Approaching Pharmacological Space: Events and Components

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

How Do Gepotidacin and Zoliflodacin Stabilize DNA Cleavage Complexes with Bacterial Type IIA Topoisomerases? 1. Experimental Definition of Metal Binding Sites

One of the challenges for experimental structural biology in the 21st century is to see chemical reactions happen. Staphylococcus aureus (S. aureus) DNA gyrase is a type IIA topoisomerase that can create temporary double-stranded DNA breaks to regulate DNA topology. Drugs, such as gepotidacin, zoliflodacin and the quinolone moxifloxacin, can stabilize these normally transient DNA strand breaks and kill bacteria. Crystal structures of uncleaved DNA with a gepotidacin precursor (2.1 Å GSK2999423) or with doubly cleaved DNA and zoliflodacin (or with its progenitor QPT-1) have been solved in the same P61 space-group (a = b ≈ 93 Å, c ≈ 412 Å). This suggests that it may be possible to observe the two DNA cleavage steps (and two DNA-religation steps) in this P61 space-group. Here, a 2.58 Å anomalous manganese dataset in this crystal form is solved, and four previous crystal structures (1.98 Å, 2.1 Å, 2.5 Å and 2.65 Å) in this crystal form are re-refined to clarify crystal contacts. The structures clearly suggest a single moving metal mechanism-presented in an accompanying (second) paper. A previously published 2.98 Å structure of a yeast topoisomerase II, which has static disorder around a crystallographic twofold axis, was published as containing two metals at one active site. Re-refined coordinates of this 2.98 Å yeast structure are consistent with other type IIA topoisomerase structures in only having one metal ion at each of the two different active sites.

Identification of selective inhibitors for phosphodiesterase 5A using e-pharmacophore modelling and large-scale virtual screening-based structure guided drug discovery approaches

The inhibition of Phosphodiesterase 5A (PDEA5) has the potential to modulate pulmonary arterial hypertension and cardiovascular diseases. Exploring the cross-reactivity of clinically available PDE5A therapeutics with PDE6A is intriguing in order to develop highly selective PDE5A compounds in cardiovascular arena. In the current study, we leveraged e-pharmacophore based screening and molecular dynamics (MD) simulation to discover more selective PDE5A inhibitors as compared to the PDE6A catalytic domain. e-Pharmacophore based mapping of the CoCoCo database (7 million compounds: ∼ 150,000,000 conformers), followed by Glide docking, MM-GBSA, and protein-inhibitor interaction analysis, revealed 1536427, 4832637 and 6788240 as stable, tight binders of PDE5A instead of PDE6A. These compounds adhere to Lipinski Rule of Five (RO5) and ADME/Tox criteria. MD simulations analysis showed that 1536427 stays stable and tightly binds to catalytic (Q-region) core of PDE5A catalytic domain as compared to sildenafil. Pronounced inward motions of the hydrophobic (H-region) and Lid region indicate the closure of PDE5A-1536427 complex, whereas this region in PDE6A-1536427 is more open. Significant differences in the interactions, stability, and dynamics of 1536427 were observed in the catalytic domain of PDE6A, demonstrating less specificity for PDE6A in comparison to PDE5A. After lead optimization and therapeutic interventions, this proposed lead may emerge as a promising PDE5A selective inhibitor.Communicated by Ramaswamy H. Sarma.

Lead optimisation of OXS007417: in vivo PK profile and hERG liability modulation to optimise a small molecule differentiation agent for the potential treatment of acute myeloid leukaemia

The development of a safe, efficacious, and widely effective differentiation therapy for AML would dramatically improve the outlook for many patients worldwide. To this aim, our laboratory has discovered a class of differentiation agents that demonstrate tumour regression in murine models in vivo. Herein, we report a lead optimisation process around compound OXS007417, which led to improved potency, solubility, metabolic stability, and off-target toxicity of this compound class. A hERG liability was investigated and successfully alleviated through addition of nitrogen atoms into key positions of the compound. OXS008255 and OXS008474 demonstrated an improved murine PK profile in respect to OXS007417 and a delay in tumour growth in a subcutaneous in vivo model using HL-60 cells.

On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme

Introduction: Given the direct association with malignant ventricular arrhythmias, cardiotoxicity is a major concern in drug design. In the past decades, computational models based on the quantitative structure-activity relationship have been proposed to screen out cardiotoxic compounds and have shown promising results. The combination of molecular fingerprint and the machine learning model shows stable performance for a wide spectrum of problems; however, not long after the advent of the graph neural network (GNN) deep learning model and its variant (e.g., graph transformer), it has become the principal way of quantitative structure-activity relationship-based modeling for its high flexibility in feature extraction and decision rule generation. Despite all these progresses, the expressiveness (the ability of a program to identify non-isomorphic graph structures) of the GNN model is bounded by the WL isomorphism test, and a suitable thresholding scheme that relates directly to the sensitivity and credibility of a model is still an open question. Methods: In this research, we further improved the expressiveness of the GNN model by introducing the substructure-aware bias by the graph subgraph transformer network model. Moreover, to propose the most appropriate thresholding scheme, a comprehensive comparison of the thresholding schemes was conducted. Results: Based on these improvements, the best model attains performance with 90.4% precision, 90.4% recall, and 90.5% F1-score with a dual-threshold scheme (active: < 1 μ M ; non-active: > 30 μ M ). The improved pipeline (graph subgraph transformer network model and thresholding scheme) also shows its advantages in terms of the activity cliff problem and model interpretability.

Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking

Graph neural networks (GNNs) have been widely used in molecular property prediction, but explaining their black-box predictions is still a challenge. Most existing explanation methods for GNNs in chemistry focus on attributing model predictions to individual nodes, edges or fragments that are not necessarily derived from a chemically meaningful segmentation of molecules. To address this challenge, we propose a method named substructure mask explanation (SME). SME is based on well-established molecular segmentation methods and provides an interpretation that aligns with the understanding of chemists. We apply SME to elucidate how GNNs learn to predict aqueous solubility, genotoxicity, cardiotoxicity and blood-brain barrier permeation for small molecules. SME provides interpretation that is consistent with the understanding of chemists, alerts them to unreliable performance, and guides them in structural optimization for target properties. Hence, we believe that SME empowers chemists to confidently mine structure-activity relationship (SAR) from reliable GNNs through a transparent inspection on how GNNs pick up useful signals when learning from data.

Structural analysis of hERG channel blockers and the implications for drug design

The repolarizing current (I_kr) produced by the hERG potassium channel forms a major component of the cardiac action potential and blocking this current by small molecule drugs can lead to life-threatening cardiotoxicity. Understanding the mechanisms of drug-mediated hERG inhibition is essential to develop a second generation of safe drugs, with minimal cardiotoxic effects. Although various computational tools and drug design guidelines have been developed to avoid binding of drugs to the hERG pore domain, there are many other aspects that are still open for investigation. This includes the use computational modelling to study the implications of hERG mutations on hERG structure and trafficking, the interactions of hERG with hERG chaperone proteins and with membrane-soluble molecules, the mechanisms of drugs that inhibit hERG trafficking and drugs that rescue hERG mutations. The plethora of available experimental data regarding all these aspects can guide the construction of much needed robust computational structural models to study these mechanisms for the rational design of safe drugs.

Toward Quantitative Models in Safety Assessment: A Case Study to Show Impact of Dose-Response Inference on hERG Inhibition Models

Due to challenges with historical data and the diversity of assay formats, in silico models for safety-related endpoints are often based on discretized data instead of the data on a natural continuous scale. Models for discretized endpoints have limitations in usage and interpretation that can impact compound design. Here, we present a consistent data inference approach, exemplified on two data sets of Ether-à-go-go-Related Gene (hERG) K+ inhibition data, for dose-response and screening experiments that are generally applicable for in vitro assays. hERG inhibition has been associated with severe cardiac effects and is one of the more prominent safety targets assessed in drug development, using a wide array of in vitro and in silico screening methods. In this study, the IC₅₀ for hERG inhibition is estimated from diverse historical proprietary data. The IC₅₀ derived from a two-point proprietary screening data set demonstrated high correlation (R = 0.98, MAE = 0.08) with IC_50s derived from six-point dose-response curves. Similar IC₅₀ estimation accuracy was obtained on a public thallium flux assay data set (R = 0.90, MAE = 0.2). The IC₅₀ data were used to develop a robust quantitative model. The model's MAE (0.47) and R² (0.46) were on par with literature statistics and approached assay reproducibility. Using a continuous model has high value for pharmaceutical projects, as it enables rank ordering of compounds and evaluation of compounds against project-specific inhibition thresholds. This data inference approach can be widely applicable to assays with quantitative readouts and has the potential to impact experimental design and improve model performance, interpretation, and acceptance across many standard safety endpoints.

In silico toxicity prediction, molecular docking studies and in vitro validation of antibacterial potential of alkaloids from Eclipta alba in designing of novel antimicrobial therapeutic strategies

The rapid emergence of various drug resistance and unfavourable aliphatic medication side effects endangers people's health. Phytocompounds with antibacterial activity and less harmful effects are known to be present in medicinal plants. Alkaloids from Eclipta alba were tested for their in vitro antibacterial capabilities and in silico docking studies against pathogenic bacteria and their target proteins in the current investigation. The alkaloid compounds verazine, ecliptine, 4-hydroxyverazine, 20-Epi-4beta-hydroxyverazine and hydroxyverazine were subjected to molecular docking studies to determine the method of binding as well as potential interactions and the docking score. The in vitro antibacterial activity of verazine alkaloid was assessed against two gram-positive and two gram-negative bacteria. Verazine alkaloid has the best inhibitory ability against DNA gyrase of E. coli (ΔG= -8.44 kcal/mol) and dihydrofolate reductase (DHFR) of S. aureus (ΔG= -10.04 kcal/mol), according to docking studies. Verazine shown substantial in vitro antibacterial activity in this investigation against all test bacteria, with MIC and MBC values of 31.25 and 62.50 µg/mL for S. aureus and 15.63 and 31.25 µg/mL for B. cereus, respectively. The results of this work highlighted the value of unique alkaloid compounds from E. alba, which may offer effective antibacterial agents and DNA gyrase, DHFR inhibitors due to their novel structural properties capable of combating antimicrobial resistance. These findings call for more investigation into the compounds' function as antibacterial agents, as well as their unique-binding locations and mechanisms.

