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In Vivo Efficacy Evaluation of
INM-4801-A Against Nosocomial
Pathogens Using Escherichia
Coli As The Model Organism
PRESEN...
In Vivo Efficacy Evaluation of INM-4801-A
Against Nosocomial
Pathogens Using Escherichia Coli As The Model
Organism
PRESEN...
• Nosocomial comes
from the Greek
words “nosus”
which means
disease and
“komeion” which
means to take care
of.
• Also call...
The increasing trend of nosocomial infection has now become a serious
problem leading to the emergence of dreadful strain...
Objectives:
 In silico Bioprospection model for identification of promising antimicrobial of
herbal origin against highly...
1.BIOPROSPECTION
Selection of microorganism
Selection of bioactivity virulent factor using classical approach
Selection...
INM-4801-A
Common name:
Indian barberry is medicinal plants used in various diseases. In differene
language it is commonly...
The classical herbal bioprospection is a technique identification of medicinal plants based on its
ethnopharmacological im...
5. Binary Coefficient matrix to Evaluate the presence / absence of Virulent factor in selected plants:
This methodology w...
1.Retrieval of 3D structure of OXA-23 beta lactamase Receptor
The experimental 3D tertiary structure of OXA-23 beta lactam...
4.Ligand Receptor Docking (Hex 6.8)
Receptor and Ligand files were imported in the Hex 6.8 software. Graphic settings and ...
FLOW CHART 1 : CALCULATION OF LETHAL DOSE (L.D. 50)
Up & Down Method
Two Rat injected
with a particular dose ‘X’
Observed ...
FLOW CHART 2 : TYPE I OF UP & DOWN TEST
The Limit Test – Performed when test material is expected to be non toxic
Injected...
1.Experimental groups and drug administration
Single dose, acute toxicity studies were conducted. Group 1 served as the c...
FLOW CHART 3 : ASSESSMENT OF EFFECTIVE DOSE
Collect Urine/Blood Sample of Mice
for Microbial Load Assessment
on every 24 H...
Pharmacokinetics parameters of the given predominant phytoconstituents INM- 4801-A was
evaluated using Pharmacoscintigraph...
3.In vivo pharmacokinetics studies
1ml of the stably radiolabelled drug was administered intravenously into the given set...
1.Keywords Hits Scoring Matrix
On the basis of the keyword hits scoring results weightage was given to various
parameters...
2.Binary (Presence-Absence) Coefficients Matrix
Out of 46 plants selected from ethanomedicinal data, 24 plants were
shown ...
Out of 24 plants
Selected (on the basis
of binary coefficient
matrix)
In which 11 plants
has higher combined
weightage sco...
On the basis of Decision
matrix and optimized
score value of 11 plants S.No. Herbal Plant µS* Optimized Score
1 Rosmarinus...
1.Active Site Analysis
Active site analysis using Dog Site Scorer revealed that pocket P0 of the OXA-23 beta –
lactamase e...
Figure 3: P0 pockets of OXA-23 beta lactamase of
MDR Acinetobacter baumanii with their
descriptors (volume, surface and de...
2.Docking of Receptor and Ligand using Hex 6.8
The process of classifying phytoligands that are most likely to interact w...
Table5: Hex 6.8 Virtual Docking Value (E value) for Predominant Phytoligands and
Standard Beta lactamase Inhibitors as con...
Figure 4: 3D Ribbon structure of OXA-23 beta-lactamase docked with punicalin as most
active phytoligand from Punica granat...
3.In Silico Toxicity Prediction of Ligands
In silico toxicity prediction analysis revealed that 22% out of the selected
p...
Figure 6: Optimization of identified potent leads with their respective E-values vs. LD50 as
decision-aid toxicity predict...
1.Gross Behavior study
No signs of toxicity were observed, in the control or treated groups.
Diarrhoea was observed in m...
255
256
257
258
259
260
261
262
263
264
265
Control group 2 (100mg/kg) group 3(500mg/kg) group 4 (1000mg/kg) group 5 (2000...
Table 8: Hematological parameter of acute toxicity study of INM-4801-A in male Sprague
dawley rats
Parameters Control 100m...
Table 9: List of Different Dose Efficacy Parameters Evaluated
Dose mg/kg Days Weight Body Temperature MacConky agar MHA
0....
Table 10: In vivo rate of clearance of INM-4801-
A of Group 1
Time
(min)
Radioactivity in
rat blood(µCi)
Radioactivity in ...
Table 12: In vivo rate of clearance of INM-
4801-A in Group 3
Time
(min)
Radioactivity in rat
blood(µCi)
Radioactivity in ...
Based on the results of the present studies, following conclusions can be drawn.On the
basis of bioprospection in silico ...
1.Jones, R.N., Low, D.E., Pfaller, M.A. (1999). Epidemiologic trends in nosocomial and community-
acquired infections due ...
