The computational prediction of toxicity

The activity of T- and B-cell links of specific protection of chicken-broilers under the influence of synbiotic preparation “Biomagn” and “Diolide” disinfectant

One of the most actual problems of modern poultry farming is to increase the preservation of chickens and ensure high intensity of their growth at all stages of rearing. The development of alternative methods of prevention and treatment of infectious diseases of poultry involves the use of preparations of natural or organic origin, which have antagonistic properties against infectious agents and are able to balance the immune response. A prominent place in the system of disease prevention and increasing the resistance of animals and poultry is the development of modern probiotic and bactericidal preparations. In view of this, we studied the influence of the synbiotic preparations “Biomagn” in combination with the disinfectant “Diolide” on the state of the T- and B-cell links of the specific immunity of broiler chickens during the period of their rearing. The preparation “Biomagn” is based on representatives of the normal commensal microflora - non-pathogenic lactic acid bacteria with antibacterial and immunomodulatory properties and additional components: magnesium chloride, chitosan, xylanase, protease, cellulase, milk thistle meal, acidity regulator, betaine and emulsifier. The specified remedy is used to ensure optimal level of metabolism, increase immune function, growth, safety of animals and poultry. The results of the research showed that the use of the synbiotic preparation “Biomagn” in combination with the disinfectant “Diolide” had a positive influence on the activity of T- and B-cell links of specific defense of broiler chickens organism during the period of their rearing. This is evidenced by an increase in the amount of T-lymphocytes (total, active and theophylline-resistant) and B-lymphocytes in the blood and an increase in their functional activity due to the redistribution of the receptor apparatus of immunocompetent cells. In particular, a decrease in the number of functionally inactive T- and B-lymphocytes in the blood and an increase in the number of cells with low and medium degree of avidity. Therefore, the use of a set of studied immunotropic preparations is a promising direction in increasing the resistance and productivity of poultry. This allows in industrial conditions of maintenance, without loss of productivity, to respond to technological factors that can lead to immunodeficiency, disease and death of poultry.

ToxDP2 Database: Toxicity prediction of dietary polyphenols

Polyphenols are bioactive substances that minimize the risk of a variety of chronic diseases. Exposure to polyphenol bioactive compounds in our diet has increased across the globe, with amplified expectations from consumers, industry, and regulators centered on the potential benefits and essential safety of these compounds. Several data resources for beneficial properties of dietary polyphenols are present; however, toxicological information remains partial. We present a dynamic web-based database to assess dietary polyphenols' safety and fulfill the toxicity data gaps in the domain of food safety. The database (ToxDP2) comprises 415 dietary polyphenolic compounds, distributed into 15 subclasses with 25,792 collected and predicted data points. This web server facilitates the exploration of polyphenols for divergent applications. The data-driven approach on the ToxDP2 provides researchers with an understanding of polyphenols structure-function-toxicity relationships beneficial for developing nutraceuticals, pharmaceuticals, herbal supplements, and formulations.

Multilabel and Missing Label Methods for Binary Quantitative Structure-Activity Relationship Models: An Application for the Prediction of Adverse Drug Reactions

The prediction of adverse drug reactions in the discovery of new medicines is highly challenging. In the task of predicting the adverse reactions of chemical compounds, information about different targets is often available. Although we can focus on every adverse drug reaction prediction separately, multilabel approaches have been proven useful in many research areas for taking advantage of the relationship among the targets. However, when approaching the prediction problem from a multilabel point of view, we have to deal with the lack of information for some labels. This missing labels problem is a relevant issue in the field of cheminformatics approaches. This paper aims to predict the adverse drug reaction of commercial drugs using a multilabel approach where the possible presence of missing labels is also taken into consideration. We propose the use of multilabel methods to deal with the prediction of a large set of 27 different adverse reaction targets. We also propose the use of multilabel methods specifically designed to deal with the missing labels problem to test their ability to solve this difficult problem. The results show the validity of the proposed approach, demonstrating a superior performance of the multilabel method compared with the single-label approach in addressing the problem of adverse drug reaction prediction.

