Investigation of miscellaneous hERG inhibition in large diverse compound collection using automated patch-clamp assay

Identification of a novel xanthine oxidoreductase inhibitor for hyperuricemia treatment with high efficacy and safety profile

Hyperuricemia is with growing incidence and of high risk to develop into gout and other metabolic diseases. The key enzyme catalyzing uric acid synthesis, xanthine oxidoreductase (XOR) is a vital target for anti-hyperuricemic drugs, while XOR inhibitors characterized as both potent and safe are currently in urgent need. In this study, a novel small molecule compound, CC15009, was identified as a specific XOR inhibitor. CC15009 exerted strongest in vitro XOR inhibitory activity among current XOR inhibitors. It also showed favorable dose-dependent uric acid-lowering effects in two different XOR substrate-induced hyperuricemic mouse models, which was significantly superior than the current first-line drug, allopurinol. Mechanically, the direct binding of CC15009 against XOR was confirmed by molecular docking and SPR analysis. The inhibition mode was competitive and reversible. Besides, the potential antioxidant activity of CC15009 was indicated by its strong inhibitory activity against the oxidized isoform of XOR, which reduced ROS generation as the byproduct. Regarding the safety concerns of current XOR inhibitors, especially in cardiovascular risks, the safety of CC15009 was comprehensively evaluated. No significant abnormality was observed in the acute, subacute toxicity tests and mini-AMES test. Notably, there was no obvious inhibition of CC15009 against cardiac ion channels, including hERG, Nav1.5, Cav1.2 at the concentration of 30 μM, indicating its lower cardiovascular risk. Taken together, our results supported CC15009 as a candidate of high efficacy and safety profile to treat hyperuricemia through direct XOR inhibition.

In Silico Human Cardiomyocyte Action Potential Modeling: Exploring Ion Channel Input Combinations

In silico modeling offers an opportunity to supplement and accelerate cardiac safety testing. With in silico modeling, computational simulation methods are used to predict electrophysiological interactions and pharmacological effects of novel drugs on critical physiological processes. The O'Hara-Rudy's model was developed to predict the response to different ion channel inhibition levels on cardiac action potential duration (APD) which is known to directly correlate with the QT interval. APD data at 30% 60% and 90% inhibition were derived from the model to delineate possible ventricular arrhythmia scenarios and the marginal contribution of each ion channel to the model. Action potential values were calculated for epicardial, myocardial, and endocardial cells, with action potential curve modeling. This study assessed cardiac ion channel inhibition data combinations to consider when undertaking in silico modeling of proarrhythmic effects as stipulated in the Comprehensive in Vitro Proarrhythmia Assay (CiPA). As expected, our data highlight the importance of the delayed rectifier potassium channel (IKr) as the most impactful channel for APD prolongation. The impact of the transient outward potassium channel (Ito) inhibition on APD was minimal while the inward rectifier (IK1) and slow component of the delayed rectifier potassium channel (IKs) also had limited APD effects. In contrast, the contribution of fast sodium channel (INa) and/or L-type calcium channel (ICa) inhibition resulted in substantial APD alterations supporting the pharmacological relevance of in silico modeling using input from a limited number of cardiac ion channels including IKr, INa, and ICa, at least at an early stage of drug development.

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.

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.

Pyrido-Dibemequine Metabolites Exhibit Improved Druglike Features, Inhibit Hemozoin Formation in Plasmodium falciparum, and Synergize with Clinical Antimalarials

Structural modification of existing chemical scaffolds to afford new molecules able to circumvent drug resistance constitutes one of the rational approaches to antimalarial drug discovery. Previously synthesized compounds based on the 4-aminoquinoline core hybridized with a chemosensitizing dibenzylmethylamine side group showed in vivo efficacy in Plasmodium berghei-infected mice despite low microsomal metabolic stability, suggesting a contribution from their pharmacologically active metabolites. Here, we report on a series of these dibemequine (DBQ) metabolites with low resistance indices against chloroquine-resistant parasites and improved metabolic stability in liver microsomes. The metabolites also exhibit improved pharmacological properties including lower lipophilicity, cytotoxicity, and hERG channel inhibition. Using cellular heme fractionation experiments, we also demonstrate that these derivatives inhibit hemozoin formation by causing a buildup of toxic "free" heme in a similar manner to chloroquine. Finally, assessment of drug interactions also revealed synergy between these derivatives and several clinically relevant antimalarials, thus highlighting their potential interest for further development.

