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Special Issue "Computational Toxicology: Predicting Potential Toxicity of Drugs and Therapeutics"

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Toxicology".

Deadline for manuscript submissions: closed (15 August 2014)

Special Issue Editor

Guest Editor
Prof. Dr. Dale Johnson (Website)

1 Emiliem, Inc., 6027 Christie Avenue, Emeryville, CA 94608, USA
2
University of California, Berkeley, Morgan Hall, Berkeley, CA 94720, USA
Phone: +1 (510) 642-7870
Fax: +1 (510) 642-0535
Interests: nephrotoxicity mechanisms; renal bioactivation of toxicants; structure-toxicity relationships

Special Issue Information

Dear Colleagues,

Computational toxicology is an expanding research area that is becoming a multi-disciplinary fusion of bioinformatics and computational sciences with molecular biology and chemistry. A major goal in therapeutics research is to create more predictive power in the field of toxicology and drug safety throughout the process from early design to marketed products. The field has become multidisciplinary, starting at the stage of chemical synthesis with the goal of reducing potential toxicity in lead compounds, as well as in impurities. Computational analysis progresses through all therapeutic development stages even in assessing potential toxicity in increased risk potential in susceptible patient populations The field relies on the application of computer technology and mathematical/computational methods to analyze, model, and predict potential toxicological effects from chemical structures, exposure characteristics determined by PK/PD modeling and from networks of biological pathways affected by therapeutic agents. The field is progressing rapidly due to increased availability of larger and better curated public databases, open-source predictive tools, and focused commercial applications. Newer technologies for large scale data acquisition, and the prediction of biological effects using systems biology methodology, have expanded the scale and complexity of inquiry to a point where data gaps in knowledge can be filled with predicted values with a high level of confidence.

Prof. Dr. Dale Johnson
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs).

Keywords

  • Structural alerts
  • SAR
  • QSAR
  • Biological pathway perturbations
  • Systems biology
  • High-throughput screening
  • Databases
  • Toxicogenomics
  • Metabolomics
  • Network pharmacology
  • Systems toxicology
  • Drug safety

Related Special Issue

Published Papers (8 papers)