Machine Learning Methods in Computational Toxicology

Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of their use in computational toxicology. The use of multiple linear regression, variants of naïve Bayes classifier, k-nearest neighbors, support vector machine, decision trees, ensemble learning, random forest, several types of neural networks, and deep learning is the focus of attention of this review. The role of fragment descriptors, graph mining, and graph kernels is highlighted. The application of unsupervised methods, such as Kohonen's self-organizing maps and related approaches, which allow for combining predictions with data analysis and visualization, is also considered. The necessity of applying a wide range of machine learning methods in computational toxicology is underlined.

A proposal for calculating the no-observed-adverse-effect level (NOAEL) for organic compounds responsible for liver toxicity based on their physicochemical properties

OBJECTIVES

Both environmental and occupational exposure limits are based on the no-observed-adverse-effect level (NOAEL), lowest-observed-adverse-effect level (LOAEL) or benchmark dose (BMD) deriving from epidemiological and experimental studies. The aim of this study is to investigate to what extent the NOAEL values for organic compounds responsible for liver toxicity calculated based on their physicochemical properties could be used for calculating occupational exposure limits.

MATERIAL AND METHODS

The distribution coefficients from air to the liver (log K(liver)) were calculated according to the Abraham solvation equation. NOAEL and LOAEL values for early effects in the liver were obtained from the literature data. The descriptors for Abraham's equation were found for 59 compounds, which were divided into 2 groups: "non-reactive" (alcohols, ketones, esters, ethers, aromatic and aliphatic hydrocarbons, amides) and "possibly reactive" (aldehydes, allyl compounds, amines, benzyl halides, halogenated hydrocarbons, acrylates).

RESULTS

The correlation coefficients between log-log K and log NOAEL for non-reactive and reactive compounds amounted to r = -0.8123 and r = -0.8045, respectively, and were statistically significant. It appears that the Abraham equation could be used to predict the NOAEL values for compounds lacking information concerning their liver toxicity.

CONCLUSIONS

In view of the tendency to limit animal testing procedures, the method proposed in this paper can improve the practice of setting exposure guidelines for the unstudied compounds.

Aconitum and delphinium diterpenoid alkaloids of local anesthetic activity: comparative QSAR analysis based on GA-MLRA/PLS and optimal descriptors approach

The duration of anesthesia (related to protein binding of a drug) and the onset time (determined by the pKa) are important characteristics in assessment of local anesthetic agents. They are known to be affected by a number of factors. Early studies of antiarrhythmic diterpenoid alkaloids from plants Aconitum and Delphinium suggested that they possess local anesthetic activity due to their ability to suppress sodium currents of excited membranes. In this study we utilized toxicity, duration, and onset of action as endpoints to construct Quantitative Structure-Activity Relationship (QSAR) models for the series of 34 diterpenoid alkaloids characterized by local anesthetic activity using genetic algorithm-based multiple linear regression analysis/partial least squares and simplified molecular input line entry system (SMILES)-based optimal descriptors approach. The developed QSAR models correctly reflected factors that determine three endpoints of interest. Toxicity correlates with descriptors describing partition and reactivity of compounds. The duration of anesthesia was encoded by the parameters defining the ability of a compound to bind at the receptor site. The size and number of H-bond acceptor atoms were found not to favor the speed of onset, while topographic electronic descriptor demonstrated strong positive effect on it. SMILES-based optimal descriptors approach resulted in overall improvement of models. This approach was shown to be more sensitive to structural peculiarities of molecules than regression methods. The results clearly indicate that obtained QSARs are able to provide distinct rationales for compounds optimization with respect to particular endpoint.

Adaptation of high-throughput screening in drug discovery-toxicological screening tests

High-throughput screening (HTS) is one of the newest techniques used in drug design and may be applied in biological and chemical sciences. This method, due to utilization of robots, detectors and software that regulate the whole process, enables a series of analyses of chemical compounds to be conducted in a short time and the affinity of biological structures which is often related to toxicity to be defined. Since 2008 we have implemented the automation of this technique and as a consequence, the possibility to examine 100,000 compounds per day. The HTS method is more frequently utilized in conjunction with analytical techniques such as NMR or coupled methods e.g., LC-MS/MS. Series of studies enable the establishment of the rate of affinity for targets or the level of toxicity. Moreover, researches are conducted concerning conjugation of nanoparticles with drugs and the determination of the toxicity of such structures. For these purposes there are frequently used cell lines. Due to the miniaturization of all systems, it is possible to examine the compound's toxicity having only 1-3 mg of this compound. Determination of cytotoxicity in this way leads to a significant decrease in the expenditure and to a reduction in the length of the study.

