Editorial: Safety Pharmacology - Risk Assessment QT Interval Prolongation and Beyond

Assessing drug safety by identifying the axis of arrhythmia in cardiomyocyte electrophysiology

Many classes of drugs can induce fatal cardiac arrhythmias by disrupting the electrophysiology of cardiomyocytes. Safety guidelines thus require all new drugs to be assessed for pro-arrhythmic risk prior to conducting human trials. The standard safety protocols primarily focus on drug blockade of the delayed-rectifier potassium current (IKr). Yet the risk is better assessed using four key ion currents (IKr, ICaL, INaL, IKs). We simulated 100,000 phenotypically diverse cardiomyocytes to identify the underlying relationship between the blockade of those currents and the emergence of ectopic beats in the action potential. We call that relationship the axis of arrhythmia. It serves as a yardstick for quantifying the arrhythmogenic risk of any drug from its profile of multi-channel block alone. We tested it on 109 drugs and found that it predicted the clinical risk labels with an accuracy of 88.1-90.8%. Pharmacologists can use our method to assess the safety of novel drugs without resorting to animal testing or unwieldy computer simulations.

Safety assessment of metarrestin in dogs: A clinical candidate targeting a subnuclear structure unique to metastatic cancer cells

Pancreatic cancer is a leading cause of cancer-related deaths in the U.S. Ninety percent of patients with stage IV pancreatic cancer die within one year of diagnosis due to complications of metastasis. A metastatic potential of cancer cells has been shown to be closely associated with formation of perinucleolar compartment (PNC). Metarrestin, a first-in-class PNC inhibitor, was evaluated for its toxicity, toxicokinetics, and safety pharmacology in beagle dogs following every other day oral (capsule) administration for 28 days to support its introduction into clinical trials. The study consisted of four dose groups: vehicle; 0.25, 0.75 and 1.50 mg/kg/dose. Metarrestin reached its maximum concentration in blood at 3 h (overall median Tmax) across all doses with a mean t1/2 over 168 h of 55.5 h. Dose dependent increase in systemic exposure (Cmax and AUClast) with no sex difference was observed on days 1 and 27. Metarrestin accumulated from Day 1 to Day 27 at all dose levels and in both sexes by an overall factor of about 2.34. No mortality occurred during the dosing period; however, treatment-related clinical signs of toxicity consisting of hypoactivity, shaking/shivering, thinness, irritability, salivation, abnormal gait, tremors, ataxia and intermittent seizure-like activity were seen in both sexes at mid and high dose groups. Treatment-related effects on body weight and food consumption were seen at the mid and high dose levels. Safety pharmacology study showed no treatment-related effects on blood pressure, heart rate, corrected QT, PR, RR, or QRS intervals, or respiratory function parameters (respiratory rate, tidal volume, minute volume). There were no histopathological changes observed, with the exception of transient thymic atrophy which was considered to be non-adverse. Based primarily on clinical signs of toxicity, the No Observed Adverse Effect Level (NOAEL) in dogs was considered to be 0.25 mg/kg metarrestin after every other day dosing for 28 days with a mean of male and female Cmax = 82.5 ng/mL and AUClast = 2521 h*ng/mL, on Day 27.

A Computational Pipeline to Predict Cardiotoxicity: From the Atom to the Rhythm

RATIONALE

Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery.

OBJECTIVE

To predict the impact of a drug from the drug chemistry on the cardiac rhythm.

METHODS AND RESULTS

In a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction.

CONCLUSIONS

We present a multiscale model framework to predict electrotoxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue-level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.

In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products

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Regulators now consider also evidences produced in silico.

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We need accepted methods to evaluate the credibility of models.

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In this paper we describe the use of the ASME V&V-40 technical standard.

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We also discuss its application to various types of modelling methods.

Historically, the evidences of safety and efficacy that companies provide to regulatory agencies as support to the request for marketing authorization of a new medical product have been produced experimentally, either in vitro or in vivo. More recently, regulatory agencies started receiving and accepting evidences obtained in silico, i.e. through modelling and simulation. However, before any method (experimental or computational) can be acceptable for regulatory submission, the method itself must be considered “qualified” by the regulatory agency. This involves the assessment of the overall “credibility” that such a method has in providing specific evidence for a given regulatory procedure. In this paper, we describe a methodological framework for the credibility assessment of computational models built using mechanistic knowledge of physical and chemical phenomena, in addition to available biological and physiological knowledge; these are sometimes referred to as “biophysical” models. Using guiding examples, we explore the definition of the context of use, the risk analysis for the definition of the acceptability thresholds, and the various steps of a comprehensive verification, validation and uncertainty quantification process, to conclude with considerations on the credibility of a prediction for a specific context of use. While this paper does not provide a guideline for the formal qualification process, which only the regulatory agencies can provide, we expect it to help researchers to better appreciate the extent of scrutiny required, which should be considered early on in the development/use of any (new) in silico evidence.

First-in-Human, Healthy Volunteers Integrated Protocol of ETC-206, an Oral Mnk 1/2 Kinase Inhibitor Oncology Drug

In the last decade, drug development has tackled substantial challenges to improve efficiency and facilitate access to innovative medicines. Integrated clinical protocols and the investigation of targeted oncology drugs in healthy volunteers (HVs) have emerged as modalities with an increase in scope and complexity of early clinical studies and first-in-human (FIH) studies in particular. However, limited work has been done to explore the impact of these two modalities, alone or in combination, on the scientific value and on the implementation of such articulated studies. We conducted an FIH study in HVs with an oncology targeted drug, an Mnk 1/2 small molecule inhibitor. In this article, we describe results, advantages, and limitations of an integrated clinical protocol with an oncology drug. We further discuss and indicate points to consider when designing and conducting similar scientifically and operationally demanding FIH studies.

Quantitative systems models illuminate arrhythmia mechanisms in heart failure: Role of the Na+ -Ca2+ -Ca2+ /calmodulin-dependent protein kinase II-reactive oxygen species feedback

Quantitative systems modeling aims to integrate knowledge in different research areas with models describing biological mechanisms and dynamics to gain a better understanding of complex clinical syndromes. Heart failure (HF) is a chronic complex cardiac disease that results from structural or functional disorders impairing the ability of the ventricle to fill with or eject blood. Highly interactive and dynamic changes in mechanical, structural, neurohumoral, metabolic, and electrophysiological properties collectively predispose the failing heart to cardiac arrhythmias, which are responsible for about a half of HF deaths. Multiscale cardiac modeling and simulation integrate structural and functional data from HF experimental models and patients to improve our mechanistic understanding of this complex arrhythmia syndrome. In particular, they allow investigating how disease-induced remodeling alters the coupling of electrophysiology, Ca²⁺ and Na⁺ handling, contraction, and energetics that lead to rhythm derangements. The Ca²⁺ /calmodulin-dependent protein kinase II, which expression and activity are enhanced in HF, emerges as a critical hub that modulates the feedbacks between these various subsystems and promotes arrhythmogenesis. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Cellular Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.