1
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Fernandes DC, Tambourgi DV. Complement System Inhibitory Drugs in a Zebrafish ( Danio rerio) Model: Computational Modeling. Int J Mol Sci 2023; 24:13895. [PMID: 37762197 PMCID: PMC10530807 DOI: 10.3390/ijms241813895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
The dysregulation of complement system activation usually results in acute or chronic inflammation and can contribute to the development of various diseases. Although the activation of complement pathways is essential for innate defense, exacerbated activity of this system may be harmful to the host. Thus, drugs with the potential to inhibit the activation of the complement system may be important tools in therapy for diseases associated with complement system activation. The synthetic peptides Cp40 and PMX205 can be highlighted in this regard, given that they selectively inhibit the C3 and block the C5a receptor (C5aR1), respectively. The zebrafish (Danio rerio) is a robust model for studying the complement system. The aim of the present study was to use in silico computational modeling to investigate the hypothesis that these complement system inhibitor peptides interact with their target molecules in zebrafish, for subsequent in vivo validation. For this, we analyzed molecular docking interactions between peptides and target molecules. Our study demonstrated that Cp40 and the cyclic peptide PMX205 have positive interactions with their respective zebrafish targets, thus suggesting that zebrafish can be used as an animal model for therapeutic studies on these inhibitors.
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Affiliation(s)
| | - Denise V. Tambourgi
- Immunochemistry Laboratory, Butantan Institute, São Paulo 05503-900, Brazil;
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2
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Ivanov SM, Lagunin AA, Filimonov DA, Poroikov VV. Relationships between the Structure and Severe Drug-Induced Liver Injury for Low, Medium, and High Doses of Drugs. Chem Res Toxicol 2022; 35:402-411. [PMID: 35172101 DOI: 10.1021/acs.chemrestox.1c00307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Assessment of structure-activity relationships (SARs) for predicting severe drug-induced liver injury (DILI) is essential since in vivo and in vitro preclinical methods cannot detect many druglike compounds disrupting liver functions. To date, plenty of SAR models for the prediction of DILI have been developed; however, none of them considered the route of drug administration and daily dose, which may introduce significant bias into prediction results. We have created a dataset of 617 drugs with parenteral and oral administration routes and consistent information on DILI severity. We have found a clear relationship between route, dose, and DILI severity. According to SAR, nearly 40% of moderate- and non-DILI-causing drugs would cause severe DILI if they were administered at high oral doses. We have proposed the following approach to predict severe DILI. New compounds recommended to be used at low oral doses (<∼10 mg daily), or parenterally, can be considered not causing severe DILI. DILI for compounds administered at medium oral doses (∼10-100 mg daily; 22.2% of drugs under consideration) can be considered unpredictable because reasonable SAR models were not obtained due to the small size and heterogeneity of the corresponding dataset. The DILI potential of the compounds recommended to be used at high oral doses (more than ∼100 mg daily) can be estimated using SAR modeling. The balanced accuracy of the approach calculated by a 10-fold cross-validation procedure is 0.803. The developed approach can be used to estimate severe DILI for druglike compounds proposed to use at low and high oral doses or parenterally at the early stages of drug development.
