Computational Tool for Fast in silico Evaluation of hERG K+ Channel Affinity

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.

Enhancing hERG Risk Assessment with Interpretable Classificatory and Regression Models

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

Using the Correlation Intensity Index to Build a Model of Cardiotoxicity of Piperidine Derivatives

The assessment of cardiotoxicity is a persistent problem in medicinal chemistry. Quantitative structure-activity relationships (QSAR) are one possible way to build up models for cardiotoxicity. Here, we describe the results obtained with the Monte Carlo technique to develop hybrid optimal descriptors correlated with cardiotoxicity. The predictive potential of the cardiotoxicity models (pIC50, Ki in nM) of piperidine derivatives obtained using this approach provided quite good determination coefficients for the external validation set, in the range of 0.90-0.94. The results were best when applying the so-called correlation intensity index, which improves the predictive potential of a model.

Pharmacophoric analogs of sotorasib-entrapped KRAS G12C in its inactive GDP-bound conformation: covalent docking and molecular dynamics investigations

For decades, KRAS G12C was considered an undruggable target. However, in recent times, a covalent inhibitor known as sotorasib was discovered and approved for the treatment of patients with KRAS G12C-driven cancers. Ever since the discovery of this drug, several preclinical efforts have focused on identifying novel therapeutic candidates that could act as covalent binders of KRAS G12C. Despite these intensive efforts, only a few KRAS G12C inhibitors have entered clinical trials. Hence, this highlights the need to develop effective drug candidates that could be used in the treatment of KRAS G12C-driven cancers. Herein, we embarked on a virtual screening campaign that involves the identification of pharmacophores of sotorasib that could act as covalent arsenals against the KRAS G12C target. To our knowledge, this is the first computational study that involves the compilation of sotorasib pharmacophores from an online chemical database against KRAS G12C. After this library of chemical entities was compiled, we conducted a covalent docking-based virtual screening that revealed three promising drug candidates (CID_146235944, CID_160070181, and CID_140956845) binding covalently to the crucial nucleophilic side chain of Cys12 and interact with the residues that form the cryptic allosteric pocket of KRAS G12C in its inactive GDP-bound conformation. Subsequently, ADMET profiling portrayed the covalent inhibitors as lead-like candidates, while 100 ns molecular dynamics was used to substantiate their stability. Although our overall computational study has shown the promising potential of the lead-like candidates in impeding oncogenic RAS signaling, more experimental efforts are needed to validate and establish their preclinical relevance. Implication of KRAS G12C in cancer and computational approach towards impeding the KRAS G12C RAS signaling.

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.

Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point

Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico. Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted.

BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers

Unintended inhibition of the human ether-à-go-go-related gene (hERG) ion channel by small molecules leads to severe cardiotoxicity. Thus, hERG channel blockage is a significant concern in the development of new drugs. Several computational models have been developed to predict hERG channel blockage, including deep learning models; however, they lack robustness, reliability and interpretability. Here, we developed a graph-based Bayesian deep learning model for hERG channel blocker prediction, named BayeshERG, which has robust predictive power, high reliability and high resolution of interpretability. First, we applied transfer learning with 300 000 large data in initial pre-training to increase the predictive performance. Second, we implemented a Bayesian neural network with Monte Carlo dropout to calibrate the uncertainty of the prediction. Third, we utilized global multihead attentive pooling to augment the high resolution of structural interpretability for the hERG channel blockers and nonblockers. We conducted both internal and external validations for stringent evaluation; in particular, we benchmarked most of the publicly available hERG channel blocker prediction models. We showed that our proposed model outperformed predictive performance and uncertainty calibration performance. Furthermore, we found that our model learned to focus on the essential substructures of hERG channel blockers via an attention mechanism. Finally, we validated the prediction results of our model by conducting in vitro experiments and confirmed its high validity. In summary, BayeshERG could serve as a versatile tool for discovering hERG channel blockers and helping maximize the possibility of successful drug discovery. The data and source code are available at our GitHub repository (https://github.com/GIST-CSBL/BayeshERG).