In Vitro Antibacterial Efficacy of Cymbopogon flexuosus Essential Oil against Aeromonas hydrophila of Fish Origin and in Silico Molecular Docking of the Essential Oil Components against DNA Gyrase-B and Their Drug-Likeness

In aquaculture, diseases caused by the Aeromonads with high antibiotic resistance are among the most common and troublesome diseases. Application of herbs is emerging as a tool in controlling these diseases. Plant extracts besides disease control, favor various physiological activities in fish. In this study, essential oil of Cymbopogon flexuosus (Poaceae family) was studied in vitro for its antibacterial efficacy against two oxytetracycline (OTC) resistant and one sensitive strains of Aeromonas hydrophila. The oil was found rich (86.93 %) in oxygenated terpenoids containing 74.15 % of citral. The oil exhibited dose dependent growth inhibition of the bacteria. Mean MIC value of the oil against the sensitive strain was recorded as 2.0 mg mL^-1 whereas MBC value was recorded as 4.0 mg mL^-1 . The oil was found effective against the OTC resistant isolates with the MIC and MBC values ranging from 2.67-3.33 and 4.0-6.67 mg mL^-1 , respectively. In silico molecular docking of the essential oil components against DNA gyrase-B, a vital macromolecule in bacterial cell, was carried out to computationally asses the efficacy of the oil against the bacteria. Some of the components of the essential oil strongly bonded with the enzyme to inhibit its efficacy. Binding energy of some components of the oil was comparable to that of the conventional antibiotic, OTC. The identified phytochemicals exhibited favorable physicochemical and pharmacokinetic properties and satisfied the rule of five (Ro5).

Structural Alterations of the "Address" Moiety of NAN Leading to the Discovery of a Novel Opioid Receptor Modulator with Reduced hERG Toxicity

The search for selective opioid ligands with desired pharmacological potency and improved safety profile has always been an area of interest. Our previous effort yielded a potent opioid modulator, NAN, a 6α-N-7'-indolyl-substituted naltrexamine derivative, which exhibited promising pharmacological activities both in vitro and in vivo. However, significant human ether-a-go-go-related gene (hERG) liability limited its further development. Therefore, a systematic structural modification on NAN was conducted in order to alleviate hERG toxicity while preserving pharmacological properties, which led to the discovery of 2'-methylindolyl derivative compound 21. Compared to NAN, compound 21 manifested overall improved pharmacological profiles. Follow-up hERG channel inhibition evaluation revealed a seven-fold decreased potency of compound 21 compared to NAN. Furthermore, several fundamental drug-like property evaluations suggested a reasonable ADME profile of 21. Collectively, compound 21 appeared to be a promising opioid modulator for further development as a novel therapeutic agent toward opioid use disorder treatments.

Ensemble of structure and ligand-based classification models for hERG liability profiling

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

Preclinical Characterization and Development on NAQ as a Mu Opioid Receptor Partial Agonist for Opioid Use Disorder Treatment

Mu opioid receptor (MOR) selective antagonists and partial agonists have clinical utility for the treatment of opioid use disorders (OUDs). However, the development of many has suffered due to their poor pharmacokinetic properties and/or rapid metabolism. Our recent efforts to identify MOR modulators have provided 17-cyclopropylmethyl-3,14β-dihydroxy-4,5α-epoxy-6α-(isoquinoline-3-carboxamido)morphinan (NAQ), a low-efficacy partial agonist, that showed sub-nanomolar binding affinity to the MOR (K i 0.6 nM) with selectivity over the delta opioid receptor (δ/μ 241) and the kappa opioid receptor (κ/μ 48). Its potent inhibition of the analgesic effect of morphine (AD50 0.46 mg/kg) and precipitation of significantly less withdrawal symptoms even at 100-fold greater dose than naloxone represents a promising molecule for further development as a novel OUD therapeutic agent. Therefore, further in vitro and in vivo characterization of its pharmacokinetics and pharmacodynamics properties was conducted to fully understand its pharmaceutical profile. NAQ showed favorable in vitro ADMET properties and no off-target binding to several classes of GPCRs, enzymes, and ion channels. Following intravenous administration, 1 mg/kg dose of NAQ showed a similar in vivo pharmacokinetic profile to naloxone; however, orally administered 10 mg/kg NAQ demonstrated significantly improved oral bioavailability over both naloxone and naltrexone. Abuse liability assessment of NAQ in rats demonstrated that NAQ functioned as a less potent reinforcer than heroin. Chronic 5 day NAQ pretreatment decreased heroin self-administration in a heroin-vs-food choice procedure similar to the clinically used MOR partial agonist buprenorphine. Taken together, these studies provide evidence supporting NAQ as a promising lead to develop novel OUD therapeutics.

