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Computational Approaches in Drug Development and Phytocompound Analysis

  • Glaucia C. PereiraEmail author
Chapter
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Abstract

A plethora of therapeutic properties can be attributed to phytocompounds, and increasing new methodologies can be employed to optimise their utilisation. In cancer research, variations of molecular docking have been employed for mapping protein-phytochemical interactions, seeking alternative approaches to chemotherapy, radiotherapy, and surgery. In neurodegenerative pathologies, progressing research on phytotherapies may lead to effective substitutes for palliative treatments. In Parkinson’s disease, for instance, temporary relief results from usage of dopamine agonists, mimicking wild-field dopamine action, in the brain, and stimulating dopamine receptors. On the contrary, plant secondary metabolites are promising, offering long-term modulation of biomarkers for neuro-dysfunction. For nearly a decade, the World Health Organization (WHO) has been reporting on critical trends in cancer, cardiovascular and neurological disorders. According to WHO, in 2005, neuropathologies resulted in 92 million DALYs (disability-adjusted life years), a measure of disease burden, in terms of reducing life expectancy. The figures are projected to peak at 103 million in 2030, representing 12% increase. The predictions for Alzheimer and other dementias indicate 66% increase, between 2005 and 2030. Therefore, it urges finding effective measures to mitigate these trends. In 2017, regenerative medicine saw 5H-purin-6-amine from Sedum sarmentosum extracts, modulating signalling pathways, contributing to increase of spermatogonial stem cell (SSC) activity. This is key in fertility studies, because 5H-purin-6-amine seems to induce SSC concentration within germ cells. However, the benefits of increasing phytochemical extracts-driven stem cell activity go beyond that, with the high-mobility group box 1 (HMGB1) acting as a key upstream mediator in tissue regeneration. Computing is fast becoming a key instrument in drug discovery. Big data analytics and process automation are the domain knowledge revolutionising the field. On one hand, governing large volumes of heterogeneous information challenges drug development. On the other, it brings about rich sources of insights employed in unveiling new medicinal phenotypes and repurposing existing ones. One singular example derives from ontologies used to characterise molecular compounds, forming the basis of large annotated standardised information systems where medicinal compounds are assembled, in dictionaries (e.g. SNOMED) and semantic networks (e.g. Unified Medical Language System). Undoubtedly, the ability to correlate this data is fundamental, endowing conventional systems with cognitive power (e.g. biotechnology empowered by cognitive analytics), to analyse genotype-phenotype pairs, target compounds’ interactions, and track diseases’ biomarkers, to both elucidate and designate potential ligand-receptor binding prone to modulate dysfunction in diseases’ signalling cascades. Within this ambit, this chapter discusses how technology has revolutionised pharmacology. Special attention is given to phytocompound interactions and their role in drug development.

Keywords

Drug discovery and development Computing Artificial intelligence Quantum computing and intractability Deep learning Reinforcement learning Omics Phytocompound analysis Molecular docking Medicinal dictionaries and semantic networks Data mining Phenotype-genotype analysis Evolutionary algorithms Generative adversarial networks 

Notes

Acknowledgements

The author is grateful for the invitation to contribute with this chapter. Equally, the author is grateful to all those who directly and indirectly contributed with the success of the research undertaken over nearly 19 years in R&D. This includes numerous sponsors—e.g. Institut Carnot (ICM—France), MCINN (Spain), the BHF (the UK), CnPq (Brazil), and Capes (Brazil). Ultimately, the author is grateful for the open access editorials that provided some of the images that illustrate this manuscript—e.g. open access Springer Nature and the US Food and Drug Administration, along with Cognizant Worldwide Limited.

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Bioengineering, Imperial College LondonLondonUK

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