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Research

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Open AccessArticle Elucidating Mechanisms of Toxicity Using Phenotypic Data from Primary Human Cell Systems—A Chemical Biology Approach for Thrombosis-Related Side Effects
Int. J. Mol. Sci. 2015, 16(1), 1008-1029; doi:10.3390/ijms16011008
Received: 5 September 2014 / Accepted: 23 December 2014 / Published: 5 January 2015
PDF Full-text (1299 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Here we describe a chemical biology approach for elucidating potential toxicity mechanisms for thrombosis-related side effects. This work takes advantage of a large chemical biology data set comprising the effects of known, well-characterized reference agents on the cell surface levels of tissue [...] Read more.
Here we describe a chemical biology approach for elucidating potential toxicity mechanisms for thrombosis-related side effects. This work takes advantage of a large chemical biology data set comprising the effects of known, well-characterized reference agents on the cell surface levels of tissue factor (TF) in a primary human endothelial cell-based model of vascular inflammation, the BioMAP® 3C system. In previous work with the Environmental Protection Agency (EPA) for the ToxCast™ program, aryl hydrocarbon receptor (AhR) agonists and estrogen receptor (ER) antagonists were found to share an usual activity, that of increasing TF levels in this system. Since human exposure to compounds in both chemical classes is associated with increased incidence of thrombosis-related side effects, we expanded this analysis with a large number of well-characterized reference compounds in order to better understand the underlying mechanisms. As a result, mechanisms for increasing (AhR, histamine H1 receptor, histone deacetylase or HDAC, hsp90, nuclear factor kappa B or NFκB, MEK, oncostatin M receptor, Jak kinase, and p38 MAPK) and decreasing (vacuolar ATPase or V-ATPase) and mTOR) TF expression levels were uncovered. These data identify the nutrient, lipid, bacterial, and hypoxia sensing functions of autophagy as potential key regulatory points controlling cell surface TF levels in endothelial cells and support the mechanistic hypothesis that these functions are associated with thrombosis-related side effects in vivo. Full article
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Open AccessArticle The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction
Int. J. Mol. Sci. 2014, 15(11), 21136-21154; doi:10.3390/ijms151121136
Received: 25 September 2014 / Accepted: 20 October 2014 / Published: 14 November 2014
Cited by 8 | PDF Full-text (5161 KB) | HTML Full-text | XML Full-text
Abstract
The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds [...] Read more.
The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage. Full article
Open AccessArticle ToxDBScan: Large-Scale Similarity Screening of Toxicological Databases for Drug Candidates
Int. J. Mol. Sci. 2014, 15(10), 19037-19055; doi:10.3390/ijms151019037
Received: 28 August 2014 / Revised: 5 September 2014 / Accepted: 25 September 2014 / Published: 21 October 2014
Cited by 1 | PDF Full-text (814 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We present a new tool for hepatocarcinogenicity evaluation of drug candidates in rodents. ToxDBScan is a web tool offering quick and easy similarity screening of new drug candidates against two large-scale public databases, which contain expression profiles for substances with known carcinogenic [...] Read more.
We present a new tool for hepatocarcinogenicity evaluation of drug candidates in rodents. ToxDBScan is a web tool offering quick and easy similarity screening of new drug candidates against two large-scale public databases, which contain expression profiles for substances with known carcinogenic profiles: TG-GATEs and DrugMatrix. ToxDBScan uses a set similarity score that computes the putative similarity based on similar expression of genes to identify chemicals with similar genotoxic and hepatocarcinogenic potential. We propose using a discretized representation of expression profiles, which use only information on up- or down-regulation of genes as relevant features. Therefore, only the deregulated genes are required as input. ToxDBScan provides an extensive report on similar compounds, which includes additional information on compounds, differential genes and pathway enrichments. We evaluated ToxDBScan with expression data from 15 chemicals with known hepatocarcinogenic potential and observed a sensitivity of 88 Based on the identified chemicals, we achieved perfect classification of the independent test set. ToxDBScan is publicly available from the ZBIT Bioinformatics Toolbox. Full article
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Open AccessArticle Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study
Int. J. Mol. Sci. 2014, 15(10), 18162-18174; doi:10.3390/ijms151018162
Received: 8 July 2014 / Revised: 9 September 2014 / Accepted: 17 September 2014 / Published: 9 October 2014
Cited by 4 | PDF Full-text (870 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and [...] Read more.
