In silico prediction of drug toxicity

It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of these. A number of expert systems are available for toxicity prediction, most of them covering a range of toxicity endpoints. Those discussed include TOPKAT, CASE, DEREK, HazardExpert, OncoLogic and COMPACT. Comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed. The consensus approach is recommended, whereby the results from several prediction systems are pooled. It is simply amazing that we can formulate any kind of QSAR. The (desired activity) is only the starting point. The truly formidable problem is that of toxicity, especially the difficult long-term toxicities resulting from chronic usage'. (Hansch & Leo [1])     

In silico discovery of beta-secretase inhibitors

Alzheimer's disease, the most common amyloid-associated disorder, accounts for the majority of the dementia diagnosed after the age of 60. The cleavage of the beta-amyloid precursor protein is initiated by beta-secretase (BACE-1), a membrane-bound aspartic protease, which has emerged as an important but difficult protein target. Here, an in silico screening approach consisting of fragment-based docking, ligand conformational search by a genetic algorithm, and evaluation of free energy of binding was used to identify low-molecular-weight inhibitors of BACE-1. More than 300,000 small molecules were docked and about 15,000 prioritized according to a linear interaction energy model with evaluation of solvation by continuum electrostatics. Eighty-eight compounds were tested in vitro, and 10 of them showed an IC(50) value lower than 100 microM in a BACE-1 enzymatic assay. Interestingly, the 10 active compounds shared a triazine scaffold. Moreover, four of them were active in an assay with mammalian cells (EC(50) < 20 microM), indicating that they are cell-permeable. Therefore, these triazine derivatives are very promising lead candidates for BACE-1 inhibition. The discoveries of this series and two other series of nonpeptidic BACE-1 inhibitors demonstrate the usefulness of our in silico high-throughput screening approach.     

In silico quest for stable phosphastannaallenes

A computational study at different levels of theory was performed for the not yet synthesized phosphastannaallenes >SnCP– in order to evaluate the strength of the SnC bond, the main postulated factor to stabilize such species, and the geometry in R₂SnCPR derivatives. The influence of the substituents with various electronic effects (H, Me, Ph, F, Cl, OMe, SiMe₃) at the Sn or P atoms of the SnCP unit on the SnC bond order was evaluated in the quest for a substituent that would stabilize the phosphastannaallenic unit. PC bond orders have also been calculated.     

Evaluation of artificial intelligence based models for chemical biodegradability prediction

This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.     

Anaerobic toxicity and biodegradability of hydrolysis products of chemical warfare agents

The toxicity and biodegradability of the main hydrolysis products of chemical warfare agents were investigated under methanogenic conditions. Among the tested substances, only MPhA does not have any toxic effect with regard to the aceticlastic methanogenic activity. The toxicity of other compounds varied between moderate (TDG, mercaptoethanol) to strong (ethanolamine, diisobutyl ester of MPhA). Biodegradability tests showed that all the products of chemical detoxification of mustard gas (ethanolamine, ethylene glycol, TDG, mercaptoethanol) can be biomineralized under methanogenic conditions. On the contrary, phosphorus-containing compounds from the chemical detoxification of nerve warfare agents (Sarin, Soman, V_x-gases) are quite persistent under these conditions.     

环境致癌物的计算机预测研究

环境致癌物可诱发人类或哺乳动物体内的肿瘤,建立环境致癌物的计算机预测模型对环境风险评价和生态安全具有重要的意义．通过构建了3780个化合物的数据集,随机选取其中3024个作为训练集,其余756个作为外部验证集;基于定量构-效关系（QSAR）方法,采用逐步判别分析和主成分分析建立数学模型．结果表明训练集非致癌物预测正确率为86．0％,可能致癌物的预测正确率为88．O％,而采用主成分建模时,非致癌物和可能致癌物的预测正确率分别为74．2％和73．1％．说明逐步判别分析法的结果优于主成分判别分析．同时确定了可能致癌物和非致癌物的分子结构参数,阐明了两者结构差异．以上结果为预测和评估环境致癌物提供参考依据．     

Probabilistic assessment of biodegradability based on metabolic pathways: CATABOL System

