When Does the IC50 Accurately Assess the Blocking Potency of a Drug?

Experimentally validated modeling of dynamic drug-hERG channel interactions reproducing the binding mechanisms and its importance in action potential duration

BACKGROUND AND OBJECTIVE

Assessment of drug cardiotoxicity is critical in the development of new compounds and modeling of drug-binding dynamics to hERG can improve early cardiotoxicity assessment. We previously developed a methodology to generate Markovian models reproducing preferential state-dependent binding properties, trapping dynamics and the onset of IKr block using simple voltage clamp protocols. Here, we test this methodology with real IKr blockers and investigate the impact of drug dynamics on action potential prolongation.

METHODS

Experiments were performed on HEK cells stably transfected with hERG and using the Nanion SyncroPatch 384i. Three protocols, P-80, P0 and P 40, were applied to obtain the experimental data from the drugs and the Markovian models were generated using our pipeline. The corresponding static models were also generated and a modified version of the O´Hara-Rudy action potential model was used to simulate the action potential duration.

RESULTS

The experimental Hill plots and the onset of IKr block of ten compounds were obtained using our voltage clamp protocols and the models generated successfully mimicked these experimental data, unlike the CiPA dynamic models. Marked differences in APD prolongation were observed when drug effects were simulated using the dynamic models and the static models.

CONCLUSIONS

These new dynamic models of ten well-known IKr blockers constitute a validation of our methodology to model dynamic drug-hERG channel interactions and highlight the importance of state-dependent binding, trapping dynamics and the time-course of IKr block to assess drug effects even at the steady-state.

The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk

BACKGROUND AND PURPOSE

Drug-induced reduction of the rapid delayed rectifier potassium current carried by the human Ether-à-go-go-Related Gene (hERG) channel is associated with increased risk of arrhythmias. Recent updates to drug safety regulatory guidelines attempt to capture each drug's hERG binding mechanism by combining in vitro assays with in silico simulations. In this study, we investigate the impact on in silico proarrhythmic risk predictions due to uncertainty in the hERG binding mechanism and physiological hERG current model.

EXPERIMENTAL APPROACH

Possible pharmacological binding models were designed for the hERG channel to account for known and postulated small molecule binding mechanisms. After selecting a subset of plausible binding models for each compound through calibration to available voltage-clamp electrophysiology data, we assessed their effects, and the effects of different physiological models, on proarrhythmic risk predictions.

KEY RESULTS

For some compounds, multiple binding mechanisms can explain the same data produced under the safety testing guidelines, which results in different inferred binding rates. This can result in substantial uncertainty in the predicted torsade risk, which often spans more than one risk category. By comparison, we found that the effect of a different hERG physiological current model on risk classification was subtle.

CONCLUSION AND IMPLICATIONS

The approach developed in this study assesses the impact of uncertainty in hERG binding mechanisms on predictions of drug-induced proarrhythmic risk. For some compounds, these results imply the need for additional binding data to decrease uncertainty in safety-critical applications.

Assessing fresh water acute toxicity with Surface-Enhanced Raman Scattering (SERS)

It's well known that the toxicity of chemicals in the environment depends not only their concentrations, but more importantly, their bio-availability. Thus, the acute toxicity test of environmental water samples is of great importance in water quality evaluation. In this work, water acute toxicity was determined via SERS approach for the first time based on the reaction between Escherichia coli (E. coli) and p-benzoquinone (BQ). The E. coli was used as the subject of toxicity assay. Under normal conditions, the BQ molecules can be transformed into Hydroquinone (HQ) by the E. coli bacteria; subsequently, the BQ will continue to react with the resulting HQ to form Quinone hydroquinone (QHQ). This process could be impaired in the presence of many toxic chemicals. Bromide modified Ag NPs was then introduced for the highly sensitive SERS detection of the product (HQ and QHQ). Several key factors that may affect water acute toxicity evaluation have been explored, which include the initial BQ and E. coli concentration, the incubation time with BQ, and the sodium chloride concentration. Later, the established system was applied for the toxicity evaluation of Cu2+. It was found that the IC50 value of Cu2+ was 0.94 mg/L, which is superior compared with literature report. This study provides a promising SERS method for assessing acute toxicity in water bodies with high sensitivity and short detection time.

Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity

BACKGROUND AND OBJECTIVE

In silico methods are gaining attention for predicting drug-induced Torsade de Pointes (TdP) in different stages of drug development. However, many computational models tended not to account for inter-individual response variability due to demographic covariates, such as sex, or physiologic covariates, such as renal function, which may be crucial when predicting TdP. This study aims to compare the effects of drugs in male and female populations with normal and impaired renal function using in silico methods.

METHODS

Pharmacokinetic models considering sex and renal function as covariates were implemented from data published in pharmacokinetic studies. Drug effects were simulated using an electrophysiologically calibrated population of cellular models of 300 males and 300 females. The population of models was built by modifying the endocardial action potential model published by O'Hara et al. (2011) according to the experimentally measured gene expression levels of 12 ion channels.

RESULTS

Fifteen pharmacokinetic models for CiPA drugs were implemented and validated in this study. Eight pharmacokinetic models included the effect of renal function and four the effect of sex. The mean difference in action potential duration (APD) between male and female populations was 24.9 ms (p<0.05). Our simulations indicated that women with impaired renal function were particularly susceptible to drug-induced arrhythmias, whereas healthy men were less prone to TdP. Differences between patient groups were more pronounced for high TdP-risk drugs. The proposed in silico tool also revealed that individuals with impaired renal function, electrophysiologically simulated with hyperkalemia (extracellular potassium concentration [K+]o = 7 mM) exhibited less pronounced APD prolongation than individuals with normal potassium levels. The pharmacokinetic/electrophysiological framework was used to determine the maximum safe dose of dofetilide in different patient groups. As a proof of concept, 3D simulations were also run for dofetilide obtaining QT prolongation in accordance with previously reported clinical values.

CONCLUSIONS

This study presents a novel methodology that combines pharmacokinetic and electrophysiological models to incorporate the effects of sex and renal function into in silico drug simulations and highlights their impact on TdP-risk assessment. Furthermore, it may also help inform maximum dose regimens that ensure TdP-related safety in a specific sub-population of patients.

Molecular Hybridization of Alkaloids Using 1,2,3-Triazole-Based Click Chemistry

Alkaloids found in multiple species, known as 'driver species', are more likely to be included in early-stage drug development due to their high biodiversity compared to rare alkaloids. Many synthetic approaches have been employed to hybridize the natural alkaloids in drug development. Click chemistry is a highly efficient and versatile reaction targeting specific areas, making it a valuable tool for creating complex natural products and diverse molecular structures. It has been used to create hybrid alkaloids that address their limitations and serve as potential drugs that mimic natural products. In this review, we highlight the recent advancements made in modifying alkaloids using click chemistry and their potential medicinal applications. We discuss the significance, current trends, and prospects of click chemistry in natural product-based medicine. Furthermore, we have employed computational methods to evaluate the ADMET properties and drug-like qualities of hybrid molecules.

Structural modeling of hERG channel-drug interactions using Rosetta

The human ether-a-go-go-related gene (hERG) not only encodes a potassium-selective voltage-gated ion channel essential for normal electrical activity in the heart but is also a major drug anti-target. Genetic hERG mutations and blockage of the channel pore by drugs can cause long QT syndrome, which predisposes individuals to potentially deadly arrhythmias. However, not all hERG-blocking drugs are proarrhythmic, and their differential affinities to discrete channel conformational states have been suggested to contribute to arrhythmogenicity. We used Rosetta electron density refinement and homology modeling to build structural models of open-state hERG channel wild-type and mutant variants (Y652A, F656A, and Y652A/F656 A) and a closed-state wild-type channel based on cryo-electron microscopy structures of hERG and EAG1 channels. These models were used as protein targets for molecular docking of charged and neutral forms of amiodarone, nifekalant, dofetilide, d/l-sotalol, flecainide, and moxifloxacin. We selected these drugs based on their different arrhythmogenic potentials and abilities to facilitate hERG current. Our docking studies and clustering provided atomistic structural insights into state-dependent drug-channel interactions that play a key role in differentiating safe and harmful hERG blockers and can explain hERG channel facilitation through drug interactions with its open-state hydrophobic pockets.