Integrated virtual screening and molecular dynamics simulation revealed promising drug candidates of p53-MDM2 interaction

In the vast majority of malignancies, the p53 tumor suppressor pathway is compromised. In some cancer cells, high levels of MDM2 polyubiquitinate p53 and mark it for destruction, thereby leading to a corresponding downregulation of the protein. MDM2 interacts with p53 via its hydrophobic pocket, and chemical entities that block the dimerization of the protein-protein complex can restore p53 activity. Thus far, only a few chemical compounds have been reported as potent arsenals against p53-MDM2. The Protein Data Bank has crystallogaphic structures of MDM2 in complex with certain compounds. Herein, we have exploited one of the complexes in the identification of new p53-MDM2 antagonists using a hierarchical virtual screening technique. The initial stage was to compile a targeted library of structurally appropriate compounds related to a known effective inhibitor, Nutlin 2, from the PubChem database. The identified 57 compounds were subjected to virtual screening using molecular docking to discover inhibitors with high binding affinity for MDM2. Consequently, five compounds with higher binding affinity than the standard emerged as the most promising therapeutic candidates. When compared to Nutlin 2, four of the drug candidates (CID_140017825, CID_69844501, CID_22721108, and CID_22720965) demonstrated satisfactory pharmacokinetic and pharmacodynamic profiles. Finally, MD simulation of the dynamic behavior of lead-protein complexes reveals the stability of the complexes after a 100,000 ps simulation period. In particular, when compared to the other three leads, overall computational modeling found CID_140017825 to be the best pharmacological candidate. Following thorough experimental trials, it may emerge as a promising chemical entity for cancer therapy.

Virtual Combinatorial Library Screening of Quinadoline B Derivatives against SARS-CoV-2 RNA-Dependent RNA Polymerase

The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest in discovering novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study wherein, using a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS-CoV-2 RdRp, we applied in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration, obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and to deeply investigate the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide-like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process.

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.

Part-II- in silico drug design: application and success

In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

Quinazolin-4(3H)-one based potential multiple tyrosine kinase inhibitors with excellent cytotoxicity

A series of quinazolin-4(3H)-one derivatives were synthesised and evaluated for their cytotoxicity against human Caucasian breast adenocarcinoma (MCF-7) and human ovarian carcinoma (A2780) cell lines. Cytotoxicity of the most tested compounds was 2- to 30-fold more than the positive control lapatinib (IC₅₀ of 2j = 3.79 ± 0.96; 3j = 0.20 ± 0.02; and lapatinib = 5.9 ± 0.74) against MCF7 cell lines except two compounds (IC₅₀ of 2 b = 15.72 ± 0.07 and 2e = 14.88 ± 0.99). On the other hand, cytotoxicity was 4 − 87 folds (IC₅₀ of 3a = 3.00 ± 1.20; 3 g = 0.14 ± 0.03) more the positive control lapatinib (IC₅₀ = 12.11 ± 1.03) against A2780 cell lines except compound 2e (IC₅₀ = 16.43 ± 1.80). Among the synthesised quinazolin-4(3H)-one derivatives, potent cytotoxic 2f-j and 3f-j were investigated for molecular mechanism of action. Inhibitory activities of the compounds were tested against multiple tyrosine protein kinases (CDK2, HER2, EGFR and VEGFR2) enzymes. As expected, all the quinazolin-4(3H)-one derivatives were showed comparable inhibitory activity against those kinases tested, especially, compound 2i and 3i showed potent inhibitory activity against CDK2, HER2, EGFR tyrosine kinases. Therefore, molecular docking analysis for quinazolin-4(3H)-one derivatives 2i and 3i were performed, and it was revealed that compounds 2i and 3i act as ATP non-competitive type-II inhibitor against CDK2 kinase enzymes and ATP competitive type-I inhibitor against EGFR kinase enzymes. However, in case of HER2, compounds 2i act as ATP non-competitive type-II inhibitor and 3i act as ATP competitive type-I inhibitor. Docking results of known inhibitors were compared with synthesised compounds and found synthesised 2i and 3i are superior than the known inhibitors in case of interactions. In addition, in silico drug likeness properties of quinazolin-4(3H)-one derivatives showed better predicted ADME values than lapatinib.

Predicting biochemical and physiological effects of natural products from molecular structures using machine learning

Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.

