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


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
1 Emiliem, Inc., 6027 Christie Avenue, Emeryville, CA 94608, USA
University of California, Berkeley, Morgan Hall, Berkeley, CA 94720, USA
Website: http://nst.berkeley.edu/faculty/johnson.html
E-Mail: daleejohnson@sbcglobal.net
Phone: +1 (510) 642-7870
Fax: +1 (510) 642-0535
Interests: 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


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).


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

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Published Papers (2 papers)

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p. 17256-17269
by , , , , ,  and
Int. J. Mol. Sci. 2014, 15(10), 17256-17269; doi:10.3390/ijms151017256
Received: 1 July 2014; in revised form: 1 September 2014 / Accepted: 9 September 2014 / Published: 26 September 2014
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p. 15767-15777
by , , , , , , ,  and
Int. J. Mol. Sci. 2014, 15(9), 15767-15777; doi:10.3390/ijms150915767
Received: 14 July 2014; in revised form: 22 August 2014 / Accepted: 2 September 2014 / Published: 5 September 2014
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: An Efficient Cloud Computing-based Tag SNP selection algorithm for huge genome data
Authors: Che-Lun Hung
Affiliation: Department of Computer Science and Communication Engineering, Providence University
Abstract: SNPs are fundamental roles for various applications including medical diagnostic, phylogenies and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Genetic variants that are near each other tend to be inherited together; these regions of linked variants are known as haplotypes. Recently, genetics researches revealed that SNPs within certain haplotype blocks induce only a few distinct common haplotypes in the majority of the population. The existence of haplotype block structure has serious implications for association-based methods for the mapping of disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. From the experimental results, the proposed method can identify longer haplotype blocks and fewer tagSNPs in several chromosome haplotype data. To enhance the performance, we also propose a parallel algorithm based on the proposed algorithm by using the Hadoop MapReduce framework to copy with data in parallel. The experiment shows that the proposed MapReduce-paralleled combinatorial algorithm performs well on the real-world data obtained from the HapMap dataset; the computation efficiency can be significantly improved proportional to the number of processors being used.

Type: Article
Title: Combinatorial measurement of CDKN1A/p21 and KIF20A expression for discrimination of DNA damage-induced clastogenicity
Authors: Rina Sakai 1,2, Yuji Morikawa 1, Chiaki Kondo 1 , Hiroyuki Oka 1, Hirofumi Miyajima 1, Kihei Kubo 2 and Takeki Uehara 1,*
Affiliation: 1 Developmental research laboratories, Shionogi & Co., Ltd. 3-1-1 Futaba-cho, Toyonaka, Osaka 561-0825, Japan; E-Mails: rina.sakai@shionogi.co.jp (R.S.); yuji.morikawa@shionogi.co.jp (Y.M.); chiaki.kondo@shionogi.co.jp (C.K.); hiroyuki.oka@shionogi.co.jp (H.O.); hirofumi.miyajima@shionogi.co.jp (H.M.); takeki.uehara@shionogi.co.jp (T.U.)
2 Department of Veterinary Science, Graduate School of Life and Environmental Sciences, Osaka Prefecture University, 1-58 Rinkuu Ourai Kita, Izumisano, Osaka 598-8531, Japan; E-Mail: kubokrad@vet.osakafu-u.ac.jp (K.K.) Author to whom correspondence should be addressed; E-Mail: takeki.uehara@shionogi.co.jp; Tel.: +81-6-6485-5086; Fax: +81-6-6375-5780.
Abstract: In vitro mammalian cytogenetic tests detect chromosomal aberrations and used for testing the genotoxicity of compounds. Our previous study demonstrated that the expression of cyclin-dependent kinase inhibitor 1A (CDKN1A/p21) is a useful genomic biomarker for identifying DNA damage-inducing clastogens. However, that study also reported that cytotoxicity greatly affects CDKN1A expression. Adequate attention therefore needs to be paid to the cytotoxic condition when interpreting alterations in CDKN1A expression from a discriminative perspective. This study aimed to identify a supportive genomic biomarker that could aid appropriate decision making in toxicogenomics-based genotoxicity screening. Human lymphoblastoid TK6 cells were treated with various classes of DNA damage-positive and -negative compounds. Microarray data analysis revealed that kinesin family member 20A (KIF20A) was the only gene downregulated on treatment with all the DNA damage-inducing compounds among the genes involved in CDKN1A-centered interactome. Downregulation of KIF20A expression was successfully confirmed using additional DNA damage-positive compounds. Our analysis also demonstrated that nucleic acid constituents falsely downregulated KIF20A, possibly via p16 activation, independent of the CDKN1A signaling pathway. Our results indicate the potential of KIF20A as a supportive biomarker for CDKN1A gene-based clastogenicity judgment and possible mechanisms involved in KIF20A downregulation in DNA damage and non-DNA damage signaling networks.

Type of Paper: Review
Title: Biomarkers of Treatment Toxicity in Combined-Modality Cancer Therapies with Radiation and Systemic Drugs: Study Design, Multiplex Methods, Molecular Networks
Authors: Anne H. Ree 1,2, Sebastian Meltzer 1,2 and Erta Kalanxhi 1
Affiliation: 1 Department of Oncology, Akershus University Hospital, P.O.Box 1000, 1478 Lørenskog, Norway; E-Mails: sebastian.meltzer@ahus.no (S.M.); erta.kalanxhi@medisin.uio.no (E.K.)
2 Institute of Clinical Medicine, University of Oslo, P.O.Box 1171 Blindern, 0318 Oslo, Norway; E-Mail: a.h.ree@medisin.uio.no (A.H.R.)
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 is 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 proteomic or transcriptomic 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.

Title: CES2 and ABCG2 primary and synchronous metastasis expression and clinical efficacy of first-line FOLFIRI regimen in metastatic colorectal carcinoma
Author: Nicola Silvestris
Affiliation: Medical Oncology Unit National Cancer Institute "Giovanni Paolo II", email: n.silvestris@oncologico.bari.it
Abstract: Enzymatic activation of Irinotecan (CPT-11) is due to carboxylesterase (CES), and its pharmacological behaviour 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 5-Fluorouracil (5-FU) (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 (TTP), (P=0.005). Moreover, Cox’ multivariate analysis revealed that TS expression was significantly associated with overall survival(OS), (P=0.01). There was no correlation with response in any of the investigated markers. Topo-I expression resulted significantly higher in liver metastases with respect to the corresponding primary tumors (P=0.000), emphasizing the role of Topo-I expression in metastatic cancer biology. CES2 expression tended to be higher in the primary tumor tissues than in lymph nodes and liver metastases tissues (P=0.05). We suggest CES2 overexpression may be a potential marker of the malignant phenotype and that metastatic cancer biology could predict the clinical outcome of the patients treated with a first-line FOLFIRI regimen. A larger study would be necessary to evaluate the potential predictive and prognostic role of CES2 and ABCG2.

Last update: 7 August 2014

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