THANKS
komal03.phr@gmail.com
In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial Pathogens Using Escherichia Coli As The Model Organism
In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial Pathogens Using Escherichia Coli As The Model Organism
In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial Pathogens Using Escherichia Coli As The Model Organism
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In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial Pathogens Using Escherichia Coli As The Model Organism

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In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial Pathogens Using Escherichia Coli As The Model Organism............Komal thesis

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In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial Pathogens Using Escherichia Coli As The Model Organism

  1. 1. In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial Pathogens Using Escherichia Coli As The Model Organism PRESENTATED BY: Komal Siddhartha
  2. 2. In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial Pathogens Using Escherichia Coli As The Model Organism PRESENTATED BY: Komal Siddhartha M. Pharm Pharmacology- IInd Year Roll No- 6433 SUPERVISOR Dr. Gaurav Kaithwas Assistant Professor Dept. of Pharmaceutical sciences BBAU, Lucknow SUPERVISOR Dr. Raman Chawla Scientist ‘D’ Dept. of CBRN defence, INMAS, DRDO, Delhi
  3. 3. • Nosocomial comes from the Greek words “nosus” which means disease and “komeion” which means to take care of. • Also called as “Hospital Acquired Infection” MEANING • Infections are considered nosocomial if they first appear 48hrs or more after hospital admission or within 30 days after discharge • Approx 9% of patients will suffer from an infection whilst in hospital – risk increases with length of stay DEFINITION • The host: Immunocompromised • Microbes:opportunistic pathogens • The environment: Infected patients • Traffic of staff and visitors • Blood products • Surgical instruments • Treatment : Usage of antibiotics FACTORS Its high time to demystify and combat these nosocomial pathogens by using herbal plants and their derivatives as alternative treatment modalities ??..... Spreads of Nosocomial infection 1
  4. 4. The increasing trend of nosocomial infection has now become a serious problem leading to the emergence of dreadful strains of microorganisms which are difficult to manage. Crude extracts of medicinal plants stand out as veritable sources of potential antimicrobial agents as they have been found to contain wide variety of antimicrobial secondary metabolites, such as tannins, terpenoids, alkaloids, and flavonoids. Thus, plant products identified for such infections by using bioprospection evidence based matrix modelling approach and molecular docking of phytoligands, was evaluated for their efficacy at in vivo using murine model system. 2
  5. 5. Objectives:  In silico Bioprospection model for identification of promising antimicrobial of herbal origin against highly virulent strain of nosocomial infection  Molecular docking analysis of predominant phytoligands for identification of promising antimicrobial of herbal origin against highly virulent strain of nosocomial infection  To evaluate Maximum Tolerable Dose of the given herbal extract INM-4801A  To evaluate Therapeutic Dose of INM-4801-A at in vivo level against highly virulent strain of nosocomial infection  To conduct the Pharmacokinetics studies on the given predominant phytoconstituent INM- 4801-A Aim: In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial Pathogens Using Escherichia Coli As The Model Organism 3
  6. 6. 1.BIOPROSPECTION Selection of microorganism Selection of bioactivity virulent factor using classical approach Selection of herbal plants using classical bioprospection approach Binary coefficients matrix to evaluate the presence/ absence of virulent factor in selected plants Fuzzy set membership analysis for decision matrix Optimization of decision matrix score 2.MOLECULAR DOCKING Retrieval of 3D structure of OXA-23 beta lactimase Receptor Preparation of Ligand Database Active Site Analysis Ligand Receptor Docking (Hex 6.8) Toxicity Predictive Analysis 3. IN VIVO DOSE EFFICACY Evaluation of Maximum Tolerable Dose of INM-4801-A Evaluation of Therapeutic Dose of INM-4801-A Pharmacokinetic study of predominant phytoconstituent INM-4801-A Radiolabelling of pure compound using technetium-99m Instant chromatography (iTLC) of radiolabelled drug for stability studes In vivo pharmacokinetic studies- I. Half life (t/2) II. Elimination rate constant (ƛ), III. Bioavailability IV. Rate of clearance 4
