Computer-aided prediction of QT-prolongation

A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy

Human Immunodeficiency Virus Type 1 (HIV-1) infection is associated with high mortality if no therapy is provided. Currently, the treatment of an HIV-1 positive patient requires that several drugs should be taken simultaneously. The resistance of the virus to an antiretroviral drug may lead to treatment failure. Our approach focuses on predicting the exposure of a particular viral variant to an antiretroviral drug or drug combination. It also aims at the prediction of drug treatment success or failure. We utilized nucleotide sequences of HIV-1 encoding protease and reverse transcriptase to perform such types of prediction. The PASS (Prediction of Activity Spectra for Substances) algorithm based on the naive Bayesian classifier was used to make a prediction. We calculated the probability of whether a sequence belonged (P₁) or did not belong (P₀) to the class associated with exposure of the viral sequence to the set of drugs that can be associated with resistance to the set of drugs. The accuracy calculated as the average Area Under the ROC (Receiver Operating Characteristic) Curve (AUC/ROC) for classifying exposure of the sequence to the HIV-1 protease inhibitors was 0.81 (±0.07), and for HIV-1 reverse transcriptase, it was 0.83 (±0.07). To predict cases of treatment effectiveness or failure, we used P₁ and P₀ values, obtained in PASS, along with the binary vector constructed based on short nucleotide descriptors and the applied random forest classifier. Average AUC/ROC prediction accuracy for the prediction of treatment effectiveness or failure for the combinations of HIV-1 protease inhibitors was 0.82 (±0.06) and of HIV-1 reverse transcriptase was 0.76 (±0.09).

Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors

Estimation of interaction of drug-like compounds with antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression. The accuracy of (Q)SAR models was validated by the fivefold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases, sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R 2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.

Cardiotoxicity screening: a review of rapid-throughput in vitro approaches

Cardiac toxicity represents one of the leading causes of drug failure along different stages of drug development. Multiple very successful pharmaceuticals had to be pulled from the market or labeled with strict usage warnings due to adverse cardiac effects. In order to protect clinical trial participants and patients, the International Conference on Harmonization published guidelines to recommend that all new drugs to be tested preclinically for hERG (Kv11.1) channel sensitivity before submitting for regulatory reviews. However, extensive studies have demonstrated that measurement of hERG activity has limitations due to the multiple molecular targets of drug compound through which it may mitigate or abolish a potential arrhythmia, and therefore, a model measuring multiple ion channel effects is likely to be more predictive. Several phenotypic rapid-throughput methods have been developed to predict the potential cardiac toxic compounds in the early stages of drug development using embryonic stem cells- or human induced pluripotent stem cell-derived cardiomyocytes. These rapid-throughput methods include microelectrode array-based field potential assay, impedance-based or Ca(2+) dynamics-based cardiomyocytes contractility assays. This review aims to discuss advantages and limitations of these phenotypic assays for cardiac toxicity assessment.

In silico assessment of adverse drug reactions and associated mechanisms

During recent years, various in silico approaches have been developed to estimate chemical and biological drug features, for example chemical fragments, protein targets, pathways, among others, that correlate with adverse drug reactions (ADRs) and explain the associated mechanisms. These features have also been used for the creation of predictive models that enable estimation of ADRs during the early stages of drug development. In this review, we discuss various in silico approaches to predict these features for a certain drug, estimate correlations with ADRs, establish causal relationships between selected features and ADR mechanisms and create corresponding predictive models.

Computational investigations of hERG channel blockers: New insights and current predictive models

Identification of potential human Ether-a-go-go Related-Gene (hERG) potassium channel blockers is an essential part of the drug development and drug safety process in pharmaceutical industries or academic drug discovery centers, as they may lead to drug-induced QT prolongation, arrhythmia and Torsade de Pointes. Recent reports also suggest starting to address such issues at the hit selection stage. In order to prioritize molecules during the early drug discovery phase and to reduce the risk of drug attrition due to cardiotoxicity during pre-clinical and clinical stages, computational approaches have been developed to predict the potential hERG blockage of new drug candidates. In this review, we will describe the current in silico methods developed and applied to predict and to understand the mechanism of actions of hERG blockers, including ligand-based and structure-based approaches. We then discuss ongoing research on other ion channels and hERG polymorphism susceptible to be involved in LQTS and how systemic approaches can help in the drug safety decision.

Pentacyclic Triterpenoids Inhibit IKKβ Mediated Activation of NF-κB Pathway: In Silico and In Vitro Evidences

Pentacyclic Triterpenoids (PTs) and their analogues as well as derivatives are emerging as important drug leads for various diseases. They act through a variety of mechanisms and a majority of them inhibit the nuclear factor kappa-beta (NF-κB) signaling pathway. In this study, we examined the effects of the naturally occurring PTs on IκB kinase-β (IKKβ), which has great scientific relevance in the NF-κB signaling pathway. On virtual screening, 109 PTs were screened through the PASS (prediction of activity spectra of substances) software for prediction of NF-κB inhibitory activity followed by docking on the NEMO/IKKβ association complex (PDB: 3BRV) and testing for compliance with the softened Lipinski’s Rule of Five using Schrodinger (LLC, New York, USA). Out of the projected 45 druggable PTs, Corosolic Acid (CA), Asiatic Acid (AA) and Ursolic Acid (UA) were assayed for IKKβ kinase activity in the cell free medium. The UA exhibited a potent IKKβ inhibitory effect on the hotspot kinase assay with IC50 of 69 μM. Whereas, CA at 50 μM concentration markedly reduced the NF-κB luciferase activity and phospho-IKKβ protein expressions. The PTs tested, attenuated the expression of the NF-κB cascade proteins in the LPS-stimulated RAW 264.7 cells, prevented the phosphorylation of the IKKα/β and blocked the activation of the Interferon-gamma (IFN-γ). The results suggest that the IKKβ inhibition is the major mechanism of the PTs-induced NF-κB inhibition. PASS predictions along with in-silico docking against the NEMO/IKKβ can be successfully applied in the selection of the prospective NF-κB inhibitory downregulators of IKKβ phosphorylation.

