Quantitative approach for cardiac risk assessment and interpretation in tuberculosis drug development

Abstract

Cardiotoxicity is among the top drug safety concerns, and is of specific interest in tuberculosis, where this is a known or potential adverse event of current and emerging treatment regimens. As there is a need for a tool, beyond the QT interval, to quantify cardiotoxicity early in drug development, an empirical decision tree based classifier was developed to predict the risk of Torsades de pointes (TdP). The cardiac risk algorithm was developed using pseudo-electrocardiogram (ECG) outputs derived from cardiac myocyte electromechanical model simulations of increasing concentrations of 96 reference compounds which represented a range of clinical TdP risk. The algorithm correctly classified 89% of reference compounds with moderate sensitivity and high specificity (71 and 96%, respectively) as well as 10 out of 12 external validation compounds and the anti-TB drugs moxifloxacin and bedaquiline. The cardiac risk algorithm is suitable to help inform early drug development decisions in TB and will evolve with the addition of emerging data.Electronic supplementary materialThe online version of this article (10.1007/s10928-018-9580-2) contains supplementary material, which is available to authorized users.

Towards Bridging Translational Gap in Cardiotoxicity Prediction: an Application of Progressive Cardiac Risk Assessment Strategy in TdP Risk Assessment of Moxifloxacin

“…However, the actual reported cases of TdP after MOXI treatment are very rare, which is consistent with the lack of pro-arrhythmic pseudoECG waveforms in any of the virtual patients simulated in Fig. 4 as well as the mid-range probability (54%) of being TdP positive as estimated by the Cardiac Risk Algorithm of Polak et al (14). Most of the reported TdP cases occurred when MOXI was administered with other cardiotoxic medications potentiating their TdP effect or physiological conditions and diseases such as hypokalemia, tachycardia, renal failure (main route of elimination for MOXI), or congenital long QT syndrome.…”

“…Although the ΔΔQTcF is a useful comparator for evaluating performance of the PBPK-based CSS simulations, it is important to note that the reliance on QTc as a metric of torsadogenic potential has significant drawbacks (13,63). This is exemplified by MOXI, as reports by Abbasi et al (64), Okada et al (39), and simulations done previously by our group (14) have shown with single cell level as well as threedimensional heart wall simulations that MOXI does not cause early after depolarization (EAD) or arrhythmia at up to 100fold supra-therapeutic concentration with healthy cardiac electrophysiology. While this is in contradiction to the reported TdP cases with MOXI in clinic at lower than 100fold exposure levels (65,66), these cases, as well as 274 records 1 of MOXI-associated TdP (obtained by mining of the US FAERS database using the OpenVigil 2.1 portal (67) for TdP cases reported with MOXI) indicates that almost all cases were multifactorial.…”

Effect of artemisinin-piperaquine treatment on the electrocardiogram of malaria patients

“…Heart rate changes are related to changes in the QTc interval. Therefore, before analyzing the effects of antimalarials on the QT interval, the data should be corrected using the appropriate formulae 4 .…”

“…Prolongation of ventricular repolarization can cause subsequent prolongation of the effective refractory period, and QT interval prolongation (QTL) is reflected in electrocardiograms (ECGs) 4 . TdP is a type of polymorphic ventricular arrhythmia associated with QTL.…”

Virtual Thorough QT (TQT) Trial—Extrapolation of In Vitro Cardiac Safety Data to In Vivo Situation Using Multi-Scale Physiologically Based Ventricular Cell-wall Model Exemplified with Tolterodine and Fesoterodine

“…These multi-scale quantitative systems toxicology and safety (QSTS) models can bridge the translational gap in cardiac safety assessment with the use of in vitro dynamic ion channel inhibition data and expected exposure levels to simulate subsequent effects on ECG profile in virtual patients, which represent an array of physiologies expected in clinical practice (13,14). Such QSTS approach allows mechanistic in vitro to in vivo extrapolation (IVIVE) of preclinical cardiac safety assessment to clinical cardiac safety risk and can aid in bridging the translational gap in TdP risk assessment (15). Recently, we have used the QSTS models to predict quantitatively the QT prolongation effect of a drug influencing multiple cardiac ion channels with electrophysiologically active metabolite(s) such as citalopram (12) and moxifloxacin, drug widely used as positive control in TQT trials (6).…”