  7. 7. INM-4801-A Common name: Indian barberry is medicinal plants used in various diseases. In differene language it is commonly called darhaldi (Bengal), chitra, darhald, rasaut, (Hindi), maradarisina, (Kerala), daruhald(Maharashtra), daruharidra, pitadaru, (Sanskrit) Scientific Classification Botanical name: Berberis aristata Kingdom: Plantae Family: Berberidaceae Order: Ranunculales Genus: Berberis Species: aristata Synonyms: B. chitria. B. coriaria Common name: Chitra, Daru haldi Indian Barberry or 5
  8. 8. The classical herbal bioprospection is a technique identification of medicinal plants based on its ethnopharmacological importance, as testified in ancient literature or otherwise in clinical literature of various countries. This process is time consuming, tedious, generally observation or experience based Evolution of new techniques of deploying dynamic search protocols, priority indexing, systemic categorization and cross-verification could be referred to as an in silico bioprospection Procedure of in Silico Bioprospection 1.Selection of Microorganism: on the basis of some important characteristics i.e., a) either no treatment regime/vaccine available or limited availability; b) evolving virulent forms from past e.g. A. baumannii 2.Selection of Bioactivity Parameter: Five parameters selected based on mechanistic aspects of antibiotic resistance of A. baumannii, including Biofilm formation, MDR efflux Pump, Outer membrane protein (OmpA), OXA-23 beta lactimase and AbaR Type resistance islands 3. Evaluation of Relevance Factor Using “Keywords hits scoring Matrix” Approach: The analysis was conducted by PubMed as selected search engine. The random search model using combination keyword as “Bioactivity Parameter + Antimicrobial activity” yielded ‘N’ hits. The first n=20 hits provided by the search engine, working on the principle of priority indexing This analysis was used to evaluate the net weightage linked to each virulence factor, Average Percentage Relevance = No of relevance hits * N×100 (n=20) 4.Selection of Herbal Plants Using Classical Bioprospection Apporach : The classical bioprospection approach accounts for investigation of the following variables based on literature review to devise a logical conclusion, resultant in selection of plants  It includes a) Ethnopharmacological importance of plant; b) Relevance of Herb in traditional medicine; c) Availability factor in localized regions; d) Any vedic literature supporting its use; e) Investigations/ prior experience on potential of the herb; f) Indirect indications, 6
  9. 9. 5. Binary Coefficient matrix to Evaluate the presence / absence of Virulent factor in selected plants: This methodology works on the principle of 0-1 binary code of absence/presence of a particular parameter in selected plants from previous step. The range of outcome of matrix lies between 1 to 5 for any plants. Based on this, all the plants having more than 03 parameters, reported in PubMed search engine (n= first 20 hits) against ‘Bioactivity Parameter + Selected Plant’ random search model, were selected 6. Weightage Matrix Based Analysis: This step includes evaluation of overall weightage of plants (Scores > 3 in previous step) by multiplying their binary score with weightage obtained in Step No.5. This is a primary step to screen the plants utilizable to subsequent analysis and removes fake positive results attributed towards investigator’s Formula = Total Parameter (5)× obtained no of parameter in selected plants( n=1,.5) Max. % relevance of a parameter This step identifies potential plant leads based on in silico bioprospection approach subjected to fuzzy set membership analysis and optimization to validate the findings 7.Fuzzy Set Membership Analysis Decision matrix: In this approach, the given mathematical relationship was used to calculate the relevance of the variety/product; μS = S-min(S)/[max(S)-min(S)] Where: μS represents the desirability values of members of the fuzzy set S. Min(S) and max(S) are minimum and maximum values, respectively, in the fuzzy set S 8. Optimization of Decision Matrix Score: In this approach the numerical value of scores obtained were converted into a leveled score by using a scaled magnitude represented by a symbol 7