In silico prediction of hERG inhibition

The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure–activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).

Role of electrostatic potential in the in silico prediction of molecular bioactivation and mutagenesis

Electrostatic potential (ESP) is a useful physicochemical property of a molecule that provides insights into inter- and intramolecular associations, as well as prediction of likely sites of electrophilic and nucleophilic metabolic attack. Knowledge of sites of metabolic attack is of paramount importance in DMPK research since drugs frequently fail in clinical trials due to the formation of bioactivated metabolites which are often difficult to measure experimentally due to their reactive nature and relatively short half-lives. Computational chemistry methods have proven invaluable in recent years as a means to predict and study bioactivated metabolites without the need for chemical syntheses, or testing on experimental animals. Additional molecular properties (heat of formation, heat of solvation and E(LUMO) - E(HOMO)) are discussed in this paper as complementary indicators of the behavior of metabolites in vivo. Five diverse examples are presented (acetaminophen, aniline/phenylamines, imidacloprid, nefazodone and vinyl chloride) which illustrate the utility of this multidimensional approach in predicting bioactivation, and in each case the predicted data agreed with experimental data described in the scientific literature. A further example of the usefulness of calculating ESP, in combination with the molecular properties mentioned above, is provided by an examination of the use of these parameters in providing an explanation for the sites of nucleophilic attack of the nucleic acid cytosine. Exploration of sites of nucleophilic attack of nucleic acids is important as adducts of DNA have the potential to result in mutagenesis.

In silico fragment-based drug design using a PASS approach

Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.

Structure-Activity Relationship Studies of a Number of α1 -Adrenoceptor Antagonists and Antiarrhythmic Agents

Arylpiperazines represent one of the most studied classes of α1 -adrenoceptor (α1 -AR) antagonists. Currently, α1 -AR antagonists are useful in the treatment of benign prostatic hyperplasia, lower urinary tract symptoms or cardiac arrhythmia. The activity of various derivatives of 1-[3-(4-arylpiperazin-1-yl)propyl]pyrrolidin-2-one as α1 -adrenergic receptor antagonists and antiarrhythmic (AA) agents was described using the qualitative inverse Structure Activity Relationship (SAR) model. The three-dimensional structures of the pyrrolidin-2-one derivatives in the basic form were obtained using AM1 semi-empirical quantum chemical calculations. All the molecules were geometry-optimized until the root-mean-square (RMS) gradient value was smaller than 10(-6) a.u. Single-point energy (SPE) calculations were performed at the DFT/B3LYP level of theory using the 6-31G** basis set. The main focus of this inverse SAR study is to find which features cause enhancing of antiarrhythmic properties between subtly different types of activity (α1 -adrenoreceptor antagonists and antiarrhythmic activities). Our SAR study involves the charge distribution in the plane of the pharmacophore model for α1 -AR. Suitable maps of the electrostatic potential were plotted based on the electronic and nuclear charge distribution obtained from the energy calculations. The results of this modelling study indicate that if the terminal arylpiperazine moiety is surrounded by regions of negative electrostatic potential, then the antiarrhythmic activity is blocked.

hERG-related drug toxicity and models for predicting hERG liability and QT prolongation

BACKGROUND

hERG K(+) channels have been recognized as a primary antitarget in safety pharmacology. Their blockade, caused by several drugs with different therapeutic indications, may lead to QT prolongation and, eventually, to potentially fatal arrhythmia, namely torsade de pointes. Therefore, a number of preclinical models have been developed to predict hERG liability early in the drug development process.

OBJECTIVE

The aim of this review is to outline the present state of the art on drug-induced hERG blockade, providing insights on the predictive value of in vitro and in silico models for hERG liability.

METHODS

On the basis of latest reports, high-throughput preclinical models have been discussed outlining advantages and limitations.

CONCLUSION

Although no single model has an absolute value, an integrated risk assessment is recommended to predict the pro-arrhythmic risk of a given drug. This prediction requires expertise from different areas and should encompass emerging issues such as interference with hERG trafficking and QT shortening.

A simple approach discriminating cardio-safe drugs from toxic ones

More than 130 FDA-approved drugs have been identified for now to prolong the QT interval and possibly lead to sudden cardiac death. Due to their toxic effect, some of these drugs have been withdrawn from the pharmaceutical market. In this study, we have formulated few rules to assess the ability to prolong QT interval and thereby discriminate between cardiotoxic and -safe drugs. These rules have clearly determined that cardio-toxic drugs are more likely to obey Lipinski rule of 5 and Oprea lead-like rule. Moreover, the cardio-toxic drugs have been found to have in common values of -0.5 to 6.5 log P, 1-5 nitrogen atoms, up to 4 oxygen atoms, 5-27 hydrophobic atoms, and 15-53 single bonds. Matthews Correlation Coefficient with the value of 0.6 was also attained and nearly 96% of the cardio-toxic drugs were successfully covered. Thus, despite the simplicity of this methodology, we have obtained interesting and informative results. The proposed set of these simple rules could be employed to infer cardio-toxicity or -safety for current and potential drugs. The present study will have important impact on decision making in the fields of drug development, molecule screening in biological assays, and other applications as well.