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Odugbemi AI, Nyirenda C, Christoffels A, Egieyeh SA. Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors. Comput Struct Biotechnol J 2024; 23:2964-2977. [PMID: 39148608 PMCID: PMC11326494 DOI: 10.1016/j.csbj.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 07/03/2024] [Accepted: 07/03/2024] [Indexed: 08/17/2024] Open
Abstract
Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.
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Affiliation(s)
- Adeshina I Odugbemi
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town 7535, South Africa
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town 7535, South Africa
- National Institute for Theoretical and Computational Sciences (NITheCS), South Africa
| | - Clement Nyirenda
- Department of Computer Science, University of the Western Cape, Cape Town 7535, South Africa
| | - Alan Christoffels
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town 7535, South Africa
- Africa Centres for Disease Control and Prevention, African Union, Addis Ababa, Ethiopia
| | - Samuel A Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town 7535, South Africa
- National Institute for Theoretical and Computational Sciences (NITheCS), South Africa
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Kotta-Loizou I, Pritsa A, Antasouras G, Vasilopoulos SN, Voulgaridou G, Papadopoulou SK, Coutts RHA, Lechouritis E, Giaginis C. Fetus Exposure to Drugs and Chemicals: A Holistic Overview on the Assessment of Their Transport and Metabolism across the Human Placental Barrier. Diseases 2024; 12:114. [PMID: 38920546 PMCID: PMC11202568 DOI: 10.3390/diseases12060114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/23/2024] [Accepted: 05/31/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND The placenta exerts a crucial role in fetus growth and development during gestation, protecting the fetus from maternal drugs and chemical exposure. However, diverse drugs and chemicals (xenobiotics) can penetrate the maternal placental barrier, leading to deleterious, adverse effects concerning fetus health. Moreover, placental enzymes can metabolize drugs and chemicals into more toxic compounds for the fetus. Thus, evaluating the molecular mechanisms through which drugs and chemicals transfer and undergo metabolism across the placental barrier is of vital importance. In this aspect, this comprehensive literature review aims to provide a holistic approach by critically summarizing and scrutinizing the potential molecular processes and mechanisms governing drugs and chemical transfer and metabolism across the placental barrier, which may lead to fetotoxicity effects, as well as analyzing the currently available experimental methodologies used to assess xenobiotics placental transfer and metabolism. METHODS A comprehensive and in-depth literature review was conducted in the most accurate scientific databases such as PubMed, Scopus, and Web of Science by using relevant and effective keywords related to xenobiotic placental transfer and metabolism, retrieving 8830 published articles until 5 February 2024. After applying several strict exclusion and inclusion criteria, a final number of 148 relevant published articles were included. RESULTS During pregnancy, several drugs and chemicals can be transferred from the mother to the fetus across the placental barrier by either passive diffusion or through placental transporters, resulting in fetus exposure and potential fetotoxicity effects. Some drugs and chemicals also appear to be metabolized across the placental barrier, leading to more toxic products for both the mother and the fetus. At present, there is increasing research development of diverse experimental methodologies to determine the potential molecular processes and mechanisms of drug and chemical placental transfer and metabolism. All the currently available methodologies have specific strengths and limitations, highlighting the strong demand to utilize an efficient combination of them to obtain reliable evidence concerning drug and chemical transfer and metabolism across the placental barrier. To derive the most consistent and safe evidence, in vitro studies, ex vivo perfusion methods, and in vivo animal and human studies can be applied together with the final aim to minimize potential fetotoxicity effects. CONCLUSIONS Research is being increasingly carried out to obtain an accurate and safe evaluation of drug and chemical transport and metabolism across the placental barrier, applying a combination of advanced techniques to avoid potential fetotoxic effects. The improvement of the currently available techniques and the development of novel experimental protocols and methodologies are of major importance to protect both the mother and the fetus from xenobiotic exposure, as well as to minimize potential fetotoxicity effects.
