2D-QSAR studies on MexAB-OprM efflux pump inhibitors of Pseudomonas aeruginos.pdf

MINISTRY OF EDUCATION AND TRAINING NGUYEN TAT THANH UNIVERSITY oOo - DISSERTATION FINAL REPORT SCIENTIFIC RESEARCH PROJECT OF STUDENT IN 2020 NAME OF DISSERTATION: BUILDING 2D-QSAR MODELS ON MEXAB-OPRM EFFLUX PUMP INHIBITORS OF PSEUDOMONAS AERUGINOSA Code of dissertation: Author of dissertation: NGUYEN TRUONG KHANH VY Scientific instructor: M.S Pharm PHAN THIEN VY Faculty: Faculty of Pharmacy Student’s name: NGUYEN TRUONG KHANH VY Class: 15DDS2C Student ID: 1511539122 Ho Chi Minh City - 2020 TABLE OF CONTENTS LIST OF ACRONYMS LIST OF FIGURES CHAPTER LITERATURE REVIEW 1.1 Antibiotics resistance and multidrug efflux systems 1.1.1 Introduction 1.1.2 Antibiotic resistance crisis 1.1.3 Multidrug-efflux system 1.2 p aeruginosa MexAB-Oprm efflux pump 12 1.2.1 Structure of p aeruginosa efflux pump 12 1.2.2 Substrates and mechanism of p aeruginosa efflux pump 14 1.3 Inhibition of MexAB-OprM efflux pump 15 1.3.1 The in vitro biological assays 17 1.3.2 Synthetic EPIs 19 1.3.3 Natural EPIs 21 1.4 Virtual screening 24 1.4.1 Structural - based virtual screening 24 1.4.2 Ligand - based virtual screening 24 1.4.3 Partial Least Squares regression 25 1.5 QSAR studies in p aeruginosa inhibitors 26 CHAPTER SUBJECTS - RESEARCH METHOD 27 2.1 Data set 27 2.1.1 Data set of MPC8 LVFX 27 2.1.2 Data set of MPC8 AZT 27 2.2 The 2D-QSAR study process on p.aeruginosa 28 2.2.1 The preparation of structure of compounds 29 2.2.2 The preparation of biological activity values 29 2.2.3 Data division 29 2.2.4 2D-descriptors calculation 29 2.2.5 2D-descriptors selection 29 2.2.6 Applicability domain (AD) of QSAR models determination Roy,Kar, &Ambure 2015 - (Roy, Kar, & Ambure, 2015) 30 2.2.7 Linear regression based on QSAR models 31 2.2.8 Model validation - Roy & Kar, 2014 - Roy and Kar 2014 (Roy & Kar, 2014) 32 2.2.9 Randomization test (Y- scrambling, Y - randomization) 34 2.3 Virtual screening 34 CHAPTER RESULT AND DISCUSSION 37 3.1 MPC8 LVFX 37 3.1.1 MPC8 LVFX QSARmodel 37 3.2 MPC8 AZT model 39 3.2.1 AZTl QSAR model 40 3.2.2 AZT-2 QSAR model 42 3.2.3 AZT-3 QSAR model 44 3.2.4 AZT-4 QSAR model 46 3.3 Virtual screening 48 3.3.1 Screening on Traditional Chinese Material (TCM) database 48 3.3.2 Screening on DrugBank database 51 3.3.3 Substances extracted from medicinal material of Nguyen Tat Thanh’s students 54 3.3.4 The ZINC database .55 CHAPTER CONCLUSION AND SUGGESTION 57 4.1 Conclusion 57 4.2 Suggestion 58 REFERENCE APPENDIX LIST OF ACRONYMS Acronyms Defined 2D Dimensions 3D Dimensions ABC The ATP binding cassette AD Applicability domain LVFX Levofloxacin AZT Aztreonam cv Cross-validation EPIs Efflux pump inhibitors EtBr Ethidium bromide LEV Levofloxacin LOO Leave-one-out MATE The multidrug and toxic compound extrusion MDR Multi-drug resistant MES Multi-drug efflux systems MFS The major facilitator MIC Minimum inhibitory concentration MLR Multiple Linear Regression MOE Molecular Operating Environment MPC8 The value of minimal concentration of an EPIs required to decrease the MIC of an antibiotic by 8-fold OLS Ordinary least squares PCA Principle component analysis PLS Partial Least Square regression QSAR Quantitative Structure - Activity Relationship RND The resistance nodulation division SMR The small multidrug resistance TCM Traditional Chinese Medical database TMDs Transmembrane domains TMHs Transmembrane helices WHO World Health Organization LIST OF FIGURES Figure 1.1 Schematic representation of the MFS, MATE, SMR, 12 Figure 1.2 Overall a model of the assembled tripartite structure 14 Figure 1.3 Structure some of synthetic efflux pump inhibitors .21 Figure 1.4 MexAB-OprM EPIs from Natural products 23 Figure 2.1 The study process of 2D-QSAR 28 Figure 2.2 Determined outlier by 3D-scatter plot in MOE 2008.10 32 Figure 2.3 The process of virtual screening 36 Figure 3.1 The correlation line between observed and predictedpMPC8 values in the training and the test set of LVFX model 39 Figure 3.2 The correlation line between observed and predicted pMPC8 values in