Evaluation of bioactive compounds from Ficus carica L. leaf extracts via high-performance thin-layer chromatography combined with effect-directed analysis

This study compares different solvent systems with the use of spontaneous fermentation on the phytochemical composition of leaf extracts from a locally grown white variety of common fig (Ficus carica Linn.). The aim was to detect and identify bioactive compounds that are responsible for acetylcholinesterase (AChE), α-amylase and cyclooxygenase-1 (COX-1) enzyme inhibition, and compounds that exhibit antimicrobial activity. Bioactive zones in chromatograms were detected by combining High-performance thin-layer chromatography (HPTLC) with enzymatic and biological assays. A new experimental protocol for measuring the relative half-maximum inhibitory concentration (IC50) was designed to evaluate the potency of the extracts compared to the potency of known inhibitors. Although the IC50 of the fig leaf extract for α-amylase and AChE inhibition were significantly higher when compared to IC50 for acarbose and donepezil, the COX-1 inhibition by the extract (IC50 = 627 µg) was comparable to that of salicylic acid (IC50 = 557 µg), and antimicrobial activity of the extract (IC50 = 375-511 µg) was similar to ampicillin (IC50 = 495 µg). Four chromatographic zones exhibited bioactivity. Compounds from detected bioactive bands were provisionally identified by comparing the band positions to coeluted standards, and by Fourier transform infrared (FTIR) spectra from eluted zones. Flash chromatography was used to separate selected extract into fractions and isolate fractions that are rich in bioactive compounds for further characterisation with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) analysis. The main constituents identified were umbelliferon (zone 1), furocoumarins psoralen and bergapten (zone 2), different fatty acids (zone 3 and 4), and pentacyclic triterpenoids (calotropenyl acetate or lupeol) and stigmasterol (zone 4).

Importance of modelling hERG binding in predicting drug-induced action potential prolongations for drug safety assessment

Reduction of the rapid delayed rectifier potassium current (IKr) via drug binding to the human Ether-à-go-go-Related Gene (hERG) channel is a well recognised mechanism that can contribute to an increased risk of Torsades de Pointes. Mathematical models have been created to replicate the effects of channel blockers, such as reducing the ionic conductance of the channel. Here, we study the impact of including state-dependent drug binding in a mathematical model of hERG when translating hERG inhibition to action potential changes. We show that the difference in action potential predictions when modelling drug binding of hERG using a state-dependent model versus a conductance scaling model depends not only on the properties of the drug and whether the experiment achieves steady state, but also on the experimental protocols. Furthermore, through exploring the model parameter space, we demonstrate that the state-dependent model and the conductance scaling model generally predict different action potential prolongations and are not interchangeable, while at high binding and unbinding rates, the conductance scaling model tends to predict shorter action potential prolongations. Finally, we observe that the difference in simulated action potentials between the models is determined by the binding and unbinding rate, rather than the trapping mechanism. This study demonstrates the importance of modelling drug binding and highlights the need for improved understanding of drug trapping which can have implications for the uses in drug safety assessment.

Modified Curcumins as Potential Drug Candidates for Breast Cancer: An Overview

Breast cancer (BC), the most common malignancy in women, results from significant alterations in genetic and epigenetic mechanisms that alter multiple signaling pathways in growth and malignant progression, leading to limited long-term survival. Current studies with numerous drug therapies have shown that BC is a complex disease with tumor heterogeneity, rapidity, and dynamics of the tumor microenvironment that result in resistance to existing therapy. Targeting a single cell-signaling pathway is unlikely to treat or prevent BC. Curcumin (a natural yellow pigment), the principal ingredient in the spice turmeric, is well-documented for its diverse pharmacological properties including anti-cancer activity. However, its clinical application has been limited because of its low solubility, stability, and bioavailability. To overcome the limitation of curcumin, several modified curcumin conjugates and curcumin mimics were developed and studied for their anti-cancer properties. In this review, we have focused on the application of curcumin mimics and their conjugates for breast cancer.