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Affiliation(s)
- Sergey M Ivanov
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow 119121, Russia.,Pirogov Russian National Research Medical University, Ostrovityanova Str., 1, Moscow 117997, Russia
| | - Alexey A Lagunin
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow 119121, Russia.,Pirogov Russian National Research Medical University, Ostrovityanova Str., 1, Moscow 117997, Russia
| | - Dmitry A Filimonov
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow 119121, Russia
| | - Vladimir V Poroikov
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow 119121, Russia
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3
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Cheng N, Chen Y, Gao W, Liu J, Huang Q, Yan C, Huang X, Ding C. An Improved Deep Learning Model: S-TextBLCNN for Traditional Chinese Medicine Formula Classification. Front Genet 2022; 12:807825. [PMID: 35003231 PMCID: PMC8727750 DOI: 10.3389/fgene.2021.807825] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/15/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose: This study proposes an S-TextBLCNN model for the efficacy of traditional Chinese medicine (TCM) formula classification. This model uses deep learning to analyze the relationship between herb efficacy and formula efficacy, which is helpful in further exploring the internal rules of formula combination. Methods: First, for the TCM herbs extracted from Chinese Pharmacopoeia, natural language processing (NLP) is used to learn and realize the quantitative expression of different TCM herbs. Three features of herb name, herb properties, and herb efficacy are selected to encode herbs and to construct formula-vector and herb-vector. Then, based on 2,664 formulae for stroke collected in TCM literature and 19 formula efficacy categories extracted from Yifang Jijie, an improved deep learning model TextBLCNN consists of a bidirectional long short-term memory (Bi-LSTM) neural network and a convolutional neural network (CNN) is proposed. Based on 19 formula efficacy categories, binary classifiers are established to classify the TCM formulae. Finally, aiming at the imbalance problem of formula data, the over-sampling method SMOTE is used to solve it and the S-TextBLCNN model is proposed. Results: The formula-vector composed of herb efficacy has the best effect on the classification model, so it can be inferred that there is a strong relationship between herb efficacy and formula efficacy. The TextBLCNN model has an accuracy of 0.858 and an F1-score of 0.762, both higher than the logistic regression (acc = 0.561, F1-score = 0.567), SVM (acc = 0.703, F1-score = 0.591), LSTM (acc = 0.723, F1-score = 0.621), and TextCNN (acc = 0.745, F1-score = 0.644) models. In addition, the over-sampling method SMOTE is used in our model to tackle data imbalance, and the F1-score is greatly improved by an average of 47.1% in 19 models. Conclusion: The combination of formula feature representation and the S-TextBLCNN model improve the accuracy in formula efficacy classification. It provides a new research idea for the study of TCM formula compatibility.
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Affiliation(s)
- Ning Cheng
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Yue Chen
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Wanqing Gao
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Jiajun Liu
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Qunfu Huang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Cheng Yan
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China.,Big Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Xindi Huang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Changsong Ding
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China.,Big Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
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4
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Gangolu J, Balaiah S, Nandi S, Roy H. Optimization and Quest of HPMC loaded Stavudine Controlled Release Dosage Development by Central Composite Design utilizing Reduced Factorial Screening Technique. BRAZ J PHARM SCI 2022. [DOI: 10.1590/s2175-97902022e201144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
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5
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Liu J, Guo W, Sakkiah S, Ji Z, Yavas G, Zou W, Chen M, Tong W, Patterson TA, Hong H. Machine Learning Models for Predicting Liver Toxicity. Methods Mol Biol 2022; 2425:393-415. [PMID: 35188640 DOI: 10.1007/978-1-0716-1960-5_15] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.
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Affiliation(s)
- Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Sugunadevi Sakkiah
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Zuowei Ji
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Gokhan Yavas
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Wen Zou
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Minjun Chen
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.
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6
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Ellison C, Hewitt M, Przybylak K. In Silico Models for Hepatotoxicity. Methods Mol Biol 2022; 2425:355-392. [PMID: 35188639 DOI: 10.1007/978-1-0716-1960-5_14] [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] [Indexed: 06/14/2023]
Abstract
In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.
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Affiliation(s)
- Claire Ellison
- Human and Natural Sciences Directorate, School of Science, Engineering and Environment, University of Salford, Manchester, UK
| | - Mark Hewitt
- School of Pharmacy, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK.
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7
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Adeluwa T, McGregor BA, Guo K, Hur J. Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors. Front Pharmacol 2021; 12:648805. [PMID: 34483896 PMCID: PMC8416433 DOI: 10.3389/fphar.2021.648805] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 07/15/2021] [Indexed: 12/31/2022] Open
Abstract
A major challenge in drug development is safety and toxicity concerns due to drug side effects. One such side effect, drug-induced liver injury (DILI), is considered a primary factor in regulatory clearance. The Critical Assessment of Massive Data Analysis (CAMDA) 2020 CMap Drug Safety Challenge goal was to develop prediction models based on gene perturbation of six preselected cell-lines (CMap L1000), extended structural information (MOLD2), toxicity data (TOX21), and FDA reporting of adverse events (FAERS). Four types of DILI classes were targeted, including two clinically relevant scores and two control classifications, designed by the CAMDA organizers. The L1000 gene expression data had variable drug coverage across cell lines with only 247 out of 617 drugs in the study measured in all six cell types. We addressed this coverage issue by using Kru-Bor ranked merging to generate a singular drug expression signature across all six cell lines. These merged signatures were then narrowed down to the top and bottom 100, 250, 500, or 1,000 genes most perturbed by drug treatment. These signatures were subject to feature selection using Fisher's exact test to identify genes predictive of DILI status. Models based solely on expression signatures had varying results for clinical DILI subtypes with an accuracy ranging from 0.49 to 0.67 and Matthews Correlation Coefficient (MCC) values ranging from -0.03 to 0.1. Models built using FAERS, MOLD2, and TOX21 also had similar results in predicting clinical DILI scores with accuracy ranging from 0.56 to 0.67 with MCC scores ranging from 0.12 to 0.36. To incorporate these various data types with expression-based models, we utilized soft, hard, and weighted ensemble voting methods using the top three performing models for each DILI classification. These voting models achieved a balanced accuracy up to 0.54 and 0.60 for the clinically relevant DILI subtypes. Overall, from our experiment, traditional machine learning approaches may not be optimal as a classification method for the current data.
