A nonlinear and time-dependent leak current in the presence of calcium fluoride patch-clamp seal enhancer [version 1; peer review: 2 approved with reservations]

Automated patch-clamp platforms are widely used and vital tools in both academia and industry to enable high-throughput studies such as drug screening. A leak current to ground occurs whenever the seal between a pipette and cell (or internal solution and cell in high-throughput machines) is not perfectly insulated from the bath (extracellular) solution. Over 1 GΩ seal resistance between pipette and bath solutions is commonly used as a quality standard for manual patch work. With automated platforms it can be difficult to obtain such a high seal resistance between the intra- and extra-cellular solutions. One suggested method to alleviate this problem is using an F− containing internal solution together with a Ca2+ containing external solution — so that a CaF2 crystal forms when the two solutions meet which ‘plugs the holes’ to enhance the seal resistance. However, we observed an unexpected nonlinear-in-voltage and time-dependent current using these solutions on an automated patch-clamp platform. We performed manual patch-clamp experiments with the automated patch-clamp solutions, but no biological cell, and observed the same nonlinear time-dependent leak current. The current could be completely removed by washing out F− ions to leave a conventional leak current that was linear and not time-dependent. We therefore conclude fluoride ions interacting with the CaF2 crystal are the origin of the nonlinear time-dependent leak current. The consequences of such a nonlinear and time-dependent leak current polluting measurements should be considered carefully if it cannot be isolated and subtracted

Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities

The hERG (human ether-a-go-go-related gene) encoded potassium ion (K+) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC50) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC50) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC50) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC50) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG

Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities

The hERG (human ether-a-go-go-related gene) encoded potassium ion (K+) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC50) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC50) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC50) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC50) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics

When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological systems are always large simplifications, model discrepancy arises—models fail to perfectly recapitulate the true data generating process. This presents a particular challenge for making accurate predictions, and especially for accurately quantifying uncertainty in these predictions. Experimentalists and modellers must choose which experimental procedures (protocols) are used to produce data used to train models. We propose to characterise uncertainty owing to model discrepancy with an ensemble of parameter sets, each of which results from training to data from a different protocol. The variability in predictions from this ensemble provides an empirical estimate of predictive uncertainty owing to model discrepancy, even for unseen protocols. We use the example of electrophysiology experiments that investigate the properties of hERG potassium channels. Here, ‘information-rich’ protocols allow mathematical models to be trained using numerous short experiments performed on the same cell. In this case, we simulate data with one model and fit it with a different (discrepant) one. For any individual experimental protocol, parameter estimates vary little under repeated samples from the assumed additive independent Gaussian noise model. Yet parameter sets arising from the same model applied to different experiments conflict—highlighting model discrepancy. Our methods will help select more suitable ion channel models for future studies, and will be widely applicable to a range of biological modelling problems

A nonlinear and time-dependent leak current in the presence of calcium fluoride patch-clamp seal enhancer

Automated patch-clamp platforms are widely used and vital tools in both academia and industry to enable high-throughput studies such as drug screening. A leak current to ground occurs whenever the seal between a pipette and cell (or internal solution and cell in high-throughput machines) is not perfectly insulated from the bath (extracellular) solution. Over 1 G seal resistance between pipette and bath solutions is commonly used as a quality standard for manual patch work. With automated platforms it can be difficult to obtain such a high seal resistance between the intra- and extra-cellular solutions. One suggested method to alleviate this problem is using an F containing internal solution together with a Ca 2+ containing external solution so that a CaF 2 crystal forms when the two solutions meet which plugs the holes' to enhance the seal resistance. However, we observed an unexpected nonlinear-in-voltage and time-dependent current using these solutions on an automated patch-clamp platform. We performed manual patch-clamp experiments with the automated patch-clamp solutions, but no biological cell, and observed the same nonlinear time-dependent leak current. The current could be completely removed by washing out F ions to leave a conventional leak current that was linear and not time-dependent. We therefore conclude fluoride ions interacting with the CaF 2 crystal are the origin of the nonlinear time-dependent leak current. The consequences of such a nonlinear and time-dependent leak current polluting measurements should be considered carefully if it cannot be isolated and subtracted