Docetaxel Loaded in Copaiba Oil-Nanostructured Lipid Carriers as a Promising DDS for Breast Cancer Treatment

Breast cancer is the neoplasia of highest incidence in women worldwide. Docetaxel (DTX), a taxoid used to treat breast cancer, is a BCS-class-IV compound (low oral bioavailability, solubility and intestinal permeability). Nanotechnological strategies can improve chemotherapy effectiveness by promoting sustained release and reducing systemic toxicity. Nanostructured lipid carriers (NLC) encapsulate hydrophobic drugs in their blend-of-lipids matrix, and imperfections prevent drug expulsion during storage. This work describes the preparation, by design of experiments (2³ factorial design) of a novel NLC formulation containing copaiba oil (CO) as a functional excipient. The optimized formulation (NLC_DTX) showed approximately 100% DTX encapsulation efficiency and was characterized by different techniques (DLS, NTA, TEM/FE-SEM, DSC and XRD) and was stable for 12 months of storage, at 25 °C. Incorporation into the NLC prolonged drug release for 54 h, compared to commercial DTX (10 h). In vitro cytotoxicity tests revealed the antiproliferative effect of CO and NLC_DTX, by reducing the cell viability of breast cancer (4T1/MCF-7) and healthy (NIH-3T3) cells more than commercial DTX. NLC_DTX thus emerges as a promising drug delivery system of remarkable anticancer effect, (strengthened by CO) and sustained release that, in clinics, may decrease systemic toxicity at lower DTX doses.

Automatic modeling of dynamic drug-hERG channel interactions using three voltage protocols and machine learning techniques: A simulation study

BACKGROUND

Assessment of drug cardiac safety is critical in the development of new compounds and is commonly addressed by evaluating the half-maximal blocking concentration of the potassium human ether-à-go-go related gene (hERG) channels. However, recent works have evidenced that the modelling of drug-binding dynamics to hERG can help to improve early cardiac safety assessment. Our goal is to develop a methodology to automatically generate Markovian models of the drug-hERG channel interactions.

METHODS

The training and the test sets consisted of 20800 and 5200 virtual drugs, respectively, distributed into 104 groups with different affinities and kinetics to the conformational states of the channel. In our system, drugs may bind to any state (individually or simultaneously), with different degrees of preference for a conformational state and the change of the conformational state of the drug bound channels may be restricted or allowed. To model such a wide range of possibilities, 12 Markovian chains are considered. Our approach uses the response of the drugs to our three previously developed voltage clamp protocols, which enhance the differences in the probabilities of occupying a certain conformational state of the channel (open, closed and inactivated). The computing tool is comprised of a classifier and a parameter optimizer and uses linear interpolation, support vector machines and a simplex method for function minimization.

RESULTS

We propose a novel methodology that automatically generates dynamic drug models using Markov model formulations and that elucidates the states where the drug binds and unbinds and the preferential binding state using data obtained from simple voltage clamp protocols that captures the preferential state-dependent binding properties, the relative affinities, trapping and non-trapping dynamics and the onset of I_Kr block. Overall, the tool correctly predicted the class of 92.04% of the drugs and the model provided by the tool accurately fitted the response of the target compound, the mean accuracy being 97.53%. Moreover, generation of the dynamic model of an I_Kr blocker from its response to our voltage clamp protocols usually takes less than an hour on a common desktop computer.

CONCLUSION

Our methodology could be very useful to model and simulate dynamic drug-hERG channel interactions. It would contribute to the improvement of the preclinical assessment of the proarrhythmic risk of drugs that inhibit I_Kr and the efficacy of antiarrhythmic I_Kr blockers.