New Insights into Ion Channels: Predicting hERG-Drug Interactions

Drug-induced long QT syndrome can be a very dangerous side effect of existing and developmental drugs. In this work, a model proposed two decades ago addressing the ion specificity of potassium channels is extended to the human ether-à-gogo gene (hERG). hERG encodes the protein that assembles into the potassium channel responsible for the delayed rectifier current in ventricular cardiac myocytes that is often targeted by drugs associated with QT prolongation. The predictive value of this model can guide a rational drug design decision early in the drug development process and enhance NCE (New Chemical Entity) retention. Small molecule drugs containing a nitrogen that can be protonated to afford a formal +1 charge can interact with hERG to prevent the repolarization of outward rectifier currents. Low-level ab initio calculations are employed to generate electronic features of the drug molecules that are known to interact with hERG. These calculations were employed to generate structure–activity relationships (SAR) that predict whether a small molecule drug containing a protonated nitrogen has the potential to interact with and inhibit the activity of the hERG potassium channels of the heart. The model of the mechanism underlying the ion specificity of potassium channels offers predictive value toward optimizing drug design and, therefore, minimizes the effort and expense invested in compounds with the potential for life-threatening inhibitory activity of the hERG potassium channel.

The Effect of a Synthetic Estrogen, Ethinylestradiol, on the hERG Block by E-4031

Inhibition of K⁺-conductance through the human ether-a-go-go related gene (hERG) channel leads to QT prolongation and is associated with cardiac arrhythmias. We previously reported that physiological concentrations of some estrogens partially suppress the hERG channel currents by interacting with the S6 residue F656 and increase the sensitivity of hERG blockade by E-4031. Although these studies suggested that clinically used synthetic estrogens with similar structures have the marked potential to alter hERG functions, the hERG interactions with synthetic estrogens have not been assessed. We therefore examined whether ethinylestradiol (EE2), a synthetic estrogen used in oral contraceptives, affects hERG function and blockade by drugs. Supratherapeutic concentrations of EE2 did not alter amplitudes or kinetics of the hERG currents elicited by train pulses at 20 mV (0.1 Hz). On the other hand, EE2 at therapeutic concentrations reduced the degree of hERG current suppression by E-4031. The administration of EE2 followed by E-4031 blockade reversed the current suppression, suggesting that the interaction of EE2 and E-4031 alters hERG at the drug-binding site. The effects of EE2 on hERG blockade raised the possibility that other estrogens, including synthetic estrogens, can alter hERG blockade by drugs that cause QT prolongation and ventricular arrhythmias.

Discovery of Investigational Drug CT1812, an Antagonist of the Sigma-2 Receptor Complex for Alzheimer's Disease

An unbiased phenotypic neuronal assay was developed to measure the synaptotoxic effects of soluble Aβ oligomers. A collection of CNS druglike small molecules prepared by conditioned extraction was screened. Compounds that prevented and reversed synaptotoxic effects of Aβ oligomers in neurons were discovered to bind to the sigma-2 receptor complex. Select development compounds displaced receptor-bound Aβ oligomers, rescued synapses, and restored cognitive function in transgenic hAPP Swe/Ldn mice. Our first-in-class orally administered small molecule investigational drug 7 (CT1812) has been advanced to Phase II clinical studies for Alzheimer's disease.

