSM from the webserver. Results: By searching the bioactive compound name in the search bar of “The Herb-CYP450 Enzyme Inhibition Predictor online database” (HCIP- http://hcip.in/), it will provide the liver enzyme inhibition profile of the selected bioactive compound. For example; Guggulsterone: CYP3A4 inhibitor. Conclusion: The Herb-CYP450 Enzyme Inhibition Predictor online database is very peculiar and easy to determine the inhibition profile of the targeted bioactive compound. Keywords: CYP450; Enzyme inhibition; Bioactive Compounds; Online database; Herb-Drug Interaction

Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib

Purpose Fedratinib (INREBIC®), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug–drug interaction (DDI) potentials for fedratinib under clinical scenarios. Methods The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp® (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data. Results The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates. Conclusions The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued.

Lack of Antiviral Activity of Darunavir against SARS-CoV-2

Given the high need and the absence of specific antivirals for treatment of COVID-19 (the disease caused by severe acute respiratory syndrome-associated coronavirus-2 [SARS-CoV-2]), human immunodeficiency virus (HIV) protease inhibitors are being considered as therapeutic alternatives. Prezcobix/Rezolsta is a fixed-dose combination of 800 mg of the HIV protease inhibitor darunavir (DRV) and 150 mg cobicistat, a CYP3A4 inhibitor, which is indicated in combination with other antiretroviral agents for the treatment of HIV infection. There are currently no definitive data on the safety and efficacy of DRV/cobicistat for treatment of COVID-19. The in vitro antiviral activity of darunavir against a clinical isolate from a patient infected with SARS-CoV-2 was assessed. DRV showed no activity against SARS-CoV-2 at clinically relevant concentrations (EC50 >100 μM). Remdesivir, used as a positive control, showed potent antiviral activity (EC50 = 0.38 μM). Overall, the data do not support the use of DRV for treatment of COVID-19.

MON-182 Increased Risk of Bleeding and Excessive Anticoagulation Using Standard Dose Low Molecular Weight Heparin (LMWH) in Cushing’s Syndrome

Background: Cushing’s Syndrome (CS) is a hypercoagulable state with increased risk of venous thrombosis events (VTE) including pulmonary embolism (PE) and deep venous thrombosis (DVT). In the absence of clear recommendations (1), a standard dose of low molecular weight heparin (LMWH) is given: 1 mg/kg q12h (with dose reduction for CrCl < 30ml/min and low BMI). Here we report an excessive level of anticoagulation (based on standard LMWH anti-Xa levels) using standard dosing requirements in 8 of 11 patients with CS complicated by VTE who received treatment with Enoxaparin. Methods: We retrospectively studied 6 women and 5 men with active hypercortisolism and VTE (6 PE and 5 DVT) treated with Enoxaparin given at doses below every 12 hours. Self-declared race/ethnicity were 7 white (one Hispanic), 2 unknown, 1 Asian, and 1 African. Anticoagulation therapy was monitored 4 hours after administration with measurement of LMWH anti-Xa (therapeutic range: 0.5-1.2 IU/mL). Results: The (subsequent) etiology of CS was ectopic ACTH secretion in 6 patients (3 pulmonary NET, 3 occult) and Cushing’s disease in 5. Median age was 53 years (range 24 - 74); median BMI 34.7 kg/M2 (range 24.9 - 52.9). Median urine cortisol was 659 mcg/24h (range 122 - 32,444; nl reference range 3.5 - 45). All had CrCl > 60 ml/min. 6 patients were taking a CYP3A4 inhibitor (Ketoconazole: n=5 or Mifepristone: n=1). 6/7 patients who received an initial dose of 1 mg/kg had supratherapeutic anti-Xa levels, from 1.4 - 2 IU/ml; five were on a CYP3A4 inhibitor. One died from a massive retroperitoneal bleed leading to organ failure. The other five required dose reduction; the median dose at which anti-Xa levels were at goal was 0.56 mg/kg (range 0.36 - 0.87 mg/kg). The sixth patient had an initial anti-Xa level of 0.2 IU/ml, which normalized on a dose of 1.1 mg/kg. One patient started at 1.3 mg/kg exhibited an anti-Xa level of 1.7 IU/ml, with appropriate anti-Xa levels at a dose of 0.6 mg/kg. 3 patients started at reduced treatment doses (0.68, 0.78 and 0.87 mg/kg) exhibited therapeutic anti-Xa levels. In summary, therapeutic anti-Xa levels were achieved at a dose of approximately 1 mg/kg in only three patients (0.87, 0.96, 1.12 mg/kg); others required reduction of the usual recommended dose to between 0.36 and 0.78 mg/kg. CYP3A4 inhibitors were used in 5/8 patients with elevated anti-Xa levels. Conclusions: Patients with Cushing’s syndrome appear to require lower than standard dose of enoxaparin; which may be only partly explained by concomitant CYP3A4 inhibitors. We suggest that anti-Xa levels be more closely monitored in CS patients to avoid morbidity and mortality caused by PE or bleeding. Further studies are needed to determine if this risk is present in patients receiving supraphysiologic doses of exogenous glucocorticoids. 1. Wagner J et al. Front Endocrinol (Lausanne). 9: 805, 2018

