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Genomics and Artificial Intelligence Working Together in Drug Discovery and Repositioning: The Advent of Adaptive Pharmacogenomics in Glioblastoma and Chronic Arterial Inflammation Therapies

  • Glaucia C. Pereira
Chapter

Abstract

The field of pharmacogenomics investigates how genomics may modulate pathological trends using information on both genotype and phenotype, with the aim of designing personalised healthcare. Homoeostasis is partially regulated through the expression of core protein groups whose functionality is determined at gene level and modulated by environmental factors. Harmful changes in physiology may promote several dis-functionalities. In prior work gene expression was used as a biomarker to assess both pathological propensity and disease progression. A growing body of pharmacogenomics research has developed new compounds, on one hand, and on the other, it has proposed novel therapeutic applications for the existing ones. Over the past decades, collective efforts have significantly increased the number of omics information available. However, efficient and deterministic in silico mechanisms that efficiently analyse and detect trends on the basis of often unknown and limited physiological information responding to challenging clinical questions are still lacking. In this context, computational automation via artificial intelligence methodologies has proven to be accurate, robust to noise, cost efficient, and dynamic dealing with massive databases and forecasting on the basis of the available information. Moreover, this set of computational techniques, based on well-established mathematical models, provide efficient ways of determining trends based on both a priori knowledge and dynamically acquired information, working successfully on incomplete datasets. Therefore, in this chapter we assess developmental similarities between two major causes of worldwide death: glioblastoma and chronic arterial inflammation; and discuss the potential applicability of two artificial intelligence approaches for drug discovery and repositioning. According to the World Health Organization (WHO) a glioblastoma multiform is the most malignant glial-type tumour (graded level IV in the WHO scale); and inflammatory diseases affecting the cardiovascular network are the cause of high mortality. As suggested, these two pathologies have several developmental similarities and share common genetic variants. Therefore, we additionally seek to discuss the main promoters presented in the current literature, aiming at benefiting from their similarities in drug discovery and repositioning, via automatic artificial intelligence pattern recognition, forecasting, and computational design.

Keywords

Genomics Artificial intelligence Drug discovery Drug repositioning Adaptive pharmacogenomics Glioblastoma Chronic arterial inflammation Genetic fingerprints Deep neural networks Reinforcement learning Inflammatory signalling cascades Cytokines Transcription factors 

Abbreviation

ADME

Absorption, distribution, metabolism, and excretion

ApoE

Apolipoprotein E

ATP

Adenosine triphosphate

ATRX

Alpha-thalassaemia X

CSNK2A1

Casein kinase 2 alpha 1

DCN

Deep convolutional networks

DLNN

Deep learning neural networks

DNA

Deoxyribonucleic acid

DNN

Deep neural networks

EGFR

Epidermal growth factor receptor

GRR

Glycine-rich regions

IDH

Isocitrate dehydrogenase

IL

Interleukin

LDL

Low-density lipoprotein

LPA

Apolipoprotein A

LZ

Leucine zipper

NfkB

Nuclear factor-kappa B

oxi-LDL

Oxidized low-density lipoprotein

PTEN

Phosphatase and tensin homolog

RHD

Rel homology domain

SMC

Smooth muscle cells

SMCs

Vascular smooth muscle cells

SPP

Salesperson problem

STAT3

Signal transducer and activator of transcription 3

TAD

Transactivation domain

TERT

Telomerase reverse transcriptase

TNP-470

Trinitrophenol 470

TP53

Tumour protein p53

VCAM1

Vascular cell adhesion molecule 1

WHO

World Health Organization

Notes

Acknowledgments

The author is grateful for all those who directly and indirectly contributed to her research. The author is particularly grateful for the support given by the sponsors and collaborators: Prof. Malik, UFMA (http://www.huufma.br/site/english/); Mr. James, GridPro (http://www.gridpro.com/); Landspitali (http://www.landspitali.is/um-landspitala/languages/english/); and the Icelandic Institute for Intelligent Machines (http://www.iiim.is).

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.BiotechnologyIcelandic Institute for Intelligent MachinesReykjavikIceland
  2. 2.Department of Computer SciencesPolytechnic Institute, University Autonoma of MadridMadridSpain
  3. 3.Department of BioengineeringImperial College LondonLondonUK

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