📒 Lancaster 2016
Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms1
- drug‐induced Torsades de Pointes (TdP) remains a critical issue in drug development.
- blockade of the KCNH2, or hERG (IKr)
- Reduction in IKr increases the action potential duration (APD), which appears as an increased QT interval in the EKG
- A major shortcoming of the hERG assay is the failure to account for multichannel drug effects.
- The goal of this study was twofold
- to develop a classifier for improved prediction of drug torsadogenicity.
- apply our classifier to identify key cellular physiological differences between torsadogenic and nontorsadogenic drugs
- Quantitative Systems Pharmacology approach : combined modeling of physiological dynamics with statistical analysis and machine‐learning
Human ventricular cell models simulate drug response
- simulated the application of 86 drugs at effective free therapeutic plasma concentration (EFTPC) in three recent, independently formulated human ventricular myocyte models (TP, GPB, ORd)
- 13 metrics in the AP and Ca trasient curves
A novel classification method identifies torsadogenic drugs
- principal components (PCs) from the metrics: first three PCs describe 88.2% of the variance
- clear division between torsadogenic and nontorsadogenic drugs
- The drug scores in the PC space were used to train a support vector machine (SVM) classifier, with accuarcy of 87.2%. Area under ROC curve (auROC) was 0.963.
- substantial improvement over classifications based on either hERG block or APD at 90% repolarization (APD90)
Ca2+ dynamics augment action potential duration to identify torsadogenic drugs
- PC scores are less clear in mechanisms explanation
- Classification based on specific simulated metrics rather than PC scores provides physiological insight into differences between drugs
- two to three metrics may allow for accurate drug classification (from PC analysis)
- APD at 50% repolarization (APD50) and diastolic (Ca2+)i => auROC = 0.962
- drugs with dramatic AP prolongation are torsadogenic. diastolic (Ca2+)i provides the additional information necessary to classify the drugs.
Risk prediction is robust across a large range of doses
- repeated the classification analysis after simulating each drug at 0.1, 1, 10, and 100 times typical clinical concentration (EFTPC)
- dose‐dependence of each drug’s distance from the decision boundary
- Amiodarone, imipramine, and solifenacin were close to the origin at EFTPC, but higher concentrations of these drugs moved them onto the correct, torsadogenic side of the decision boundary.
- The classification algorithm correctly predicted the torsadogenicity of donepezil, previously regarded safe.
A synthetic population stratifies drug risk
- generated a synthetic population of 24 individuals by randomly varying ionic current parameters and calibrating the population variability to experimental data
- Simulating drug effects in a synthetic population allows us to compute probabilities of TdP risk
- Ibutilide, a class III antiarrhythmic with a well‐established risk of Torsades, is predicted to be dangerous in the majority (71%) of individuals
- nitrendipine is a dihydropyridine Ca2+‐channel blocker with no known risk of Torsades that is predicted to be safe in all individuals in the population
- nilotinib: safe in the majority of individuals, but torsadogenic in a subset (12.5%).
Off‐target interactions influence drug risk
- we generated 100 hypothetical drugs that alter the activity of nine ion channels, pumps and transporters distinct from those targeted by the original drug set.
- predict the influence of each parameter on drug risk
- alterations to the Na+‐Ca2+ exchanger have the largest effect on TdP risk, with increased current leading to decreased risk. The Na+‐K+ ATPase, background Ca2+ current and the SERCA pump followed in significance
- increases in the activity of the Na+‐K+ ATPase and the Na+‐Ca2+ exchanger were protective, whereas decreases were predicted to be torsadogenic
- slow delayed rectifier (GKs) and the inward rectifier (GK1): blocking either K+ current is predicted to be torsadogenic, and enhancing either is protective
- Quantitative Systems Pharmacology (QSP) strategy, running extensive mechanistic simulations based on results from a large drug dataset.
- provide deeper insight into the physiological mechanisms of drug action than the “snapshot” one obtains using high‐throughput measurements, such as ion channel block or altered gene expression
- the value of simulations to not only predict cellular drug responses, but to direct experimental studies
- we do not assume the primacy of particular physiological responses, such as AP prolongation. Instead, we calculate from simulations a wide range of metrics, and then systematically determine which are most informative
- drug‐induced changes in diastolic (Ca2+) were nearly as important as changes in APD to differentiate between torsadogenic and nontorsadogenic drugs
- Na+‐K+ ATPase, Na+‐Ca2+ exchanger, background Ca2+ current, and the SERCA pump as the most significant potential modulators of TdP risk
- peak INa inhibition does not account for specific effects on the late Na+ current (INaL)
- Our method also does not include drug effects on channel trafficking, which plays a role in the torsadogenesis of some drugs
Models and simulations
- (1) OVVR14; (2) ten Tusscher and Panfilov12; and (3) Grandi, Pasqualini, and Bers
- stimulated at rates of 2 Hz, 1 Hz, and 0.5 Hz to steady state
- under 24 different conditions: at 3 pacing rates × 8 cell types
- reported half‐maximal inhibitory concentrations (IC50) for channel inhibition. We obtained these data for 86 drugs published in the studies by Kramer et al. and Mirams et al. as training set.
- We defined the torsadogenicity of the drugs in our training set using the CredibleMeds database (https://www.crediblemeds.org)
Classification of drugs
- 331 metrics => PCs => training a SVM classifier15 with a linear kernel