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.

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.

Use of Solvent Mapping for Characterizing the Binding Site and for Predicting the Inhibition of the Human Ether-á-Go-Go-Related K+ Channel

Molecular dynamics was used to optimize the droperidol-hERG complex obtained from docking. To accommodate the inhibitor, residues T623, S624, V625, G648, Y652, and F656 did not move significantly during the simulation, while F627 moved significantly. Binding sites in cryo-EM structures and in structures obtained from molecular dynamics simulations were characterized using solvent mapping and Atlas ligands, which were negative images of the binding site, were generated. Atlas ligands were found to be useful for identifying human ether-á-go-go-related potassium channel (hERG) inhibitors by aligning compounds to them or by guiding the docking of compounds in the binding site. A molecular dynamics optimized structure of hERG led to improved predictions using either compound alignment to the Atlas ligand or docking. The structure was also found to be suitable to define a strategy for lowering inhibition based on the proposed binding mode of compounds in the channel.

The utility of hERG channel inhibition data in the derivation of occupational exposure limits

Inhibition of the human ether-à-go-go (hERG) channel may lead to QT prolongation and fatal arrhythmia. While pharmaceutical drug candidates that exhibit potent hERG channel inhibition often fail early in development, many drugs with both cardiac and non-cardiac indications proceed to market. In this study, the relationship between in vitro hERG channel inhibition and published occupational exposure limit (OEL) was evaluated. A total of 23 cardiac drugs and 44 drugs with non-cardiac indications with published hERG channel IC₅₀ and published OELs were identified. There was an apparent relationship between hERG IC₅₀ potency and the OEL for cardiac and non-cardiac drugs. Twenty cardiac and non-cardiac drugs were identified that had a potent hERG IC₅₀ (≤25 μM) and a contrastingly large OEL value (≥100 μg/m³). OELs or hazard banding corresponding to ≤100 μg/m³ should be sufficiently protective of effects following occupational exposure to the majority of APIs with hERG IC₅₀ values ≤ 100 μM. It is important to consider hERG IC₅₀ values and possible cardiac effects when deriving OEL values for drugs, regardless of indication. These considerations may be particularly important early in the drug development process for establishing exposure control bands for drugs that do not yet have full clinical safety data.

In Silico Models for Predicting Acute Systemic Toxicity

In this chapter, we give a brief overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. Emphasis is placed on quantitative structure-activity relationship (QSAR) models implemented by means of a range of software tools. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered.

The Bradykinin B2 Receptor Agonist (NG291) Causes Rapid Onset of Transient Blood-Brain Barrier Disruption Without Evidence of Early Brain Injury

Background: The blood-brain barrier (BBB) describes the brain's highly specialized capillaries, which form a dynamic interface that maintains central nervous system (CNS) homeostasis. The BBB supports the CNS, in part, by preventing the entry of potentially harmful circulating molecules into the brain. However, this specialized function is challenging for the development of CNS therapeutics. Several strategies to facilitate drug delivery into the brain parenchyma via disruption of the BBB have been proposed. Bradykinin has proven effective in disrupting mechanisms across the blood-tumor barrier. Unfortunately, bradykinin has limited therapeutic value because of its short half-life and the undesirable biological activity elicited by its active metabolites. Objective: To evaluate NG291, a stable bradykinin analog, with selective agonist activity on the bradykinin-B2 receptor and its ability to disrupt the BBB transiently. Methods: Sprague Dawley rats and CD-1 mice were subjected to NG291 treatment (either 50 or 100 μg/kg, intravenously). Time and dose-dependent BBB disruption were evaluated by histological analysis of Evans blue (EB) extravasation. Transcellular and paracellular BBB leakage were assessed by infiltration of 99mTc-albumin (66.5 KDa) and 14C-sucrose (340 Da) radiolabeled probes into the brains of CD-1 mice treated with NG291. NG291 influence on P-glycoprotein (P-gp) efflux pump activity was evaluated by quantifying the brain accumulation of 3H-verapamil, a known P-gp substrate, in CD-1 mice. Results: NG291-mediated BBB disruption was localized, dose-dependent, and reversible as measured by EB extravasation. 99mTc-albumin leakage was significantly increased by 50 μg/kg of NG291, whereas 100 μg/kg of NG291 significantly augmented both 14C-sucrose and 99mTc-albumin leakage. NG291 enhanced P-gp efflux transporter activity and was unable to increase brain uptake of the P-gp substrate pralidoxime. NG291 did not evoke significant short-term neurotoxicity, as it did not increase brain water content, the number of Fluoro-Jade C positive cells, or astrocyte activation. Conclusion: Our findings strongly suggest that NG291 increases BBB permeability by two different mechanisms in a dose-dependent manner and increases P-gp efflux transport. This increased permeability may facilitate the penetration into the brain of therapeutic candidates that are not P-gp substrates.

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.

