The Systems Biology Approach to Drug Development: Application to Toxicity Assessment of Cardiac Drugs

2010 ◽  
Vol 88 (1) ◽  
pp. 130-134 ◽  
Author(s):  
B Rodriguez ◽  
K Burrage ◽  
D Gavaghan ◽  
V Grau ◽  
P Kohl ◽  
...  
Keyword(s):  
Systems Biology ◽  
Drug Development ◽  
Toxicity Assessment ◽  
Development Application

scholarly journals An integrated dataset for in silico drug discovery

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  
...  
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Drug Development ◽  
Data Integration ◽  
In Silico ◽  
Drug Repositioning ◽  
Integrated Systems ◽  
Integration Platform ◽  
Treatment Indications ◽  
New Treatment

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.

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

2012 ◽  
Vol 11 ◽  
pp. CIN.S8185 ◽  
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.

Genomics, systems biology and drug development for infectious diseases

2007 ◽  
Vol 3 (12) ◽  
pp. 841 ◽  
Author(s):  
Tomoyo Sakata ◽  
Elizabeth A. Winzeler
Keyword(s):  
Systems Biology ◽  
Infectious Diseases ◽  
Drug Development

Toxicity Assessment of 7 Anticancer Compounds in Zebrafish

2014 ◽  
Vol 33 (2) ◽  
pp. 98-105 ◽  
Author(s):  
Xiao-Ping Gao ◽  
Feng Feng ◽  
Xiao-Qi Zhang ◽  
Xiao-Xin Liu ◽  
Yu-Bin Wang ◽  
...  
Keyword(s):  
Drug Development ◽  
Mycophenolic Acid ◽  
Drug Toxicity ◽  
Toxicity Assessment ◽  
Gambogic Acid ◽  
Zebrafish Model ◽  
Anticancer Compounds ◽  
Pectoral Fins ◽  
Pericardial Edema ◽  
Vertebrate Model

Toxicity is one of the major reasons for failure in drug development. Zebrafish, as an ideal vertebrate model, could also be used to evaluate drug toxicity. In this study, we aimed to show the predictability and highlight novel findings of toxicity in zebrafish model. Seven anticancer compounds, including triptolide (TP), gambogic acid (GA), mycophenolic acid (MPA), curcumin, auranofin, thalidomide, and taxol, were assessed in zebrafish for their toxicity. Three compounds (GA, TP, and taxol) showed highest acute lethality, with 50% lethal concentration ≈ 1 μmol/L. Missing tails, severe pericardial edema, and enlarged yolk sacs were observed in MPA-treated embryos. The development of pectoral fins was severely disturbed in thalidomide-, GA-, and TP-treated embryos. Bradycardia was observed in MPA- and thalidomide-treated groups. Our findings suggested that the zebrafish are a good model for toxicity assessment of anticancer compounds.

What Does Systems Biology Mean for Drug Development?

2008 ◽  
Vol 15 (15) ◽  
pp. 1520-1528 ◽  
Author(s):  
Andre Schrattenholz ◽  
Vukic Soskic
Keyword(s):  
Systems Biology ◽  
Drug Development

Systems Biology: A Powerful Tool for Drug Development

2018 ◽  
Vol 18 (20) ◽  
pp. 1745-1754 ◽  
Author(s):  
Sneha Rai ◽  
Utkarsh Raj ◽  
Pritish Kumar Varadwaj
Keyword(s):  
Systems Biology ◽  
Drug Development ◽  
Clinical Symptoms ◽  
Drug Effects ◽  
New Drugs ◽  
Data Types ◽  
Disease States ◽  
Biological Responses ◽  
Disease Biology ◽  
Individualize Medicine

The conventional way of characterizing a disease consists of correlating clinical symptoms with pathological findings. Although this approach for many years has assisted clinicians in establishing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is the major challenge in the development of effective and safe medicines. Therefore, the process of drug development must consider biological responses in both pathological and physiological conditions. Consequently, a quantitative and holistic systems biology approach could aid in understanding complex biological systems by providing an exceptional platform to integrate diverse data types with molecular as well as pathway information, leading to development of predictive models for complex diseases. Furthermore, an increase in knowledgebase of proteins, genes, metabolites from high-throughput experimental data accelerates hypothesis generation and testing in disease models. The systems biology approach also assists in predicting drug effects, repurposing of existing drugs, identifying new targets, facilitating development of personalized medicine and improving decision making and success rate of new drugs in clinical trials.

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