The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson’s Disease

Miao, Zhijun; Bai, Jinwei; Shen, Li; Singla, Rajeev K.

doi:10.1155/2021/9990020

Cited by 5 publications

(3 citation statements)

References 70 publications

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“…Using computational processing, it is possible to find informative patterns from previous information so that candidate compounds can be selected on the basis of evidence, even for intractable diseases. 79,80) As an example, we focus on studies on AD.…”

Section: Automated De Novo Design Of Natural Productslike Compoundsmentioning

confidence: 99%

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Ogawa

Daiki

Hosoki

2023

CHEMICAL & PHARMACEUTICAL BULLETIN

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Computational approaches to drug development are rapidly growing in popularity and have been usedto produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

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Section: Automated De Novo Design Of Natural Productslike Compoundsmentioning

confidence: 99%

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Ogawa

Daiki

Hosoki

2023

CHEMICAL & PHARMACEUTICAL BULLETIN

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“…Artificial intelligence models based on deep-machine learning can simulate, model and analyze natural compound-based nanoparticles, their bioavailability, and their interaction with therapeutic targets or other medicines (Miao et al, 2021;Shen et al, 2021). Prostate organoids can help simulate the tumor microenvironment and assess the effect of nanoformulations in a more realistic context, before moving toward animal and clinical studies.…”

Section: Future Research Perspectivesmentioning

confidence: 99%

Natural Products for the Management of Castration-Resistant Prostate Cancer: Special Focus on Nanoparticles Based Studies

Singla

Sai

Chopra

et al. 2021

Front. Cell Dev. Biol.

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Prostate cancer is the most common type of cancer among men and the second most frequent cause of cancer-related mortality around the world. The progression of advanced prostate cancer to castration-resistant prostate cancer (CRPC) plays a major role in disease-associated morbidity and mortality, posing a significant therapeutic challenge. Resistance has been associated with the activation of androgen receptors via several mechanisms, including alternative dehydroepiandrosterone biosynthetic pathways, other androgen receptor activator molecules, oncogenes, and carcinogenic signaling pathways. Tumor microenvironment plays a critical role not only in the cancer progression but also in the drug resistance. Numerous natural products have shown major potential against particular or multiple resistance pathways as shown by in vitro and in vivo studies. However, their efficacy in clinical trials has been undermined by their unfavorable pharmacological properties (hydrophobic molecules, instability, low pharmacokinetic profile, poor water solubility, and high excretion rate). Nanoparticle formulations can provide a way out of the stalemate, employing targeted drug delivery, improved pharmacokinetic drug profile, and transportation of diagnostic and therapeutic agents via otherwise impermeable biological barriers. This review compiles the available evidence regarding the use of natural products for the management of CRPC with a focus on nanoparticle formulations. PubMed and Google Scholar search engines were used for preclinical studies, while ClinicalTrials.gov and PubMed were searched for clinical studies. The results of our study suggest the efficacy of natural compounds such as curcumin, resveratrol, apigenin, quercetin, fisetin, luteolin, kaempferol, genistein, berberine, ursolic acid, eugenol, gingerol, and ellagic acid against several mechanisms leading to castration resistance in preclinical studies, but fail to set the disease under control in clinical studies. Nanoparticle formulations of curcumin and quercetin seem to increase their potential in clinical settings. Using nanoparticles based on betulinic acid, capsaicin, sintokamide A, niphatenones A and B, as well as atraric acid seems promising but needs to be verified with preclinical and clinical studies.

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“…Computer-Aided Drug Discovery (CADD) based on the “big data,” surpasses the traditional methods of drug discovery that rely on ligand and structure-based drug designing tools ( Sairam et al, 2002 ; Hazra et al, 2012 ; Joon et al, 2021 ). Howbeit, this demands a standardized multi-tiered data integration with robust algorithms for mining, structuring, and analysis of the accumulated clinical data ( Shen et al, 2020 ; Miao et al, 2021 ). Also, this is crucial for assessing the precision of drug-target interactions generated by molecular simulation studies.…”

Section: Introductionmentioning

confidence: 99%

Translational Informatics for Natural Products as Antidepressant Agents

Singla

Joon

Shen

et al. 2022

Front. Cell Dev. Biol.

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Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

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The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson’s Disease

Cited by 5 publications

References 70 publications

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Natural Products for the Management of Castration-Resistant Prostate Cancer: Special Focus on Nanoparticles Based Studies

Translational Informatics for Natural Products as Antidepressant Agents

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