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Affiliation(s)
- Temidayo Adeluwa
- Department of Biomedical Sciences, University of North Dakota, Grand Forks, ND, United States
| | - Brett A McGregor
- Department of Biomedical Sciences, University of North Dakota, Grand Forks, ND, United States
| | - Kai Guo
- Department of Biomedical Sciences, University of North Dakota, Grand Forks, ND, United States.,Department of Neurology, University of Michigan, Ann Arbor, MI, United States
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota, Grand Forks, ND, United States
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8
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Coffin AB, Boney R, Hill J, Tian C, Steyger PS. Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings. Front Neurol 2021; 12:725566. [PMID: 34489859 PMCID: PMC8418111 DOI: 10.3389/fneur.2021.725566] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 07/22/2021] [Indexed: 12/24/2022] Open
Abstract
Over 100 drugs and chemicals are associated with permanent hearing loss, tinnitus, and vestibular deficits, collectively known as ototoxicity. The ototoxic potential of drugs is rarely assessed in pre-clinical drug development or during clinical trials, so this debilitating side-effect is often discovered as patients begin to report hearing loss. Furthermore, drug-induced ototoxicity in adults, and particularly in elderly patients, may go unrecognized due to hearing loss from a variety of etiologies because of a lack of baseline assessments immediately prior to novel therapeutic treatment. During the current pandemic, there is an intense effort to identify new drugs or repurpose FDA-approved drugs to treat COVID-19. Several potential COVID-19 therapeutics are known ototoxins, including chloroquine (CQ) and lopinavir-ritonavir, demonstrating the necessity to identify ototoxic potential in existing and novel medicines. Furthermore, several factors are emerging as potentiators of ototoxicity, such as inflammation (a hallmark of COVID-19), genetic polymorphisms, and ototoxic synergy with co-therapeutics, increasing the necessity to evaluate a drug's potential to induce ototoxicity under varying conditions. Here, we review the potential of COVID-19 therapies to induce ototoxicity and factors that may compound their ototoxic effects. We then discuss two models for rapidly detecting the potential for ototoxicity: mammalian auditory cell lines and the larval zebrafish lateral line. These models offer considerable value for pre-clinical drug development, including development of COVID-19 therapies. Finally, we show the validity of in silico screening for ototoxic potential using a computational model that compares structural similarity of compounds of interest with a database of known ototoxins and non-ototoxins. Preclinical screening at in silico, in vitro, and in vivo levels can provide an earlier indication of the potential for ototoxicity and identify the subset of candidate therapeutics for treating COVID-19 that need to be monitored for ototoxicity as for other widely-used clinical therapeutics, like aminoglycosides and cisplatin.
