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Systems Network Pharmaco-Toxicology in the Study of Herbal Medicines

  • Alessandro BurianiEmail author
  • Stefano Fortinguerra
  • Maria Carrara
  • Olavi Pelkonen
Chapter

Abstract

Analytic “‘omic” techniques and systems biology driven bioinformatics have increasingly been a game changer in the study of herbal drugs, thanks to the simultaneous detection of entire molecular families in a given biological system, and the ability to collect, classify, network, and visualize a large number of analytical data through bioinformatics. The genomics area has been at the vanguard of this evolution. Other “‘omic” techniques, such as proteomics and metabonomics, are providing a fast-growing body of data both on biological targets and on phytocomplexes and their interactions. This has favored a more global view of biological processes, describing how perturbations can influence the steady state of a large number of the components of the system and their relations, changing the system as a whole. It is thus apparent that biological responses induced by phytocomplexes represent the net output of changes in the properties of a very large number of molecules, all acting in an interdependent fashion to form a highly connected network.

“‘Omic” techniques and systems biology are applied in herbal medicine at various levels, and provide novel strategies that can be exploited both for herbal drug research and medical use, in applications ranging from drug quality control to patient stratification. Network pharmaco-toxicology represents one of the most important applications of this new approach. Building up networks of molecular interactions between phytocomplex components and pharmaco-toxicological processes can provide a powerful predictive tool in herbal medicine. There is an increasing number of Web-based systems biology platforms, continuously fed with “'omics” data, providing a view of the complete biological system modulated by a given drug that can be used for predictive pharmacology and toxicology. Systems toxicology promises to be the best context for providing a mechanistic understanding of toxicological effects, thus allowing the prediction of responses to phytochemicals.

Keywords

‘omics Pharmacogenomics Genomics Whole genome sequencing Natural drugs Proteomics Metabonomics Metabolomics Microbiome Systems biology Network pharmacology Network toxicology Herbal medicines Phytocomplex Holistic Traditional Chinese medicine Ayurveda Jamu Kampo Traditional Iranian medicine 

Abbreviations

ADME

Adsorption, distribution, metabolism, excretion

ADMET

Adsorption, distribution, metabolism, excretion and toxicology

AOP

Adverse outcome pathway

AST

Addition and subtraction theory

CMAP

Connectivity map

DCHD

Da Chaihu decoction

DILI

Drug-induced liver injury

FDA

Food and Drug Administration

HMDB

Human metabolome database

HMSP

Herbal medicine systems pharmacology

HS

UK National Health System

KEGG

Kyoto Encyclopedia of Genes and Genomes

LBVS

Ligand-based virtual screening

LINCS

Library of integrated network-based cellular signatures

MHD

Ma-huang decoction

MS

Mass spectrometry

NGS

Next generation sequencing

NIH

(United States) National Institutes of Health

NMR

Nuclear magnetic resonance

PEA

Probability ensemble approach

PGP

Personal genome project

PoT

Pathways of toxicity

PRM

Parallel reaction monitoring

QSARs

Quantitative structure-activity relationships

SBVS

Structure-based virtual screening

SNPs

Single nucleotide polymorphisms

SRM

Selected reaction monitoring

TCM

Traditional Chinese medicine

TCMSP

Traditional Chinese medicine systems pharmacology database

VS

Virtual screening

WES

Whole exome sequencing

XCHD

Xiao Chaihu decoction

WGS

Whole genome sequencing

Notes

Acknowledgements

We would like to thank Roberta Sato at the Library of the Department of Pharmaceutical and Pharmacological Sciences of the University of Padova for her technical assistance with database search and bibliography, and Mariagnese Barbera for text revision.

Conflict of Interest

Alessandro Buriani and Stefano Fortinguerra are in charge of the Personalized Medicine service of the Gruppo Data Medica Padova.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Alessandro Buriani
    • 1
    • 2
    Email author
  • Stefano Fortinguerra
    • 1
    • 2
  • Maria Carrara
    • 2
  • Olavi Pelkonen
    • 3
  1. 1.Center Maria Paola Belloni Regazzo for Pharmacogenomics and Personalized MedicineGruppo Data MedicaPadovaItaly
  2. 2.Department of Pharmacological and Pharmaceutical SciencesUniversity of PadovaPadovaItaly
  3. 3.Department of Pharmacology and ToxicologyUniversity of OuluOuluFinland

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