Corrigendum to “A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm” [Toxicology and Applied Pharmacology volume 394C (2020) 114961]

© 2020 The Author(s) The authors regret that one affiliation address is mistaken in the published paper. Matthew Bridgland-Taylor's affiliation was incorrectly listed as Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom. The correct affiliation is Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom. The authors would like to apologise for any inconvenience caused

A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm

© 2020 Introduction: hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed. Methods: A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates. Results: A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used. Discussion: This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment

Atomic-Level Simulation Study of <i>n</i>‑Hexane Pyrolysis on Silicon Carbide Surfaces

Ethylene production plays a key role in the petrochemical industry. The severe operation conditions of ethylene thermal cracking, such as high-temperature and coke-formation, pose challenges for the development of new corrosion-resistant and coking-resistant materials for ethylene reactor radiant coils tubes (RCTs). We investigated the performance of ceramic materials such as silicon carbide (SiC) in severe pyrolysis conditions by using reactive force field molecular dynamics (ReaxFF MD) simulation method. Our results indicate that β-SiC surface remains fully stable at 1500 K, whereas increased temperature results in melted interface. At 2500 K, fully grown cross-linked-graphene-like polycyclic aromatic hydrocarbon coking structure on SiC surfaces was observed. Such coking was particularly severe in the carbon-side of the surface slab. The coking structures were mainly derived from surface atoms at the initial 3.0 ns, as a result of the loss of interfacial hydroxyl layer and further hydrothermal corrosion. The SiC substrate surface enhances the ethylene cracking rate and also leads to different intermediate-state compounds. Our fundamental research will have significant and broad impact on both petrochemical industry and academic research in materials science, petrochemistry, and combustion chemistry

Deep learning-enabled natural language processing to identify directional pharmacokinetic drug–drug interactions

Abstract Background During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmacokinetic (PK) drug-drug interactions (DDIs) with another drug (precipitant). While many natural language processing (NLP) methods for DDI have been published, most were designed to evaluate if (and what kind of) DDI relationships exist in the text, without identifying the direction of DDI (object vs. precipitant drug). Here we present a method for the automatic identification of the directionality of a PK DDI from literature or drug labels. Methods We reannotated the Text Analysis Conference (TAC) DDI track 2019 corpus for identifying the direction of a PK DDI and evaluated the performance of a fine-tuned BioBERT model on this task by following the training and validation steps prespecified by TAC. Results This initial attempt showed the model achieved an F-score of 0.82 in identifying sentences as containing PK DDI and an F-score of 0.97 in identifying object versus precipitant drugs in those sentences. Discussion and conclusion Despite a growing list of NLP methods for DDI extraction, most of them use a common set of corpora to perform general purpose tasks (e.g., classifying a sentence into one of several fixed DDI categories). There is a lack of coordination between the drug development and biomedical informatics method development community to develop corpora and methods to perform specific tasks (e.g., extract clinical exposure changes due to PK DDI). We hope that our effort can encourage such a coordination so that more “fit for purpose” NLP methods could be developed and used to facilitate the drug development process

Protein–Ligand Interaction Detection with a Novel Method of Transient Induced Molecular Electronic Spectroscopy (TIMES): Experimental and Theoretical Studies

Protein–ligand interaction detection without disturbances (e.g., surface immobilization, fluorescent labeling, and crystallization) presents a key question in protein chemistry and drug discovery. The emergent technology of transient induced molecular electronic spectroscopy (TIMES), which incorporates a unique design of microfluidic platform and integrated sensing electrodes, is designed to operate in a label-free and immobilization-free manner to provide crucial information for protein–ligand interactions in relevant physiological conditions. Through experiments and theoretical simulations, we demonstrate that the TIMES technique actually detects protein–ligand binding through signals generated by surface electric polarization. The accuracy and sensitivity of experiments were demonstrated by precise measurements of dissociation constant of lysozyme and <i>N</i>-acetyl-d-glucosamine (NAG) ligand and its trimer, NAG₃. Computational fluid dynamics (CFD) computation is performed to demonstrate that the surface’s electric polarization signal originates from the induced image charges during the transition state of surface mass transport, which is governed by the overall effects of protein concentration, hydraulic forces, and surface fouling due to protein adsorption. Hybrid atomistic molecular dynamics (MD) simulations and free energy computation show that ligand binding affects lysozyme structure and stability, producing different adsorption orientation and surface polarization to give the characteristic TIMES signals. Although the current work is focused on protein–ligand interactions, the TIMES method is a general technique that can be applied to study signals from reactions between many kinds of molecules