In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This study has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, were developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given

Defining molecular initiating events in the adverse outcome pathway framework for risk assessment.

Consumer and environmental safety decisions are based on exposure and hazard data, interpreted using risk assessment approaches. The adverse outcome pathway (AOP) conceptual framework has been presented as a logical sequence of events or processes within biological systems which can be used to understand adverse effects and refine current risk assessment practices in ecotoxicology. This framework can also be applied to human toxicology and is explored on the basis of investigating the molecular initiating events (MIEs) of compounds. The precise definition of the MIE has yet to reach general acceptance. In this work we present a unified MIE definition: an MIE is the initial interaction between a molecule and a biomolecule or biosystem that can be causally linked to an outcome via a pathway. Case studies are presented, and issues with current definitions are addressed. With the development of a unified MIE definition, the field can look toward defining, classifying, and characterizing more MIEs and using knowledge of the chemistry of these processes to aid AOP research and toxicity risk assessment. We also present the role of MIE research in the development of in vitro and in silico toxicology and suggest how, by using a combination of biological and chemical approaches, MIEs can be identified and characterized despite a lack of detailed reports, even for some of the most studied molecules in toxicology.The authors acknowledge the financial support of Unilever.This is the accepted manuscript. The final published version is available from ACS at http://dx.doi.org/10.1021/tx500345

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This study has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, were developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given

Relationship Between Adverse Outcome Pathways and Chemistry-Based in Silico Models to Predict Toxicity

The current landscape of Adverse Outcome Pathways (AOPs) provides a means of organising information relating to the adverse effects elicited following exposure to chemicals. As such, AOPs are an excellent driver for the development and application of in silico models for predictive toxicology allowing for the direct relationship between chemistry and adverse effects to be established. Information may be extracted from AOPs to support the creation of (quantitative) structure-activity relationships ((Q)SARs) as well as to increase confidence in grouping and read-across. Any part of an AOP can be linked to these various types of in silico models. There is, however, an emphasis on using information from known Molecular Initiating Events (MIEs) to create models including 2D and 3D structural alerts, SARs and QSARs. MIEs can be classified according to the nature of the interaction e.g. covalent reactivity, oxidative stress, phototoxicity, chronic receptor mediated, acute enzyme inhibition, unspecific, physical and other effects. Different types of MIEs require different approaches to their in silico modelling. Modelling Key Events and Key Event Relationships is useful if they represent the rate limiting step or key determinant of toxicity. Modelling of metabolism and chemical interactions will become part of AOP networks, which are also driving species-specific extrapolation and respective adaptation of models. With more information and data being captured, in silico approaches will increasingly support the application of knowledge from AOPs to build weight of evidence and support risk assessment, e.g. in the context of Integrated Assessment and Testing Approaches (IATAs)