Transforming Type II to Type I c-Met kinase inhibitors via combined scaffold hopping and structure-guided synthesis of new series of 1,3,4-thiadiazolo[2,3-c]-1,2,4-triazin-4-one derivatives

Novel 1,3,4-thiadiazolo[2,3-c]-1,2,4-triazin-4-one derivatives 3a-e, 4a-f and 5a-f were designed as Type I c-Met kinase inhibitors based on scaffold hopping of our previous Type II c-Met kinase lead. Target compounds were then synthesized under the guidance of molecular docking analysis to identify the potential inhibitors that fit the binding pocket of c-Met kinase in the characteristic manner as the reported Type I c-Met kinase inhibitors. All synthesized derivatives were evaluated for their c-Met kinase inhibitory activity at 10 µM concentration, where 3d, 5d and 5f displayed >80% inhibition. Further IC₅₀ investigation of these compounds identified 5d as the most potent c-Met kinase inhibitor with IC₅₀ value of 1.95 µM. Moreover, 5d showed selective antitumor activity against c-Met over-expressing colon HCT-116 and lung A549 adenocarcinoma cells with IC₅₀ values of 6.18 and 10.6 µg/ml, respectively. More significantly, 5d effectively inhibited c-Met phosphorylation in the Western blot experiment. Also, 5d induced cellular apoptosis in HCT-116 cancer cells as well as cell cycle arrest with accumulation of cells in G2/M phase. Finally, kinase selectivity profiling of 5d against nine oncogenic kinases revealed its selectivity to only Tyro3 kinase (% inhibition = 80%, IC₅₀ = 3 µM). All these experimental findings clearly demonstrate that 5d is a potential dual acting inhibitor against c-Met and Tyro3 kinases, standing out as a viable lead that deserves further investigation and development to new generation of antitumor agents.

Structural optimization, synthesis and in vitro synergistic anticancer activities of combinations of new N3-substituted dihydropyrimidine calcium channel blockers with cisplatin and etoposide

T-type calcium channels are considered potential drug targets to combat cancer. Combining T-type calcium channel blockers with conventional chemotherapy drugs represents a promising strategy towards successful cancer treatment. From this perspective, we report in this study the design and synthesis of a novel series of N3-sustituted dihydropyrimidines (DHPMs) as anticancer adjuvants to cisplatin (Cis) and etoposide (Eto). Full spectral characterization of the new compounds was done using FT-IR, ¹H NMR, ¹³C NMR, and HRMS. Structure elucidation was confirmed by 2D NMR ¹H-H COSY, HSQC and NOESY experiments. Novel derivatives were tested for their Ca²⁺ channel blocking activity by employing the whole cell patch-clamp technique. Results demonstrated that most compounds were potential T-type calcium channel blockers with the triazole-based C12 and C13 being the most selective agents against Ca_V3.2 channel. Further electrophysiological studies demonstrated that C12 and C13 inhibited Ca_V3.2 currents with respective affinity of 2.26 and 1.27 µM, and induced 5 mV hyperpolarizing shifts in the half-inactivation potential. Subsequently, C12 and C13 were evaluated for their anticancer activities alone and in combination with Cis and Eto against A549 and MDA-MB 231 cancer cells. Interestingly, both compounds exhibited potential anticancer effects with IC₅₀ values < 5 µM. Combination studies revealed that both compounds had synergistic effects (combination index CI < 1) on Cis and Eto through induction of apoptosis (p53 activation and up-regulation of BAX and p21 gene expression). Importantly, in silico physicochemical and ADMET assessment of both compounds revealed their potential drug-like properties with decreased risk of cardiac toxicity. Hence, C12 and C13 are promising anticancer adjuvants through inhibition of Ca_V3.2 T-type calcium channels, thereby serving as eminent leads for further modification.

Flavonoids and hERG channels: Friends or foes?

The cardiac action potential is regulated by several ion channels. Drugs capable to block these channels, in particular the human ether-à-go-go-related gene (hERG) channel, also known as K_V11.1 channel, may lead to a potentially lethal ventricular tachyarrhythmia called "Torsades de Pointes". Thus, evaluation of the hERG channel off-target activity of novel chemical entities is nowadays required to safeguard patients as well as to avoid attrition in drug development. Flavonoids, a large class of natural compounds abundantly present in food, beverages, herbal medicines, and dietary food supplements, generally escape this assessment, though consumed in consistent amounts. Continuously growing evidence indicates that these compounds may interact with the hERG channel and block it. The present review, by examining numerous studies, summarizes the state-of-the-art in this field, describing the most significant examples of direct and indirect inhibition of the hERG channel current operated by flavonoids. A description of the molecular interactions between a few of these natural molecules and the Rattus norvegicus channel protein, achieved by an in silico approach, is also presented.