Evaluating a Specific Dual ROCK Inhibitor against Bleomycin-Induced Idiopathic Pulmonary Fibrosis in Rats

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal lung disease. Rho-associated protein kinases (ROCK) 1/2 are promising therapeutic targets for the treatment of IPF. However, a single inhibition of each of them is insufficient to prevent bleomycin-induced lung fibrosis. The current work reported that bleomycin-induced lung fibrosis can be reduced by dual inhibition of ROCK1/2 with compound 1. We evaluated the dual-selective ROCK1/2 inhibition activity of compound 1, its toxicity, and its preliminary efficacy on bleomycin-induced lung fibrosis. In vitro, compound 1 served as the ROCK1/2 inhibitor with half-maximal inhibitory concentration (IC50) values of 165 ±10.4 nM for ROCK1 and 16.1 ± 2.82 nM for ROCK2. In NIH/3T3 cells, compound 1 inhibited the mRNA expression of COL 1A1 and α-SMA. At therapeutic levels, compound 1 exhibited neither hepatic nor cardiac toxicity, also no CYP450 enzyme inhibition. In vivo, compound 1 had good pharmacokinetic properties, and its oral administration reduced bleomycin-induced pulmonary fibrosis in rats. All the outcomes prove the drug-like characteristics of compound 1 for the treatment of IPF.

Deep eutectic solvent for spent lithium-ion battery recycling: comparison with inorganic acid leaching

Deep eutectic solvents (DESs) as novel green solvents are potential options to replace inorganic acids for hydrometallurgy. Compared with inorganic acids, the physicochemical properties of DESs and their applications in recycling of spent lithium-ion batteries were summarized. The viscosity, metal solubility, toxicological properties and biodegradation of DESs depend on the hydrogen bond donor (HBD) and acceptor (HBA). The viscosity of ChCl-based DESs increased according to the HBD in the following order: alcohols < carboxylic acids < sugars < inorganic salts. The strongly coordinating HBDs increased the solubility of metal oxide via surface complexation reactions followed by ligand exchange for chloride in the bulk solvent. Interestingly, the safety and degradability of DESs reported in the literature are superior to those of inorganic acids. Both DESs and inorganic acids have excellent metal leaching efficiencies (>99%). However, the reaction kinetics of DESs are 2-3 orders of magnitude slower than those of inorganic acids. A significant advantage of DESs is that they can be regenerated and recycled multiple times after recovering metals by electrochemical deposition or precipitation. In the future, the development of efficient and selective DESs still requires a lot of attention.

Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk

BACKGROUND AND OBJECTIVE

In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets.

METHODS

The effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database.

RESULTS

Population of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of I_Kr, I_Ks, and I_NaK and higher conductances of I_CaL, I_NaL, and I_NCX, which are more prone to develop TdP.

CONCLUSIONS

The methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.

Assessment of proarrhythmogenic risk for chloroquine and hydroxychloroquine using the CiPA concept

Chloroquine and hydroxychloroquine have been proposed recently as therapy for SARS-CoV-2-infected patients, but during 3 months of extensive use concerns were raised related to their clinical effectiveness and arrhythmogenic risk. Therefore, we estimated for these compounds several proarrhythmogenic risk predictors according to the Comprehensive in vitro Proarrhythmia Assay (CiPA) paradigm. Experiments were performed with either CytoPatch™2 automated or manual patch-clamp setups on HEK293T cells stably or transiently transfected with hERG1, hNav1.5, hKir2.1, hKv7.1+hMinK, and on Pluricyte® cardiomyocytes (Ncardia), using physiological solutions. Dose-response plots of hERG1 inhibition fitted with Hill functions yielded IC50 values in the low micromolar range for both compounds. We found hyperpolarizing shifts of tens of mV, larger for chloroquine, in the voltage-dependent activation but not inactivation, as well as a voltage-dependent block of hERG current, larger at positive potentials. We also found inhibitory effects on peak and late I_Na and on I_K1, with IC50 of tens of μM and larger for chloroquine. The two compounds, tested on Pluricyte® cardiomyocytes using the β-escin-perforated method, inhibited I_Kr, I_CaL, I_{Na peak}, but had no effect on I_f. In current-clamp they caused action potential prolongation. Our data and those from literature for I_to were used to compute proarrhythmogenic risk predictors B_net (Mistry HB, 2018) and Q_net (Dutta S et al., 2017), with hERG1 blocking/unblocking rates estimated from time constants of fractional block. Although the two antimalarials are successfully used in autoimmune diseases, and chloroquine may be effective in atrial fibrillation, assays place these drugs in the intermediate proarrhythmogenic risk group.