Selective Fatty Acid Amide Hydrolase Inhibitors as Potential Novel Antiepileptic Agents

Temporal lobe epilepsy is the most common form of epilepsy, and current antiepileptic drugs are ineffective in many patients. The endocannabinoid system has been associated with an on-demand protective response to seizures. Blocking endocannabinoid catabolism would elicit antiepileptic effects, devoid of psychotropic effects. We herein report the discovery of selective anandamide catabolic enzyme fatty acid amide hydrolase (FAAH) inhibitors with promising antiepileptic efficacy, starting from a further investigation of our prototypical inhibitor 2a. When tested in two rodent models of epilepsy, 2a reduced the severity of the pilocarpine-induced status epilepticus and the elongation of the hippocampal maximal dentate activation. Notably, 2a did not affect hippocampal dentate gyrus long-term synaptic plasticity. These data prompted our further endeavor aiming at discovering new antiepileptic agents, developing a new set of FAAH inhibitors (3a-m). Biological studies highlighted 3h and 3m as the best performing analogues to be further investigated. In cell-based studies, using a neuroblastoma cell line, 3h and 3m could reduce the oxinflammation state by decreasing DNA-binding activity of NF-kB p65, devoid of cytotoxic effect. Unwanted cardiac effects were excluded for 3h (Langendorff perfused rat heart). Finally, the new analogue 3h reduced the severity of the pilocarpine-induced status epilepticus as observed for 2a.

Trends in application of advancing computational approaches in GPCR ligand discovery

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

The Acid/Base Characterization of Molecules with Epigenetic Activity

The acidic and basic functional groups in a molecule strongly influence its physicochemical properties, affinity for a macromolecule, pharmacokinetics, and toxicity. For instance, basicity has been correlated with molecular promiscuity, hERG blockade, and phospholipidosis. Nonetheless, no systematic characterization of the acid/base profile of epigenomic databases has been reported. This study describes an analysis of the acidic ionization constant distribution of a library of 7820 compounds with reported activity against epigenetic targets. Furthermore, the epigenomics database's acid/base profile was compared to the reference libraries of food chemicals, natural products, and approved drugs. It was found that the acid/base profile of histone lysine demethylase ligands is more similar to previously approved drugs, and histone acetyltransferase ligands have acidic and basic functional groups largely found in food chemicals and natural products; this support the potential of these libraries for finding new epigenetic inhibitors.

Synthesis, Biological Evaluation and Molecular Docking Study of Cyclic Diarylheptanoids as Potential Anticancer Therapeutics

BACKGROUND

Cancer is one of the leading causes of mortality globally. To cope with cancer, it is necessary to develop anticancer drugs. Bioactive natural products, i.e. diarylheptanoids, have gained significant attention of researchers owing to their intriguing structures and potent biological activities. In this article, considering the development of anticancer drugs with enhanced selectivity towards cancerous cells, a series of Cyclic Diarylheptanoids (CDHs) are designed, synthesized and evaluated their biological activity.

OBJECTIVE

To establish an easy route for the synthesis of diarylheptanoids, and evaluate their antiproliferative, and topoisomerase-I & -IIα inhibitory activities, for developing potential anticancer drugs among CDHs.

METHODS

Diarylheptanoids were synthesized from reported linear diarylheptanoids using the classical Ullmann reaction. Antibacterial activity was evaluated by the filter paper disc diffusion method. Cell viability was assessed by measuring mitochondrial dehydrogenase activity with a Cell Counting Kit (CCK-8). Topoisomerases I and II (topo-I and -IIα) inhibitory activity was measured by the assessment of relaxation of supercoiled pBR322 plasmid DNA. IFD protocol of Schrodinger Maestro v11.1 was used to characterize the binding pattern of studied compounds with the ATPase domain of the human topo-IIα.

RESULTS

The synthesized CDHs were evaluated for their biological activities (antibacterial, antiproliferative, and topoisomerase-I & -IIα inhibitory activities, respectively). Leading to obtain a series of anticancer agents with the least inhibitory activities against different microbes, improving their selectivity for cancer cells. In brief, most of the synthesized CDHs had excellent antiproliferative activity against T47D (human breast cancer cell line). Pterocarine possessed the strongest activity (2i; IC50 = 0.63µM) against T47D. The cyclic diarylheptanoid 2b induced 30% inhibition of topoisomerase-IIα activity at 100μM compared with the reference of etoposide, which induced 72% inhibition. Among the tested compounds, galeon (2h) displayed very low activity against four bacterial strains. Compounds 2b, 2h, and 2i formed hydrogen bonds with Thr215, Asn91, Asn120, Ala167, Lys168 and Ile141 residues, which are important for binding of ligand compound to the ATPase binding site of topoisomerase IIα by acting as ATP competitive molecule validated by docking study. In silico Absorption, Distribution, Metabolism and Excretion (ADME) analysis revealed the predicted ADME parameters of the studied compounds which showed recommended values.