  10. 10. 1.Retrieval of 3D structure of OXA-23 beta lactamase Receptor The experimental 3D tertiary structure of OXA-23 beta lactamase of Acinetobacter baumanii was retrieved from the RCSB Protein Data Bank as pdb file and Hydrogen atoms were introduced into the enzyme structure using Argus Lab to customize it as the receptor molecule for rigid docking 2.Preparation of Ligand Database The predominant phytoconstituents and standard chemotherapeutic agents were drawn using ACD Chemsketch Hydrogen atoms were introduced into the ligand structure using Argus Lab to customize them for rigid docking The hydrogenated ligand molecules were then converted into pdb format using Open Babel interface as required for rigid docking 3.Active Site Analysis DoG Site Scorer, was used to predict the possible binding sites in the 3D structure of OXA-23 receptor Predictions were based on the difference of gaussian filter to detect potential pockets on the protein surface. Procedure: Docking is the identification of low energy binding mode of ligand within the active site of a receptor or macromolecule whose structure is known Categories of Docking: 1) Protein-ligand docking 2) Protein-Protein Docking Type of Molecular docking: 1)Rigid Docking 2) Flexible Docking Docking Programe: MOE-DOCK; FRED; FLOG; Hex 6.2; AADS etc 8
  11. 11. 4.Ligand Receptor Docking (Hex 6.8) Receptor and Ligand files were imported in the Hex 6.8 software. Graphic settings and Docking parameters were customized as follows and rigid docking was performed. E values of the docking predicted the free energy of docking, which served as the basis for ranking phytoligands in increasing order of their docking abilities The parameters used for the docking process were: a. Correlation type: Shape and Electro only b. FFT mode: 3D fast lite c. Grid Dimension: 0.8 d. Receptor range: 180° e. Ligand range: 180° f. Twist range: 360° 5. Toxicity Predictive Analysis Toxicity prediction analysis of predominant phytoconstituents was conducted using consensus clustering prediction methodology in rat model system (www.epa.gov/nrmrl/std/qsar/TEST). Oral Lethal Dose (LD50), Bioaccumulation factor, Developmental toxicity and Mutagenicity of the ligand were used as the descriptors to filter the predominant phytoligands on the basis of being toxicants or non-toxicants respectively. Subsequently, global properties, describing the size, shape and chemical features of the predicted pockets were calculated so as to estimate simple score for each pocket, based on a linear combination of three descriptors i.e., volume, hydrophobicity and enclosure  For each queried input structure, a druggability score between 0-to-1 was obtained. Higher the druggability score, higher the physiological relevance of the pocket as potential target 9
  12. 12. FLOW CHART 1 : CALCULATION OF LETHAL DOSE (L.D. 50) Up & Down Method Two Rat injected with a particular dose ‘X’ Observed for a period of 24Hrs for any mortality Rat Dies Rat Lives Increase the Dose by a factor of 1.5 ≈ (X + 1.5X) Decrease the Dose by a factor of 0.7 ≈ (X – 0.7X) Maximum Non Lethal DoseMinimum Lethal Dose 10
  13. 13. FLOW CHART 2 : TYPE I OF UP & DOWN TEST The Limit Test – Performed when test material is expected to be non toxic Injected with 2000mg/kg dose Observed for a period of 24Hrs for any mortality Rat Dies Rat Lives Inject 4 Rats with same dose – 2000mg/kg Conduct the Main Test 3 or more rat dies3 or more rat survives L.D. 50 is more than 2000mg/kg L.D. 50 is less than 2000mg/kg Conduct the Main Test Or More Or More OECD Guideline : 420 and 425 11
  14. 14. 1.Experimental groups and drug administration Single dose, acute toxicity studies were conducted. Group 1 served as the control group (Peptone water) and the other groups II, III, IV and V were treated with the INM-4801-A (100, 200, 500, 700, 1000 and 2000mg/kg) respectively.  Before commencing the experiment, the body weight of rats were recorded. All animals except group I were administered with a single oral dose of INM-4801-A at 100, 200, 500, 700, 1000 and 2000mg/kg body weight. 2.Behavioral study After dosing, all animals in this study were observed for gross behavior parameter at 1h, 2h, 3h, 24h and 48 h.  The observed result was recorded as sign of toxicity/number of animals studied. Signs of toxicity and mortality were observed daily for 7 days and were monitored daily for change in body weight 3. Hematological parameter On 8th day, blood was collected from retro orbital method from all animals. The hematological parameters were determined using an hematological analyzer. 4.Dose Efficacy Study Efficacy studies were performed using INM-4801-A by assessing the microbial load of control group rats (vehicle) as well as the inoculated group rats (108 CFU/mL of Escherichia coli) which were then compared to the microbial counts obtained after treatment with a given concentration of the herbal extract, administered orally. This was done for different concentration of the INM-4801-A i.e. 0.5, 1, 2, 4 & 8mg/kg. 12