A computational method for identifying an optimal combination of existing drugs to repair the action potentials of SQT1 ventricular myocytes

Mutations are known to cause perturbations in essential functional features of integral membrane proteins, including ion channels. Even restricted or point mutations can result in substantially changed properties of ion currents. The additive effect of these alterations for a specific ion channel can result in significantly changed properties of the action potential (AP). Both AP shortening and AP prolongation can result from known mutations, and the consequences can be life-threatening. Here, we present a computational method for identifying new drugs utilizing combinations of existing drugs. Based on the knowledge of theoretical effects of existing drugs on individual ion currents, our aim is to compute optimal combinations that can ‘repair’ the mutant AP waveforms so that the baseline AP-properties are restored. More specifically, we compute optimal, combined, drug concentrations such that the waveforms of the transmembrane potential and the cytosolic calcium concentration of the mutant cardiomyocytes (CMs) becomes as similar as possible to their wild type counterparts after the drug has been applied. In order to demonstrate the utility of this method, we address the question of computing an optimal drug for the short QT syndrome type 1 (SQT1). For the SQT1 mutation N588K, there are available data sets that describe the effect of various drugs on the mutated K⁺ channel. These published findings are the basis for our computational analysis which can identify optimal compounds in the sense that the AP of the mutant CMs resembles essential biomarkers of the wild type CMs. Using recently developed insights regarding electrophysiological properties among myocytes from different species, we compute optimal drug combinations for hiPSC-CMs, rabbit ventricular CMs and adult human ventricular CMs with the SQT1 mutation. Since the ‘composition’ of ion channels that form the AP is different for the three types of myocytes under consideration, so is the composition of the optimal drug.

Poly-pharmacology (using multiple drugs to treat disease) has been proposed for improving cardiac anti-arrhythmic therapy for at least two decades. However, the specific arrhythmia contexts in which polytherapy is likely to be both safe and effective have remained elusive. Type 1 short QT syndrome (SQT1) is a rare form of cardiac arrhythmia that results from mutations to the human Ether-á-go-go Related Gene (hERG) potassium channel. Functionally, these mutations are remarkably consistent in that they permit the channel to open earlier during each heart beat. While hundreds of compounds are known to inhibit hERG channels, the specific effect of SQT1 mutations that allows for early channel opening also limits the ability of most of those compounds to correct SQT1 dysfunction. Here, we have applied a suite of ventricular cardiomyocyte computational models to ask whether polytherapy may offer a more effective therapeutic strategy in SQT1, and if so, what the likely characteristics of that strategy are. Our analyses suggest that simultaneous induction of late sodium current and partial hERG blockade offers a promising strategy. While no activators of late sodium current have been clinically approved, several experimental compounds are available and may provide a basis for interrogating this strategy. The method presented here can be used to compute optimal drug combinations provided that the effect of each drug on every relevant ion channel is known.

Methylene blue associated with maghemite nanoparticles has antitumor activity in breast and ovarian carcinoma cell lines

Background

Cancer constitutes group of diseases responsible for the second largest cause of global death, and it is currently considered one of the main public health concerns nowadays. Early diagnosis associated with the best choice of therapeutic strategy, is essential to achieve success in cancer treatment. In women, breast cancer is the second most common type, whereas ovarian cancer has the highest lethality when compared to other neoplasms of the female genital system. The present work, therefore, proposes the association of methylene blue with citrate-coated maghemite nanoparticles (MAGCIT–MB) as a nanocomplex for the treatment of breast and ovarian cancer.