A recursive molecular docking coupled with energy-based pose-rescoring and MD simulations to identify hsGC βH-NOX allosteric modulators for cardiovascular dysfunctions

Modulating the activity of human soluble guanylate cyclase (hsGC) through allosteric regulation of the βH-NOX domain has been considered as an immediate treatment for cardiovascular disorder (CVDs). Currently available βH-NOX domain-specific agonists including cinaciguat are unable to deal with the conundrum raised due to oxidative stress in the case of CVDs and their associated comorbidities. Therefore, the idea of investigating novel compounds for allosteric regulation of hsGC activation has been rekindled to circumvent CVDs. Current study aims to identify novel βH-NOX domain-specific compounds that can selectively turn on sGC functions by modulating the conformational dynamics of the target protein. Through a comprehensive computational drug-discovery approach, we first executed a target-based performance assessment of multiple docking (PLANTS, QVina, LeDock, Vinardo, Smina) scoring functions based on multiple performance metrices. QVina showed the highest capability of selecting true-positive ligands over false positives thus, used to screen 4.8 million ZINC15 compounds against βH-NOX domain. The docked ligands were further probed in terms of contact footprint and pose reassessment through clustering analysis and PLANTS docking, respectively. Subsequently, energy-based AMBER rescoring of top 100 low-energy complexes, per-residue energy decomposition analysis, and ADME-Tox analysis yielded the top three compounds i.e. ZINC000098973660, ZINC001354120371, and ZINC000096022607. The impact of three selected ligands on the internal structural dynamics of the βH-NOX domain was also investigated through molecular dynamics simulations. The study revealed potential electrostatic interactions for better conformational dialogue between βH-NOX domain and allosteric ligands that are critical for the activation of hsGC as compared to the reference compound.

Design, Synthesis, Docking and Computational Pharmacokinetic Profiling of New Pyrazolinyl Thiazolinone Biheterocycles as Potent Antimicrobial Agents

Background:

Development of potential antimicrobial agents is the main aim in the drug discovery process to overcome the problem of drug resistance. Pyrazolines and thiazolinones are extensively used as building blocks for the synthesis of diverse and medicinally important compounds.

Methods:

In this present work, a new series of functionalized pyrazolinyl-thiazolinone biheterocycles is designed and synthesized from N-pyrazolinecarbothioamide. Antimicrobial screening is carried out in order to discover their potential towards six bacterial and four fungal strains. The zone of inhibition (ZI in mm) was determined by the disc diffusion method and minimum inhibitory concentration (MIC in μg/mL) by macro dilution method. The druggability of these new entities is done through in silico pharmacokinetic profiling using Maestro 2017-1 interface of Schrӧdinger software.

Results and Disscusion:

Compounds 4c and 4e with chloro and iodo substituents on Nphenylacetamide ring displayed good inhibitory antibacterial activity against the tested bacterial strains with minimum MIC values when compared to the reference drug tetracycline. Compound 3 with an acetic acid derivative showed high antifungal activity among all the tested derivatives. Compound 3 not only showed antifungal activity but also qualified druggability test with no violation of Lipinski rule of five.

Conclusion:

The capability of the synthesized pyrazolinyl-thiazolinone derivatives was performed to efficiently inhibit the growth of microorganisms against selected bacterial and fungal strains. Further, these compounds are found to be effectively bound to the active sites of attractive target Escherichia coli FabH.

Development of multitarget inhibitors for the treatment of pain: Design, synthesis, biological evaluation and molecular modeling studies

Multitarget-directed ligands are a promising class of drugs for discovering innovative new therapies for difficult to treat diseases. In this study, we designed dual inhibitors targeting the human fatty acid amide hydrolase (FAAH) enzyme and human soluble epoxide hydrolase (sEH) enzyme. Targeting both of these enzymes concurrently with single target inhibitors synergistically reduces inflammatory and neuropathic pain; thus, dual FAAH/sEH inhibitors are likely to be powerful analgesics. Here, we identified the piperidinyl-sulfonamide moiety as a common pharmacophore and optimized several inhibitors to have excellent inhibition profiles on both targeted enzymes simultaneously. In addition, several inhibitors show good predicted pharmacokinetic properties. These results suggest that this series of inhibitors has the potential to be further developed as new lead candidates and therapeutics in pain management.

Preclinical Evaluation of Acylhydrazone SB-AF-1002 as a Novel Broad-Spectrum Antifungal Agent

The incidence of invasive fungal infections is rising due to the increase in susceptible populations. Current clinically available drugs have therapeutic limitations due to toxicity, a narrow spectrum of activity, and, more importantly, the consistent rise of fungal species that are intrinsically resistant or that develop resistance due to prolonged therapy. Thus, there is an urgent need for new broad-spectrum antifungal agents with low toxicity and a novel mechanism of action. We previously reported a new class of potent antifungal compounds, acylhydrazones, that target the fungal sphingolipid pathway. Based upon our initial lead molecules, (E)-N'-(5-bromo-2-hydroxybenzylidene)-2-methylbenzohydrazide and D13, we performed a structure-activity relationship study, synthesizing ca. 300 new compounds. Of these, 5 compounds were identified to be the most promising for further studies, based on their broad-spectrum activity and low toxicity in mammalian cells lines. Among these top 5 lead compounds, we report here the impressive in vivo activity of 2,4-dibromo-N'-(5-bromo-2-hydroxybenzylidene)benzohydrazide (SB-AF-1002) in several models of systemic fungal infection. Our data show that SB-AF-1002 is efficacious and outperforms current standard-of-care drugs in models of invasive fungal infections, such as cryptococcosis, candidiasis, and aspergillosis. Specifically, animals treated with SB-AF-1002 not only survived the infection but also showed a clearing of fungal cells from key organs. Moreover, SB-AF-1002 was very effective in an aspergillosis model as a prophylactic therapy. SB-AF-1002 also displayed acceptable pharmacokinetic properties in mice, similar to those of the parent compound, D13. These results clearly indicate that our novel acylhydrazones constitute a new class of highly potent and efficacious antifungal agents which warrant further development for the treatment of invasive fungal infections.