  15. 15. FLOW CHART 3 : ASSESSMENT OF EFFECTIVE DOSE Collect Urine/Blood Sample of Mice for Microbial Load Assessment on every 24 Hrs. Mice Lives Herbal Extract was effective at the given concentration Mice Dead Herbal Extract was Found to be ineffective Microbial Inoculum Herbal Extract Dilutions INM-4801-A dilutions given to different sets of mice Observed till Lethality Period Assigned as Effective Dose
  16. 16. Pharmacokinetics parameters of the given predominant phytoconstituents INM- 4801-A was evaluated using Pharmacoscintigraphy Pure compound was tagged with radioactive 99Tc (technetium) and its tagging efficiency was monitored by iTLC (instant chromatography). Tagged active compound was then administered intravenously in given sets of rats and its accumulation in vivo was assessed by calculating the radioactivity in blood samples collected at periodic intervals 1.Radiolabelling of pure compound using technetium-99 1gm pure INM- 4801-A was weighed accurately, and then dissolved in an appropriate solvent (0.5mL of distilled water). 200µl of stannous chloride and hydrochloric acid stock solution was added into INM-4801-A solution. The above solution pH was then adjusted to 7 using 1N sodium hydroxide (NaOH) solutions. Finally, volume was made up to 1mL using distilled water. Few drops of sodium pertechnetate solution (20mCi) were then added so as to introduce the radionuclide into the reaction mixture and then the reaction mixture was kept aside for 30 minutes. 2.Instant chromatography (iTLC) of radiolabelled drug for stability studies A single minute spot of the radiolabelled drug was spotted onto the iTLC strip and was allowed to dry. The strip was then suspended into a pre saturated beaker containing acetone as mobile phase. The mobile phase was allowed to run 1/3rd of the strip and then strip was removed carefully from the mobile phase. 13
  17. 17. 3.In vivo pharmacokinetics studies 1ml of the stably radiolabelled drug was administered intravenously into the given sets of rats (3rats). After an interval of 30 minutes, blood sample was collected and radioactivity count was assessed using a gamma counter and calibrator. This step was repeated after 30 minutes for 5 consecutive intervals. Amount of radioactivity at each time interval is directly proportional to the drug present in body. Graph of radioactivity versus time was plotted and used to calculate t1/2, elimination rate constant , bioavailability and rate of clearance The strip of iTLC was divided into the upper 1/3rd and lower 2/3rd portion and cut accordingly. Radioactivity count was assessed for both upper and lower portions. If the lower portion shows higher counts as compared to the upper portion then it can be said that the drug is bound to the radionuclide , making the moiety heavier and hence the moiety reside in the lower portion of the iTLC strip owing to an higher counts of radioactivity in the lower portion. Same iTLC procedure was repeated after every 1hr., so as to assess the extent of drug radiolabelling with the ensuing time 14
  18. 18. 1.Keywords Hits Scoring Matrix On the basis of the keyword hits scoring results weightage was given to various parameters selected for screening of herbal plants with respect to antimicrobial activity Weightage was decided according to the percentage relevance obtained for each parameter Table 1: Relative weightage for each parameter assigned on the basis of percentage relevance S.No. Parameter selected Total hits Hit score Relatives %Relevance 1 Beta lactamase inhibitor 22 20 7 35% 2 MDR pump inhibition 103 20 5 25% 3 Outer membrane protein OmpA inhibition 26 2 4 20% 4 Biofilm formation inhibition 35 20 3 15% 5 AbaR-Type Resistance Island 20 20 1 5% 15
  19. 19. 2.Binary (Presence-Absence) Coefficients Matrix Out of 46 plants selected from ethanomedicinal data, 24 plants were shown to contain either 3 or more than 3 characteristic and hence illustrated a better score as compared to other plants e.g. Eucalyptus globules, Thymus vulgaris, Menthe piperita, Andrographis paniculata, Camellia sinensis, Rosmarinus officinalis, Punica granatum, Terminalia arjuna, Lawsonia inermis and Allium sativum, Zingiber officinale 30 5 24 Plants with Binary Matrix score 1 plants with Binary Matrix score 2 Plants with Bainary Matrix score >3 Figure 1: Binary Matrix Scores for Herbal Plants 16