Fragment descriptors in structure-property modeling and virtual screening

This chapter reviews the application of fragment descriptors at different stages of virtual screening: filtering, similarity search, and direct activity assessment using QSAR/QSPR models. Several case studies are considered. It is demonstrated that the power of fragment descriptors stems from their universality, very high computational efficiency, simplicity of interpretation, and versatility.

Reflections on the origins and evolution of genetic toxicology and the Environmental Mutagen Society

This article traces the development of the field of mutagenesis and its metamorphosis into the research area we now call genetic toxicology. In 1969, this transitional event led to the founding of the Environmental Mutagen Society (EMS). The charter of this new Society was to "encourage interest in and study of mutagens in the human environment, particularly as these may be of concern to public health." As the mutagenesis field unfolded and expanded, new wording appeared to better describe this evolving area of research. The term "genetic toxicology" was coined and became an important subspecialty of the broad area of toxicology. Genetic toxicology is now set for a thorough reappraisal of its methods, goals, and priorities to meet the challenges of the 21st Century. To better understand these challenges, we have revisited the primary goal that the EMS founders had in mind for the Society's main mission and objective, namely, the quantitative assessment of genetic (hereditary) risks to human populations exposed to environmental agents. We also have reflected upon some of the seminal events over the last 40 years that have influenced the advancement of the genetic toxicology discipline and the extent to which the Society's major goal and allied objectives have been achieved. Additionally, we have provided suggestions on how EMS can further advance the science of genetic toxicology in the postgenome era. Any oversight or failure to make proper acknowledgment of individuals, events, or the citation of relevant references in this article is unintentional.

Photochemical reactions involved in the phototoxicity of the anticonvulsant and antidepressant drug lamotrigine (Lamictal)

Lamotrigine (LTG) [3,5-diamino-6-(2,3-dichlorophenyl)-1,2,4-triazine], an anticonvulsant and antidepressant drug Lamictal, produces a (photo)toxic response in some patients. LTG absorbs UV light, generating singlet oxygen (1O2) with a quantum yield of 0.22 in CH2Cl2, 0.11 in MeCN and 0.01 in D2O. A small production of superoxide radical anion was also detected in acetonitrile. Thus, LTG is a moderate photosensitizer producing phototoxicity and oxidizing linoleic acid. LTG is a weak 1O2 quencher (k(q) = 3.2 x 10(5) M(-1) s(-1) in MeCN), but its photodecomposition products in dimethyl sulfoxide (DMSO) quenched 1O2 very efficiently. Upon intense UV irradiation from a xenon lamp, LTG was photobleached rapidly in DMSO and slowly in acetonitrile, alcohol and water. The rate increased significantly when laser pulses at 266 nm were employed. The photobleaching products generated 1O2 twice as strongly as LTG. Photobleaching was usually accompanied by the release of chloride anions, which increased in the presence of ascorbic acid. This suggests the formation of aryl radicals via dechlorination, a process which may be responsible for the photoallergic response observed in some patients. Our results demonstrate that LTG is a moderate generator of 1O2 prone to photodechlorination, especially in a reducing environment, which can contribute to the reported phototoxicity of LTG.

LF, Lopes C. Chemical carcinogenesis

The use of chemical compounds benefits society in a number of ways. Pesticides, for instance, enable foodstuffs to be produced in sufficient quantities to satisfy the needs of millions of people, a condition that has led to an increase in levels of life expectancy. Yet, at times, these benefits are offset by certain disadvantages, notably the toxic side effects of the chemical compounds used. Exposure to these compounds can have varying effects, ranging from instant death to a gradual process of chemical carcinogenesis. There are three stages involved in chemical carcinogenesis. These are defined as initiation, promotion and progression. Each of these stages is characterised by morphological and biochemical modifications and result from genetic and/or epigenetic alterations. These genetic modifications include: mutations in genes that control cell proliferation, cell death and DNA repair - i.e. mutations in proto-oncogenes and tumour suppressing genes. The epigenetic factors, also considered as being non-genetic in character, can also contribute to carcinogenesis via epigenetic mechanisms which silence gene expression. The control of responses to carcinogenesis through the application of several chemical, biochemical and biological techniques facilitates the identification of those basic mechanisms involved in neoplasic development. Experimental assays with laboratory animals, epidemiological studies and quick tests enable the identification of carcinogenic compounds, the dissection of many aspects of carcinogenesis, and the establishment of effective strategies to prevent the cancer which results from exposure to chemicals.