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Affiliation(s)
- Ioly Kotta-Loizou
- Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK;
| | - Agathi Pritsa
- Department of Nutritional Sciences and Dietetics, School of Health Sciences, International Hellenic University, 57400 Thessaloniki, Greece; (G.V.); (S.K.P.)
| | - Georgios Antasouras
- Department of Food Science and Nutrition, School of the Environment, University of the Aegean, 81400 Lemnos, Greece; (G.A.); (E.L.); (C.G.)
| | - Spyridon N. Vasilopoulos
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou Campus, 15780 Athens, Greece;
| | - Gavriela Voulgaridou
- Department of Nutritional Sciences and Dietetics, School of Health Sciences, International Hellenic University, 57400 Thessaloniki, Greece; (G.V.); (S.K.P.)
| | - Sousana K. Papadopoulou
- Department of Nutritional Sciences and Dietetics, School of Health Sciences, International Hellenic University, 57400 Thessaloniki, Greece; (G.V.); (S.K.P.)
| | - Robert H. A. Coutts
- Department of Clinical, Pharmaceutical and Biological Sciences, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK;
| | - Eleftherios Lechouritis
- Department of Food Science and Nutrition, School of the Environment, University of the Aegean, 81400 Lemnos, Greece; (G.A.); (E.L.); (C.G.)
| | - Constantinos Giaginis
- Department of Food Science and Nutrition, School of the Environment, University of the Aegean, 81400 Lemnos, Greece; (G.A.); (E.L.); (C.G.)
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Xie J, Chen S, Lei J, Yang Y. DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model. J Chem Inf Model 2024; 64:2554-2564. [PMID: 38267393 DOI: 10.1021/acs.jcim.3c01466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
In molecular optimization, one popular way is R-group decoration on molecular scaffolds, and many efforts have been made to generate R-groups based on deep generative models. However, these methods mostly use information on known binding ligands, without fully utilizing target structure information. In this study, we proposed a new method, DiffDec, to involve 3D pocket constraints by a modified diffusion technique for optimizing molecules through molecular scaffold decoration. For end-to-end generation of R-groups with different sizes, we designed a novel fake atom mechanism. DiffDec was shown to be able to generate structure-aware R-groups with realistic geometric substructures by the analysis of bond angles and dihedral angles and simultaneously generate multiple R-groups for one scaffold on different growth anchors. The growth anchors could be provided by users or automatically determined by our model. DiffDec achieved R-group recovery rates of 69.67% and 45.34% in the single and multiple R-group decoration tasks, respectively, and these values were significantly higher than competing methods (37.33% and 26.85%). According to the molecular docking study, our decorated molecules obtained a better average binding affinity than baseline methods. The docking pose analysis revealed that DiffDec could decorate scaffolds with R-groups that exhibited improved binding affinities and more favorable interactions with the pocket. These results demonstrated the potential and applicability of DiffDec in real-world scaffold decoration for molecular optimization.
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Affiliation(s)
- Junjie Xie
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
- AixplorerBio Inc., Jiaxing 314031, China
| | - Sheng Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
- AixplorerBio Inc., Jiaxing 314031, China
| | - Jinping Lei
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
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Evbuomwan IO, Alejolowo OO, Elebiyo TC, Nwonuma CO, Ojo OA, Edosomwan EU, Chikwendu JI, Elosiuba NV, Akulue JC, Dogunro FA, Rotimi DE, Osemwegie OO, Ojo AB, Ademowo OG, Adeyemi OS, Oluba OM. In silico modeling revealed phytomolecules derived from Cymbopogon citratus (DC.) leaf extract as promising candidates for malaria therapy. J Biomol Struct Dyn 2024; 42:101-118. [PMID: 36974933 DOI: 10.1080/07391102.2023.2192799] [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: 01/02/2023] [Accepted: 03/10/2023] [Indexed: 03/29/2023]
Abstract