  20. 20. Out of 24 plants Selected (on the basis of binary coefficient matrix) In which 11 plants has higher combined weightage score And show immence potential of acting as theraputic agents against Nosocomial infection 3.Simple Additive Weightage Matrix Table 2: Weightage Matrix Scores for herbal plants screened on the basis of binary matrix scores (Scores > 3) Herbl Plant (Weightage) β- lactamase inhibition (5) MDR pump inhibition (3.57) Outer membrane protein inhibition (2.85) Biofilm formation inhibition (2.14) AbaR Resistance Island (0.71) Total weightage Thymus vulgaris + + + + _ 13.56 Mentha piperita + + + + _ 13.56 Camellia sinensis + + + + _ 13.56 Eucalyptus globules + + + + _ 13.56 Rosmarinus officinalis + + + + _ 13.56 Nelumbo nucifera - + + + _ 8.56 Punica granatum _ + + + 8.56 Terminalia arjuna + + + _ + 12.13 Allium sativum + + _ + _ 10.71 Lawsonia innermis + _ + + - 9.99 Andrographis paniculata _ + _ + + 6.42 17
  21. 21. On the basis of Decision matrix and optimized score value of 11 plants S.No. Herbal Plant µS* Optimized Score 1 Rosmarinus officinalis 6.67 ++++( 4) 2 Eucalyptus globules 6.67 ++++(4) 3 Thymus vulgaris 6.67 ++++(4) 4 Menthe piperita 6.67 ++++(4) 5 Camellia sinensis 6.67 ++++(4) 6 Nelumbo nucifera 6.67 ++++(4) 7 Terminalia arjuna 5.23 +++(3) 8 Allium sativum 3.83 ++(2) 9 Lawsonia inermis 3.09 ++(2) 10 Andrographis paniculata 0.47 +(1) 4. Fuzzy Set Membership Decision Matrix & Optimized Scoring 6 plants found higher % relevance to be chosen as potent agents against NI Among these Rosmarinus officinalis, Eucalyptus globules, Thymus vulgaris Menthe piperita, Camellia sinensis, Nelumbo nucifera Held topmost position with 100% relevance Table 3: Fuzzy Set Membership Analysis for herbal plants screened on the basis of Weightage Matrix scores 18
  22. 22. 1.Active Site Analysis Active site analysis using Dog Site Scorer revealed that pocket P0 of the OXA-23 beta – lactamase enzyme was found to be energetically favorable for performing molecular docking studies, attributed to its descriptors (Figure 3), i.e., larger surface area, greater depth, less solvent-exposed surface, spontaneity of binding and higher hydrophobic character than other pockets . Table4: Pocket Discriptor Table of OXA-23 beta lactamase enzyme 19
  23. 23. Figure 3: P0 pockets of OXA-23 beta lactamase of MDR Acinetobacter baumanii with their descriptors (volume, surface and depth) and scores based on Active Site Analysis using DoG Site Scorer Figure 2: 3D structure of OXA-23 beta lactamase of MDR Acinetobacter baumanii 20
  24. 24. 2.Docking of Receptor and Ligand using Hex 6.8 The process of classifying phytoligands that are most likely to interact with a particular receptor is based on the predicted free-energy of binding  Lowering the value of free energy change (E value) promotes spontaneity of binding interaction between the predominant phytoligand and targeted receptor Energy of docking (E values) was calculated using Hex 6.8 and revealed predominant phytoconstituents, including Punicalin, Arjunolic acid, Epicatechin gallate, Catechin, Andrographolide, Luteolin, ellagic acid and nuciferin; which have an E value in the range: -309.31 to -250.99 Kcal/mol These natural plant products exhibited significant ability (p < 0.05) to inhibit Acinetobacter baumanii as compared to standard chemotherapeutic inhibitors namely Meropenem (-268.02); Imipenem(-264.89); Tazobactam (-238.04 Kcal/mol) and Clavulanic acid (-213.86 Kcal/mol), (Table 5; Figure 4) 21
  25. 25. Table5: Hex 6.8 Virtual Docking Value (E value) for Predominant Phytoligands and Standard Beta lactamase Inhibitors as controls Predominant Phytoligands Rank E-value* Punicalin (Punica granatum; Anar) 1 -309.31 Arjunolic acid (Terminalia arjuna; Arjuna) 2 -287.62 Meropenem (Standard Inhibitor) 3 -268.02 Epicatechin gallate (Camellia sinensis; Green Tea) 4 -268.01 Catechin (Rosmarinus officinalis; Rosemary) 5 -265.35 Imipenem (Standard Inhibitor) 6 -264.89 Andrographolide (Andrographis paniculata; Kalmegh) 7 -261.12 Luteolin (Thymus vulgaris; Thyme) 8 -258.68 Ellagic acid (Punica granatum; Anar) 9 -256.16 Nuciferine (Nelumbo nucifera; Lotus) 10 -250.99 Tazobactum (Standard Inhibitor) 11 -238.04 Clavulanic acid (Standard Inhibitor) 12 -213.86 Methyl cinammate (Alpinia officinarum; Rasna) 13 -211.51 l-menthol (Mentha piperat;, Peppermint oil) 14 -199.70 Lawsone (Lawsonia inermis; Mehndi) 15 -188.70 Allicin ((Allium sativum; Lehsun) 16 -185.02 Cineole (Eucalyptus globules; Eucalyptus) 17 -168.13 * Virtual Docking Score (free energy change) evaluated using Hex 6.8. 22
  26. 26. Figure 4: 3D Ribbon structure of OXA-23 beta-lactamase docked with punicalin as most active phytoligand from Punica granatum (Anar) with E value = -309.31Kcal/mol evaluated using Hex 6.8 23