the training and the test set of AZT l model 42 Figure 3.3 The correlation line between observed and predicted pMPC8 values in the training and the test set of AZT_2 model 44 Figure 3.4 The correlation line between observed and predicted pMPC8 values in the training and the test set of AZT model 46 Figure 3.5 The correlation line between observed and predicted pMPC8 values in the training and the test set of AZT model 48 Figure 3.6 A virtual screening result of natural compounds 49 Figure 3.7 A virtual screening result of Drugbank database 52 Figure 3.8 G-9 (spergulin A) from Glinus oppositifolius 54 Figure 3.9 A virtual screening results of ZinC database 55 LIST OF TABLES Table 1.1 Six family of Efflux pumps Table 1.2 Substrates that MexAB-OprM efflux pumpcan expel 15 Table 1.3 EPIs from natural source 23 Table 2.1 Data set of MPC8LFVX model 27 Table 2.2 Data set of MPC8 AZT model 28 Table 2.3 The virtual screening database 35 Table 3.1 Descriptors definition of LVFX QSAR model 37 Table 3.2 The correlation matrix between descriptors 37 Table 3.3 The validation result of LVFX 38 Table 3.4 The AZT QSAR model 39 Table 3.5 Descriptors definition of AZT QSAR model 40 Table 3.6 The correlation matrix between descriptors 40 Table 3.7 The validation result of AZT model 41 Table 3.8 The correlation matrix between descriptors 42 Table 3.9 The validation result of AZT model 43 Table 3.10 The correlation matrix between descriptors 44 Table 3.11 The validation result of AZT model 45 Table 3.12 The correlation matrix between descriptors 46 Table 3.13 The validation result of AZT model 47 Table 3.14 Top natural compounds had strongest predicted values from virtual screening with theirs structure and natural materials (LVFX model) 50 Table 3.15 Top natural compounds had the strongest predicted values from virtual screening with theirs structure and natural materials (AZT l model) 51 Table 3.16 Medicines had strongest predicted MPC8 values from Drugbank database 53 Table 3.17 Compounds had strongest predicted MPC8 values 56 CHAPTER LITERATURE REVIEW 1.1 Antibiotics resistance and multidrug efflux systems 1.1.1 Introduction Today, antibiotic resistance poses a significant threat to the modem healthcare provision, especially in Gram-Negative bacteria Bacteria may be a single drug or multidrug-resistant (MDR) There are many resistance mechanisms that were developed since the antimicrobial agents were produced and actually used The mechanisms involved in antimicrobial agent resistance are, modifying enzymes, alternation of the target site of antimicrobials, and prevention of antimicrobials accumulation inside the bacteria cells Recently, the latest ones are accomplished through two mechanisms: alternation outer membrane permeability and efflux pump (Auda, Salman, & Auda, 2020) Besides that, overexpression of the efflux pump has been progressively discovered to be related to multidrug resistance in clinical isolates To successfully combat the ascending number of drug-resistant and multidrug-resistant bacteria, we must expand our knowledge of the molecular mechanisms underlying microbial antibiotic resistance In addition, these multidrug-efflux systems also known as transport proteins which play a significant role in transporting the toxic agents, namely antibiotics, biocides and dye to the outer environment (Blanco et al., 2016) In particular, MexAB- OprM system is able to pump out a wide range of antibiotics, such as P-lactams, fluoroquinolones, tetracyline, macrolides, chloramphenicol, novobiocin, trimethoprim, as well as a series of detergents, dyes, organic solvents, fatty acid, synthesis inhibitors, and homoserine lactone (W Chen et al., 2016) Next, the rise of MDR bacterial pathogens had led to widespread and extensive use of carbapenems Subsequently, the incidence of carbapenems resistance in p.aeruginosa is now ascending worldwide Recent data show about 20% of clinical isolates in the US are non-susceptible to meropenem, increasing 65-78% among MDR isolates Globally, imipenem resistance in p.aeruginosa ranges from 16 to 