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Affiliation(s)
| | | | - Jordan Hill
- Washington State University Vancouver, Vancouver, WA, United States
| | - Cong Tian
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE, United States
| | - Peter S. Steyger
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE, United States
- National Center for Rehabilitative Auditory Research, Portland, OR, United States
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9
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Jaganathan K, Tayara H, Chong KT. Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets. Int J Mol Sci 2021; 22:8073. [PMID: 34360838 PMCID: PMC8348336 DOI: 10.3390/ijms22158073] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/18/2021] [Accepted: 07/23/2021] [Indexed: 02/05/2023] Open
Abstract
Drug-induced liver toxicity is one of the significant safety challenges for the patient's health and the pharmaceutical industry. It causes termination of drug candidates in clinical trials and also the retractions of approved drugs from the market. Thus, it is essential to identify hepatotoxic compounds in the initial stages of drug development process. The purpose of this study is to construct quantitative structure activity relationship models using machine learning algorithms and systematical feature selection methods for molecular descriptor sets. The models were built from a large and diverse set of 1253 drug compounds and were validated internally with 10-fold cross-validation. In this study, we applied a variety of feature selection techniques to extract the optimal subset of descriptors as modeling features to improve the prediction performance. Experimental results suggested that the support vector machine-based classifier had achieved a better classification accuracy with reduced molecular descriptors. The final optimal model provides an accuracy of 0.811, a sensitivity of 0.840, a specificity of 0.783 and Mathew's correlation coefficient of 0.623 with an internal validation set. Furthermore, this model outperformed the prior studies while evaluated in both the internal and external test sets. The utilization of distinct optimal molecular descriptors as modeling features produce an in silico model with a superior performance.
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Affiliation(s)
- Keerthana Jaganathan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
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10
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Zhuo Y, Zhang Y, Li M, Wu H, Gong S, Hu X, Fu Y, Shen X, Sun B, Wu JL, Li N. Hepatotoxic evaluation of toosendanin via biomarker quantification and pathway mapping of large-scale chemical proteomics. Food Chem Toxicol 2021; 153:112257. [PMID: 34000341 DOI: 10.1016/j.fct.2021.112257] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/15/2021] [Accepted: 05/07/2021] [Indexed: 01/15/2023]
Abstract
Drug-induced liver injury (DILI) is a major side effect, sometimes can't be exactly evaluated by current approaches partly as the covalent modification of drug or its reactive metabolites (RMs) with proteins is a possible reason. In this study, we developed a rapid, sensitive, and specific analytical method to assess the hepatotoxicity induced by drug covalently modified proteins based on the quantification of the modified amino acids using toosendanin (TSN), a hepatotoxic chemical, as an example. TSN RM-protein adducts both in rat liver and blood showed good correlation with the severity of hepatotoxicity. Thus, TSN RM-protein adducts in serum can potentially serve as minimally invasive biomarkers of hepatotoxicity. Meanwhile, large-scale chemical proteomics analysis showed that at least 84 proteins were modified by TSN RMs in rat liver, and the bioinformatics analysis revealed that TSN might induce hepatotoxicity through multi-target protein-protein interaction especially involved in energy metabolism. These findings suggest that our approach may serve as a valuable tool to evaluate DILI and investigate the possible mechanism, especially for complex compounds.
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Affiliation(s)
- Yue Zhuo
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, 999078, PR China; Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Yida Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, 999078, PR China; State Key Laboratory of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, PR China
| | - Meng Li
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, 999078, PR China
| | - Haiying Wu
- Department of Emergency, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shilin Gong
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, 999078, PR China
| | - Xiaolan Hu
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, 999078, PR China
| | - Yu Fu
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, 999078, PR China
| | - Xinzi Shen
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, 999078, PR China
| | - Baoqing Sun
- State Key Laboratory of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, PR China
| | - Jian-Lin Wu
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, 999078, PR China.
| | - Na Li
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, 999078, PR China.
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11
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Wang J, Bwayi M, Florke Gee RR, Chen T. PXR-mediated idiosyncratic drug-induced liver injury: mechanistic insights and targeting approaches. Expert Opin Drug Metab Toxicol 2020; 16:711-722. [PMID: 32500752 PMCID: PMC7429329 DOI: 10.1080/17425255.2020.1779701] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 06/04/2020] [Indexed: 01/03/2023]
Abstract
INTRODUCTION The human liver is the center for drug metabolism and detoxification and is, therefore, constantly exposed to toxic chemicals. The loss of liver function as a result of this exposure is referred to as drug-induced liver injury (DILI). The pregnane X receptor (PXR) is the primary regulator of the hepatic drug-clearance system, which plays a critical role in mediating idiosyncratic DILI. AREAS COVERED This review is focused on common mechanisms of PXR-mediated DILI and on in vitro and in vivo models developed to predict and assess DILI. It also provides an update on the development of PXR antagonists that may manage PXR-mediated DILI. EXPERT OPINION DILI can be caused by many factors, and PXR is clearly linked to DILI. Although emerging data illustrate how PXR mediates DILI and how PXR activity can be modulated, many questions concerning the development of effective PXR modulators remain. Future research should be focused on determining the mechanisms regulating PXR functions in different cellular contexts.