  27. 27. 3.In Silico Toxicity Prediction of Ligands In silico toxicity prediction analysis revealed that 22% out of the selected phytoligands (~13) were found to be non toxic on the basis of their higher Lethal Dose (Oral rat LD50). Highest LD50 was found in case of Punicalin (7727.39 mg/kg) 17% of the selected phytoligands exhibited low bioaccumulation factor with lowest in case of Andrographoloid (0.9 units)  11% of phytoligands were found to be non-toxic on the basis of their negligible developmental toxicity while 50% were found to be non- mutagenic, as given in Figure5. 22% 17% 11% 50% Non Toxic (High LD50) Negligible Bioaccumulation Developemental Non Toxicant Non Mutagenic Figure 5: Categorization of pre-dominant Phytoligands (~13) based on Lethal Dose (50%); Bioaccumulation Factor; Developmental toxicant; Mutagenicity 24
  28. 28. Figure 6: Optimization of identified potent leads with their respective E-values vs. LD50 as decision-aid toxicity predictive descriptors 0 1 2 3 4 5 6 7 8 9 10 Values E Value of Phyto-Ligands Bioaccumulati on Mutagenicity Developmental Toxicity Figure 7: Optimization of identified potent leads with their respective E-values vs. Bioaccumulation Factor, Mutagenicity and developmental toxicity as decision-aid toxicity predictive descriptors. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 OralLD50 E-Value of Phyto-Ligands LD50 25
  29. 29. 1.Gross Behavior study No signs of toxicity were observed, in the control or treated groups. Diarrhoea was observed in male rats at the dose of 2000 mg/kg at 1 h, 2 h and 3 h. After 24 h all the animals were normal Body weight changes are indication of adverse effects of drugs and chemicals and it will be significant if the body weight loss is more than 10% from the initial body weight occurred Body weight changes statistically not significant when compared to the control group 2.Body Weight Group No Dose (mg/kg) Weight in gram DAY 1 DAY 2 DAY 3 Group 1 0 (Vehicle) 259±4.86 262.4±8.96 263.4±5.49 Group 2 100 mg/kg 260.±1.85 263±2.28 267.2±1.72 Group 3 500 mg/kg 259.4±2.72 262.2±2.4 266.4±2.15 Group 4 1000 mg/kg 261.8±4.17 265.4±3.22 269.4±1.62 Group 5 2000mg/kg 261.8±3.0 264±2.60 267.8±2.31 Table 7: Mean body weight of rats at different doses of INM-4801-A
  30. 30. 255 256 257 258 259 260 261 262 263 264 265 Control group 2 (100mg/kg) group 3(500mg/kg) group 4 (1000mg/kg) group 5 (2000 mg/kg) weightingm Day 1 Day 7 Day 14 Figure 8: Mean body weight of rat at different doses of INM-4801-A 27 3.Hematological parameters Blood parameters analysis is relevant to risk evaluation as the haematological system has a higher predictive value for toxicity in humans (91%) when assay involve rodents and non-rodents. Haematological analyses (haemoglobin, red blood cell, leukocyte), HCT, monocytes, lymphocytes, eosinophils and platelet counts) all the parameters were statistically not significant when compared to the control group
  31. 31. Table 8: Hematological parameter of acute toxicity study of INM-4801-A in male Sprague dawley rats Parameters Control 100mg/kg 500mg/kg 1000mg/kg 2000mg/kg WBC (103/mm3) 11.06±1.38 11.70±1.67 13.32±1.15 13.04±1.91 12.69±2.67 RBC (106/mm3) 7.77±0.36 6.29±1.62 7.65±0.53 7.65±0.55 7.28±1.62 HGB (g/dl) 14.68±1.16 14.59±0.80 14.05±1.25 13.25±2.20 14.58±0.80 HCT % 32.77±2.64 31.17±4.80 30.07±4.11 32.81±2.62 32.17± 4.80 PLT (103/ml) 700.8±51.84 700.6±16.54 705.2±37.34 709.6±27.17 700.6±16.54 LY (103/mm3) 80.74±4.21 78.35±6.63 81.15±2.85 78.76±4.90 79.35±6.63 MO (103/mm3) 4.77±0.27 3.42±0.43 3.164±0.27 3.97±0.50 3.42±0.43 EO (103/mm3) 0.68±0.24 0.74±0.21 0.692±0.23 0.63±0.23 0.64±0.21 No Adverse Effect observed up till 2000mg/Kg - Designated as Maximum Tolerated Dose
  32. 32. Table 9: List of Different Dose Efficacy Parameters Evaluated Dose mg/kg Days Weight Body Temperature MacConky agar MHA 0.5mg/kg Day 0 259±1.89 96±0.23 0 103± 1.63 Day 1 243.33±1.1 106±1.2 10.33±1.24 117.33±2 Day 2 245±1.56 103±0.3 8± 1.21 109±6.48 Day 3 248.6±1.4 100±0.3 7.33±1.24 106.67±6.18 Day 4 252±2.1 99± 0.22 3.67±1.24 105±5.71 Day 5 257.66±1.22 97±0.21 1±0.81 94±3.74 1mg/kg Day 0 257.67±1.24 97±0.24 0 105.33± 2 Day 1 242.33±2 104±0.9 13±1.24 114.67±2 Day 2 247.3±1.24 100±0.3 9±1.54 109±1.63 Day 3 248±1.63 98±0.31 3.33±1.24 97±1.63 Day 4 253.67±1.24 96± 0.42 0.33±0.47 92.67±2 Day 5 256±1.63 96±0.21 0.3±0.47 82±1.63 2mg/kg Day 0 259.33±1.24 96±0.12 0 104±1.63 Day 1 243±1.34 105±0.4 12±1.63 117.67±1.24 Day 2 246± 1.24 103± 0.4 9±1.34 110.33±1.24 Day 3 248.7±1.24 101±0.4 7±0.81 105.3±3.09 Day 4 252.1±1.24 98±0.11 3±0.81 102±2.44 Day 5 256.23±1.52 97±0.10 0.33±0.3 86.67±1.24 4mg/kg Day 0 259.12±1.24 96±0.23 0 105±2.16 Day 1 244±1.90 106±0.2 10±1.21 116±1.24 Day 2 246±1.23 104±0.2 8±1.64 109.67±3.29 Day 3 249.1±1.24 102±0.31 6±0.81 104.33±2.62 Day 4 253.7±1.24 97±0.42 4.33±0.4 102.67±2.05 Day 5 257.11±1.24 97±0.12 0.47 90±1.63