hERG toxicity assessment: Useful guidelines for drug design

All along the drug development process, one of the most frequent adverse side effects, leading to the failure of drugs, is the cardiac arrhythmias. Such failure is mostly related to the capacity of the drug to inhibit the human ether-à-go-go-related gene (hERG) cardiac potassium channel. The early identification of hERG inhibition properties of biological active compounds has focused most of attention over the years. In order to prevent the cardiac side effects, a great number of in silico, in vitro and in vivo assays have been performed. The main goal of these studies is to understand the reasons of these effects, and then to give information or instructions to scientists involved in drug development to avoid the cardiac side effects. To evaluate anticipated cardiovascular effects, early evaluation of hERG toxicity has been strongly recommended for instance by the regulatory agencies such as U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Thus, following an initial screening of a collection of compounds to find hits, a great number of pharmacomodulation studies on the novel identified chemical series need to be performed including activity evaluation towards hERG. We provide in this concise review clear guidelines, based on described examples, illustrating successful optimization process to avoid hERG interactions as cases studies and to spur scientists to develop safe drugs.

In Silico and In Vitro Anti-Helicobacter Pylori Effects of Combinations of Phytochemicals and Antibiotics

Helicobacter pylori infection is a WHO class 1 carcinogenic factor of gastric adenocarcinoma. In the past decades, many studies have demonstrated the increasing trend of antibiotic resistance and pointed out the necessity of new effective treatment. This study was aimed at identifying phytochemicals that can inhibit H. pylori and possibly serve as adjuvant treatments. Here, in silico molecular docking and drug-like properties analyses were performed to identify potential inhibitors of urease, shikimate kinase and aspartate-semialdehyde dehydrogenase. These three enzymes are targets of the treatment of H. pylori. Susceptibility and synergistic testing were performed on the selected phytochemicals and the positive control antibiotic, amoxicillin. The in-silico study revealed that oroxindin, rosmarinic acid and verbascoside are inhibitors of urease, shikimate kinase and aspartate-semialdehyde dehydrogenase, respectively, in which, oroxindin has the highest potency against H. pylori, indicated by a minimum inhibitory concentration (MIC) value of 50 μg/mL. A combination of oroxindin and amoxicillin demonstrated additive effects against H. pylori, as indicated by a fractional inhibitory concentration (FIC) value of 0.75. This study identified phytochemicals that deserve further investigation for the development of adjuvant therapeutic agents to current antibiotics against H. pylori.

Prediction of hERG K+ channel blockage using deep neural networks

Human ether-a-go-go-related gene (hERG) K+ channel blockage may cause severe cardiac side-effects and has become a serious issue in safety evaluation of drug candidates. Therefore, improving the ability to avoid undesirable hERG activity in the early stage of drug discovery is of significant importance. The purpose of this study was to build predictive models of hERG activity by deep neural networks. For each combination of sampling methods and descriptors, deep neural networks with different architectures were implemented to build classification models. The optimal model M15 with three hidden layers, undersampling method, and 2D descriptors yielded the prediction accuracy of 0.78 and F1 score of 0.75 on the test set as well as accuracy of 0.77 and F1 score of 0.34 on the external validation set, outperforming the other 35 models including 9 random forest models. Particularly, the optimal model M15 achieved the highest F1 score and the second highest accuracy when compared with other five methods from four groups using different machine learning algorithms with the same external validation set. It can be believed that this model has powerful capability on prediction of hERG toxicity, which is of great benefit for developing novel drug candidates.

Computational determination of hERG-related cardiotoxicity of drug candidates

Background

Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates.

Result

In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models.

Conclusion

The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.

Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity

Blockade of the human ether-à-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drugs. In this study, we develop a deep learning approach, termed deephERG, for prediction of hERG blockers of small molecules in drug discovery and postmarketing surveillance. In total, we assemble 7,889 compounds with well-defined experimental data on the hERG and with diverse chemical structures. We find that deephERG models built by a multitask deep neural network (DNN) algorithm outperform those built by single-task DNN, naı̈ve Bayes (NB), support vector machine (SVM), random forest (RF), and graph convolutional neural network (GCNN). Specifically, the area under the receiver operating characteristic curve (AUC) value for the best model of deephERG is 0.967 on the validation set. Furthermore, based on 1,824 U.S. Food and Drug Administration (FDA) approved drugs, 29.6% drugs are computationally identified to have potential hERG inhibitory activities by deephERG, highlighting the importance of hERG risk assessment in early drug discovery. Finally, we showcase several novel predicted hERG blockers on approved antineoplastic agents, which are validated by clinical case reports, experimental evidence, and the literature. In summary, this study presents a powerful deep learning-based tool for risk assessment of hERG-mediated cardiotoxicities in drug discovery and postmarketing surveillance.

