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Yang Y, Qian Z, Wu C, Cheng Y, Yang B, Shao J, Zhao J, Zhu X, Jia X, Feng L. Differential absorption and metabolic characteristics of organic acid components in pudilan xiaoyan oral liquid between young rats and adult rats. JOURNAL OF ETHNOPHARMACOLOGY 2024; 334:118528. [PMID: 38972526 DOI: 10.1016/j.jep.2024.118528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 07/01/2024] [Accepted: 07/04/2024] [Indexed: 07/09/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Pudilan Xiaoyan Oral Liquid (PDL) is a proprietary Chinese medicinal preparation approved by the State for treating acute pharyngitis in both adults and children (Approval No. Z20030095). It is worth noting that children exhibit unique physiopathological characteristics compared to adults. However, the in vivo regulatory characteristics of PDL in treating acute pharyngitis in children remain incompletely understood. AIM OF THE STUDY The differential absorption and metabolism characteristics of the main pharmacological components in PDL in young and adult rats were investigated with a view to providing a reference for preclinical data of PDL in medication for children. MATERIALS AND METHODS This study utilized UPLC-Q-TOF-MS to investigate the pharmacodynamic material basis of PDL. The focus was on the gastrointestinal digestion and absorption characteristics of organic acid components in PDL (PDL-OAC), known as the primary pharmacodynamic components in this formulation. The research combined in vitro dynamic simulation and a Quadruple single-pass intestinal perfusion model to examine these characteristics. The permeability properties of PDL-OAC were evaluated using an artificial parallel membrane model. Additionally, an acute pharyngitis model was established to evaluate the histopathological condition of the pharynx in young rats using H&E staining. The levels of IL-1β, TNF-α, IL-6, and IL-10 in blood and pharyngeal tissue homogenates of young rats were quantified using ELISA kits. RESULTS A total of 91 components were identified in PDL, including 33 organic acids, 24 flavonoids, 14 alkaloids, 5 terpenoids and coumarins, 3 sugars, and 12 amino acids. The PDL-OAC exhibited a significant reduction in IL-1β, TNF-α, IL-6, and IL-10 levels in the pharyngeal tissues of young rats with acute pharyngitis. Results from dynamic simulation studies of gastrointestinal fluids revealed that the PDL-OAC (Specifically chlorogenic acid (CGA), gallic acid (GA), chicoric acid (CRA), and caffeic acid (CA)) were effectively stabilized in the gastrointestinal fluids of both children and adults in vitro. Young rats, characterized by thinner intestinal walls and higher permeability, efficiently absorbed the four organic acids across the entire intestinal segment. The absorption of CGA, GA, and CRA followed a concentration-dependent pattern, with CGA and GA absorption being influenced by exocytosis. CONCLUSION The efficacy of the PDL-OAC in treating acute pharyngitis was demonstrated in young rats. The absorption rate of these components was observed to be faster in young rats compared to adult rats, underscoring the need for dedicated studies on the drug's usage in children. This research provides valuable insights for the appropriate clinical use of PDL in pediatric patients.
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Affiliation(s)
- Yanjun Yang
- School of Traditional Chinese Pharmacy, Innovation Center for Industry-Education Integration of Pediatrics and Traditional Chinese Medicine, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211198, PR China.
| | - Zhouyang Qian
- School of Traditional Chinese Pharmacy, Innovation Center for Industry-Education Integration of Pediatrics and Traditional Chinese Medicine, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211198, PR China.
| | - Chenhui Wu
- School of Traditional Chinese Pharmacy, Innovation Center for Industry-Education Integration of Pediatrics and Traditional Chinese Medicine, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211198, PR China.
| | - Yue Cheng
- School of Traditional Chinese Pharmacy, Innovation Center for Industry-Education Integration of Pediatrics and Traditional Chinese Medicine, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211198, PR China.
| | - Bing Yang
- School of Traditional Chinese Pharmacy, Innovation Center for Industry-Education Integration of Pediatrics and Traditional Chinese Medicine, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211198, PR China.
| | - Jianguo Shao
- Jiangsu Key Laboratory of Chinese Medicine and Characteristic Preparations for Paediatrics, Jumpcan Pharmaceutical Co., Ltd., Taixing, 225400, PR China.