Temperature Effects on Kinetics of K V 11.1 Drug Block Have Important Consequences for In Silico Proarrhythmic Risk Prediction s

ABSTRACT Drug block of voltage-gated potassium channel subtype 11.1 human ether-a-go-go related gene (K v 11.1) (hERG) channels, encoded by the KCNH2 gene, is associated with reduced repolarization of the cardiac action potential and is the predominant cause of acquired long QT syndrome that can lead to fatal cardiac arrhythmias. Current safety guidelines require that potency of K V 11.1 block is assessed in the preclinical phase of drug development. However, not all drugs that block K V 11.1 are proarrhythmic, meaning that screening on the basis of equilibrium measures of block can result in high attrition of potentially low-risk drugs. The basis of the next generation of drug-screening approaches is set to be in silico risk prediction, informed by in vitro mechanistic descriptions of drug binding, including measures of the kinetics of block. A critical issue in this regard is characterizing the temperature dependence of drug binding. Specifically, it is important to address whether kinetics relevant to physiologic temperatures can be inferred or extrapolated from in vitro data gathered at room temperature in high-throughout systems. Here we present the first complete study of the temperature-dependent kinetics of block and unblock of a proarrhythmic drug, cisapride, to K V 11.1. Our data highlight a complexity to binding that manifests at higher temperatures and can be explained by accumulation of an intermediate, non-blocking encounter-complex. These results suggest that for cisapride, physiologically relevant kinetic parameters cannot be simply extrapolated from those measured at lower temperatures; rather, data gathered at physiologic temperatures should be used to constrain in silico models that may be used for proarrhythmic risk prediction

Spider-Venom Peptides as Bioinsecticides

Over 10,000 arthropod species are currently considered to be pest organisms. They are estimated to contribute to the destruction of ~14% of the world’s annual crop production and transmit many pathogens. Presently, arthropod pests of agricultural and health significance are controlled predominantly through the use of chemical insecticides. Unfortunately, the widespread use of these agrochemicals has resulted in genetic selection pressure that has led to the development of insecticide-resistant arthropods, as well as concerns over human health and the environment. Bioinsecticides represent a new generation of insecticides that utilise organisms or their derivatives (e.g., transgenic plants, recombinant baculoviruses, toxin-fusion proteins and peptidomimetics) and show promise as environmentally-friendly alternatives to conventional agrochemicals. Spider-venom peptides are now being investigated as potential sources of bioinsecticides. With an estimated 100,000 species, spiders are one of the most successful arthropod predators. Their venom has proven to be a rich source of hyperstable insecticidal mini-proteins that cause insect paralysis or lethality through the modulation of ion channels, receptors and enzymes. Many newly characterized insecticidal spider toxins target novel sites in insects. Here we review the structure and pharmacology of these toxins and discuss the potential of this vast peptide library for the discovery of novel bioinsecticides

In Vitro and In Silico Risk Assessment in Acquired Long QT Syndrome: The Devil Is in the Details

Acquired long QT syndrome, mostly as a result of drug block of the Kv11. 1 potassium channel in the heart, is characterized by delayed cardiac myocyte repolarization, prolongation of the T interval on the ECG, syncope and sudden cardiac death due to the polymorphic ventricular arrhythmia Torsade de Pointes (TdP). In recent years, efforts are underway through the Comprehensive in vitro proarrhythmic assay (CiPA) initiative, to develop better tests for this drug induced arrhythmia based in part on in silico simulations of pharmacological disruption of repolarization. However, drug binding to Kv11.1 is more complex than a simple binary molecular reaction, meaning simple steady state measures of potency are poor surrogates for risk. As a result, there is a plethora of mechanistic detail describing the drug/Kv11.1 interaction—such as drug binding kinetics, state preference, temperature dependence and trapping—that needs to be considered when developing in silico models for risk prediction. In addition to this, other factors, such as multichannel pharmacological profile and the nature of the ventricular cell models used in simulations also need to be considered in the search for the optimum in silico approach. Here we consider how much of mechanistic detail needs to be included for in silico models to accurately predict risk and further, how much of this detail can be retrieved from protocols that are practical to implement in high throughout screens as part of next generation of preclinical in silico drug screening approaches