A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity. Full article
Open AccessArticle Combinatorial Measurement of CDKN1A/p21 and KIF20A Expression for Discrimination of DNA Damage-Induced Clastogenicity
Int. J. Mol. Sci. 2014, 15(10), 17256-17269; doi:10.3390/ijms151017256
Received: 1 July 2014 / Revised: 1 September 2014 / Accepted: 9 September 2014 / Published: 26 September 2014
Cited by 1 | PDF Full-text (1801 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In vitro mammalian cytogenetic tests detect chromosomal aberrations and are used for testing the genotoxicity of compounds. This study aimed to identify a supportive genomic biomarker could minimize the risk of misjudgments and aid appropriate decision making in genotoxicity testing. Human lymphoblastoid [...] Read more.
In vitro mammalian cytogenetic tests detect chromosomal aberrations and are used for testing the genotoxicity of compounds. This study aimed to identify a supportive genomic biomarker could minimize the risk of misjudgments and aid appropriate decision making in genotoxicity testing. Human lymphoblastoid TK6 cells were treated with each of six DNA damage-inducing genotoxins (clastogens) or two genotoxins that do not cause DNA damage. Cells were exposed to each compound for 4 h, and gene expression was comprehensively examined using Affymetrix U133A microarrays. Toxicogenomic analysis revealed characteristic alterations in the expression of genes included in cyclin-dependent kinase inhibitor 1A (CDKN1A/p21)-centered network. The majority of genes included in this network were upregulated on treatment with DNA damage-inducing clastogens. The network, however, also included kinesin family member 20A (KIF20A) downregulated by treatment with all the DNA damage-inducing clastogens. Downregulation of KIF20A expression was successfully confirmed using additional DNA damage-inducing clastogens. Our analysis also demonstrated that nucleic acid constituents falsely downregulated the expression of KIF20A, possibly via p16 activation, independently of the CDKN1A signaling pathway. Our results indicate the potential of KIF20A as a supportive biomarker for clastogenicity judgment and possible mechanisms involved in KIF20A downregulation in DNA damage and non-DNA damage signaling networks. Full article
Open AccessArticle CES2, ABCG2, TS and Topo-I Primary and Synchronous Metastasis Expression and Clinical Outcome in Metastatic Colorectal Cancer Patients Treated with First-Line FOLFIRI Regimen
Int. J. Mol. Sci. 2014, 15(9), 15767-15777; doi:10.3390/ijms150915767
Received: 14 July 2014 / Revised: 22 August 2014 / Accepted: 2 September 2014 / Published: 5 September 2014
Cited by 6 | PDF Full-text (969 KB) | HTML Full-text | XML Full-text
Abstract
Enzymatic activation of irinotecan (CPT-11) is due to carboxylesterase (CES), and its pharmacological behavior is influenced by drug resistance-related proteins. We previously reported that the clinical response and prognosis of metastatic colorectal cancer (mCRC) patients did not differ in tumors with different [...] Read more.
Enzymatic activation of irinotecan (CPT-11) is due to carboxylesterase (CES), and its pharmacological behavior is influenced by drug resistance-related proteins. We previously reported that the clinical response and prognosis of metastatic colorectal cancer (mCRC) patients did not differ in tumors with different thymidylate synthase (TS) or topoisomerase-I (Topo-I) expression. Using immunohistochemistry (IHC), we evaluated the biological role of CES2 and the expression of breast cancer resistance protein (BCRP/ABCG2) in 58 consecutive mCRC patients, who had undergone a first-line CPT-11/5-FU/leucovirin (FOLFIRI) regimen. The expression of these proteins was also examined in a group of synchronous lymph nodes and liver metastases. Furthermore, all samples were revaluated for TS and Topo-I expression. High expression of CES2, ABCG2, TS and Topo-I was observed in 55%, 56%, 38% and 49% of patients, respectively. There was a significant association between high TS and high ABCG2 expression (p = 0.049). Univariate analysis showed that only TS expression significantly impacted on time to progression (p = 0.005). Moreover, Cox’ multivariate analysis revealed that TS expression was significantly associated with overall survival (p = 0.01). No significant correlation was found between investigated markers expression and clinical response. Topo-I expression resulted in being significantly higher in liver metastases with respect to the corresponding primary tumors (p < 0.0001), emphasizing the role of Topo-I expression in metastatic cancer biology. In primary tumor tissues, CES2 expression tended to be higher than that observed in liver metastasis tissues (p = 0.05). These preliminary data may suggest CES2 over-expression as a potential marker of malignant phenotype. In light of these findings, we suggest that Topo-I expression together with TS expression could be associated with metastatic progression of CRC. Further studies are warranted with the aim of evaluating the potential predictive and prognostic role of CES2 and ABCG2 in larger series of patients. Full article