The emergence of varying levels of resistance to currently available antimalarial drugs significantly threatens global health. This factor heightens the urgency to explore bioactive compounds from natural products with a view to discovering and developing newer antimalarial drugs with novel mode of actions. Therefore, we evaluated the inhibitory effects of sixteen phytocompounds from Cymbopogon citratus leaf extract against Plasmodium falciparum drug targets such as P. falciparum circumsporozoite protein (PfCSP), P. falciparum merozoite surface protein 1 (PfMSP1) and P. falciparum erythrocyte membrane protein 1 (PfEMP1). In silico approaches including molecular docking, pharmacophore modeling and 3D-QSAR were adopted to analyze the inhibitory activity of the compounds under consideration. The molecular docking results indicated that a compound swertiajaponin from C. citratus exhibited a higher binding affinity (-7.8 kcal/mol) to PfMSP1 as against the standard artesunate-amodiaquine (-6.6 kcal/mol). Swertiajaponin also formed strong hydrogen bond interactions with LYS29, CYS30, TYR34, ASN52, GLY55 and CYS28 amino acid residues. In addition, quercetin another compound from C. citratus exhibited significant binding energies -6.8 and -8.3 kcal/mol with PfCSP and PfEMP1, respectively but slightly lower than the standard artemether-lumefantrine with binding energies of -7.4 kcal/mol against PfCSP and -8.7 kcal/mol against PfEMP1. Overall, the present study provides evidence that swertiajaponin and other phytomolecules from C. citratus have modulatory properties toward P. falciparum drug targets and thus may warrant further exploration in early drug discovery efforts against malaria. Furthermore, these findings lend credence to the folkloric use of C. citratus for malaria treatment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ikponmwosa Owen Evbuomwan
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
- Department of Food Science and Microbiology, Landmark University, Omu-Aran, Nigeria
| | - Omokolade Oluwaseyi Alejolowo
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
| | | | - Charles Obiora Nwonuma
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
| | - Oluwafemi Adeleke Ojo
- Phytomedicine, Molecular Toxicology and Computational Biochemistry Research Group, Department of Biochemistry, Bowen University, Iwo, Nigeria
| | - Evelyn Uwa Edosomwan
- Department of Animal and Environmental Biology, University of Benin, Benin City, Nigeria
| | | | | | | | | | - Damilare Emmanuel Rotimi
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
| | | | | | - Olusegun George Ademowo
- Department of Pharmacology and Therapeutics, Faculty of Basic Medical Sciences, University of Ibadan, Ibadan, Nigeria
- Drug Research Laboratory, Institute of Advanced Medical Research and Training (IMRAT), College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oluyomi Stephen Adeyemi
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
- Laboratory of Sustainable Animal Environment, Graduate School of Agricultural Science, Tohoku University, Osaki, Miyagi, Japan
| | - Olarewaju Michael Oluba
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
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Nada H, Gul AR, Elkamhawy A, Kim S, Kim M, Choi Y, Park TJ, Lee K. Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer-Design, Synthesis, and In Vitro Evaluation. ACS OMEGA 2023; 8:31784-31800. [PMID: 37692247 PMCID: PMC10483653 DOI: 10.1021/acsomega.3c02799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023]
Abstract
The epidermal growth factor receptor (EGFR) is vital for regulating cellular functions, including cell division, migration, survival, apoptosis, angiogenesis, and cancer. EGFR overexpression is an ideal target for anticancer drug development as it is absent from normal tissues, marking it as tumor-specific. Unfortunately, the development of medication resistance limits the therapeutic efficacy of the currently approved EGFR inhibitors, indicating the need for further development. Herein, a machine learning-based application that predicts the bioactivity of novel EGFR inhibitors is presented. Clustering of the EGFR small-molecule inhibitor (∼9000 compounds) library showed that N-substituted quinazolin-4-amine-based compounds made up the largest cluster of EGFR inhibitors (∼2500 compounds). Taking advantage of this finding, rational drug design was used to design a novel series of 4-anilinoquinazoline-based EGFR inhibitors, which were first tested by the developed artificial intelligence application, and only the compounds which were predicted to be active were then chosen to be synthesized. This led to the synthesis of 18 novel compounds, which were subsequently evaluated for cytotoxicity and EGFR inhibitory activity. Among the tested compounds, compound 9 demonstrated the most potent antiproliferative activity, with 2.50 and 1.96 μM activity over MCF-7 and MDA-MB-231 cancer cell lines, respectively. Moreover, compound 9 displayed an EGFR inhibitory activity of 2.53 nM and promising apoptotic results, marking it a potential candidate for breast cancer therapy.