CONCLUSION

A series of CDHs were synthesized and evaluated for their antibacterial, antiproliferative, and topo-I & -IIα inhibitory activities. SARs study, molecular docking study and in silico ADME analysis were conducted. Five compounds exhibited excellent and selective antiproliferative activity against the human breast cancer cell line (T47D). Among them, a compound 2h showed topo-IIα activity by 30% at 100µM, which represented a moderate intensity of inhibition compared with etoposide. Three of them formed hydrogen bonds with Thr215, Asn91, Asn120, and Ala167 residues, which are considered as crucial residues for binding to the ATPase domain of topoisomerase IIα. According to in silico drug-likeness property analysis, three compounds are expected to show superiority over etoposide in case of absorption, distribution, metabolism and excretion.

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the "Big Data" Era

The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against classical machine learning for a typical drug-discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by the human ether-à-go-go-related gene (hERG), leads to a prolonged QT interval of the cardiac action potential and is a significant safety pharmacology target for the development of new medicines. Several computational approaches have been employed to develop prediction models for the assessment of hERG liabilities of small molecules including recent work using deep learning methods. Here, we perform a comprehensive comparison of hERG effect prediction models based on classical approaches (random forests and gradient boosting) and modern AI methods [deep neural networks (DNNs) and recurrent neural networks (RNNs)]. The training set (∼9000 compounds) was compiled by integrating the hERG bioactivity data from the ChEMBL database with experimental data generated from an in-house, high-throughput thallium flux assay. We utilized different molecular descriptors including the latent descriptors, which are real-value continuous vectors derived from chemical autoencoders trained on a large chemical space (>1.5 million compounds). The models were prospectively validated on ∼840 in-house compounds screened in the same thallium flux assay. The best results were obtained with the XGBoost method and RDKit descriptors. The comparison of models based only on latent descriptors revealed that the DNNs performed significantly better than the classical methods. The RNNs that operate on SMILES provided the highest model sensitivity. The best models were merged into a consensus model that offered superior performance compared to reference models from academic and commercial domains. Furthermore, we shed light on the potential of AI methods to exploit the big data in chemistry and generate novel chemical representations useful in predictive modeling and tailoring a new chemical space.

Identification of new IDH2R140Q inhibitors by discriminatory analysis-based molecular docking and biological evaluation

Isocitrate dehydrogenase 2 (IDH2) is a key enzyme in the regulation of cell metabolism. Its mutated type can lead to the accumulation of 2-hydroxyglutarate, which is often related to malignancies such as acute myeloid leukemia. Therefore, it is necessary to find new inhibitors targeting mutant IDH2. Discriminatory analysis-based molecular docking was employed to screen the ChemDiv compound library, which resulted in the identification of three new IDH2^R140Q inhibitors with moderate-to-good IC₅₀ values. Among them, compounds 1 and 3 displayed good selectivity against other mutant or wild-type IDH proteins. The most potent compound 1, bearing the [1,2,4]triazolo[1,5-a]pyrimidin scaffold, was subjected to dynamic simulations to provide more information on the binding mode with IDH2^R140Q , providing structural clues to further optimize compound 1 as a new mutant IDH2 inhibitor.