Review

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Open AccessReview Biomarkers of Treatment Toxicity in Combined-Modality Cancer Therapies with Radiation and Systemic Drugs: Study Design, Multiplex Methods, Molecular Networks
Int. J. Mol. Sci. 2014, 15(12), 22835-22856; doi:10.3390/ijms151222835
Received: 6 August 2014 / Revised: 23 November 2014 / Accepted: 2 December 2014 / Published: 9 December 2014
Cited by 4 | PDF Full-text (2814 KB) | HTML Full-text | XML Full-text
Abstract
Organ toxicity in cancer therapy is likely caused by an underlying disposition for given pathophysiological mechanisms in the individual patient. Mechanistic data on treatment toxicity at the patient level are scarce; hence, probabilistic and translational linkages among different layers of data information, [...] Read more.
Organ toxicity in cancer therapy is likely caused by an underlying disposition for given pathophysiological mechanisms in the individual patient. Mechanistic data on treatment toxicity at the patient level are scarce; hence, probabilistic and translational linkages among different layers of data information, all the way from cellular targets of the therapeutic exposure to tissues and ultimately the patient’s organ systems, are required. Throughout all of these layers, untoward treatment effects may be viewed as perturbations that propagate within a hierarchically structured network from one functional level to the next, at each level causing disturbances that reach a critical threshold, which ultimately are manifested as clinical adverse reactions. Advances in bioinformatics permit compilation of information across the various levels of data organization, presumably enabling integrated systems biology-based prediction of treatment safety. In view of the complexity of biological responses to cancer therapy, this communication reports on a “top-down” strategy, starting with the systematic assessment of adverse effects within a defined therapeutic context and proceeding to transcriptomic and proteomic analysis of relevant patient tissue samples and computational exploration of the resulting data, with the ultimate aim of utilizing information from functional connectivity networks in evaluation of patient safety in multimodal cancer therapy. Full article
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Open AccessReview Computer-Aided Targeting of the PI3K/Akt/mTOR Pathway: Toxicity Reduction and Therapeutic Opportunities
Int. J. Mol. Sci. 2014, 15(10), 18856-18891; doi:10.3390/ijms151018856
Received: 18 August 2014 / Revised: 21 September 2014 / Accepted: 8 October 2014 / Published: 20 October 2014
Cited by 5 | PDF Full-text (2195 KB) | HTML Full-text | XML Full-text
Abstract
The PI3K/Akt/mTOR pathway plays an essential role in a wide range of biological functions, including metabolism, macromolecular synthesis, cell growth, proliferation and survival. Its versatility, however, makes it a conspicuous target of many pathogens; and the consequential deregulations of this pathway often [...] Read more.
The PI3K/Akt/mTOR pathway plays an essential role in a wide range of biological functions, including metabolism, macromolecular synthesis, cell growth, proliferation and survival. Its versatility, however, makes it a conspicuous target of many pathogens; and the consequential deregulations of this pathway often lead to complications, such as tumorigenesis, type 2 diabetes and cardiovascular diseases. Molecular targeted therapy, aimed at modulating the deregulated pathway, holds great promise for controlling these diseases, though side effects may be inevitable, given the ubiquity of the pathway in cell functions. Here, we review a variety of factors found to modulate the PI3K/Akt/mTOR pathway, including gene mutations, certain metabolites, inflammatory factors, chemical toxicants, drugs found to rectify the pathway, as well as viruses that hijack the pathway for their own synthetic purposes. Furthermore, this evidence of PI3K/Akt/mTOR pathway alteration and related pathogenesis has inspired the exploration of computer-aided targeting of this pathway to optimize therapeutic strategies. Herein, we discuss several possible options, using computer-aided targeting, to reduce the toxicity of molecularly-targeted therapy, including mathematical modeling, to reveal system-level control mechanisms and to confer a low-dosage combination therapy, the potential of PP2A as a therapeutic target, the formulation of parameters to identify patients who would most benefit from specific targeted therapies and molecular dynamics simulations and docking studies to discover drugs that are isoform specific or mutation selective so as to avoid undesired broad inhibitions. We hope this review will stimulate novel ideas for pharmaceutical discovery and deepen our understanding of curability and toxicity by targeting the PI3K/Akt/mTOR pathway. Full article

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