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Affiliation(s)
- Hossam Nada
- BK21
FOUR Team and Integrated Research Institute for Drug Development,
College of Pharmacy, Dongguk University-Seoul, Goyang 10326, Republic of Korea
| | - Anam Rana Gul
- Department
of Chemistry, Chung-Ang University, 84 Heukseok-ro, Seoul 06974, South Korea
| | - Ahmed Elkamhawy
- BK21
FOUR Team and Integrated Research Institute for Drug Development,
College of Pharmacy, Dongguk University-Seoul, Goyang 10326, Republic of Korea
- Department
of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
| | - Sungdo Kim
- BK21
FOUR Team and Integrated Research Institute for Drug Development,
College of Pharmacy, Dongguk University-Seoul, Goyang 10326, Republic of Korea
| | - Minkyoung Kim
- BK21
FOUR Team and Integrated Research Institute for Drug Development,
College of Pharmacy, Dongguk University-Seoul, Goyang 10326, Republic of Korea
| | - Yongseok Choi
- College
of Life Sciences and Biotechnology, Korea
University, Seoul 02841, Republic of Korea
| | - Tae Jung Park
- Department
of Chemistry, Chung-Ang University, 84 Heukseok-ro, Seoul 06974, South Korea
| | - Kyeong Lee
- BK21
FOUR Team and Integrated Research Institute for Drug Development,
College of Pharmacy, Dongguk University-Seoul, Goyang 10326, Republic of Korea
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Andrade-Ochoa S, Sánchez-Aldana D, Rodríguez-Valdez LM, Nevárez-Moorillón GV. In vitro and Quantitative and Structure Activity Relationship (QSAR) evaluation of the antifungal activity of terpenoid constituents of essential oils against Alternaria alternata and Fusarium oxysporum. BIOMEDICA : REVISTA DEL INSTITUTO NACIONAL DE SALUD 2023; 43:156-169. [PMID: 37721910 PMCID: PMC10588966 DOI: 10.7705/biomedica.6883] [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: 01/30/2023] [Accepted: 05/09/2023] [Indexed: 09/20/2023]
Abstract
INTRODUCTION Fungal genera Alternaria and Fusarium include human and plant pathogenic species. Several antifungals have been used for their control, but excessive use has contributed to resistance development in pathogens. An alternative to searching for and developing new antifungal agents is using essential oils and their main components, which have biological activities of interest in medicine and food production. OBJECTIVE To evaluate in vitro and in silico the antifungal activities of terpenoids against Alternaria alternata and Fusarium oxysporum. MATERIALS AND METHODS The minimum inhibitory concentration and minimum fungicidal concentration values of 27 constituents of essential oils used against Alternaria alternata and Fusarium oxysporum were evaluated in vitro. In addition, using genetic algorithms, quantitative models of the structure-activity relationship were used to identify the structural and physicochemical properties related to antifungal activity. RESULTS The evaluated compounds proved to be effective antifungals. Thymol was the most active with a minimum inhibitory concentration of 91.6 ± 28.8 μg/ml for A. alternata and F. oxysporum. Quantitative structure-activity relationship models revealed the octanolwater cleavage ratio as the molecular property, and the phenols as the main functional group contributing to antifungal activity. CONCLUSION Terpenoids exhibit relevant antifungal activities that should be incorporated into the study of medicinal chemistry. Inclusion of in silico assays in the in vitro evaluation is a valuable tool in the search for and rational design of terpene derivatives as new potential antifungal agents.
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Affiliation(s)
- Sergio Andrade-Ochoa
- Facultad de Ciencias Químicas, Universidad Autónoma de Chihuahua, Chihuahua, México; Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Chihuahua, México.