Cinnamides Target Leishmania amazonensis Arginase Selectively

Caffeic acid and related natural compounds were previously described as Leishmania amazonensis arginase (L-ARG) inhibitors, and against the whole parasite in vitro. In this study, we tested cinnamides that were previously synthesized to target human arginase. The compound caffeic acid phenethyl amide (CAPA), a weak inhibitor of human arginase (IC₅₀ = 60.3 ± 7.8 μM) was found to have 9-fold more potency against L-ARG (IC₅₀ = 6.9 ± 0.7 μM). The other compounds that did not inhibit human arginase were characterized as L-ARG, showing an IC₅₀ between 1.3–17.8 μM, and where the most active was compound 15 (IC₅₀ = 1.3 ± 0.1 μM). All compounds were also tested against L. amazonensis promastigotes, and only the compound CAPA showed an inhibitory activity (IC₅₀ = 80 μM). In addition, in an attempt to gain an insight into the mechanism of competitive L-ARG inhibitors, and their selectivity over mammalian enzymes, we performed an extensive computational investigation, to provide the basis for the selective inhibition of L-ARG for this series of compounds. In conclusion, our results indicated that the compounds based on cinnamoyl or 3,4-hydroxy cinnamoyl moiety could be a promising starting point for the design of potential antileishmanial drugs based on selective L-ARG inhibitors.

Development of In Vitro Corneal Models: Opportunity for Pharmacological Testing

The human eye is a specialized organ with a complex anatomy and physiology, because it is characterized by different cell types with specific physiological functions. Given the complexity of the eye, ocular tissues are finely organized and orchestrated. In the last few years, many in vitro models have been developed in order to meet the 3Rs principle (Replacement, Reduction and Refinement) for eye toxicity testing. This procedure is highly necessary to ensure that the risks associated with ophthalmic products meet appropriate safety criteria. In vitro preclinical testing is now a well-established practice of significant importance for evaluating the efficacy and safety of cosmetic, pharmaceutical, and nutraceutical products. Along with in vitro testing, also computational procedures, herein described, for evaluating the pharmacological profile of potential ocular drug candidates including their toxicity, are in rapid expansion. In this review, the ocular cell types and functionality are described, providing an overview about the scientific challenge for the development of three-dimensional (3D) in vitro models.

A Series of Isatin-Hydrazones with Cytotoxic Activity and CDK2 Kinase Inhibitory Activity: A Potential Type II ATP Competitive Inhibitor

Isatin derivatives potentially act on various biological targets. In this article, a series of novel isatin-hydrazones were synthesized in excellent yields. Their cytotoxicity was tested against human breast adenocarcinoma (MCF7) and human ovary adenocarcinoma (A2780) cell lines using MTT assay. Compounds 4j (IC50 = 1.51 ± 0.09 µM) and 4k (IC50 = 3.56 ± 0.31) showed excellent activity against MCF7, whereas compound 4e showed considerable cytotoxicity against both tested cell lines, MCF7 (IC50 = 5.46 ± 0.71 µM) and A2780 (IC50 = 18.96± 2.52 µM), respectively. Structure-activity relationships (SARs) revealed that, halogen substituents at 2,6-position of the C-ring of isatin-hydrazones are the most potent derivatives. In-silico absorption, distribution, metabolism and excretion (ADME) results demonstrated recommended drug likeness properties. Compounds 4j (IC50 = 0.245 µM) and 4k (IC50 = 0.300 µM) exhibited good inhibitory activity against the cell cycle regulator CDK2 protein kinase compared to imatinib (IC50 = 0.131 µM). A molecular docking study of 4j and 4k confirmed both compounds as type II ATP competitive inhibitors that made interactions with ATP binding pocket residues, as well as lacking interactions with active state DFG motif residues.

Synthesis of Novel Potent Biologically Active N-Benzylisatin-Aryl Hydrazones in Comparison with Lung Cancer Drug ‘Gefitinib’

Developing anticancer therapeutics with no/few side effects is a challenge for medicinal chemists. The absence of antibacterial activity of an anticancer drug removes its detrimental effect toward intestinal flora and therefore leads to reduced side effects. Here, a series of novel N-benzylisatin-aryl-hydrazones was designed, synthesized and evaluated for their antimicrobial and antiproliferative activities with SAR and ADME studies, aiming to develop anticancer drugs with no antimicrobial, yet high antiproliferative activities. The results were then compared with the effects of first-line treatments for lung cancer drug Gefitinib. Novel N-benzylisatin-aryl-hydrazones were synthesized from isatin and benzyl bromide in three steps with good to moderate yields. Antimicrobial activity was tested with six Gram-positive/negative bacterial strains, antifungal activity with a fungal strain and antiproliferative activity against ‘A549’ and ‘HeLa cell lines’, respectively. As expected, synthesized hydrazones reveled no effects on any of the strains of bacteria and fungi up to 100-µg/disc concentration. However, four compounds showed two-to-four fold antiproliferative activity over Gefitinib. For instance, IC50 of a compound (6c) shows concentration of 4.35 µM, whereas gefitinib shows 15.23 µM against ‘A549.’ ADME predicted studies reveled that our synthesized hydrazones exhibited higher ADME values than the Gefitinib. Therefore, our synthesized hydrazones can be an excellent scaffold for the development of anticancer therapeutics after considering further investigations.

Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors

Histone deacetylases (HDACs) are a class of epigenetic modulators overexpressed in numerous types of cancers. Consequently, HDAC inhibitors (HDACIs) have emerged as promising antineoplastic agents. Unfortunately, the most developed HDACIs suffer from poor selectivity towards a specific isoform, limiting their clinical applicability. Among the isoforms, HDAC1 represents a crucial target for designing selective HDACIs, being aberrantly expressed in several malignancies. Accordingly, the development of a predictive in silico tool employing a large set of HDACIs (aminophenylbenzamide derivatives) is herein presented for the first time. Software Phase was used to derive a 3D-QSAR model, employing as alignment rule a common-features pharmacophore built on 20 highly active/selective HDAC1 inhibitors. The 3D-QSAR model was generated using 370 benzamide-based HDACIs, which yielded an excellent correlation coefficient value (R² = 0.958) and a satisfactory predictive power (Q² = 0.822; Q²_F3 = 0.894). The model was validated (r²_{ext_ts} = 0.794) using an external test set (113 compounds not used for generating the model), and by employing a decoys set and the receiver-operating characteristic (ROC) curve analysis, evaluating the Güner-Henry score (GH) and the enrichment factor (EF). The results confirmed a satisfactory predictive power of the 3D-QSAR model. This latter represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity.

100th Anniversary of Macromolecular Science Viewpoint: Synthetic Protein Hydrogels

Our bodies are composed of soft tissues made of various proteins. In contrast, most hydrogels designed for biological applications are made of synthetic polymers. Recently, it is increasingly recognized that genetically synthesized proteins can be tailored as building blocks of hydrogels with biological, chemical, and mechanical properties similar to native soft tissues. In this Viewpoint, we summarize recent progress in synthetic protein hydrogels. We compare the structural and mechanical properties of different protein building blocks. We discuss various biocompatible cross-linking strategies based on covalent chemical reactions and noncovalent physical interactions. We introduce how stimulus-responsive conformational changes or intermolecular interactions at the molecular level can be used to engineer responsive hydrogels. We highlight that hydrogel network structures are as important as the protein sequences for the properties and functions of protein hydrogels and should be carefully designed. Despite great progress and potentials of synthetic protein hydrogels, there are still quite a few unsettled challenges and unexploited opportunities, providing abundant room for future investigation and development, particularly as this field is quickly expanding beyond its initial stage. We discuss a number of possible directions, including optimizing protein production and reducing cost, engineering anisotropic hydrogels to better mimic native tissues, rationally designing hydrogel mechanical properties, investigating interplays of hydrogels and residing cells for 3D cell culture and organoid construction, and evaluating long-term cytotoxicity and immune response.

Structure-activity relationships study of isothiocyanates for H2S releasing properties: 3-Pyridyl-isothiocyanate as a new promising cardioprotective agent

Introduction

The gasotransmitter hydrogen sulphide (H₂S), an endogenous ubiquitous signalling molecule, is known for its beneficial effects on different mammalian systems. H₂S exhibits cardioprotective activity against ischemia/reperfusion (I/R) or hypoxic injury.

Methods

A library of forty-five isothiocyanates, selected for their different chemical properties, has been evaluated for its hydrogen sulfide (H₂S) releasing capacity. The obtained results allowed to correlate several factors such as steric hindrance, electronic effects and position of the substituents to the observed H₂S production. Moreover, the chemical-physical profiles of the selected compounds have been studied by an in silico approach and from a combination of the obtained results, 3-pyridyl-isothiocyanate (25) has been selected as the most promising one. A detailed pharmacological characterization of its cardioprotective action has been performed.

Results

The results herein obtained strongly indicate 3-pyridyl-isothiocyanate (25) as a suitable pharmacological option in anti-ischemic therapy. The cardioprotective effects of compound 25 were tested in vivo and found to exhibit a positive effect.