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Affiliation(s)
- Jingheng Wang
- Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA
| | - Monicah Bwayi
- Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA
| | - Rebecca R. Florke Gee
- Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA
- Graduate School of Biomedical Sciences, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Taosheng Chen
- Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA
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12
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Roy H, Nandi S. In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery. Curr Pharm Des 2020; 25:3292-3305. [PMID: 31481001 DOI: 10.2174/1381612825666190903155935] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 09/01/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly. METHODS To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status. RESULTS The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound. CONCLUSION It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.
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Affiliation(s)
- Harekrishna Roy
- Nirmala College of Pharmacy, Mangalagiri, Guntur, Affiliated to Acharya Nagarjuna University, Andhra Pradesh-522503, India
| | - Sisir Nandi
- Department of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research, Affiliated to Uttarakhand Technical University, Kashipur-244713, India
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13
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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14
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Garzel B, Zhang L, Huang SM, Wang H. A Change in Bile Flow: Looking Beyond Transporter Inhibition in the Development of Drug-induced Cholestasis. Curr Drug Metab 2020; 20:621-632. [PMID: 31288715 DOI: 10.2174/1389200220666190709170256] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/22/2019] [Accepted: 06/12/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND Drug-induced Liver Injury (DILI) has received increasing attention over the past decades, as it represents the leading cause of drug failure and attrition. One of the most prevalent and severe forms of DILI involves the toxic accumulation of bile acids in the liver, known as Drug-induced Cholestasis (DIC). Traditionally, DIC is studied by exploring the inhibition of hepatic transporters such as Bile Salt Export Pump (BSEP) and multidrug resistance-associated proteins, predominantly through vesicular transport assays. Although this approach has identified numerous drugs that alter bile flow, many DIC drugs do not demonstrate prototypical transporter inhibition, but rather are associated with alternative mechanisms. METHODS We undertook a focused literature search on DIC and biliary transporters and analyzed peer-reviewed publications over the past two decades or so. RESULTS We have summarized the current perception regarding DIC, biliary transporters, and transcriptional regulation of bile acid homeostasis. A growing body of literature aimed to identify alternative mechanisms in the development of DIC has been evaluated. This review also highlights current in vitro approaches used for prediction of DIC. CONCLUSION Efforts have continued to focus on BSEP, as it is the primary route for hepatic biliary clearance. In addition to inhibition, drug-induced BSEP repression or the combination of these two has emerged as important alternative mechanisms leading to DIC. Furthermore, there has been an evolution in the approaches to studying DIC including 3D cell cultures and computational modeling.
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Affiliation(s)
- Brandy Garzel
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration (FDA), Silver Spring, MD 20993, United States.,Becton Dickinson, 54 Loveton Circle, Sparks, MD 21152, United States
| | - Lei Zhang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration (FDA), Silver Spring, MD 20993, United States.,Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, FDA, Silver Spring, MD 20993, United States
| | - Shiew-Mei Huang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration (FDA), Silver Spring, MD 20993, United States
| | - Hongbing Wang
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, United States
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15
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Chierici M, Francescatto M, Bussola N, Jurman G, Furlanello C. Predictability of drug-induced liver injury by machine learning. Biol Direct 2020; 15:3. [PMID: 32054490 PMCID: PMC7020573 DOI: 10.1186/s13062-020-0259-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 01/30/2020] [Indexed: 12/13/2022] Open
Abstract
Background Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucial advantage. In 2018, the Critical Assessment Massive Data Analysis group proposed the CMap Drug Safety challenge focusing on DILI prediction. Methods and results The challenge data included Affymetrix GeneChip expression profiles for the two cancer cell lines MCF7 and PC3 treated with 276 drug compounds and empty vehicles. Binary DILI labeling and a recommended train/test split for the development of predictive classification approaches were also provided. We devised three deep learning architectures for DILI prediction on the challenge data and compared them to random forest and multi-layer perceptron classifiers. On a subset of the data and for some of the models we additionally tested several strategies for balancing the two DILI classes and to identify alternative informative train/test splits. All the models were trained with the MAQC data analysis protocol (DAP), i.e., 10x5 cross-validation over the training set. In all the experiments, the classification performance in both cross-validation and external validation gave Matthews correlation coefficient (MCC) values below 0.2. We observed minimal differences between the two cell lines. Notably, deep learning approaches did not give an advantage on the classification performance. Discussion We extensively tested multiple machine learning approaches for the DILI classification task obtaining poor to mediocre performance. The results suggest that the CMap expression data on the two cell lines MCF7 and PC3 are not sufficient for accurate DILI label prediction. Reviewers This article was reviewed by Maciej Kandula and Paweł P. Labaj.