Core battery safety pharmacology testing - An assessment of its utility in early drug development

Requirements for safety pharmacology testing have been in place since the issue of initial regulatory guidance over 20 years ago. An evaluation of such testing, supporting first clinical entry of 105 small molecule drug candidates over the last decade, showed that a "core battery" of in vitro electrophysiological (hERG), conscious non-rodent telemetry cardiovascular, rodent central nervous system (CNS) (modified Irwin's or functional observational battery [FOB] test) and respiratory function (plethysmography) studies was performed. Routine use of the latter 2 studies appears to have limited utility, with only 21% and 28% of studies, respectively, giving findings of which none were identified as of obvious concern to moving the affected drugs into the clinic. The use of a stand-alone hERG assay does not appear to be particular sensitive in predicting proarrythmic risk as a tool by itself. Telemetry study testing had utility especially for identifying effects on QTc interval (about 10% of studies), resulting on some occasions in a lower clinical starting dose and/or increased awareness for potential effects on the cardiovascular system in the Phase I study. Overall, this investigation provides information supporting an overhaul of the current "box ticking" core battery approach used for safety pharmacology testing. However, in order to achieve a more focused examination to investigate potential undesirable pharmacodynamic effects of a new candidate drug and also support 3Rs (Replacement, Reduction and Refinement) thinking in performing unnecessary studies, there will not only need to be a sea change by drug developers but also a change in current regulatory guidance.

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.

Novel thienopyrimidine analogues as potential metabotropic glutamate receptors inhibitors and anticancer activity: Synthesis, In-vitro, In-silico, and SAR approaches

There is a continuous need in drug development approach for synthetic anticancer analogues with new therapeutic targets to diminish chemotherapeutic resistance of cancer cells. This study presents new group of synthetic thienopyrimidine analogues (1-9) aims as mGluR-1 inhibitors with anticancer activity. In-vitro antiproliferative assessment was carried out using viability assay against cancer cell lines (MCF-7, A-549 and PC-3) compared to WI-38 normal cell line. Analogues showed variable anticancer activity with IC₅₀ ranging from 6.60 to 121 µg/mL with compound 7b is the most potent analogue against the three cancer cell lines (MCF-7; 6.57 ± 0.200, A-549; 6.31 ± 0.400, PC-3;7.39 ± 0.500 µg/mL) compared to Doxorubicin, 5-Flurouracil and Riluzole controls. Selected compounds were tested as mGluR-1 inhibitors in MCF-7 cell line and results revealed compound 7b induced significant reduction in extracellular glutamate release (IC₅₀; 4.96 ± 0.700 µM) compared to other analogues and next to Riluzole (IC₅₀; 2.80 ± 0.500 µM) of the same suggested mode of action. Furthermore, both cell cycle and apoptosis assays confirmed the potency of compound 7b for early apoptosis of MCF-7 at G₂/M phase and apoptotic positive cell shift to (91.4%) compared to untreated control (19.6%) and Raptinal positive control (51.4%). On gene expression level, compound 7b induced over-expression of extrinsic (FasL, TNF-α and Casp-8), intrinsic (Cyt-C, Casp-3, Bax) apoptotic genes with down-regulation of anti-apoptotic Bcl-2 gene with boosted Bax/Bcl-2 ratio to 2.6-fold increase. Molecular docking and dynamic studies confirmed the biological potency through strong binding and stability modes of 7b where it was faster in reaching the equilibrium point and achieving the stability than Riluzole over 20 ns MD. These results suggest compound 7b as a promising mGluR inhibitory scaffold with anticancer activity that deserves further optimization and in-depth In-vivo and clinical investigations.