Conclusion

Results strongly suggest that isothiocyanate-based H₂S-releasing drugs, such as compound 25, can trigger a ‘‘pharmacological pre-conditioning” and could represent a suitable pharmacological option in antiischemic therapy.

Organic Isothiocyanates as Hydrogen Sulfide Donors

Significance: Hydrogen sulfide (H₂S), the "new entry" in the series of endogenous gasotransmitters, plays a fundamental role in regulating the biological functions of various organs and systems. Consequently, the lack of adequate levels of H₂S may represent the etiopathogenetic factor of multiple pathological alterations. In these diseases, the use of H₂S donors represents a precious and innovative opportunity. Recent Advances: Natural isothiocyanates (ITCs), sulfur compounds typical of some botanical species, have long been investigated because of their intriguing pharmacological profile. Recently, the ITC moiety has been proposed as a new H₂S-donor chemotype (with a l-cysteine-mediated reaction). Based on this recent discovery, we can clearly observe that almost all the effects of natural ITCs can be explained by the H₂S release. Consistently, the ITC function was also used as an original H₂S-releasing moiety for the design of synthetic H₂S donors and original "pharmacological hybrids." Very recently, the chemical mechanism of H₂S release, resulting from the reaction between l-cysteine and some ITCs, has been elucidated. Critical Issues: Available literature gives convincing demonstration that H₂S is the real player in ITC pharmacology. Further, countless studies have been carried out on natural ITCs, but this versatile moiety has been used only rarely for the design of synthetic H₂S donors with optimal drug-like properties. Future Directions: The development of more ITC-based synthetic H₂S donors with optimal drug-like properties and selectivity toward specific tissues/pathologies seem to represent a stimulating and indispensable prospect of future experimental activities.

Calculation of absolute binding free energies between the hERG channel and structurally diverse drugs

The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel that plays an essential role in the repolarization of action potentials in cardiac muscle. However, various drugs can block the ion current by binding to the hERG channel, resulting in potentially lethal cardiac arrhythmia. Accordingly, in silico studies are necessary to clarify the mechanisms of how these drugs bind to the hERG channel. Here, we used the experimental structure of the hERG channel, determined by cryo-electron microscopy, to perform docking simulations to predict the complex structures that occur between the hERG channel and structurally diverse drugs. The absolute binding free energies for the models were calculated using the MP-CAFEE method; calculated values were well correlated with experimental ones. By applying the regression equation obtained here, the affinity of a drug for the hERG channel can be accurately predicted from the calculated value of the absolute binding free energy.

The molecular determinants of R-roscovitine block of hERG channels

Human ether-à-go-go-related gene (Kv11.1, or hERG) is a potassium channel that conducts the delayed rectifier potassium current (I_Kr) during the repolarization phase of cardiac action potentials. hERG channels have a larger pore than other K⁺channels and can trap many unintended drugs, often resulting in acquired LQTS (aLQTS). R-roscovitine is a cyclin-dependent kinase (CDK) inhibitor that induces apoptosis in colorectal, breast, prostate, multiple myeloma, other cancer cell lines, and tumor xenografts, in micromolar concentrations. It is well tolerated in phase II clinical trials. R-roscovitine inhibits open hERG channels but does not become trapped in the pore. Two-electrode voltage clamp recordings from Xenopus oocytes expressing wild-type (WT) or hERG pore mutant channels (T623A, S624A, Y652A, F656A) demonstrated that compared to WT hERG, T623A, Y652A, and F656A inhibition by 200 μM R-roscovitine was ~ 48%, 29%, and 73% weaker, respectively. In contrast, S624A hERG was inhibited more potently than WT hERG, with a ~ 34% stronger inhibition. These findings were further supported by the IC₅₀ values, which were increased for T623A, Y652A and F656A (by ~5.5, 2.75, and 42 fold respectively) and reduced 1.3 fold for the S624A mutant. Our data suggest that while T623, Y652, and F656 are critical for R-roscovitine-mediated inhibition, S624 may not be. Docking studies further support our findings. Thus, R-roscovitine’s relatively unique features, coupled with its tolerance in clinical trials, could guide future drug screens.