Use of computer-assisted prediction of toxic effects of chemical substances

The current revision of the European policy for the evaluation of chemicals (REACH) has lead to a controversy with regard to the need of additional animal safety testing. To avoid increases in animal testing but also to save time and resources, alternative in silico or in vitro tests for the assessment of toxic effects of chemicals are advocated. The draft of the original document issued in 29th October 2003 by the European Commission foresees the use of alternative methods but does not give further specification on which methods should be used. Computer-assisted prediction models, so-called predictive tools, besides in vitro models, will likely play an essential role in the proposed repertoire of "alternative methods". The current discussion has urged the Advisory Committee of the German Toxicology Society to present its position on the use of predictive tools in toxicology. Acceptable prediction models already exist for those toxicological endpoints which are based on well-understood mechanism, such as mutagenicity and skin sensitization, whereas mechanistically more complex endpoints such as acute, chronic or organ toxicities currently cannot be satisfactorily predicted. A potential strategy to assess such complex toxicities will lie in their dissection into models for the different steps or pathways leading to the final endpoint. Integration of these models should result in a higher predictivity. Despite these limitations, computer-assisted prediction tools already today play a complementary role for the assessment of chemicals for which no data is available or for which toxicological testing is impractical due to the lack of availability of sufficient compounds for testing. Furthermore, predictive tools offer support in the screening and the subsequent prioritization of compound for further toxicological testing, as expected within the scope of the European REACH program. This program will also lead to the collection of high-quality data which will broaden the database for further (Q)SAR approaches and will in turn increase the predictivity of predictive tools.

Prediction of genotoxicity of various environmental pollutants by artificial neural network simulation

In order to evaluate human carcinogenic risks, genotoxicity data such as animal cancer bioassay are often not available. In this study, to assess the relevance of indicator of carcinogenic risks, we used the "molecular diversity approach" to estimate the genotoxicity based upon Salmonella genotoxicity test using the umu test and systemic toxicity data of the 82 environmental chemicals predicted by neural network simulation. The 82 environmental chemicals were randomly selected for this study according to the production and usage in Japan. Even in this challenging trial for QSTR (Quantitative Structure Toxicity Relationship) study, approaches using artificial neural networks can account for about 94% of the variation in the genotoxicity results derived by the umu-test.

Computational prediction of human drug metabolism

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.

Relationship between chemical structure and the occupational asthma hazard of low molecular weight organic compounds

AIMS

To investigate quantitatively, relationships between chemical structure and reported occupational asthma hazard for low molecular weight (LMW) organic compounds; to develop and validate a model linking asthma hazard with chemical substructure; and to generate mechanistic hypotheses that might explain the relationships.

METHODS

A learning dataset used 78 LMW chemical asthmagens reported in the literature before 1995, and 301 control compounds with recognised occupational exposures and hazards other than respiratory sensitisation. The chemical structures of the asthmagens and control compounds were characterised by the presence of chemical substructure fragments. Odds ratios were calculated for these fragments to determine which were associated with a likelihood of being reported as an occupational asthmagen. Logistic regression modelling was used to identify the independent contribution of these substructures. A post-1995 set of 21 asthmagens and 77 controls were selected to externally validate the model.

RESULTS

Nitrogen or oxygen containing functional groups such as isocyanate, amine, acid anhydride, and carbonyl were associated with an occupational asthma hazard, particularly when the functional group was present twice or more in the same molecule. A logistic regression model using only statistically significant independent variables for occupational asthma hazard correctly assigned 90% of the model development set. The external validation showed a sensitivity of 86% and specificity of 99%.

CONCLUSIONS

Although a wide variety of chemical structures are associated with occupational asthma, bifunctional reactivity is strongly associated with occupational asthma hazard across a range of chemical substructures. This suggests that chemical cross-linking is an important molecular mechanism leading to the development of occupational asthma. The logistic regression model is freely available on the internet and may offer a useful but inexpensive adjunct to the prediction of occupational asthma hazard.