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7
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Singh R, Kumar P, Sindhu J, Devi M, Kumar A, Lal S, Singh D. Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies. Comput Biol Med 2023; 157:106776. [PMID: 36947906 DOI: 10.1016/j.compbiomed.2023.106776] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/20/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
α-Amylase (EC.3.2.1.1) is a ubiquitous digestive endoamylase. The abrupt rise in blood glucose levels due to the hydrolysis of carbohydrates by α-amylase at a faster rate is one of the main reasons for type 2 diabetes. The inhibitors prevent the action of digestive enzymes, slowing the digestion of carbs and eventually assisting in the management of postprandial hyperglycemia. In the course of developing α-amylase inhibitors, we have screened 2-aryliminothiazolidin-4-one based analogs for their in vitro α-amylase inhibitory potential and employed various in silico approaches for the detailed exploration of the bioactivity. The DNSA bioassay revealed that compounds 5c, 5e, 5h, 5j, 5m, 5o and 5t were more potent than the reference drug (IC60 value = 22.94 ± 0.24 μg mL-1). The derivative 5o with -NO2 group at both the rings was the most potent analog with an IC60 value of 19.67 ± 0.20 μg mL-1 whereas derivative 5a with unsubstituted aromatic rings showed poor inhibitory potential with an IC60 value of 33.40 ± 0.15 μg mL-1. The reliable QSAR models were developed using the QSARINS software. The high value of R2ext = 0.9632 for model IM-9 showed that the built model can be applied to predict the α-amylase inhibitory activity of the untested molecules. A consensus modelling approach was also employed to test the reliability and robustness of the developed QSAR models. Molecular docking and molecular dynamics were employed to validate the bioassay results by studying the conformational changes and interaction mechanisms. A step further, these compounds also exhibited good ADMET characteristics and bioavailability when tested for in silico pharmacokinetics prediction parameters.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India.
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India
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Kasoju N, Remya NS, Sasi R, Sujesh S, Soman B, Kesavadas C, Muraleedharan CV, Varma PRH, Behari S. Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology. CSI TRANSACTIONS ON ICT 2023; 11:11-30. [PMCID: PMC10089382 DOI: 10.1007/s40012-023-00380-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 03/27/2023] [Indexed: 04/12/2024]
Abstract
Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.
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Affiliation(s)
- Naresh Kasoju
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - N. S. Remya
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - Renjith Sasi
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - S. Sujesh
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - Biju Soman
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - C. Kesavadas
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - C. V. Muraleedharan
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - P. R. Harikrishna Varma
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - Sanjay Behari
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
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9
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Zhou Y, Al‐Jarf R, Alavi A, Nguyen TB, Rodrigues CHM, Pires DEV, Ascher DB. kinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors. Protein Sci 2022; 31:e4453. [PMID: 36305769 PMCID: PMC9597374 DOI: 10.1002/pro.4453] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 11/20/2022]
Abstract
Protein phosphorylation acts as an essential on/off switch in many cellular signaling pathways. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer‐aided drug discovery has been proven a useful and cost‐effective approach for facilitating prioritization and enrichment of screening libraries, but limited effort has been devoted providing insights on what makes a potent kinase inhibitor. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin‐dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand–kinase inhibition constants (pKi) and classifying different types of inhibitors based on their favorable binding modes. kinCSM predictive models were built using supervised learning and leveraged the concept of graph‐based signatures to capture both physicochemical properties and geometry properties of small molecules. CDK2 inhibitors were accurately identified with Matthew's Correlation Coefficients (MCC) of up to 0.74, and inhibition constants predicted with Pearson's correlation of up to 0.76, both with consistent performances of 0.66 and 0.68 on a nonredundant blind test, respectively. kinCSM was also able to identify the potential type of inhibition for a given molecule, achieving MCC of up to 0.80 on cross‐validation and 0.73 on the blind test. Analyzing the molecular composition of revealed enriched chemical fragments in CDK2 inhibitors and different types of inhibitors, which provides insights into the molecular mechanisms behind ligand–kinase interactions. kinCSM will be an invaluable tool to guide future kinase drug discovery. To aid the fast and accurate screening of CDK2 inhibitors, kinCSM is freely available at https://biosig.lab.uq.edu.au/kin_csm/.