A structure-based computational workflow to predict liability and binding modes of small molecules to hERG

Off-target interactions of drugs with the human ether-à-go-go related gene 1 (hERG1) channel have been associated with severe cardiotoxic conditions leading to the withdrawal of many drugs from the market over the last decades. Consequently, predicting drug-induced hERG-liability is now a prerequisite in any drug discovery campaign. Understanding the atomic level interactions of drug with the channel is essential to guide the efficient development of safe drugs. Here we utilize the recent cryo-EM structure of the hERG channel and describe an integrated computational workflow to characterize different drug-hERG interactions. The workflow employs various structure-based approaches and provides qualitative and quantitative insights into drug binding to hERG. Our protocol accurately differentiated the strong blockers from weak and revealed three potential anchoring sites in hERG. Drugs engaging in all these sites tend to have high affinity towards hERG. Our results were cross-validated using a fluorescence polarization kit binding assay and with electrophysiology measurements on the wild-type (WT-hERG) and on the two hERG mutants (Y652A-hERG and F656A-hERG), using the patch clamp technique on HEK293 cells. Finally, our analyses show that drugs binding to hERG disrupt and hijack certain native—structural networks in the channel, thereby, gaining more affinity towards hERG.

In silico toxicity evaluation of Salubrinal and its analogues

This paper reports on a comprehensive in silico toxicity assessment of Salubrinal and its analogues containing a cinnamic acid residue or quinoline ring using the online servers admetSAR, ADMETlab, ProTox, ADVERPred, Pred-hERG and Vienna LiverTox. Apart from rare exceptions, in all 55 studied structures, mild or practical absence of acute toxicity was predicted for rats (III or IV toxicity class). Cardiotoxic, hepatotoxic and immunotoxic effects were predicted for Salubrinal and its analogues. We constructed models of the main predicted anti-targets hERG, BSEP, MRP3, MRP4 and AhR using the principle of homologous modeling. Molecular docking studies were carried out with the obtained models. We carried out molecular docking for all targets using AutoDock Vina, implemented in the PyRx 0.8 software package. According to the results of molecular docking, the compounds analyzed are potential moderate or weak hERG blockers. Induction of cholestasis and, as a consequence, liver damage by these drugs, directly related to inhibition of BSEP, MRP3 and MRP4, most likely will not be observed. Interaction with AhR for the studied compounds is impossible for steric reasons and, as a consequence, toxic effects on the immune and other organ systems associated with the activation of the AhR signaling pathway are excluded.

Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development

In drug development, preclinical safety and pharmacokinetics assessments of candidate drugs to ensure the safety profile are a must. While in vivo and in vitro tests are traditionally used, experimental determinations have disadvantages, as they are usually time-consuming and costly. In silico predictions of these preclinical endpoints have each been developed in the past decades. However, only a few web-based tools have integrated different models to provide a simple one-step platform to help researchers thoroughly evaluate potential drug candidates. To efficiently achieve this approach, a platform for preclinical evaluation must not only predict key ADMET (absorption, distribution, metabolism, excretion and toxicity) properties but also provide some guidance on structural modifications to improve the undesired properties. In this review, we organized and compared several existing integrated web servers that can be adopted in preclinical drug development projects to evaluate the subject of interest. We also introduced our new web server, Virtual Rat, as an alternative choice to profile the properties of drug candidates. In Virtual Rat, we provide not only predictions of important ADMET properties but also possible reasons as to why the model made those structural predictions. Multiple models were implemented into Virtual Rat, including models for predicting human ether-a-go-go-related gene (hERG) inhibition, cytochrome P450 (CYP) inhibition, mutagenicity (Ames test), blood-brain barrier penetration, cytotoxicity and Caco-2 permeability. Virtual Rat is free and has been made publicly available at https://virtualrat.cmdm.tw/.

A Pooled Analysis of Three Randomized Phase I/IIa Clinical Trials Confirms Absence of a Clinically Relevant Effect on the QTc Interval by Umibecestat

Umibecestat, an orally active β‐secretase inhibitor, reduces the production of amyloid beta‐peptide that accumulates in the brain of patients with Alzheimer’s disease. The echocardiogram effects of umibecestat, on QTcF (Fridericia‐corrected QT), on PR and QRS and heart rate (HR), were estimated by concentration‐effect modeling. Three phase I/II studies with durations up to 3 months, with 372 healthy subjects over a wide age range, including both sexes and 2 ethnicities, were pooled, providing a large data set with good statistical power. No clinically relevant effect on QTcF, PR interval, QRS duration, or HR were observed up to supratherapeutic doses. The upper bound of 90% confidence intervals of the ∆QTcF was below the 10 ms threshold of regulatory concern for all concentrations measured. Prespecified sensitivity analysis confirmed the results in both sexes, in those over and below 60 years, and in Japanese subjects. All conclusions were endorsed by the US Food and Drug Administration (FDA).