  33. 33. Table 10: In vivo rate of clearance of INM-4801- A of Group 1 Time (min) Radioactivity in rat blood(µCi) Radioactivity in rat blood with background (µCi) 30min 0.82 9.62 120min 0.30 9.0 180min 0.12 8.60 240min 0.02 8.20 300min 0.0 8.00 30 120 180 240 300 y = -0.006x + 9.755 7.8 8 8.2 8.4 8.6 8.8 9 9.2 9.4 9.6 9.8 0 100 200 300 400 AmountofRadioactivity (µCi) Time (min) Figure9: In vivo clearance rate of INM-4801-A in Group 1 Table 11: In vivo rate of clearance of INM- 4801-A of Group 2 Time (min) Radioactivity in rat blood(µCi) Radioactivity in rat blood with background (µCi) 30min 0.90 9.4 120min 0.40 9.1 180min 0.22 8.7 240min 0.12 8.3 300min 0.03 8.0 y = -0.005x + 9.638 7.8 8 8.2 8.4 8.6 8.8 9 9.2 9.4 9.6 0 100 200 300 400 AmountofRadioactivity (µCi) Time (min) Figure10: In vivo clearance rate of INM-4801-A in Group 2 29
  34. 34. Table 12: In vivo rate of clearance of INM- 4801-A in Group 3 Time (min) Radioactivity in rat blood(µCi) Radioactivity in rat blood with background(µCi) 30min 0.85 9.8 120min 0.30 9.3 180min 0.20 9.1 240min 0.16 8.6 300min 0.07 8.2 30 120 180 240 300 y = -0.005x + 10.02 7.8 8.3 8.8 9.3 9.8 10.3 0 50 100 150 200 250 300 350 AmountofRadioactivity (µCi) Time (min) Figure11: In vivo clearence rate of INM- 4801-A in Group3 Table 13: List of Different Pharmacokinetic Parameters calculated Parameter Rat 1 Rat 2 Rat 3 Mean±S.D Half life(t1/2)* (Min) 113.60 138.6 138,6 126±12.5 AUC(m2)** 3.32 3.51 4.72 3.85±0.62 Elimination rate rate constant*** 0.141 0.149 0.10 0.13±0.02 MRT(min)*** * 7.09 6.711 10 7.93±1.47 Bioavailabili ty in %***** 42.13 47.06 42.48 43.89±2.25 Rate of clearance**** **(L/min/Kg) 0.1269 0.1341 0.09 0.11±0.01 30
  35. 35. Based on the results of the present studies, following conclusions can be drawn.On the basis of bioprospection in silico modelling revealed that 11 out of 46 plants have maximum potential to inhibit nosocomial infection caused by MDR A.baumanni. Phytoligand of these 11 plants was further study by molecular docking and. On the basis of Molecular docking we found that the punicalin as most active Phytoligand bind to the same active site OXA-23 with lower binding energy ( E value = -309.31 Kcal/mol) than standard inhibitor . In silico toxicity prediction analysis revealed that highest LD50 was found in case of Punicalin (7727.39 mg/kg). Andrographaloid (0.9 units) exhibited low bioaccumulation factor. 11% of phyto-ligands were found to be non-toxic on the basis of their negligible developmental toxicity while 50% were found to be non- mutagenic. In acute toxicity study we found that herbal extract INM-4801A was not toxic even at 2000mg/kg and Minimum Inhibitory concentration of INM-4810A was found to be 1mg/kg. Overall, it may be suggested that as resistance to old antibiotics spreads, the development of new antimicrobial agent of herbal origin may be useful as potential antimicrobial agents. However, further research for the validation is still required 31
  36. 36. 1.Jones, R.N., Low, D.E., Pfaller, M.A. (1999). Epidemiologic trends in nosocomial and community- acquired infections due to antibiotic-resistant gram-positive bacteria: the role of streptogramins and other newer compounds. Diagnostic Microbiology and Infectious Diseases; 33(2): 101-112 2.Berkley, J.A., Lowe, B.S., Mwangi, I. (2005). Bacteremia among children admitted to a rural hospital in Kenya. New England Journal of Medicine; 352: 39–47 3.Archibald, L.K. (2004). Gram-negative hospital acquired infections: a growing concern. Infection control and Hospital Epidemiology; 25(10): 809-811. 4.Markovic-Denic,L.(2009). Nosocomial infections prevalence study in a Serbian university hospital. Vojnosanit Pregl, 66(11): 868-75 5.Shears, P. (2007). Poverty and infection in the developing world: healthcare-related infections and infection control in the tropics. Journal of Hospital Infections; 67: 217-224 6.Tacconelli, E., De Angelis, G., Cataldo, M.A., Pozzi E., Cauda, R. (2008). "Does antibiotic exposure increase the risk of methicillin-resistant Staphylococcus aureus (MRSA) isolation? A systematic review and meta-analysis.” Journal of Antimicrobial Chemotherapy; 61 (1): 26–38 7.Weinstein, R.A. (1991). Epidemiology and control of nosocomial infections in adult intensive care units. American Journal of Medicine; 91: 179-84.
  37. 37. THANKS komal03.phr@gmail.com

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