Quantitative structure property relationship modeling of excipient properties for prediction of formulation characteristics

Gaikwad, Vinod L.; Bhatia, Neela M.; Desai, Sujit A.; Bhatia, Manish S.

doi:10.1016/j.carbpol.2016.05.114

Cited by 14 publications

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“…As one of the basic and hot topics in chemometrics research, this has attracted much attention and been widely used, for instance, in treatment technology for organic micropollutants ( Huang et al, 2020 ), migration and transformation of organic pollutants, development and design of drugs, graph signal processing ( Matsushita et al, 2019 ), and environmental-related research ( Song et al, 2020 ). A QSAR model is developed by classification and/or regression analysis of select descriptors contributing toward targeted properties ( Gaikwad et al, 2016 ). The related analyses implemented by ML mainly include the partial least square (PLS), random forest (RF), K-nearest neighbors (KNN), error back propagation training (EBPT), discrimination analysis (DA), PLS-DA, support vector machine (SVM), and other single classifier algorithms ( Jiang et al, 2020 ; Maharao et al, 2020 ; Spiegel and Senderowitz, 2020 ).…”

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confidence: 99%

Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis—An Assessment Combining Machine Learning–Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

et al. 2021

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Background: Dianbaizhu (Gaultheria leucocarpa var. yunnanensis), a traditional Chinese/ethnic medicine (TC/EM), has been used to treat rheumatoid arthritis (RA) for a long time. The anti–rheumatic arthritis fraction (ARF) of G. yunnanensis has significant anti-inflammatory and analgesic activities and is mainly composed of methyl salicylate glycosides, flavonoids, organic acids, and others. The effective ingredients and rudimentary mechanism of ARF remedying RA have not been elucidated to date.Purpose: The aim of the present study is to give an insight into the effective components and mechanisms of Dianbaizhu in ameliorating RA, based on the estimation of the absorption, distribution, metabolism, and excretion (ADME) properties, analysis of network pharmacology, and in vivo and in vitro validations.Study design and methods: The IL-1β–induced human fibroblast-like synoviocytes of RA (HFLS-RA) model and adjuvant-induced arthritis in the rat model were adopted to assess the anti-RA effect of ARF. The components in ARF were identified by using UHPLC-LTQ-Orbitrap-MSn. The quantitative structure–activity relationship (QSAR) models were developed by using five machine learning algorithms, alone or in combination with genetic algorithms for predicting the ADME properties of ARF. The molecular networks and pathways presumably referring to the therapy of ARF on RA were yielded by using common databases and visible software, and the experimental validations of the key targets conducted in vitro.Results: ARF effectively relieved RA in vivo and in vitro. The five optimized QSAR models that were developed showed robustness and predictive ability. The characterized 48 components in ARF had good biological potency. Four key signaling pathways were obtained, which were related to both cytokine signaling and cell immune response. ARF suppressed IL-1β–induced expression of EGFR, MMP 9, IL2, MAPK14, and KDR in the HFLS-RA .Conclusions: ARF has good druggability and high exploitation potential. Methyl salicylate glycosides and flavonoids play essential roles in attuning RA. ARF may partially attenuate RA by regulating the expression of multi-targets in the inflammation–immune system. These provide valuable information to rationalize ARF and other TC/EMs in the treatment of RA.

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