Toward in vivo-relevant hERG safety assessment and mitigation strategies based on relationships between non-equilibrium blocker binding, three-dimensional channel-blocker interactions, dynamic occupancy, dynamic exposure, and cellular arrhythmia

Novel quinoline substituted autophagy inhibitors attenuate Zika virus replication in ocular cells

Zika virus (ZIKV) is a re-emerging RNA virus that is known to cause ocular and neurological abnormalities in infants. ZIKV exploits autophagic processes in infected cells to enhance its replication and spread. Thus, autophagy inhibitors have emerged as a potent therapeutic target to combat RNA viruses, with Hydroxychloroquine (HCQ) being one of the most promising candidates. In this study, we synthesized several novel small-molecule quinoline derivatives, assessed their antiviral activity, and determined the underlying molecular mechanisms. Among the nine synthesized analogs, two lead candidates, labeled GL-287 and GL-382, significantly attenuated ZIKV replication in human ocular cells, primarily by inhibiting autophagy. These two compounds surpassed the antiviral efficacy of HCQ and other existing autophagy inhibitors, such as ROC-325, DC661, and GNS561. Moreover, unlike HCQ, these novel analogs did not exhibit cytotoxicity in the ocular cells. Treatment with compounds GL-287 and GL-382 in ZIKV-infected cells increased the abundance of LC3 puncta, indicating the disruption of the autophagic process. Furthermore, compounds GL-287 and GL-382 effectively inhibited the ZIKV-induced innate inflammatory response in ocular cells. Collectively, our study demonstrates the safe and potent antiviral activity of novel autophagy inhibitors against ZIKV.

Enhancing hERG Risk Assessment with Interpretable Classificatory and Regression Models

The human Ether-à-go-go-Related Gene (hERG) is a transmembrane protein that regulates cardiac action potential, and its inhibition can induce a potentially deadly cardiac syndrome. In vitro tests help identify hERG blockers at early stages; however, the high cost motivates searching for alternative, cost-effective methods. The primary goal of this study was to enhance the Pred-hERG tool for predicting hERG blockage. To achieve this, we developed new QSAR models that incorporated additional data, updated existing classificatory and multiclassificatory models, and introduced new regression models. Notably, we integrated SHAP (SHapley Additive exPlanations) values to offer a visual interpretation of these models. Utilizing the latest data from ChEMBL v30, encompassing over 14,364 compounds with hERG data, our binary and multiclassification models outperformed both the previous iteration of Pred-hERG and all publicly available models. Notably, the new version of our tool introduces a regression model for predicting hERG activity (pIC50). The optimal model demonstrated an R2 of 0.61 and an RMSE of 0.48, surpassing the only available regression model in the literature. Pred-hERG 5.0 now offers users a swift, reliable, and user-friendly platform for the early assessment of chemically induced cardiotoxicity through hERG blockage. The tool provides versatile outcomes, including (i) classificatory predictions of hERG blockage with prediction reliability, (ii) multiclassificatory predictions of hERG blockage with reliability, (iii) regression predictions with estimated pIC50 values, and (iv) probability maps illustrating the contribution of chemical fragments for each prediction. Furthermore, we implemented explainable AI analysis (XAI) to visualize SHAP values, providing insights into the contribution of each feature to binary classification predictions. A consensus prediction calculated based on the predictions of the three developed models is also present to assist the user's decision-making process. Pred-hERG 5.0 has been designed to be user-friendly, making it accessible to users without computational or programming expertise. The tool is freely available at http://predherg.labmol.com.br.

Electrophysiological Changes in the Rabbit Ventricular Wedge and Human-Induced Pluripotent Stem-Cell Derived (IPSC) Cardiomyocytes Translate to Severe Arrhythmia Observed in a Canine Toxicology Study, Not Predicted by Standard In Vitro Ion Channel Assays

During drug discovery, small molecules are typically assayed in vitro for secondary pharmacology effects, which include ion channels relevant to cardiac electrophysiology. Compound A was an irreversible inhibitor of myeloperoxidase investigated for the treatment of peripheral artery disease. Oral doses in dogs at ≥5 mg/kg resulted in cardiac arrhythmias in a dose-dependent manner (at Cmax, free ≥1.53 μM) that progressed in severity with time. Nevertheless, a panel of 13 different cardiac ion channel (K, Na, and Ca) assays, including hERG, failed to identify pharmacologic risks of the molecule. Compound A and a related Compound B were evaluated for electrophysiological effects in the isolated rabbit ventricular wedge assay. Compounds A and B prolonged QT and Tp-e intervals at ≥1 and ≥.3 μM, respectively, and both prolonged QRS at ≥5 μM. Compound A produced early after depolarizations and premature ventricular complexes at ≥5 μM. These data indicate both compounds may be modulating hERG (Ikr) and Nav1.5 ion channels. In human IPSC cardiomyocytes, Compounds A and B prolonged field potential duration at ≥3 μM and induced cellular dysrhythmia at ≥10 and ≥3 μM, respectively. In a rat toxicology study, heart tissue: plasma concentration ratios for Compound A were ≥19X at 24 hours post-dose, indicating significant tissue distribution. In conclusion, in vitro ion channel assays may not always identify cardiovascular electrophysiological risks observed in vivo, which can be affected by tissue drug distribution. Risk for arrhythmia may increase with a "trappable" ion channel inhibitor, particularly if cardiac tissue drug levels achieve a critical threshold for pharmacologic effects.

