Organotypic Models in Drug Development “Tumor Models and Cancer Systems Biology for the Investigation of Anticancer Drugs and Resistance Development”

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

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Cancer Systems Biology

Cancer Systems Biology - Chapman & Hall/CRC Mathematical & Computational Biology ◽

10.1201/9781439811863-s1 ◽

2010 ◽

pp. 1-1 ◽

Cited By ~ 1

Keyword(s):

Systems Biology ◽

Cancer Systems Biology

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The pharmacological audit trail (PhAT): Use of tumor models to address critical issues in the preclinical development of targeted anticancer drugs

Drug Discovery Today Disease Models ◽

10.1016/j.ddmod.2017.07.002 ◽

2016 ◽

Vol 21 ◽

pp. 23-32 ◽

Cited By ~ 1

Author(s):

Olivia Rossanese ◽

Suzanne Eccles ◽

Caroline Springer ◽

Amanda Swain ◽

Florence I. Raynaud ◽

...

Keyword(s):

Anticancer Drugs ◽

Tumor Models ◽

Preclinical Development ◽

Audit Trail ◽

Critical Issues

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An Introduction to the Mathematical Modeling in the Study of Cancer Systems Biology

Cancer Informatics ◽

10.1177/1176935118799754 ◽

2018 ◽

Vol 17 ◽

pp. 117693511879975 ◽

Cited By ~ 2

Author(s):

Abdallah K Alameddine ◽

Frederick Conlin ◽

Brian Binnall

Keyword(s):

Mathematical Modeling ◽

Systems Biology ◽

Cancer Progression ◽

Malignant Tumors ◽

Deterministic System ◽

Introductory Overview ◽

Cancer Systems Biology ◽

Heterogeneous Processes ◽

Global Systems ◽

Deficient Mismatch Repair

Background: Frequently occurring in cancer are the aberrant alterations of regulatory onco-metabolites, various oncogenes/epigenetic stochasticity, and suppressor genes, as well as the deficient mismatch repair mechanism, chronic inflammation, or those deviations belonging to the other cancer characteristics. How these aberrations that evolve overtime determine the global phenotype of malignant tumors remains to be completely understood. Dynamic analysis may have potential to reveal the mechanism of carcinogenesis and can offer new therapeutic intervention. Aims: We introduce simplified mathematical tools to model serial quantitative data of cancer biomarkers. We also highlight an introductory overview of mathematical tools and models as they apply from the viewpoint of known cancer features. Methods: Mathematical modeling of potentially actionable genomic products and how they proceed overtime during tumorigenesis are explored. This report is intended to be instinctive without being overly technical. Results: To date, many mathematical models of the common features of cancer have been developed. However, the dynamic of integrated heterogeneous processes and their cross talks related to carcinogenesis remains to be resolved. Conclusions: In cancer research, outlining mathematical modeling of experimentally obtained data snapshots of molecular species may provide insights into a better understanding of the multiple biochemical circuits. Recent discoveries have provided support for the existence of complex cancer progression in dynamics that span from a simple 1-dimensional deterministic system to a stochastic (ie, probabilistic) or to an oscillatory and multistable networks. Further research in mathematical modeling of cancer progression, based on the evolving molecular kinetics (time series), could inform a specific and a predictive behavior about the global systems biology of vulnerable tumor cells in their earlier stages of oncogenesis. On this footing, new preventive measures and anticancer therapy could then be constructed.

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PROSPECTIVE AND RETROSPECTIVE ANIMAL MODEL USED IN THE PHARMACOLOGICAL SCREENING OF ANTI-CANCER DRUG

International Journal of Current Pharmaceutical Research ◽

10.22159/ijcpr.2018v10i4.28472 ◽

2018 ◽

Vol 10 (4) ◽

pp. 13

Author(s):

Shamima Nasreen Ahmed ◽

Biswajit Das ◽

Jashabir Chakraborty

Keyword(s):

Drug Development ◽

Anticancer Drug ◽

Drug Screening ◽

Rapid Development ◽

The Body ◽

Cytotoxic Agents ◽

Cancer Drug ◽

Tumor Models ◽

Preclinical Screening ◽

Anticancer Drug Development

Cancer is a disease characterized by uncontrolled proliferation of cells that have transformed from the normal cells of the body. The widely used cancer drugs suffers from the drawback of high toxicity not within the reach of a common man. This urgently necessitating the screening of these compounds. This review focuses on the major contributions of preclinical screening models to anticancer drug development over the years till recent times, from the empirical drug screening of cytotoxic agents against uncharacterized tumor models to the target-orientated drug screening of agents with defined mechanisms of action,, a general transition has been observed. The newer approaches to anticancer drug development involve the molecular characterization of models along with an appreciation of the pharmacodynamics and pharmacokinetic properties of compounds [e. g., the US National Cancer Institute (NCI) in vitro 60-cell line panel, hollow fibre assay, and s. c. xenograft]. In vivo tumor models including orthotopic, metastatic, and genetically engineered mouse models are also reviewed. The preclinical screening efforts of the European are also included. In 2015 with the rapid development of cancer modeling in zebrafish, great opportunities exist for chemical screens to find anticancer drug since 1970 the European Organisation for Research and Treatment of Cancer and Cancer Research UK, have been collaborating with the NCI in the acquisition and screening of compounds.

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A Systems Biology Approach in Therapeutic Response Study for Different Dosing Regimens—a Modeling Study of Drug Effects on Tumor Growth using Hybrid Systems

Cancer Informatics ◽

10.4137/cin.s8185 ◽

2012 ◽

Vol 11 ◽

pp. CIN.S8185 ◽

Cited By ~ 3

Author(s):

Xiangfang Li ◽

Lijun Qian ◽

Michale L. Bittner ◽

Edward R. Dougherty

Keyword(s):

Systems Biology ◽

Drug Development ◽

Tumor Growth ◽

Hybrid Systems ◽

Anticancer Agents ◽

Therapeutic Response ◽

Therapeutic Effects ◽

Systems Model ◽

Optimal Drug ◽

Dosing Regimens

Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drug's pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.

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