Inhibition of K+-conductance through the human ether-a-go-go related gene (hERG) channel leads to QT prolongation and is associated with cardiac arrhythmias. We previously reported that physiological concentrations of some estrogens partially suppress the hERG channel currents by interacting with the S6 residue F656 and increase the sensitivity of hERG blockade by E-4031. Although these studies suggested that clinically used synthetic estrogens with similar structures have the marked potential to alter hERG functions, the hERG interactions with synthetic estrogens have not been assessed. We therefore examined whether ethinylestradiol (EE2), a synthetic estrogen used in oral contraceptives, affects hERG function and blockade by drugs. Supratherapeutic concentrations of EE2 did not alter amplitudes or kinetics of the hERG currents elicited by train pulses at 20 mV (0.1 Hz). On the other hand, EE2 at therapeutic concentrations reduced the degree of hERG current suppression by E-4031. The administration of EE2 followed by E-4031 blockade reversed the current suppression, suggesting that the interaction of EE2 and E-4031 alters hERG at the drug-binding site. The effects of EE2 on hERG blockade raised the possibility that other estrogens, including synthetic estrogens, can alter hERG blockade by drugs that cause QT prolongation and ventricular arrhythmias.
Several drugs proposed for the treatment of COVID-19 have reported cases of cardiac adverse events such as ventricular arrhythmias. To properly weigh risks against potential benefits in a timely manner, mathematical modeling of drug disposition and drug action can be useful for predicting patient response.Here we explored the potential effects on cardiac electrophysiology of 4 COVID-19 proposed treatments: lopinavir, ritonavir, chloroquine, and azithromycin, including combination therapy involving these drugs. To address this, we combined simulations of pharmacokinetics (PK) with mechanistic mathematical modeling of human ventricular myocytes to predict adverse events caused by these treatments. We utilized a mechanistic model to construct heterogenous populations of 4 patient groups (healthy male, healthy female, diseased male, and diseased female) each with 1000 members, and studied the varied responses of drugs and combinations on each population. To determine appropriate drug concentrations for recommended COVID-19 regimen, we implemented PK models for each drug and incorporated these values into the mechanistic model. We found that: (1) drug combinations can lead to greater cellular action potential (AP) prolongation, analogous to QT prolongation, compared with drugs given in isolation; (2) simulations of chloroquine with azithromycin caused a significantly greater increase in AP duration (DAPDz190 ms) compared to lopinavir with ritonavir (DAPDz6 ms); (3) drug effects on different patient populations revealed that females with preexisting heart disease are more susceptible to drug-induced arrhythmias as 85 members formed arrhythmias, and less than 20 in each of the other three; and (4) logistic regression analysis performed on the population showed that higher levels of the sodium-calcium exchanger may predispose certain females with heart failure to drug-induced arrhythmias. Overall, these results illustrate how PK and mechanistic modeling can be combined to precisely predict cardiac arrhythmia susceptibility of COVID-19 therapies.
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