Current Drug Safety - Volume 7, Issue 4, 2012

Classification data related to the Liver-Related Adverse Effects of Drugs have been studied with the CORAL software (http://www.insilico.eu/coral). Two datasets which contain compounds with two serum enzyme markers of liver toxicity: alanine aminotransferase (ALT, n=187) and aspartate aminotransferase (AST, n=209) are analyzed. Statistical quality of the prediction for ALT activity is n=35, Sensitivity = 0.5556, Specificity = 0.8077, and Accuracy = 0.7429. In the case of AST activity the prediction is characterized by n=42, Sensitivity = 0.6875, Specificity = 0.7692, and Accuracy = 0.7381. A number of structural alerts which can be related to the studied activities are revealed. It is the first attempt to build up the classification QSAR model by means of the Monte Carlo technique based on representation of the molecular structure by SMILES using the CORAL software.

A simple/sensitive high-performance liquid chromatographic method, with ultraviolet (UV) detection, was developed for phenylurea-herbicide analysis, which involves preconcentration using solid-phase extraction. Mobile phase was acetonitrile/water at flow-rate of 1mL·min–1 with direct UV absorbance detection at 210nm. Analyte separation studied on a C18 column was applied successfully to herbicide analysis in soft drink’s brands and tap water. Good linearity/repeatability was observed for all pesticides. Retention times increase as: metoxuron < monuron < diuron < matazachlor < linuron. They are modelled by structure–property relations. The effect of different types of features is analyzed: electronic, solvation, lipophilic and steric, etc. Formation enthalpy and molecular dipole moment are calculated with MOPAC–AM1. Most important properties are hydration free energy and dipole moment. Results are improved if competitive conformation with higher dipole moment is considered at 1.1kJ·mol–1. Plastic evolution is an evolutionary perspective conjugating the effect of acquired characters, and relations that emerge among the principles of evolutionary indeterminacy, morphologic determination and natural selection. Plastic evolution is applied to design co-ordination index Ic, which is used to characterize phenylurea herbicides and compared to molecular dipole moment for retention time. Parametres needed to calculate Ic are formation enthalpy and molecular weight/surface area. Ic improves multivariable regression equations for retention and is predictive when it is used together with dipole and hydration free energy. Correction introduced in retention is produced in the correct direction. Hierarchical quantitative structure–property relationship provided simplified properties analysis. Structural classification is based on the presence of two Cl/O/N atoms.

Drug-likeness and toxicity prediction of compounds are so important as to estimate their bioactivities. In rational design of drugs, looking for safe rather than only highly active synthetic targets has increasingly became mandatory. In this context, structure-based methods to model toxicities of drug-like compounds arise as fundamental tasks to achieve safer drugs. Accordingly, the MIA-QSAR method, which has been widely applied to model bioactivities of several classes of compounds, can also be used to predict toxicities of drug-like compounds. In fact, the MIA-based approach has shown to be accurate to model bioactivities, boiling points, NMR chemical shifts and electrophoretic profiles, but it has been used to model cytotoxicities for the first time in this work, in order to contribute for studies to develop safer drugs. The QSAR modeling of bioactivities (pEC50) and cytotoxicities (CCIC50) of a series of HIV-1 protease inhibitors, some ritonavir derivatives, is reported in this work using the MIA-QSAR approach. The statistical quality of both models indicates that pEC50 and CCIC50 of ritonavir analogs can be reliably predicted using this method; therefore, improved drugs can be designed.

In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the “Galvez data set”, that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.

Undesirable toxicity is still a major block in the drug discovery process. Obviously, capable techniques that identify poor effects at a very early stage of product development and provide reasonable toxicity estimates for the huge number of untested compounds are needed. In silico techniques are very useful for this purpose, because of their advantage in reducing time and cost. These case studies give the description of in silico validation techniques and applied modeling methods for the prediction of toxicity of chemical compounds. In silico toxicity prediction techniques can be classified into two categories: Molecular Modeling and methods that derive predictions from experimental data. Molecular modeling is a computational approach to mimic the behavior of molecules, from small molecules (e.g. in conformational analysis) to biomolecules. But the same approaches can also be applied for toxicological purposes, if the mechanism is receptor mediated. Quantitative Structure-Toxicity Relationships (QSTRs) models are typical examples for the prediction of toxicity which relates variations in the molecular structures to toxicity. There are many applied modeling techniques in QSTR such as Partial Least Squares, Artificial Neural Networks, and Principal Component Regression (PCR). The applicability of these techniques in predictive toxicology will be discussed with different examples of sets of chemical compounds.

Adverse drug reactions (ADRs) are a main problem faced by drug companies and regulatory authorities. Not only do they contribute heavily to late-phase failure of drug development and withdrawal of drugs from the market, they also pose significant health risks to patients. Rare and severe ADRs are even harder to detect, and sufficient attention has not been paid to them. Torsade de pointes (TdP), an atypical ventricular tachycardia which is potentially life-threatening, is one of them. The objective of this project is to develop a computational model to predict TdP-causing potential of drug candidates. A total of 260 marketed drugs were collected and screened for their potential to cause TdP. 103 drugs were classified as TdP+ and 157 were likely to be TdP-. One-class classification methods were used to construct multiple base models. A model dependent applicability domain estimation method was used to determine the applicability of the base models for future dataset. A final ensemble model was constructed based on selected base models and it had sensitivity and specificity value of 78.4% and 90% respectively when estimated using external cross validation method. The result suggests that the ensemble model developed in this study is potentially useful for facilitating the prediction of TdP in drug candidates. The ensemble model is made available via the free software, PaDEL-DDPredictor.