Acylhydrazones as Antifungal Agents Targeting the Synthesis of Fungal Sphingolipids

The incidence of invasive fungal infections has risen dramatically in recent decades. Current antifungal drugs are either toxic, likely to interact with other drugs, have a narrow spectrum of activity, or induce fungal resistance. Hence, there is a great need for new antifungals, possibly with novel mechanisms of action. Previously our group reported an acylhydrazone called BHBM that targeted the sphingolipid pathway and showed strong antifungal activity against several fungi. In this study, we screened 19 derivatives of BHBM. Three out of 19 derivatives were highly active against Cryptococcus neoformansin vitro and had low toxicity in mammalian cells. In particular, one of them, called D13, had a high selectivity index and showed better activity in an animal model of cryptococcosis, candidiasis, and pulmonary aspergillosis. D13 also displayed suitable pharmacokinetic properties and was able to pass through the blood-brain barrier. These results suggest that acylhydrazones are promising molecules for the research and development of new antifungal agents.

Experimental and In Silico Analysis of Cordycepin and its Derivatives as Endometrial Cancer Treatment

The aim of this study was to investigate the inhibition effects of cordycepin and its derivatives on endometrial cancer cell growth. Cytotoxicity MTT assays, clonogenic assays, and flow cytometry were used to observe the effects on apoptosis and regulation of the cell cycle of Ishikawa cells under various concentrations of cordycepin, cisplatin, and combinations of the two. Validated in silico docking simulations were performed on 31 cordycepin derivatives against adenosine deaminase (ADA) to predict their binding affinities and hence their potential tendency to be metabolized by ADA. Cordycepin has a significant dose-dependent inhibitory effect on cell proliferation. The combination of cordycepin and cisplatin produced greater inhibition effects than did cordycepin alone. Apoptosis investigations confirmed the ability of cordycepin to induce the apoptosis of Ishikawa cells. The in silico results indicate that compound MRS5698 is least metabolized by ADA and has acceptable drug likeness and safety profiles. This is the first study to confirm the cytotoxic effects of cordycepin on endometrial cancer cells. This study also identified cordycepin derivatives with promising pharmacological and pharmacokinetic properties for further investigation in the development of new treatments for endometrial cancer.

Technological Advances in Cardiovascular Safety Assessment Decrease Preclinical Animal Use and Improve Clinical Relevance

Cardiovascular (CV) safety liabilities are significant concerns for drug developers and preclinical animal studies are predominately where those liabilities are characterized before patient exposures. Steady progress in technology and laboratory capabilities is enabling a more refined and informative use of animals in those studies. The application of surgically implantable and telemetered instrumentation in the acute assessment of drug effects on CV function has significantly improved historical approaches that involved anesthetized or restrained animals. More chronically instrumented animals and application of common clinical imaging assessments like echocardiography and MRI extend functional and in-life structural assessments into the repeat-dose setting. A growing portfolio of circulating CV biomarkers is allowing longitudinal and repeated measures of cardiac and vascular injury and dysfunction better informing an understanding of temporal pathogenesis and allowing earlier detection of undesirable effects. In vitro modeling systems of the past were limited by their lack of biological relevance to the in vivo human condition. Advances in stem cell technology and more complex in vitro modeling platforms are quickly creating more opportunity to supplant animals in our earliest assessments for liabilities. Continuing improvement in our capabilities in both animal and nonanimal modeling should support a steady decrease in animal use for primary liability identification and optimize the translational relevance of the animal studies we continue to do.

Modeling of the hERG K+ Channel Blockage Using Online Chemical Database and Modeling Environment (OCHEM)

Human ether-a-go-go related gene (hERG) K+ channel plays an important role in cardiac action potential. Blockage of hERG channel may result in long QT syndrome (LQTS), even cause sudden cardiac death. Many drugs have been withdrawn from the market because of the serious hERG-related cardiotoxicity. Therefore, it is quite essential to estimate the chemical blockage of hERG in the early stage of drug discovery. In this study, a diverse set of 3721 compounds with hERG inhibition data was assembled from literature. Then, we make full use of the Online Chemical Modeling Environment (OCHEM), which supplies rich machine learning methods and descriptor sets, to build a series of classification models for hERG blockage. We also generated two consensus models based on the top-performing individual models. The consensus models performed much better than the individual models both on 5-fold cross validation and external validation. Especially, consensus model II yielded the prediction accuracy of 89.5 % and MCC of 0.670 on external validation. This result indicated that the predictive power of consensus model II should be stronger than most of the previously reported models. The 17 top-performing individual models and the consensus models and the data sets used for model development are available at https://ochem.eu/article/103592.

Natural products modulating the hERG channel: heartaches and hope

This review covers natural products modulating the hERG potassium channel. Risk assessment strategies, structural features of blockers, and the duality target/antitarget are discussed.