Benzoic acid derivatives as potent antibiofilm agents against Klebsiella pneumoniae biofilm

Klebsiella pneumoniae is responsible for a significant proportion of human urinary tract infections, and its biofilm is a major virulence. One potential approach to controlling biofilm-associated infections is targeting the adhesin MrkD1P to disrupt biofilm formation. We employed Schrodinger's Maestro tool with the OPLS 2005 force field to dock compounds with the target protein. Two benzoic acid derivatives, 3-hydroxy benzoic acid and 2,5-dihydroxybenzoic acid, had strong binding free energies (-55.57 and -18.68 kcal/mol) and were the most potent compounds. The in-vitro experiments were conducted to validate the in-silico results. The results showed that both compounds effectively inhibited biofilm formation at low concentrations (4 and 8 mg/mL, respectively) and had antibiofilm activity, restricting cell attachment. Both compounds demonstrated a strong biofilm inhibitory effect, with 97% and 89% reduction in biofilm by 3-hydroxy benzoic acid and 2,5-dihydroxybenzoic acid, respectively. These findings suggest that natural compounds can be a potential source of new drugs to combat biofilm-associated infections. The study highlights the potential of targeting adhesin MrkD1P as an effective approach to controlling biofilm-associated infections caused by K. pneumoniae. The results may have implications for the development of new therapies for biofilm-associated infections and pave the way for future research in this area.

hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties

Human ether-a-go-go-related gene (hERG) potassium channel is well-known contributor to drug-induced cardiotoxicity and therefore an extremely important target when performing safety assessments of drug candidates. Ligand-based approaches in connection with quantitative structure active relationships (QSAR) analyses have been developed to predict hERG toxicity. Availability of the recent published cryogenic electron microscopy (cryo-EM) structure for the hERG channel opened the prospect for using structure-based simulation and docking approaches for hERG drug liability predictions. In recent time, the idea of combining structure- and ligand-based approaches for modeling hERG drug liability has gained momentum offering improvements in predictability when compared to ligand-based QSAR practices alone. The present article demonstrates uniting the structure-based SILCS (site-identification by ligand competitive saturation) approach in conjunction with physicochemical properties to develop predictive models for hERG blockade. This combination leads to improved model predictability based on Pearson's R and percent correct (represents rank-ordering of ligands) metric for different validation sets of hERG blockers involving diverse chemical scaffold and wide range of pIC50 values. The inclusion of the SILCS structure-based approach allows determination of the hERG region to which compounds bind and the contribution of different chemical moieties in the compounds to blockade, thereby facilitating the rational ligand design to minimize hERG liability.

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

Drug-induced blockade of the human ether-à-go-go-related gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligand-based approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein–ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUC_MAX = 0.86 ± 0.01) and negative predictive values (NPV_MAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein–ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

Prediction of potential drug interactions between repurposed COVID-19 and antitubercular drugs: an integrational approach of drug information software and computational techniques data

Introduction:

Tuberculosis is a major respiratory disease globally with a higher prevalence in Asian and African countries than rest of the world. With a larger population of tuberculosis patients anticipated to be co-infected with COVID-19 infection, an ongoing pandemic, identifying, preventing and managing drug–drug interactions is inevitable for maximizing patient benefits for the current repurposed COVID-19 and antitubercular drugs.

Methods:

We assessed the potential drug–drug interactions between repurposed COVID-19 drugs and antitubercular drugs using the drug interaction checker of IBM Micromedex®. Extensive computational studies were performed at a molecular level to validate and understand the drug–drug interactions found from the Micromedex drug interaction checker database at a molecular level. The integrated knowledge derived from Micromedex and computational data was collated and curated for predicting potential drug–drug interactions between repurposed COVID-19 and antitubercular drugs.