In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM

Natural products continue to be major sources of bioactive compounds and drug candidates not only because of their unique chemical structures but also because of their overall favorable metabolism and pharmacokinetic properties. The number of publicly accessible natural product databases has increased significantly in the past few years. However, the systematic ADME/Tox profile has been reported on a limited basis. For instance, BIOFACQUIM was recently published as a public database of natural products from Mexico, a country with a rich source of biomolecules. However, its ADME/Tox profile has not been reported. Herein, we discuss the results of an in-depth in silico ADME/Tox profile of natural products in BIOFACQUIM and other large public collections of natural products. It was concluded that the absorption and distribution profiles of compounds in BIOFACQUIM are similar to those of approved drugs, while the metabolism profile is comparable to that in the other natural product databases. The excretion profile of compounds in BIOFACQUIM is different from that of the approved drugs, but their predicted toxicity profile is comparable. This work further contributes to the deeper characterization of natural product collections as major sources of bioactive compounds with therapeutic potential.

Computational Assessment of the Pharmacological Profiles of Degradation Products of Chitosan

Chitosan is a natural polymer revealing an increased potential to be used in different biomedical applications, including drug delivery systems, and tissue engineering. It implies the evaluation of the organism response to the biomaterial implantation. Low-molecular degradation products, the chito-oligomers, are resulting mainly from the influence of enzymes, which are found in the organism fluids. Within this study, we have performed the computational assessment of pharmacological profiles and toxicological effects on human health of small chito-oligomers with distinct molecular weights, deacetylation degrees, and acetylation patterns. Our approach is based on the fact that regulatory agencies and researchers in the drug development field rely on the use of modeling to predict biological effects and to guide decision making. To be considered as valid for regulatory purposes, every model that is used for predictions should be associated with a defined toxicological endpoint and has appropriate robustness and predictivity. Within this context, we have used FAF-Drugs4, SwissADME, and PreADMET tools to predict the oral bioavailability of chito-oligomers and SwissADME, PreADMET, and admetSAR2.0 tools to predict their pharmacokinetic profiles. The organs and genomic toxicities have been assessed using admetSAR2.0 and PreADMET tools but specific computational facilities have been also used for predicting different toxicological endpoints: Pred-Skin for skin sensitization, CarcinoPred-EL for carcinogenicity, Pred-hERG for cardiotoxicity, ENDOCRINE DISRUPTOME for endocrine disruption potential and Toxtree for carcinogenicity and mutagenicity. Our computational assessment showed that investigated chito-oligomers reflect promising pharmacological profiles and limited toxicological effects on humans, regardless of molecular weight, deacetylation degree, and acetylation pattern. According to our results, there is a possible inhibition of the organic anion transporting peptides OATP1B1 and/or OATP1B3, a weak potential of cardiotoxicity, a minor probability of affecting the androgen receptor, and phospholipidosis. Consequently, these results may be used to guide or to complement the existing in vitro and in vivo toxicity tests, to optimize biomaterials properties and to contribute to the selection of prototypes for nanocarriers.

Advances with support vector machines for novel drug discovery

INTRODUCTION

Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds. Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges. Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.

The High Cost of Stroke and Stroke Cytoprotection Research

Acute ischemic stroke is inadequately treated in the USA and worldwide due to a lengthy history of neuroprotective drug failures in clinical trials. The majority of victims must endure life-long disabilities that not only affect their livelihood, but also have an enormous societal economic impact. The rapid development of a neuroprotective or cytoprotective compound would allow future stroke victims to receive a treatment to reduce disabilities and further promote recovery of function. This opinion article reviews in detail the enormous costs associated with developing a small molecule to treat stroke, as well as providing a timely overview of the cell-death time-course and relationship to the ischemic cascade. Distinct temporal patterns of cell-death of neurovascular unit components provide opportunities to intervene and optimize new cytoprotective strategies. However, adequate research funding is mandatory to allow stroke researchers to develop and test their novel therapeutic approach to treat stroke victims.