Covering: 1996–December 2016

The human Ether-à-go-go Related Gene (hERG) channel is a voltage-gated potassium channel playing an essential role in the normal electrical activity in the heart. It is involved in the repolarization and termination of action potentials in excitable cardiac cells. Mutations in the hERG gene and hERG channel blockage by small molecules are associated with increased risk of fatal arrhythmias. Several drugs have been withdrawn from the market due to hERG channel-related cardiotoxicity. Moreover, as a result of its notorious ligand promiscuity, this ion channel has emerged as an important antitarget in early drug discovery and development. Surprisingly, the hERG channel blocking profile of natural compounds present in frequently consumed botanicals (i.e. dietary supplements, spices, and herbal medicinal products) is not routinely assessed. This comprehensive review will address these issues and provide a critical compilation of hERG channel data for isolated natural products and extracts over the past two decades (1996–2016). In addition, the review will provide (i) a solid basis for the molecular understanding of the physiological functions of the hERG channel, (ii) the translational potential of in vitro/in vivo results to cardiotoxicity in humans, (iii) approaches for the identification of hERG channel blockers from natural sources, (iv) future perspectives for cardiac safety guidelines and their applications within phytopharmaceuticals and dietary supplements, and (v) novel applications of hERG channel modulation (e.g. as a drug target).

Prediction of hERG Liability - Using SVM Classification, Bootstrapping and Jackknifing

Drug-induced QT prolongation leads to life-threatening cardiotoxicity, mostly through blockage of the human ether-à-go-go-related gene (hERG) encoded potassium ion (K+ ) channels. The hERG channel is one of the most important antitargets to be addressed in the early stage of drug discovery process, in order to avoid more costly failures in the development phase. Using a thallium flux assay, 4,323 molecules were screened for hERG channel inhibition in a quantitative high throughput screening (qHTS) format. Here, we present support vector classification (SVC) models of hERG channel inhibition with the averaged area under the receiver operator characteristics curve (AUC-ROC) of 0.93 for the tested compounds. Both Jackknifing and bootstrapping have been employed to rebalance the heavily biased training datasets, and the impact of these two under-sampling rebalance methods on the performance of the predictive models is discussed. Our results indicated that the rebalancing techniques did not enhance the predictive power of the resulting models; instead, adoption of optimal cutoffs could restore the desirable balance of sensitivity and specificity of the binary classifiers. In an external validation set of 66 drug molecules, the SVC model exhibited an AUC-ROC of 0.86, further demonstrating the utility of this modeling approach to predict hERG liabilities.

Synergistic antibacterial effects of herbal extracts and antibiotics on methicillin-resistant Staphylococcus aureus: A computational and experimental study

Antibiotic resistance has become a serious global concern, and the discovery of antimicrobial herbal constituents may provide valuable solutions to overcome the problem. In this study, the effects of therapies combining antibiotics and four medicinal herbs on methicillin-resistant Staphylococcus aureus (MRSA) were investigated. Specifically, the synergistic effects of Magnolia officinalis, Verbena officinalis, Momordica charantia, and Daphne genkwa in combination with oxacillin or gentamicin against methicillin-resistant (ATCC43300) and methicillin-susceptible (ATCC25923) S. aureus were examined. In vitro susceptibility and synergistic testing were performed to measure the minimum inhibitory concentration and fractional inhibitory concentration (FIC) index of the antibiotics and medicinal herbs against MRSA and methicillin-susceptible S. aureus. To identify the active constituents in producing these synergistic effects, in silico molecular docking was used to investigate the binding affinities of 139 constituents of the four herbs to the two common MRSA inhibitory targets, penicillin binding proteins 2a (PBP2a) and 4 (PBP4). The physicochemical and absorption, distribution, metabolism, and excretion properties and drug safety profiles of these compounds were also analyzed. D. genkwa extract potentiated the antibacterial effects of oxacillin against MRSA, as indicated by an FIC index value of 0.375. M. officinalis and V. officinalis produced partial synergistic effects when combined with oxacillin, whereas M. charantia was found to have no beneficial effects in inhibiting MRSA. Overall, tiliroside, pinoresinol, magnatriol B, and momorcharaside B were predicted to be PBP2a or PBP4 inhibitors with good drug-like properties. This study identifies compounds that deserve further investigation with the aim of developing therapeutic agents to modulate the effect of antibiotics on MRSA. Impact statement Antibiotic resistant is a well-known threat to global health and methicillin-resistant Staphylococcus aureus is one of the most significant ones. These resistant bacteria kill thousands of people every year and therefore a new effective antimicrobial treatment is necessary. This study identified the herbs and their associated bioactive ingredients that can potential the effects of current antibiotics. These herbs have long history of human usage in China and have well-defined monograph in the Chinese Pharmacopeia. These indicate their relatively high clinical safety and may have a quicker drug development process than that of a new novel antibiotic. Based on the results of this study, the authors will perform further in vitro and animal studies, aiming to accumulate significant data for the application of clinical trial.

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches

Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

Current and Future Perspectives on the Development, Evaluation, and Application of in Silico Approaches for Predicting Toxicity

Exploiting non-testing approaches to predict toxicity early in the drug discovery development cycle is a helpful component in minimizing expensive drug failures due to toxicity being identified in late development or even during clinical trials. Changes in regulations in the industrial chemicals and cosmetics sectors in recent years have prompted a significant number of advances in the development, application, and assessment of non-testing approaches, such as (Q)SARs. Many efforts have also been undertaken to establish guiding principles for performing read-across within category and analogue approaches. This review offers a perspective, as taken from these sectors, of the current status of non-testing approaches, their evolution in light of the advances in high-throughput approaches and constructs such as adverse outcome pathways, and their potential relevance for drug discovery. It also proposes a workflow for how non-testing approaches could be practically integrated within testing and assessment strategies.