Results

In vitro studies showed that T-47D and A2780 cancer cell lines underwent a significant reduction in cell viability after treatment with MAGCIT–MB, an event not observed in non-tumor (HNTMC and HUVEC) cells and MDA-MB-231, a triple-negative breast cancer cell line. Flow cytometry experiments suggest that the main mechanism of endocytosis involved in the interiorization of MAGCIT–MB is the clathrin pathway, whereas both late apoptosis and necrosis are the main types of cell death caused by the nanocomplex. Scanning electron microscopy and light microscopy reveal significant changes in the cell morphology. Quantification of reactive oxygen species confirmed the MAGCIT–MB cytotoxic mechanism and its importance for the treatment of tumor cells. The lower cytotoxicity of individual solution of maghemite nanoparticles with citrate (MAGCIT) and free methylene blue (MB) shows that their association in the nanocomplex is responsible for its enhanced therapeutic potential in the treatment of breast and ovarian cancer in vitro.

Conclusions

Treatment with MAGCIT–MB induces the death of cancer cells but not normal cells. These results highlight the importance of the maghemite core for drug delivery and for increasing methylene blue activity, aiming at the treatment of breast and ovarian cancer.

Graphic Abstract

Cardiac TdP risk stratification modelling of anti-infective compounds including chloroquine and hydroxychloroquine

Hydroxychloroquine (HCQ), the hydroxyl derivative of chloroquine (CQ), is widely used in the treatment of rheumatological conditions (systemic lupus erythematosus, rheumatoid arthritis) and is being studied for the treatment and prevention of COVID-19. Here, we investigate through mathematical modelling the safety profile of HCQ, CQ and other QT-prolonging anti-infective agents to determine their risk categories for Torsade de Pointes (TdP) arrhythmia. We performed safety modelling with uncertainty quantification using a risk classifier based on the qNet torsade metric score, a measure of the net charge carried by major currents during the action potential under inhibition of multiple ion channels by a compound. Modelling results for HCQ at a maximum free therapeutic plasma concentration (free C max) of approximately 1.2 µM (malaria dosing) indicated it is most likely to be in the high-intermediate-risk category for TdP, whereas CQ at a free C max of approximately 0.7 µM was predicted to most likely lie in the intermediate-risk category. Combining HCQ with the antibacterial moxifloxacin or the anti-malarial halofantrine (HAL) increased the degree of human ventricular action potential duration prolongation at some or all concentrations investigated, and was predicted to increase risk compared to HCQ alone. The combination of HCQ/HAL was predicted to be the riskiest for the free C max values investigated, whereas azithromycin administered individually was predicted to pose the lowest risk. Our simulation approach highlights that the torsadogenic potentials of HCQ, CQ and other QT-prolonging anti-infectives used in COVID-19 prevention and treatment increase with concentration and in combination with other QT-prolonging drugs.

Dual-Site Binding of Quaternary Ammonium Ions as Internal K+-Ion Channel Blockers: Nonclassical (C-H···O) H Bonding vs Dispersive (C-H···H-C) Interaction

A molecular-level study of the influence of the alkyl chain length of quaternary ammonium ions (QAs) on the blocking action and the mode of binding with the bacterial KcsA K⁺-ion channel is carried out by molecular dynamics (MD) simulations as well as quantum mechanics/molecular mechanics (QM/MM) methods. The present work unveils distinct modes of binding for different QAs, due to differences in size and hydrophobicity. The QAs bind near the channel gate as well as at the central cavity, leading to a possible dual-site blocking action. Small-sized tetraethylammonium (TEA) and tetrabutylammonium (TBA) ions enter inside the channel cavity in the open state of KcsA but bind strongly in the closed state. TEA binds to the polar hydroxyl group of threonine residues situated at the channel gate via nonclassical H-bonding interaction (C-H···O), while TBA binds to a second binding site, the central cavity, with hydrophobic benzyl and sec-butyl side chains of phenylalanine and isoleucine residues via alkyl-π and hydrophobic interactions (C-H···H-C). On the contrary, large tetrahexylammonium (THA) and tetraoctylammonium (TOA) ions bind the hydrophobic side-chain methyl and isopropyl of alanine and valine at the channel gate both in the open and closed states, thereby restricting the free movement of large QAs toward the center of the cavity. However, the binding to the hydrophobic benzyl and sec-butyl side chains of phenylalanine and isoleucine residues in the closed state is thermodynamically preferable. Also, the binding energy is found to increase with an increase in the alkyl chain length from ethyl (-16.4 kcal/mol) to octyl (-65.5 kcal/mol), due to an almost linear increase in dispersive interaction.