35% (Young et al., 2019) Various drug efflux pumps have been characterized thus far The EPI is a promising agent that expected to restore the biological activities of existing antibiotics, broaden the spectrum of antibacterial medicines, increase its sensitive and intracellular concentration (A Lamut, Peterlin, Kikelj, & Tomasic, 2019) Over the past decades, the research of MexAB-OprM efflux pump and its inhibitors have been conducted, a large number of EPIs were discovered and tested in vitro, but up to now none of them have been used to cure clinical infections It has a disadvantage that their toxic property hindering their clinical application Moreover, the processes of medicine discovery can take up to 10-15 years and cost an average of approximately 2.0-2.6 billion dollars Pharmaceutical scientists figured out that the direction of research is building 2D-QSAR models, which has predictive capabilities for p aeruginosa MexAB-OprM efflux pump inhibitory activities of other chemical entities from natural sources and clinical medicines As a result, it can save a lot of time and money for US Thus, the aims of this study are: - Creating EPIs database of MexAB-OprM - Finding the 2D molecular descriptors, which were highly accurateness with biological activities (MPC8) - Building a high precise QSAR model that can predict capabilities for p aeruginosa MexAB-OprM efflux pump inhibitory activity of other chemical entities - Screening and searching EPIs from virtual screening database 1.1.2 Antibiotic resistance crisis Nowadays, the prevalence of antibiotic resistance is intensifying, and multidrug­ resistant has been identified as a serious threat to human health “Antimicrobial resistance is one of the most urgent health risks of our time and threatens to undo a century of medical progress,” said Dr Tedros Adhanom Ghebreyesus, the World Health Organization (WHO) Director-General (WHO, 2019a) The reasons that lead to antibiotic resistance in hospitals, communities and also animal farm are caused by uncontrollably using antibiotics in human and in animal production Drug-resistant diseases could cause 10 million deaths each year by 2050 and damage to the economy as catastrophic as the 2008-2009 global financial crisis By 2030, antimicrobial resistance could force up to 24 million people into extreme poverty (WHO, 2019b) Therefore, the world has already been aware of the health consequence as crucial medicines become loss of efficiency In recent years, it is shown that the ratio of antimicrobial resistance in Gram-Negative bacteria including p aeruginosa that is rapidly growing In detail, p aeruginosa is listed in the most essential group of all risk factors include multidrug-resistant bacteria that pose a specific threat in hospitals, nursing homes, and among person whose care requires devices such as ventilators and blood catheters (Organization, 2017) In 2000, p aeruginosa displays resistance to a variety of antibiotics, including aminoglycosides, quinolones and P-lactams Up to now, this bacteria almost has become resistant to a huge number of antibiotics, including Carbapenem and Fluoroquinolones A review by Dreier and Ruggerone (2015), resistance nodulation division (RND)-type efflux pumps from p.aeruginosa have an astonishing array of substrates For instance, the MexAB-OprM pump, one of an estimated 12 systems in p aeruginosa, transports several clinically - important antibiotics from diverse classes, fluoroquinolones, macrolides, including ft and tetracyclines - lactams, aminoglycosides, Moreover, The World Health Organization (WHO) has recently listed Carbapenem-resistant p aeruginosa as one of three bacterial species in which there is a critical need for the development of new antibiotics to treat infections (Pang, Raudonis, Glick, Lin, & Cheng, 2019) 1.1.3 Multidrug-efflux system There are several ways that bacteria can develop their resistance to antibiotics For example, its target alteration, enzymatic inactivation, permeability change, and efflux effect Then, the over-expression of the efflux pump has