Estudo do metabolismo do 7-nitroindazol: uma abordagem in silico

TCC(graduação) - Universidade Federal de Santa Catarina. Centro de Ciências da Saúde. Farmácia.A sepse é um problema mundial de saúde que afeta cerca de 30 milhões de pessoas anualmente. É definida como uma resposta desregulada do hospedeiro frente a um processo infeccioso associado à disfunção orgânica. Muitos trabalhos relatam o envolvimento do óxido nítrico (NO) no desenvolvimento da sepse, por meio de evidências do aumento dos níveis plasmáticos de nitrito e nitrato em pacientes sépticos e no choque séptico. Embora o NO ou peroxinitrito apresentem ação bactericida, níveis plasmáticos excessivos são contraprodutivos, como já evidenciado pelo seu envolvimento na hipotensão e hiporresponsividade à vasoconstritores e na redução da perfusão tecidual. O 7-nitroindazol (7-NI) é uma substância conhecida por sua capacidade de inibir seletivamente a enzima óxido nítrico sintase neuronal (NOS-1), ao se ligar reversivelmente ao grupo heme desta enzima, impedindo a ligação do cofator BH4 e competindo com a L-arginina. Estudo in vivo mostraram que o 7-NI foi eficaz em melhorar a resposta aos vasoconstritores na fase tardia da sepse, além de reduzir a mortalidade dos animais em modelo de CLP e pneumosepse. Apesar de ser promissor para o tratamento da sepse, estudos farmacocinéticos do 7-NI são escassos e não há relatos de estudos de metabolismo na literatura. Assim, este trabalho teve como objetivo por meio de estudo in silico, investigar as vias de metabolização e os prováveis metabólitos do 7-NI. Os possíveis sítios de metabolismo (SoMs) do 7-NI foram avaliados pelos softwares SMARTCyp e Rs-WebPredictor. Para a identificação dos prováveis metabólitos os softwares BioTransformer e MetaUltra foram empregados. Os resultados obtidos com o emprego dos softwares SMARTCyp e Rs-WebPredictor demonstraram que os principais SoMs estão localizados nas posições orto, meta e para em relação ao grupamento nitro do anel aromático. O software Rs-WebPredictor também indicou os nitrogênios do anel de cinco membros e o próprio grupo nitro como possíveis SoMs. Os metabólitos preditos pelos softwares Biotransformer e MetaUltra envolvem principalmente reações oxidação. Além disso, reações de conjugação também foram preditas pelo software MetaUltra. Os resultados obtidos no presente estudo poderão ser utilizados para orientar investigações futuras sobre as propriedades farmacocinétics e toxicológicas do 7-NI.Sepsis is a global health issue that affects more than 30 million people worldwide every year. Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Several studies have reported the involvement of the nitric oxide (NO) in the sepsis, by the elevated circulating nitrite/nitrate, the stable byproducts of NO, observed in septic patients and septic shock. Although NO or peroxynitrite has bactericidal action, excessive plasma levels are counterproductive, as already evidenced by its involvement in hypotension and hyporesponsiveness to vasoconstrictors and in the reduction of tissue perfusion. The 7-nitroindazole (7-NI) is a substance known for its ability to selectively inhibit the enzyme neuronal nitric oxide synthase (NOS-1) by reversibly binding to the heme group of this enzyme, preventing the binding of the BH4 cofactor and competing with L-arginine. In vivo studies have demonstrated that 7-NI was able to improve the response to vasoconstrictors in the late stage of sepsis, in addition to reducing animal mortality in the CLP and pneumosepsis models. Despite being promising for the treatment of sepsis, pharmacokinetic studies are scarce and there are no reports of metabolism studies for 7-NI in the literature. Thus, in this study in silico metabolism studies were carried out to predict the metabolism pathways and probable metabolites of 7-NI. The possible sites of metabolism (SoMs) of the 7-NI were evaluated using the softwares SMARTCyp e Rs-WebPredictor. To identify the probable metabolites of 7-NI, the softwares BioTransformer e MetaUltra were employed. The results obtained by using both SMARTCyp and Rs-WebPredictor softwares demonstrated that the main SoMs for 7-NI are located in the ortho, meta or para positions of the aromatic ring, in relation to the nitro groups. Rs-WebPredictor software also predicted the nitrogens of the five-membered ring and the nitro group as possible SoMs. The metabolites identified were originated from oxidation reactions as predicted by BioTransformer and MetaUltra softwares. Besides, conjugation reactions were predicted by using MetaUltra software. The results obtained in the present study can be used to guide future investigations on the pharmacokinetic and toxicological properties of 7-NI