In Silico Classifiers for the Assessment of Drug Proarrhythmicity

Drug-induced torsade de pointes (TdP) is a life-threatening ventricular arrhythmia responsible for the withdrawal of many drugs from the market. Although currently used TdP risk-assessment methods are effective, they are expensive and prone to produce false positives. In recent years, in silico cardiac simulations have proven to be a valuable tool for the prediction of drug effects. The objective of this work is to evaluate different biomarkers of drug-induced proarrhythmic risk and to develop an in silico risk classifier. Cellular simulations were performed using a modified version of the O'Hara et al. ventricular action potential model and existing pharmacological data (IC₅₀ and effective free therapeutic plasma concentration, EFTPC) for 109 drugs of known torsadogenic risk (51 positive). For each compound, four biomarkers were tested: T_x (drug concentration leading to a 10% prolongation of the action potential over the EFTPC), T_qNet (net charge carried by ionic currents when exposed to 10 times the EFTPC with respect to the net charge in control), T_triang (triangulation for a drug concentration of 10 times the EFTPC over triangulation in control), and T_EAD (drug concentration originating early afterdepolarizations over EFTPC). Receiver operating characteristic (ROC) curves were built for each biomarker to evaluate their individual predictive quality. At the optimal cutoff point, accuracies for T_x, T_qNet, T_triang, and T_EAD were 89.9, 91.7, 90.8, and 78.9% respectively. The resulting accuracy of the hERG IC₅₀ test (current biomarker) was 78.9%. When combining T_x, T_qNet and T_triang into a classifier based on decision trees, the prediction improves, achieving an accuracy of 94.5%. The sensitivity analysis revealed that most of the effects on the action potential are mainly due to changes in I_Kr, I_CaL, I_NaL and I_Ks. In fact, considering that drugs affect only these four currents, TdP risk classification can be as accurate as when considering effects on the seven main currents proposed by the CiPA initiative. Finally, we built a ready-to-use tool (based on more than 450 000 simulations), which can be used to quickly assess the proarrhythmic risk of a compound. In conclusion, our in silico tool can be useful for the preclinical assessment of TdP-risk and to reduce costs related with new drug development. The TdP risk-assessment tool and the software used in this work are available at https://riunet.upv.es/handle/10251/136919.

In silico models for evaluating proarrhythmic risk of drugs

Safety evaluation of drugs requires examination of the risk of generating Torsade de Pointes (TdP) because it can lead to sudden cardiac death. Until recently, the QT interval in the electrocardiogram (ECG) has been used in the evaluation of TdP risk because the QT interval is known to be associated with the development of TdP. Although TdP risk evaluation based on QT interval has been successful in removing drugs with TdP risk from the market, some safe drugs may have also been affected due to the low specificity of QT interval-based evaluation. For more accurate evaluation of drug safety, the comprehensive in vitro proarrhythmia assay (CiPA) has been proposed by regulatory agencies, industry, and academia. Although the CiPA initiative includes in silico evaluation of cellular action potential as a component, attempts to utilize in silico simulation in drug safety evaluation are expanding, even to simulating human ECG using biophysical three-dimensional models of the heart and torso under the effects of drugs. Here, we review recent developments in the use of in silico models for the evaluation of the proarrhythmic risk of drugs. We review the single cell, one-dimensional, two-dimensional, and three-dimensional models and their applications reported in the literature and discuss the possibility of utilizing ECG simulation in drug safety evaluation.