Identification of new NIK inhibitors by discriminatory analysis-based molecular docking and biological evaluation

NF-κB inducing kinase (NIK) is a key regulator in the noncanonical nuclear factor κB cells (NF-κB) signaling pathway. Dysregulation of NIK is often related with autoimmune disorders and malignancies. However, the number of reported NIK inhibitors is scarce. Discriminatory analysis-based molecular docking was used to examine the accuracy of the binding conformation and to estimate the binding affinity, leading to the identification of several new NIK inhibitors with moderate IC50 (ranging from 48.9 to 103.4 μM). Among them, compound 5, the most potent one (IC50 48.9 ± 6.9 μM), also showed moderate antiproliferation activity against cancer SW1990 cells, with an IC50 value of 20.1 ± 6.0 μM. Further dynamic simulations were performed to provide more in-depth details on the binding conformation of compound 5 and the NIK protein, providing some structural clues for further optimization of compound 5 as a novel NIK inhibitor.

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.

Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns

The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average.

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.

Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting

In this paper, we explore the impact of combining different in silico prediction approaches and data sources on the predictive performance of the resulting system. We use inhibition of the hERG ion channel target as the endpoint for this study as it constitutes a key safety concern in drug development and a potential cause of attrition. We will show that combining data sources can improve the relevance of the training set in regard of the target chemical space, leading to improved performance. Similarly we will demonstrate that combining multiple statistical models together, and with expert systems, can lead to positive synergistic effects when taking into account the confidence in the predictions of the merged systems. The best combinations analyzed display a good hERG predictivity. Finally, this work demonstrates the suitability of the SOHN methodology for building models in the context of receptor based endpoints like hERG inhibition when using the appropriate pharmacophoric descriptors.

Comprehensive 3D-QSAR Model Predicts Binding Affinity of Structurally Diverse Sigma 1 Receptor Ligands

The Sigma 1 Receptor (S1R) has attracted intense interest as a pharmaceutical target for various therapeutic indications, including the treatment of neuropathic pain and the potentiation of opioid analgesia. Efforts by drug developers to rationally design S1R antagonists have been spurred recently by the 2016 publication of the high-resolution X-ray crystal structure of the ligand-bound human S1R. Until now, however, the absence in the published literature of a single, large-scale, and comprehensive quantitative structure-activity relationship (QSAR) model that encompasses a structurally diverse collection of S1R ligands has impaired rapid progress. To our best knowledge, the present study represents the first report of a statistically robust and highly predictive 3D-QSAR model (R2 = 0.92, Q2 = 0.62, Rpred2 = 0.81) based on the X-ray crystal structure of human S1R and constructed from a pooled compilation of 180 S1R antagonists that encompass five structurally diverse chemical families investigated using identical experimental protocols. Best practices, as recommended by the Organization for Economic Cooperation and Development (OECD: http://www.oecd.org/ ), were adopted for pooling data from disparate sources and for QSAR model development and both internal and external model validation. The practical utility of the final 3D-QSAR model was tested by virtual screening of the DrugBank database of FDA approved drugs supplemented by eight reported S1R antagonists. Among the top-ranked 40 DrugBank hits, four approved drugs which were previously unknown as S1R antagonists were tested using in vitro radiolabeled human S1R binding assays. Of these, two drugs (diphenhydramine and phenyltoloxamine) exhibited potent S1R binding affinity with Ki = 58 nM and 160 nM, respectively. As diphenhydramine is approved as an antiallergic, and phenyltoloxamine as an analgesic and sedative, each of these compounds represents a viable starting point for a drug discovery campaign aimed at the development of novel S1R antagonists for a wide range of therapeutic indications.

Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities

The hERG (human ether-a-go-go-related gene) encoded potassium ion (K⁺) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC₅₀) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC₅₀) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC₅₀) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC₅₀) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC₅₀/Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca²⁺-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca²⁺/late Na⁺ currents and Na⁺/Ca²⁺-exchanger, reduced Na⁺/K⁺-pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na⁺ and Ca²⁺ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca²⁺-transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

Discriminatory analysis based molecular docking study for in silico identification of epigallocatechin-3-gallate (EGCG) derivatives as B-RafV600E inhibitors

Virtual screening and biological testing were utilized to identify novel B-Raf^V600E inhibitors.