been progressively discovered to be related to multidrug resistance in clinical isolates These proteins are found in both Gram - Positive and Gram - Negative as well as in Eukaryotic and organism such as Candida albicans, Plasmodium falciparum, and cancer cells Thus, a major contribution to this intrinsic multidrag resistance is provided by a number of broadly-multidrag efflux systems such as MexAB-OprM in p.aeruginosa Efflux pumps can push out toxic substrates, including antibiotics, biocides, and dyes from within cells into the external environment Besides that, some bacterial efflux pumps can extrude selectively specific antibiotics, others may transport a range of structurally different compounds and also create an MDR phenotype (Blanco et al., 2016) Gram-positive bacteria mostly carry cytoplasmic membrane single polypeptide efflux pumps In contrast, Gram-Negative bacteria is more complicated Its efflux pumps usually have tripartite which is located in the inner membrane and outer membrane through periplasm that transverse via membrane fusion protein (MFP) (Fernandez & Hancock, 2012) Moreover, two major classes of efflux pumps are found depending on transportation energy through the pumps: secondary multidrug transporters and ATP - binding cassette - ABC Most of the efflux pumps of clinically relevant are belong to secondary multidrug transporters However, ABC efflux pumps require energy resulted from the hydrolysis of ATP It can be classified into six different families; four of them are proton motive force utilizers which are: Multidrug and toxic compound extrusion - MATE, Major facilitators - MF, Resistance-nodulation-division - RND and Small multidrug resistance of staphylococcal multi-resistance - SMR The fifth families, ABC (ATP binding cassette), utilize the energy of ATP hydrolysis for compound exportation Additionally, the newly family was explored - PACE Family of efflux pumps are all illustrated in Table 1.1 Table 1.1 Six family of Efflux pumps Family of Efflux Pumps Energy source Organisms Substrates used for extrusion ABC superfamily Lactococcus lactis, Staphylococcus aureus, E coll Multiple antibacterial drugs Hydrolysis of ATP SMR family Acinertobacter baumannii, Staphylococcus aureus Acriflavine Benzalkonium Proton motive force (PMF) MFS family Staphylococcus aureus, E coli Acriflavine Benzalkonium Na+ Proton motive force (PMF) Fluoroqinolones Aminoglycosides Cationic drugs Proton motive force (PMF) Multiple antibacterial drugs Proton motive force (PMF) RND-type family Staphylococcus aureus, E coli, Vibrio parahaemolyticus Psedomonas aeruginosa, E colt PACE family has not yet been structurally characterized MATE family Structural properties Multiple transmembrane helices of varying amino acids in the ATP-binding cassette helices and primary structure comprises of 100-200 amino acids 12 or 14 transmembrane helices 12 putative transmembrane helices spanning the membrane Multi-subunit complex Whereas in occasions, particularly in the case of Gram-positive organisms, the efflux pumps can work independently of any other protein On the contrary, in Gram-negative bacteria, tripartite complexes comprising the inner-membrane efflux pump, an outer membrane protein and a membrane fusion protein, are able to traverse both bacterial membranes The genes encoding the fusion protein and the efflux pump usually form an operon, which is frequently flanked by an upstream gene encoding a local regulator of ... regulator of their expression Bacteria can express its MES from more than one family and /or more than one type of efflux pump belonging to the same family In addition, efflux pumps can consist of either... assay of the inhibition of a substrate -efflux, mediate by efflux pumps at different concentrations Up to now, a detection of promising synergy is processing Minimum potentiation concentration (MPC)... antibiotics out of the cell 1.3 Inhibition of MexAB-OprM efflux pump Mechanisms of drug efflux contribute to the resistance of bacteria to many classes of chemotherapeutic agents Drug efflux results