Regulators globally are facing increasing challenges to make decisions regarding subtle effects and risks of pharmacodynamic compounds found in the environment. There is a recognized need for rapid screening tools as well as development of methods to support the further prioritization and testing of these compounds. It is becoming evident that the current standardized experimental designs and predictive models are not specific enough to predict the chronic and long term toxicity of active pharmaceutical ingredients. Novel approaches to firstly screen and prioritize the compounds e.g. by (quantitative) structure-activity relationship modeling and inter-species extrapolation tools are currently proving difficult to develop for chronic effects. Novel toxicogenomic high through-put screening tools may be useful for more rapidly developing toxicogenomic hypothesis and molecular initiating events especially for pharmacodynamic compounds with relatively well described mechanisms of action. The molecular initiating events can then be entered into the proposed adverse outcome pathway which can then indicate the relevant test design to demonstrate the anticipated adverse chronic effects.

Among the different chemotherapeutic classes available today, the 6-fluoroquinolone (6-FQ) antibacterials are still one of the most effective cures in fighting tuberculosis (TB). Nowadays, the development of novel 6-FQs for treatment of TB mainly depends on understanding how the structural modifications of the main quinolone scaffold at specific positions affect the anti-mycobacterial activity. Alongside the structure-activity relationship (SAR) studies of the 6-FQ antibacterials, which can be considered as a golden rule in the development of novel active antitubercular 6-FQs, the structure side effects relationship (SSER) of these drugs must be also taken into account. In the present study we focus on a proficient implementation of the existing knowledge-based expert systems for design of novel 6-FQ antibacterials with possible enhanced biological activity against Mycobaterium tuberculosis as well as lower toxicity. Following the SAR in silico studies of the quinolone antibacterials against M. tuberculosis performed in our laboratory, a large set of 6-FQs was selected. Several new 6-FQ derivatives were proposed as drug candidates for further research and development. The 6- FQs identified as potentially effective against M. tuberculosis were subjected to an additional SSER study for prediction of their toxicological profile. The assessment of structurally-driven adverse effects which might hamper the potential of new drug candidates is mandatory for an effective drug design. We applied publicly available knowledge-based (expert) systems and Quantitative Structure-Activity Relationship (QSAR) models in order to prepare a priority list of active compounds. A preferred order of drug candidates was obtained, so that the less harmful candidates were identified for further testing. TOXTREE expert system as well as some QSAR models developed in the framework of EC funded project CAESAR were used to assess toxicity. CAESAR models were developed according to the OECD principles for the validation of QSAR and they turn to be appropriate tools for in silico tests regarding five different toxicity endpoints. Those endpoints with high relevance for REACH are: bioconcentration factor, skin sensitization, carcinogenicity, mutagenicity, and developmental toxicity. We used the above-mentioned freely available models to select a set of less harmful active 6-FQs as candidates for clinical studies.

Selenium is an essential nutritional element to mammalians necessary for the active function of different oxidant enzymes, as glutathione peroxidase (GPx), thioredoxin reductases (TrxR), and iodothyronine deiodinases (IDD). The anti-oxidative effect of selenium is pivotal for the human physiology. Oxidative stress is associated with various diseases, such as cardiovascular disease, diabetes mellitus or cancer, and is also associated with the majority of surgical procedures. Particularly, the use of cardiopulmonary bypass for open cardiac surgery with aortic clamping is always related to oxidative stress due to ischemia and reperfusion. Whereas myocardial protection with different temperatures and cardioplegic solutions has become more efficient, reperfusion is often followed by the activation of an injurious oxidative cascade. The pathogenesis of ischemia/reperfusion injury depends on many factors, among them, reactive nitrogen species (RNS) and reactive oxygen species (ROS) are considered as initiators of the injury. ROS formed during oxidative stress can initiate lipid peroxidation, oxidize proteins to inactive states and cause DNA strand breaks. ROS production is physiologically controlled by free radical scavengers such as GPx and TrxR, and superoxide dismutase systems. GPx and TrxR are seleno-cysteine dependent enzymes, and their activity is known to be related to selenium availability. Furthermore, selenium has been reported to regulate gene expression of these selenoproteins as a cofactor and there is some evidence that selenium supplementation can attenuate the oxidative stress and decrease the complications after cardiac surgery. However, other clinical studies failed to demonstrate an association between selenium deficiency and cardiovascular outcomes. The aim of our review is to summarize the experimental and clinical evidence of preoperative selenium supplementation and therapy after cardiac surgery, focusing on the pathophysiology of oxidative stress and the clinical usage of selenium.

The atypical antipsychotic olanzapine is increasingly being used for various psychiatric conditions. There are few reports of olanzapine overdose in adolescent population. We report a case of 16-year-old adolescent who ingested 750 mg olanzapine, the highest reported nonlethal dose of olanzapine in adolescents. He presented with tachycardia, hypotension, generalized myoclonus, hyperpyrexia, muscular rigidity, leukocytosis and elevated creatine phosphokinase (CPK) levels. He recovered from the toxicity with minimal intervention.