A novel mechanistic modeling approach has been developed that assesses chemical biodegradability in a quantitative manner. It is an expert system predicting biotransformation pathway working together with a probabilistic model that calculates probabilities of the individual transformations. The expert system contains a library of hierarchically ordered individual transformations and matching substructure engine. The hierarchy in the expert system was set according to the descending order of the individual transformation probabilities. The integrated principal catabolic steps are derived from set of metabolic pathways predicted for each chemical from the training set and encompass more than one real biodegradation step to improve the speed of predictions. In the current work, we modeled O 2 yield during OECD 302 C (MITI I) test. MITI-I database of 532 chemicals was used as a training set. To make biodegradability predictions, the model only needs structure of a chemical. The output is given as percentage of theoretical biological oxygen demand (BOD). The model allows for identifying potentially persistent catabolic intermediates and their molar amounts. The data in the training set agreed well with the calculated BODs ( r 2 =0.90) in the entire range i.e. a good fit was observed for readily, intermediate and difficult to degrade chemicals. After introducing 60% ThOD as a cut off value the model predicted correctly 98% ready biodegradable structures and 96% not ready biodegradable structures. Crossvalidation by four times leaving 25% of data resulted in Q 2 =0.88 between observed and predicted values. Presented approach and obtained results were used to develop computer software for biodegradability prediction CATABOL.     

Probabilistic assessment of biodegradability based on metabolic pathways: catabol system

A novel mechanistic modeling approach has been developed that assesses chemical biodegradability in a quantitative manner. It is an expert system predicting biotransformation pathway working together with a probabilistic model that calculates probabilities of the individual transformations. The expert system contains a library of hierarchically ordered individual transformations and matching substructure engine. The hierarchy in the expert system was set according to the descending order of the individual transformation probabilities. The integrated principal catabolic steps are derived from set of metabolic pathways predicted for each chemical from the training set and encompass more than one real biodegradation step to improve the speed of predictions. In the current work, we modeled O2 yield during OECD 302 C (MITI I) test. MITI-I database of 532 chemicals was used as a training set. To make biodegradability predictions, the model only needs structure of a chemical. The output is given as percentage of theoretical biological oxygen demand (BOD). The model allows for identifying potentially persistent catabolic intermediates and their molar amounts. The data in the training set agreed well with the calculated BODs (r2 = 0.90) in the entire range i.e. a good fit was observed for readily, intermediate and difficult to degrade chemicals. After introducing 60% ThOD as a cut off value the model predicted correctly 98% ready biodegradable structures and 96% not ready biodegradable structures. Crossvalidation by four times leaving 25% of data resulted in Q2 = 0.88 between observed and predicted values. Presented approach and obtained results were used to develop computer software for biodegradability prediction CATABOL.     

In silico chemical library screening and experimental validation of a novel 9-aminoacridine based lead-inhibitor of human S-adenosylmethionine decarboxylase

In silico chemical library screening (virtual screening) was used to identify a novel lead compound capable of inhibiting S-adenosylmethionine decarboxylase (AdoMetDC). AdoMetDC is intimately involved in the biosynthesis of polyamines, which are essential for tumor progression and are elevated in numerous types of tumors. Therefore, inhibition of this enzyme provides an attractive target for the discovery of novel anticancer drugs. We performed virtual screening using a computer model derived from the X-ray crystal structure of human AdoMetDC and the National Cancer Institute's Diversity Set (1990 compounds). Our docking study suggested several compounds that could serve as drug candidates since their docking modes and scores revealed potential inhibitory activity toward AdoMetDC. Experimental testing of the top-scoring compounds indicated that one of these compounds (NSC 354961) possesses an IC50 in the low micromolar range. A search of the entire NCI compound collection for compounds similar to NSC 354961 yielded two additional compounds that exhibited activity in the experimental assay but with significantly diminished potency relative to NSC 354961. In this report, we disclose the activity of NSC 354961 against AdoMetDC and its probable binding mode based on computational modeling. We also discuss the importance of virtual screening in the context of enzymes that are not readily amenable to high-throughput assays, thereby demonstrating the efficacy of virtual screening, combined with selective experimental testing, in identifying new potential drug candidates.     

In silico screening of metal-organic frameworks in separation applications

Porous materials such as metal-organic frameworks (MOFs) and zeolitic imidazolate frameworks (ZIFs) offer considerable potential for separating a variety of mixtures such as those relevant for CO(2) capture (CO(2)/H(2), CO(2)/CH(4), CO(2)/N(2)), CH(4)/H(2), alkanes/alkenes, and hydrocarbon isomers. There are basically two different separation technologies that can be employed: (1) a pressure swing adsorption (PSA) unit with a fixed bed of adsorbent particles, and (2) a membrane device, wherein the mixture is allowed to permeate through a micro-porous crystalline layer. In view of the vast number of MOFs, and ZIFs that have been synthesized there is a need for a systematic screening of potential candidates for any given separation task. Also of importance is to investigate how MOFs and ZIFs stack up against the more traditional zeolites such as NaX and NaY with regard to their separation characteristics. This perspective highlights the potency of molecular simulations in determining the choice of the best MOF or ZIF for a given separation task. A variety of metrics that quantify the separation performance, such as adsorption selectivity, working capacity, diffusion selectivity, and membrane permeability, are determined from a combination of Configurational-Bias Monte Carlo (CBMC) and Molecular Dynamics (MD) simulations. The practical utility of the suggested screening methodology is demonstrated by comparison with available experimental data.     