P280 Concomitant use of ivabradine and cyp3a4 inhibitors in critical cardiac patients

Background In recent years there have been warnings concerning drug-induced life-threatening arrhythmias. Drug interactions can increase the risk of QT interval prolongation via interaction of pharmacokinetic mechanism. Some drugs such as Ivabradine does not affect the repolarization or affect the QT interval themselves. However, it can increase the risk of QT prolongation when taken with drugs that block the metabolic breakdown which inhibit the CYP3A4 enzyme particularly. Predisposing factors of QT prolongation include female sex, age over 65 years, brady-arrhythmia, electrolyte disturbances (hypokalemia, hypomagnesaemia), cardiac disease (congestive heart failure, ventricular hypertrophy, myocardial infarction, atrial fibrillation), impaired hepatic/renal function and hypothyroidism. Purpose To assess potential Ivabradine – CYP3A4 inhibitor interactions in a Cardiac Critical Care Unit (CCCU). Method We prospectively observed patients admitted at CCCU received Ivabradine and CYP3A4 inhibitor (QTprolonging agent) concomitantlyfrom Feb 2018 to July 2018 at National Heart Institute, Malaysia. We use a clinical drug decision support system (CDDSS) to identify the potential drug-drug interactions (PDDI) and assessed the likelihood of drug - drug interactions (DDI) using Drug Interaction Probability Scale (DIPS). Results Patients admitted at CCCU co-administered with Ivabradine and CYP3A4 inhibitors (amiodarone/azithromycin) were analyzed. The severity level for both potential Ivabradine – Azithromycin and Ivabradine – Amiodarone interactions were alerted by the CDDSS as Major. Total 10 (M = 70%) patients were screened. The mean age was ± 57 years old. They had no previous exposure to both or one of the medications before. All patients had underlying cardiac disease. The left ventricular ejection fractions (LVEF) ranged from 15% - 65%. Adverse drug event (ADE) occurred in 7 (70%) patients [Male = 5 (71%), Female = 2 (67%)]. 70% of patients had prolonged QT interval induced by Ivabradine – Amiodarone and Ivabradine – Azithromycin All patients had QTc interval < 500 ms before co-administration and had a change of ± 100 ms after coadministration. The onset of ADE ranged from day 2 to day 5 in all patients. One had life threatening arrhythmia; ventricular fibrillation and require defibrillator and another one patient had non-sustained ventricular tachycardia. The most common precipitating factor was underlying cardiac disease. All suspected precipitating drug was discontinued. DIPS revealed; 57% patients scored 9 points (highly probable DDI) and the rest scored between 5-7 points (probable DDI). Conclusion Ivabradine potentially associated to fatal arrhythmia when it is co-administered with CYP3A4 inhibitor. Hence, the physician should reconsider such combination within the correct clinical context to avoid cardiovascular deterioration especially in a critical care setting.