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Affiliation(s)
- Yunzhuo Zhou
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Raghad Al‐Jarf
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Azadeh Alavi
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Thanh Binh Nguyen
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Carlos H. M. Rodrigues
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Douglas E. V. Pires
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia,School of Computing and Information SystemsUniversity of MelbourneMelbourneVictoriaAustralia
| | - David B. Ascher
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
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10
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Zhao W, Chen D, Zheng X, Lu Y. Serial fuzzy system algorithm for predicting biological activity of anti-breast cancer compounds. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04134-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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11
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Computational Methods in Cooperation with Experimental Approaches to Design Protein Tyrosine Phosphatase 1B Inhibitors in Type 2 Diabetes Drug Design: A Review of the Achievements of This Century. Pharmaceuticals (Basel) 2022; 15:ph15070866. [PMID: 35890163 PMCID: PMC9322956 DOI: 10.3390/ph15070866] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 07/10/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Protein tyrosine phosphatase 1B (PTP1B) dephosphorylates phosphotyrosine residues and is an important regulator of several signaling pathways, such as insulin, leptin, and the ErbB signaling network, among others. Therefore, this enzyme is considered an attractive target to design new drugs against type 2 diabetes, obesity, and cancer. To date, a wide variety of PTP1B inhibitors that have been developed by experimental and computational approaches. In this review, we summarize the achievements with respect to PTP1B inhibitors discovered by applying computer-assisted drug design methodologies (virtual screening, molecular docking, pharmacophore modeling, and quantitative structure–activity relationships (QSAR)) as the principal strategy, in cooperation with experimental approaches, covering articles published from the beginning of the century until the time this review was submitted, with a focus on studies conducted with the aim of discovering new drugs against type 2 diabetes. This review encourages the use of computational techniques and includes helpful information that increases the knowledge generated to date about PTP1B inhibition, with a positive impact on the route toward obtaining a new drug against type 2 diabetes with PTP1B as a molecular target.
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12
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Pandey V, Sharma K, Raghav N. Ligand-based modeling of semicarbazones and thiosemicarbazones derivatives as Cathepsin B, H, and L inhibitors: A multi-target approach. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.132612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Yu D, Wang L, Wang Y. Recent Advances in Application of Computer-Aided Drug Design in Anti-Influenza A Virus Drug Discovery. Int J Mol Sci 2022; 23:ijms23094738. [PMID: 35563129 PMCID: PMC9105300 DOI: 10.3390/ijms23094738] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 04/22/2022] [Accepted: 04/23/2022] [Indexed: 02/06/2023] Open
Abstract
Influenza A is an acute respiratory infectious disease caused by the influenza A virus, which seriously threatens global human health and causes substantial economic losses every year. With the emergence of new viral strains, anti-influenza drugs remain the most effective treatment for influenza A. Research on traditional, innovative small-molecule drugs faces many challenges, while computer-aided drug design (CADD) offers opportunities for the rapid and effective development of innovative drugs. This literature review describes the general process of CADD, the viral proteins that play an essential role in the life cycle of the influenza A virus and can be used as therapeutic targets for anti-influenza drugs, and examples of drug screening of viral target proteins by applying the CADD approach. Finally, the main limitations of current CADD strategies in anti-influenza drug discovery and the field's future directions are discussed.
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Affiliation(s)
| | | | - Ye Wang
- Correspondence: ; Tel.: +86-431-8515-5249
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14
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Spiegel J, Senderowitz H. A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening. Int J Mol Sci 2021; 23:43. [PMID: 35008467 PMCID: PMC8744642 DOI: 10.3390/ijms23010043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 12/30/2022] Open
Abstract
Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.
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Affiliation(s)
| | - Hanoch Senderowitz
- Department of Chemistry, Bar-Ilan University, Ramat-Gan 5290002, Israel;
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15
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Safarzadeh Kozani P, Safarzadeh Kozani P, Hamidi M, Valentine Okoro O, Eskandani M, Jaymand M. Polysaccharide-based hydrogels: properties, advantages, challenges, and optimization methods for applications in regenerative medicine. INT J POLYM MATER PO 2021. [DOI: 10.1080/00914037.2021.1962876] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Pooria Safarzadeh Kozani
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Pouya Safarzadeh Kozani
- Department of Medical Biotechnology, Faculty of Paramedicine, Guilan University of Medical Sciences, Rasht, Iran
- Student Research Committee, Medical Biotechnology Research Center, School of Nursing, Midwifery, and Paramedicine, Guilan University of Medical Sciences, Rasht, Iran
| | - Masoud Hamidi
- Department of Medical Biotechnology, Faculty of Paramedicine, Guilan University of Medical Sciences, Rasht, Iran
- BioMatter-Biomass Transformation Lab. (BTL), École Polytechnique de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Oseweuba Valentine Okoro
- BioMatter-Biomass Transformation Lab. (BTL), École Polytechnique de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Morteza Eskandani
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mehdi Jaymand
- Nano Drug Delivery Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
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16
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Sabe VT, Ntombela T, Jhamba LA, Maguire GEM, Govender T, Naicker T, Kruger HG. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem 2021; 224:113705. [PMID: 34303871 DOI: 10.1016/j.ejmech.2021.113705] [Citation(s) in RCA: 184] [Impact Index Per Article: 61.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/12/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022]
Abstract
Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.