In silico allergenicity prediction of several lipid transfer proteins

Non-specific lipid transfer proteins (nsLTPs) are common allergens and they are particularly widespread within the plant kingdom. They have a highly conserved three-dimensional structure that generate a strong cross-reactivity among the members of this family. In the last years several web tools for the prediction of allergenicity of new molecules based on their homology with known allergens have been released, and guidelines to assess potential allergenicity of proteins through bioinformatics have been established. Even if such tools are only partially reliable yet, they can provide important indications when other kinds of molecular characterization are lacking. The potential allergenicity of 28 amino acid sequences of LTPs homologs, either retrieved from the UniProt database or in silico deduced from the corresponding EST coding sequence, was predicted using 7 publicly available web tools. Moreover, their similarity degree to their closest known LTP allergens was calculated, in order to evaluate their potential cross-reactivity. Finally, all sequences were studied for their identity degree with the peach allergen Pru p 3, considering the regions involved in the formation of its known conformational IgE-binding epitope. Most of the analyzed sequences displayed a high probability to be allergenic according to all the software employed. The analyzed LTPs from bell pepper, cassava, mango, mungbean and soybean showed high homology (>70%) with some known allergenic LTPs, suggesting a potential risk of cross-reactivity for sensitized individuals. Other LTPs, like for example those from canola, cassava, mango, mungbean, papaya or persimmon, displayed a high degree of identity with Pru p 3 within the consensus sequence responsible for the formation, at three-dimensional level, of its major conformational epitope. Since recent studies highlighted how in patients mono-sensitized to peach LTP the levels of IgE seem directly proportional to the chance of developing cross-reactivity to LTPs from non-Rosaceae foods, and these chances increase the more similar the protein is to Pru p 3, these proteins should be taken into special account for future studies aimed at evaluating the risk of cross-allergenicity in highly sensitized individuals.     

In silico predictions of hERG channel blockers in drug discovery: from ligand-based and target-based approaches to systems chemical biology

The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several drugs with different therapeutic indications and recognized as hERG blockers were recently withdrawn due to the risk of QT prolongation, arrhythmia and Torsade de Pointes. In silico techniques can provide a priori knowledge of hERG blockers, thus reducing the costs associated with screening assays. Significant progress has been made in structure-based and ligand-based drug design and a number of models have been developed to predict hERG blockage. Although approaches such as homology modeling in combination with docking and molecular dynamics bring us closer to understand the drug-channel interactions whereas QSAR and classification models provide a faster assessment and detection of hERG-related drug toxicity, limitation on the applicability domain of the current models and integration of data from diverse in vitro approaches are still issues to challenge. Therefore, this review will emphasize on current methods to predict hERG blockers and the need of consistent data to improve the quality and performance of the in silico models. Finally, integration of network-based analysis on drugs inducing potentially long-QT syndrome and arrhythmia will be discussed as a new perspective for a better understanding of the drug responses in systems chemical biology.     

In silico calculation of acidity constants of carbonic acid conformers

In order to explore the aqueous acid chemistry of carbonic acid, we employ a constrained ab initio molecular dynamics (AIMD) technique to study acid dissociations of its three conformers including CC (cis-cis), CT (cis-trans), and TT (trans-trans). The simulations of reagent states reveal similar hydration characteristics for them: the hydroxyls donate H-bonds to solvating waters but no obvious H-bonding exists between hydroxyl oxygen atoms and waters. It is found that the CC conformer dissociates spontaneously to bicarbonate within picoseconds whereas the other two can stay for relatively long simulation times. This suggests that CC has the strongest acidity among the three conformers and it is not stable in water. The simulations indicate that the symmetrical hydroxyls of TT conformer have a pKa value of 3.11 and the two asymmetrical hydroxyls of CT show different pKa values: 2.60 and 3.75, respectively. Overall, these results confirm the recent experimental measurement: about 4.0 for deuterated carbonic acid. By analyzing the dissociation processes, it is revealed that the differences of the acid constants stem from the initial steps of hydroxyls stretches. This simulation study provides a quantitative and microscopic basis for better understanding the reactivity of aqueous carbonate species.