| | - Jing Zhao
- Jiangsu Key Laboratory of Chinese Medicine and Characteristic Preparations for Paediatrics, Jumpcan Pharmaceutical Co., Ltd., Taixing, 225400, PR China.
| | - Xiangjun Zhu
- Jiangsu Health Development Research Center, National Health and Family Planning Commission Contraceptives Adverse Reaction Surveillance Center, Nanjing, 210036, PR China.
| | - Xiaobin Jia
- School of Traditional Chinese Pharmacy, Innovation Center for Industry-Education Integration of Pediatrics and Traditional Chinese Medicine, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211198, PR China.
| | - Liang Feng
- School of Traditional Chinese Pharmacy, Innovation Center for Industry-Education Integration of Pediatrics and Traditional Chinese Medicine, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211198, PR China.
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Ta GH, Weng CF, Leong MK. Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA. Toxicology 2024; 503:153739. [PMID: 38307191 DOI: 10.1016/j.tox.2024.153739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/22/2024] [Accepted: 01/28/2024] [Indexed: 02/04/2024]
Abstract
Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.
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Affiliation(s)
- Giang H Ta
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan
| | - Ching-Feng Weng
- Institute of Respiratory Disease Department of Basic Medical Science Xiamen Medical College, Xiamen 361023, Fujian, China
| | - Max K Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan.
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Djuris J, Cvijic S, Djekic L. Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration. Pharmaceuticals (Basel) 2024; 17:177. [PMID: 38399392 PMCID: PMC10892858 DOI: 10.3390/ph17020177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/23/2023] [Accepted: 12/29/2023] [Indexed: 02/25/2024] Open
Abstract
The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.
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Affiliation(s)
- Jelena Djuris
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia; (S.C.); (L.D.)
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Komura H, Watanabe R, Mizuguchi K. The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery. Pharmaceutics 2023; 15:2619. [PMID: 38004597 PMCID: PMC10675155 DOI: 10.3390/pharmaceutics15112619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.
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Affiliation(s)
- Hiroshi Komura
- University Research Administration Center, Osaka Metropolitan University, 1-2-7 Asahimachi, Abeno-ku, Osaka 545-0051, Osaka, Japan
| | - Reiko Watanabe
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
| | - Kenji Mizuguchi
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
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Guizze F, Serra CHR, Giarolla J. PAMAM Dendrimers: A Review of Methodologies Employed in Biopharmaceutical Classification. J Pharm Sci 2022; 111:2662-2673. [PMID: 35850238 DOI: 10.1016/j.xphs.2022.07.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 07/11/2022] [Accepted: 07/11/2022] [Indexed: 11/08/2022]
Abstract
The oral route is the preferred way of drug administration for most drugs, whose treatment success is directly related to the compound intestinal absorption. This absorption process, in its turn, is influenced by several factors impacting the drug bioavailability, which is extremely dependent on the maximum solubility and permeability. However, optimizing these last two factors, without chemical structural modification, is challenging. Although poly(amidoamine) dendrimers (PAMAM) are an innovative and promising strategy as drug delivery compounds, there are few studies that determine the permeability and solubility of PAMAM-drugs derivatives. Considering this scenario, this paper aimed to carry out a literature review of the last five years concerning biopharmaceutical characterizations of dendrimer delivery systems. In vitro methodologies, such as the Parallel artificial membrane permeability assay (PAMPA) (non-cellular based model) and Caco-2 cells (cellular based model), used for the permeability evaluation in the early stages of drug discovery proved to be the most promising methodologies. As a result, we discussed, for instance, that through the usage of PAMPA it was possible to evaluate the higher capacity for transdermal delivery of DNA of TAT-conjugated PAMAM, when in comparison with unmodified PAMAM dendrimer with a P<0.05. We also presented the importance of choosing the best methods of biopharmaceutical characterization, which will be essential to guarantee the efficacy and safety of the drug candidate.
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Affiliation(s)
- Felipe Guizze
- School of Pharmaceutical Sciences, Department of Pharmacy, University of São Paulo, Avenida Professor Lineu Prestes, 580, 05508-000, São Paulo, Brazil
| | - Cristina Helena Reis Serra
- School of Pharmaceutical Sciences, Department of Pharmacy, University of São Paulo, Avenida Professor Lineu Prestes, 580, 05508-000, São Paulo, Brazil.
| | - Jeanine Giarolla
- School of Pharmaceutical Sciences, Department of Pharmacy, University of São Paulo, Avenida Professor Lineu Prestes, 580, 05508-000, São Paulo, Brazil.