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Affiliation(s)
- Victor T Sabe
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Thandokuhle Ntombela
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Lindiwe A Jhamba
- HIV Pathogenesis Program, School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Glenn E M Maguire
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa; School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Thavendran Govender
- Faculty of Science and Agriculture, Department of Chemistry, University of Zululand, KwaDlangezwa, 3886, South Africa
| | - Tricia Naicker
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Hendrik G Kruger
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
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Kell DB, Samanta S, Swainston N. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J 2020; 477:4559-4580. [PMID: 33290527 PMCID: PMC7733676 DOI: 10.1042/bcj20200781] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/11/2020] [Accepted: 11/12/2020] [Indexed: 12/15/2022]
Abstract
The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Soumitra Samanta
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
| | - Neil Swainston
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
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18
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Zhao Z, Kang K, Yue J, Ji X, Qiao H, Fan P, Zheng X. Research progress in biological activities of isochroman derivatives. Eur J Med Chem 2020; 210:113073. [PMID: 33310287 DOI: 10.1016/j.ejmech.2020.113073] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 11/16/2020] [Accepted: 11/29/2020] [Indexed: 10/22/2022]
Abstract
Isochromans are well recognized heterocyclic compounds in drug discovery which produce diverse therapeutically related applications in pharmacological practices. Medicinal chemistry investigators have synthesized drug-like isochroman candidates with multiple medicinal features including central nervous system (CNS), antioxidant, antimicrobial, antihypertensive, antitumor and anti-inflammatory agents. Simultaneously, SAR (Structure-Activity Relationship) analysis has drawn attentions among medicinal chemists, along with a great deal of derivatives have been derived for potential targets. In this article, we thoroughly summarize the biological activities and part of typical SAR for isochroman derivatives reported on existing literatures and patents, wishing to provide an overall retrospect and prospect on the isochroman analogues.
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Affiliation(s)
- Zefeng Zhao
- College of Acupuncture & Massage, Shaanxi University of Chinese Medicine, Xixian New Area, Shaanxi Province, 712046, PR China; Shaanxi Key Laboratory of Acupuncture & Medicine, Xixian New Area, Shaanxi Province, 712046, PR China; School of Pharmacy, Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Biomedicine Key Laboratory of Shaanxi Province, Northwest University, 229 Taibai Road, Xi'an, 710069, PR China
| | - Kaiwen Kang
- Shaanxi Key Laboratory of Acupuncture & Medicine, Xixian New Area, Shaanxi Province, 712046, PR China
| | - Jiangxin Yue
- Shaanxi Key Laboratory of Acupuncture & Medicine, Xixian New Area, Shaanxi Province, 712046, PR China
| | - Xiaotong Ji
- Shaanxi Key Laboratory of Acupuncture & Medicine, Xixian New Area, Shaanxi Province, 712046, PR China
| | - Haifa Qiao
- College of Acupuncture & Massage, Shaanxi University of Chinese Medicine, Xixian New Area, Shaanxi Province, 712046, PR China; Shaanxi Key Laboratory of Acupuncture & Medicine, Xixian New Area, Shaanxi Province, 712046, PR China.
| | - Peinan Fan
- School of Pharmacy, Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Biomedicine Key Laboratory of Shaanxi Province, Northwest University, 229 Taibai Road, Xi'an, 710069, PR China
| | - Xiaohui Zheng
- School of Pharmacy, Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Biomedicine Key Laboratory of Shaanxi Province, Northwest University, 229 Taibai Road, Xi'an, 710069, PR China
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