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Affiliation(s)
- Marco Chierici
- Fondazione Bruno Kessler, Via Sommarive 18, Trento, 38123, Italy.
| | | | - Nicole Bussola
- Fondazione Bruno Kessler, Via Sommarive 18, Trento, 38123, Italy.,Department CIBIO, University of Trento, Via Sommarive 9, Trento, 38123, Italy
| | - Giuseppe Jurman
- Fondazione Bruno Kessler, Via Sommarive 18, Trento, 38123, Italy
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16
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Lovejoy CA, Keogh B, Maruthappu M. How will artificial intelligence affect diagnosis and treatment of liver disease? Dig Liver Dis 2019; 51:1350-1352. [PMID: 31315815 DOI: 10.1016/j.dld.2019.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 06/25/2019] [Indexed: 12/11/2022]
Affiliation(s)
| | - Bruce Keogh
- Birmingham Women's and Children's Hospital NHS Foundation Trust, United Kingdom
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17
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He S, Zhang C, Zhou P, Zhang X, Ye T, Wang R, Sun G, Sun X. Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis. Int J Mol Sci 2019; 20:ijms20153633. [PMID: 31349548 PMCID: PMC6695972 DOI: 10.3390/ijms20153633] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/08/2019] [Accepted: 07/18/2019] [Indexed: 02/06/2023] Open
Abstract
Currently, hundreds of herbal products with potential hepatotoxicity were available in the literature. A comprehensive summary and analysis focused on these potential hepatotoxic herbal products may assist in understanding herb-induced liver injury (HILI). In this work, we collected 335 hepatotoxic medicinal plants, 296 hepatotoxic ingredients, and 584 hepatoprotective ingredients through a systematic literature retrieval. Then we analyzed these data from the perspectives of phylogenetic relationship and structure-toxicity relationship. Phylogenetic analysis indicated that hepatotoxic medicinal plants tended to have a closer taxonomic relationship. By investigating the structures of the hepatotoxic ingredients, we found that alkaloids and terpenoids were the two major groups of hepatotoxicity. We also identified eight major skeletons of hepatotoxicity and reviewed their hepatotoxic mechanisms. Additionally, 15 structural alerts (SAs) for hepatotoxicity were identified based on SARpy software. These SAs will help to estimate the hepatotoxic risk of ingredients from herbs. Finally, a herb-ingredient network was constructed by integrating multiple datasets, which will assist to identify the hepatotoxic ingredients of herb/herb-formula quickly. In summary, a systemic analysis focused on HILI was conducted which will not only assist to identify the toxic molecular basis of hepatotoxic herbs but also contribute to decipher the mechanisms of HILI.
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Affiliation(s)
- Shuaibing He
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China
- Key Laboratory of new drug discovery based on Classic Chinese medicine prescription, Chinese Academy of Medical Sciences, Beijing 100193, China
| | - Chenyang Zhang
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China
- Key Laboratory of new drug discovery based on Classic Chinese medicine prescription, Chinese Academy of Medical Sciences, Beijing 100193, China
| | - Ping Zhou
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China
- Key Laboratory of new drug discovery based on Classic Chinese medicine prescription, Chinese Academy of Medical Sciences, Beijing 100193, China
| | - Xuelian Zhang
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China
- Key Laboratory of new drug discovery based on Classic Chinese medicine prescription, Chinese Academy of Medical Sciences, Beijing 100193, China
| | - Tianyuan Ye
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China
- Key Laboratory of new drug discovery based on Classic Chinese medicine prescription, Chinese Academy of Medical Sciences, Beijing 100193, China
| | - Ruiying Wang
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China
- Key Laboratory of new drug discovery based on Classic Chinese medicine prescription, Chinese Academy of Medical Sciences, Beijing 100193, China
| | - Guibo Sun
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of new drug discovery based on Classic Chinese medicine prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Xiaobo Sun
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of new drug discovery based on Classic Chinese medicine prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
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