Results:

A total of 91 potential drug–drug interactions along with their severity and level of documentation were identified from Micromedex between repurposed COVID-19 drugs and antitubercular drugs. We identified 47 pharmacodynamic, 42 pharmacokinetic and 2 unknown DDIs. The majority of our molecular modelling results were in line with drug–drug interaction data obtained from the drug information software. QT prolongation was identified as the most common type of pharmacodynamic drug–drug interaction, whereas drug–drug interactions associated with cytochrome P450 3A4 (CYP3A4) and P-glycoprotein (P-gp) inhibition and induction were identified as the frequent pharmacokinetic drug–drug interactions. The results suggest antitubercular drugs, particularly rifampin and second-line agents, warrant high alert and monitoring while prescribing with the repurposed COVID-19 drugs.

Conclusion:

Predicting these potential drug–drug interactions, particularly related to CYP3A4, P-gp and the human Ether-à-go-go-Related Gene proteins, could be used in clinical settings for screening and management of drug–drug interactions for delivering safer chemotherapeutic tuberculosis and COVID-19 care. The current study provides an initial propulsion for further well-designed pharmacokinetic-pharmacodynamic-based drug–drug interaction studies.

Plain Language Summary

Discovery of IACS-9779 and IACS-70465 as Potent Inhibitors Targeting Indoleamine 2,3-Dioxygenase 1 (IDO1) Apoenzyme

Indoleamine 2,3-dioxygenase 1 (IDO1), a heme-containing enzyme that mediates the rate-limiting step in the metabolism of l-tryptophan to kynurenine, has been widely explored as a potential immunotherapeutic target in oncology. We developed a class of inhibitors with a conformationally constrained bicyclo[3.1.0]hexane core. These potently inhibited IDO1 in a cellular context by binding to the apoenzyme, as elucidated by biochemical characterization and X-ray crystallography. A SKOV3 tumor model was instrumental in differentiating compounds, leading to the identification of IACS-9779 (62) and IACS-70465 (71). IACS-70465 has excellent cellular potency, a robust pharmacodynamic response, and in a human whole blood assay was more potent than linrodostat (BMS-986205). IACS-9779 with a predicted human efficacious once daily dose below 1 mg/kg to sustain >90% inhibition of IDO1 displayed an acceptable safety margin in rodent toxicology and dog cardiovascular studies to support advancement into preclinical safety evaluation for human development.

Probing the Dynamic Structure-Function and Structure-Free Energy Relationships of the Coronavirus Main Protease with Biodynamics Theory

The SARS-CoV-2 main protease (M^pro) is of major interest as an antiviral drug target. Structure-based virtual screening efforts, fueled by a growing list of apo and inhibitor-bound SARS-CoV/CoV-2 M^pro crystal structures, are underway in many laboratories. However, little is known about the dynamic enzyme mechanism, which is needed to inform both assay development and structure-based inhibitor design. Here, we apply biodynamics theory to characterize the structural dynamics of substrate-induced M^pro activation under nonequilibrium conditions. The catalytic cycle is governed by concerted dynamic structural rearrangements of domain 3 and the m-shaped loop (residues 132–147) on which Cys145 (comprising the thiolate nucleophile and half of the oxyanion hole) and Gly143 (comprising the second half of the oxyanion hole) reside. In particular, we observed the following: (1) Domain 3 undergoes dynamic rigid-body rotation about the domain 2–3 linker, alternately visiting two primary conformational states (denoted as M₁^pro ↔ M₂^pro); (2) The Gly143-containing crest of the m-shaped loop undergoes up and down translations caused by conformational changes within the rising stem of the loop (Lys137–Asn142) in response to domain 3 rotation and dimerization (denoted as M_1/down^pro ↔ 2·M_2/up^pro) (noting that the Cys145-containing crest is fixed in the up position). We propose that substrates associate to the M_1/down^pro state, which promotes the M_2/down^pro state, dimerization (denoted as 2·M_2/up^pro–substrate), and catalysis. Here, we explore the state transitions of M^pro under nonequilibrium conditions, the mechanisms by which they are powered, and the implications thereof for efficacious inhibition under in vivo conditions.

Toward in vivo relevant drug design

Current early and preclinical drug discovery are rooted in decades-old empirical principles describing structure-free energy and structure-function relationships under equilibrium conditions that frequently break down under in vivo conditions. Improved prediction of efficacy and toxicity depends on a paradigm shift to in vivo-relevant principles describing the true nonequilibrium/nonlinear dynamic (NLD) nature of cellular systems. Here, we outline a holistic, in vivo-relevant first principles theory ('Biodynamics'), in which cellular function/dysfunction, and pharmaco-/toxicodynamic effects are considered as emergent behaviors of multimolecular systems powered by covalent and noncovalent free energy sources. The reduction to practice of Biodynamics theory consists of in silico simulations performed at the atomistic and molecular systems levels, versus empirical models fit to in vitro data under the classical paradigm.