EXPLORATION OF THE SRX-PRX AXIS AS A SMALL-MOLECULE TARGET

Lung cancer is a leading cause of cancer-related mortality irrespective of gender. The Sulfiredoxin (Srx) and Peroxiredoxin (Prx) are a group of thiol-based antioxidant proteins that plays an essential role in non-small cell lung cancer. Understanding the molecular characteristics of the Srx-Prx interaction may help design the strategies for future development of therapeutic tools. Based on existing literature and preliminary data from our lab, we hypothesized that the Srx plays a critical role in lung carcinogenesis and targeting the Srx-Prx axis or Srx alone may facilitate future development of targeted therapeutics for prevention and treatment of lung cancer. First, we demonstrated the oncogenic role of Srx in urethane-induced lung carcinogenesis in genetically modified FVB mice. The Srx-null mice showed resistance to urethane-induced lung cancer. Second, we demonstrated the Srx and Prx sites important for Srx-Prx interaction. The orientation of this arm is demonstrated to cause some steric hindrance for the Srx-Prx interaction as it substantially reduces the rate of association between Srx and Prx. Finally, we carried out virtual screening to identify molecules that can successfully target Srx-Prx interaction. Multiple in-silico filters were used to minimize the number of chemicals to be tested. We identified ISO1 as an inhibitor of the Srx-Prx interaction. KD value for Srx-ISO1 interaction is calculated to be 42 nM. Together, these data helps to identify an inhibitor (ISO1) of the Srx-Prx interaction that can be further pursued to be developed as a chemotherapeutic tool

Pragmatic Approaches to Using Computational Methods To Predict Xenobiotic Metabolism

In this study the performance of a selection of computational models for the prediction of metabolites and/or sites of metabolism was investigated. These included models incorporated in the MetaPrint2D-React, Meteor, and SMARTCyp software. The algorithms were assessed using two data sets: one a homogeneous data set of 28 Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and paracetamol (DS1) and the second a diverse data set of 30 top-selling drugs (DS2). The prediction of metabolites for the diverse data set (DS2) was better than for the more homogeneous DS1 for each model, indicating that some areas of chemical space may be better represented than others in the data used to develop and train the models. The study also identified compounds for which none of the packages could predict metabolites, again indicating areas of chemical space where more information is needed. Pragmatic approaches to using metabolism prediction software have also been proposed based on the results described here. These approaches include using cutoff values instead of restrictive reasoning settings in Meteor to reduce the output with little loss of sensitivity and for directing metabolite prediction by preselection based on likely sites of metabolism

Development of in silico models for the prediction of toxicity incorporating ADME information

Drug discovery is a process that requires a significant investment in both time and resources. Although recent developments have reduced the number of drugs failing at the later stages of development due to poor pharmacokinetic and/or toxicokinetic profiles, late stage attrition of drug candidates remains a problem. Additionally, there is a need to reduce animal testing for toxicological risk assessment for ethical and financial reasons. In silico methods offer an alternative that can address these challenges. A variety of computational approaches have been developed in the last two decades, these must be evaluated to ensure confidence in their use. The research presented in this thesis has assessed a range of existing tools for the prediction of toxicity and absorption, distribution, metabolism and elimination (ADME) parameters with an emphasis on absorption and xenobiotic metabolism. These two ADME properties largely determine bioavailability of a drug and, in turn, also influence toxicity. In vitro (Caco-2 cells and the parallel artificial membrane permeation assay) and in silico approaches, such as various druglikeness filters, can be used to estimate human intestinal absorption; a comparison between different methods was performed to identify relative strengths and weaknesses of the approaches. In terms of xenobiotic metabolism it is not only important to predict metabolites correctly, but it is also crucial to identify those compounds that can be biotransformed into species that can covalently bind to biomolecules. Structural alerts are routinely used to screen for such potential reactive metabolites. The balance between sensitivity and specificity of such reactive metabolite alerts has been discussed in the context of correctly predicting reactive metabolites of pharmaceuticals (using data available from DrugBank). Off-target toxicity, exemplified by human Ether-à-go-go-Related Gene (hERG) channel inhibition, was also explored. A number of novel structural alerts for hERG toxicity were developed based on groups of structurally similar compounds. Finally, the importance of predicting potential ecotoxicological effects of drugs was also considered. The utility of zebrafish embryos to distinguish between baseline and excess toxicity was investigated. In evaluating this selection of existing tools, improvements to the methods have been proposed where possible