A Scientific and Animal Welfare Assessment of the OECD Health Effects Test Guidelines for the Safety Testing of Chemicals under the European Union REACH System

We have assessed each of the OECD Health Effects Test Guidelines (TGs) that were included in an annex to the Internet consultation issued by the European Commission relating to the Registration, Evaluation and Authorisation of Chemicals (REACH) legislation for the testing of new and existing chemical substances. Each guideline has been analysed with respect to its design and its scientific and animal welfare implications, the extent to which it makes use of modern techniques, and its suitability to be used in the REACH system for the testing of large numbers of chemicals. The scientific basis of the test and its justification are considered, as well as the numbers of animals required, and the potential adverse effects on them. The prospects and possibilities for applying the Three Rs ( reduction, refinement and replacement) to each of the TGs are also discussed. We have proposed an overall testing strategy for how these TGs and other methods could best be deployed for chemicals testing, should it be necessary to fill data gaps. Certain TGs have been omitted from the strategy, when we have considered them to be unnecessary for chemicals testing. A series of recommendations has been made for improving the TGs with regard to both their scientific content and ways in which they could be better designed in relation to optimising the use of the animals concerned, and minimising adverse welfare consequences to them. Our investigations show that there is an urgent need to update the TGs to reflect modern techniques and methods, and to use current approaches for applying refinement strategies to improve the scientific and animal welfare aspects of the procedures used. Improvements can and should be made in all aspects of toxicity testing, from sample preparation, and animal housing, care and feeding, to dose formulation, test material administration, and the histopathological and clinical analysis of tissue samples. Opportunities for streamlining individual assays are very limited, but testing could be made more efficient by: a) only undertaking studies that provide relevant data; b) making greater use of screens and preliminary testing; c) applying some tests simultaneously to the same animals; d) using one sex; and e) eliminating redundant tests. In conclusion, it is clear that, as they stand, the OECD Health Effects TGs are unsuitable for use in the European Union REACH system, for which potentially very large numbers of laboratory animals will be needed for the testing of a very large number of chemicals.

Separation and detection methods for covalent drug–protein adducts

Covalent binding of reactive metabolites of drugs to proteins has been a predominant hypothesis for the mechanism of toxicity caused by numerous drugs. The development of efficient and sensitive analytical methods for the separation, identification, quantification of drug-protein adducts have important clinical and toxicological implications. In the last few decades, continuous progress in analytical methodology has been achieved with substantial increase in the number of new, more specific and more sensitive methods for drug-protein adducts. The methods used for drug-protein adduct studies include those for separation and for subsequent detection and identification. Various chromatographic (e.g., affinity chromatography, ion-exchange chromatography, and high-performance liquid chromatography) and electrophoretic techniques [e.g., sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), two-dimensional SDS-PAGE, and capillary electrophoresis], used alone or in combination, offer an opportunity to purify proteins adducted by reactive drug metabolites. Conventionally, mass spectrometric (MS), nuclear magnetic resonance, and immunological and radioisotope methods are used to detect and identify protein targets for reactive drug metabolites. However, these methods are labor-intensive, and have provided very limited sequence information on the target proteins adducted, and thus the identities of the protein targets are usually unknown. Moreover, the antibody-based methods are limited by the availability, quality, and specificity of antibodies to protein adducts, which greatly hindered the identification of specific protein targets of drugs and their clinical applications. Recently, the use of powerful MS technologies (e.g., matrix-assisted laser desorption/ionization time-of-flight) together with analytical proteomics have enabled one to separate, identify unknown protein adducts, and establish the sequence context of specific adducts by offering the opportunity to search for adducts in proteomes containing a large number of proteins with protein adducts and unmodified proteins. The present review highlights the separation and detection technologies for drug-protein adducts, with an emphasis on methodology, advantages and limitations to these techniques. Furthermore, a brief discussion of the application of these techniques to individual drugs and their target proteins will be outlined.

Chemical substructures in drug discovery

The widespread use of HTS and combinatorial chemistry techniques has led to the generation of large amounts of pharmacological data, which, in turn, has catalyzed the development of computational methods designed to reduce the time and cost in identifying molecules suitable for pharmaceutical development. This review focuses on the use of substructure-based in silico techniques for lead discovery, an effective and increasingly popular approach for augmenting the chance of selecting drug-like compounds for preclinical and clinical development.

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.