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Wu YW, Ta GH, Lung YC, Weng CF, Leong MK. In Silico Prediction of Skin Permeability Using a Two-QSAR Approach. Pharmaceutics 2022; 14:961. [PMID: 35631545 PMCID: PMC9143389 DOI: 10.3390/pharmaceutics14050961] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/23/2022] [Accepted: 04/26/2022] [Indexed: 11/17/2022] Open
Abstract
Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.
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Affiliation(s)
- Yu-Wen Wu
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (Y.-W.W.); (G.H.T.); (Y.-C.L.)
| | - Giang Huong Ta
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (Y.-W.W.); (G.H.T.); (Y.-C.L.)
| | - Yi-Chieh Lung
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (Y.-W.W.); (G.H.T.); (Y.-C.L.)
| | - Ching-Feng Weng
- Institute of Respiratory Disease and Functional Physiology Section, Department of Basic Medical Science, Xiamen Medical College, Xiamen 361023, China;
| | - Max K. Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (Y.-W.W.); (G.H.T.); (Y.-C.L.)
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Best practices in current models mimicking drug permeability in the gastrointestinal tract - an UNGAP review. Eur J Pharm Sci 2021; 170:106098. [PMID: 34954051 DOI: 10.1016/j.ejps.2021.106098] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/19/2021] [Accepted: 12/15/2021] [Indexed: 12/21/2022]
Abstract
The absorption of orally administered drug products is a complex, dynamic process, dependent on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but in vitro and ex vivo tools provide initial screening approaches are important tools for direct assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.
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Wilson CG, Aarons L, Augustijns P, Brouwers J, Darwich AS, De Waal T, Garbacz G, Hansmann S, Hoc D, Ivanova A, Koziolek M, Reppas C, Schick P, Vertzoni M, García-Horsman JA. Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective. Eur J Pharm Sci 2021; 172:106100. [PMID: 34936937 DOI: 10.1016/j.ejps.2021.106100] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 01/09/2023]
Abstract
This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area. This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.
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Affiliation(s)
- Clive G Wilson
- Strathclyde Institute of Pharmacy & Biomedical Sciences, Glasgow, U.K.
| | | | | | | | | | | | | | | | | | | | - Mirko Koziolek
- NCE Formulation Sciences, Abbvie Deutschland GmbH & Co. KG, Germany
| | | | - Philipp Schick
- Department of Biopharmaceutics and Pharmaceutical Technology, Center of Drug Absorption and Transport, University of Greifswald, Germany
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Wang X, Sun Y, Ling L, Ren X, Liu X, Wang Y, Dong Y, Ma J, Song R, Yu A, Wei J, Fan Q, Guo M, Zhao T, Dao R, She G. Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis-An Assessment Combining Machine Learning-Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment. Front Pharmacol 2021; 12:704040. [PMID: 34671253 PMCID: PMC8520986 DOI: 10.3389/fphar.2021.704040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 08/18/2021] [Indexed: 12/26/2022] Open
Abstract
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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Affiliation(s)
- Xiuhuan Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Youyi Sun
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Ling Ling
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Xueyang Ren
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Xiaoyun Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Yu Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Ying Dong
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Jiamu Ma
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Ruolan Song
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Axiang Yu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Jing Wei
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Qiqi Fan
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Miaoxian Guo
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Tiantian Zhao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Rina Dao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Gaimei She
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
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Ta GH, Jhang CS, Weng CF, Leong MK. Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability. Pharmaceutics 2021; 13:pharmaceutics13020174. [PMID: 33525340 PMCID: PMC7911528 DOI: 10.3390/pharmaceutics13020174] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 01/09/2021] [Accepted: 01/21/2021] [Indexed: 12/26/2022] Open
Abstract
Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.
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Affiliation(s)
- Giang Huong Ta
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (G.H.T.); (C.-S.J.)
| | - Cin-Syong Jhang
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (G.H.T.); (C.-S.J.)
| | - Ching-Feng Weng
- Department of Physiology, School of Basic Medical Science, Xiamen Medical College, Xiamen 361023, China;
| | - Max K. Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (G.H.T.); (C.-S.J.)
- Correspondence: ; Tel.: +886-3-890-3609
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