In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial

Pathogens Using Escherichia Coli As The Model Organism

PRESENTATED BY:Komal Siddhartha

In Vivo Efficacy Evaluation of INM-4801-A Against Nosocomial

Pathogens Using Escherichia Coli As The Model Organism

PRESENTATED BY:Komal

SiddharthaM. Pharm

Pharmacology- IInd YearRoll No- 6433 SUPERVISOR

Dr. Gaurav Kaithwas

Assistant Professor Dept. of Pharmaceutical

sciencesBBAU, Lucknow

SUPERVISORDr. Raman

ChawlaScientist ‘D’

Dept. of CBRN defence, INMAS, DRDO, Delhi

• 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

antibioticsFACTORS

Its high time to demystify and combat these nosocomial pathogens by using herbal plants and their derivatives as alternative treatment modalities

??.....

INTRODUCTIONSpreads of Nosocomial infection

1

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.

RESEARCH HYPOTHESIS

2

AIM AND OBJECTIVES

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

PLAN OF WORK 1.BIOPROSPECTION Selection of microorganismSelection of bioactivity virulent factor using classical approachSelection of herbal plants using classical bioprospection approachBinary coefficients matrix to evaluate the presence/ absence of virulent factor in selected plantsFuzzy set membership analysis for decision matrixOptimization of decision matrix score 2.MOLECULAR DOCKINGRetrieval 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-AEvaluation of Therapeutic Dose of INM-4801-APharmacokinetic study of predominant phytoconstituent INM-4801-ARadiolabelling of pure compound using technetium-99mInstant chromatography (iTLC) of radiolabelled drug for stability studesIn vivo pharmacokinetic studies-

I. Half life (t/2)II. Elimination rate constant (ƛ),III. Bioavailability IV. Rate of clearance

4

INM-4801-ACommon 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

Plant Profile

5

MATERIAL AND

METHODOLOGY

I. IN SILICO BIOPROSPECTIONThe 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 basedEvolution 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 Bioprospection1.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 indexingThis 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

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 parameterThis 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

II.MOLECULAR DOCKING AND IN SILICO TOXICITY ANALYSIS

1.Retrieval of 3D structure of OXA-23 beta lactamase ReceptorThe 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 ChemsketchHydrogen 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 receptorPredictions 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 knownCategories of Docking: 1) Protein-ligand docking 2) Protein-Protein DockingType of Molecular docking: 1)Rigid Docking 2) Flexible DockingDocking Programe: MOE-DOCK; FRED; FLOG; Hex 6.2; AADS etc

8

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 onlyb. FFT mode: 3D fast litec. Grid Dimension: 0.8d. Receptor range: 180°e. Ligand range: 180°f. Twist range: 360°

5. Toxicity Predictive AnalysisToxicity 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

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

ACUTE TOXICITY STUDY

10

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/kgConduct the Main Test

Or MoreOr More

OECD Guideline : 420 and 425 11

1.Experimental groups and drug administrationSingle 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 studyAfter 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 parameterOn 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.

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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 wasFound to beineffective

Microbial Inoculum Herbal Extract Dilutions

INM-4801-A dilutions given to different sets of mice

Observed till Lethality

Period

Assigned as Effective Dose

Pharmacokinetics parameters of the given predominant phytoconstituents INM- 4801-A was evaluated using PharmacoscintigraphyPure 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 studiesA 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.

PHARMACOKINETICS STUDIES

13

3.In vivo pharmacokinetics studies1ml 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

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Results and Discussion

I. IN SILICO BIOPROSPECTION1.Keywords Hits Scoring MatrixOn the basis of the keyword hits scoring results weightage was given to various parameters selected for screening of herbal plants with respect to antimicrobial activityWeightage was decided according to the percentage relevance obtained for each parameter

Table 1: Relative weightage for each parameter assigned on the basis of percentage relevanceS.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%

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2.Binary (Presence-Absence) Coefficients MatrixOut 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

305

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 Plants16

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 + + + + _ 13.56Mentha piperita + + + + _ 13.56

Camellia sinensis + + + + _ 13.56Eucalyptus

globules+ + + + _ 13.56

Rosmarinus officinalis

+ + + + _ 13.56

Nelumbo nucifera - + + + _ 8.56Punica granatum _ + + + 8.56Terminalia arjuna + + + _ + 12.13

Allium sativum + + _ + _ 10.71Lawsonia innermis + _ + + - 9.99

Andrographis paniculata

_ + _ + + 6.42

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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 vulgarisMenthe piperita, Camellia sinensis, Nelumbo nuciferaHeld topmost position with 100% relevance

Table 3: Fuzzy Set Membership Analysis for herbal plants screened on the basis of Weightage Matrix scores

18

II. MOLECULAR DOCKING AND TOXICITY1.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

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

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

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 -309.31Arjunolic acid (Terminalia arjuna; Arjuna) 2 -287.62 Meropenem (Standard Inhibitor) 3 -268.02Epicatechin 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 -258.68Ellagic acid (Punica granatum; Anar) 9 -256.16Nuciferine (Nelumbo nucifera; Lotus) 10 -250.99Tazobactum (Standard Inhibitor) 11 -238.04Clavulanic acid (Standard Inhibitor) 12 -213.86Methyl cinammate (Alpinia officinarum; Rasna) 13 -211.51l-menthol (Mentha piperat;, Peppermint oil) 14 -199.70Lawsone (Lawsonia inermis; Mehndi) 15 -188.70Allicin ((Allium sativum; Lehsun) 16 -185.02Cineole (Eucalyptus globules; Eucalyptus) 17 -168.13* Virtual Docking Score (free energy change) evaluated using Hex 6.8.

22

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

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 Bioaccumula-tion

Developemental Non Tox-icant

Non Mutagenic

Figure 5: Categorization of pre-dominant Phytoligands (~13) based on Lethal Dose (50%); Bioaccumulation Factor; Developmental toxicant; Mutagenicity

24

Figure 6: Optimization of identified potent leads with their respective E-values vs. LD50 as decision-aid toxicity predictive descriptors

0

2

4

6

8

10Bioaccumula-tion

Mutagenicity

Developmental Toxicity

E Value of Phyto-Ligands

Valu

es

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.

309.3

1(punic

alin)

287.62

(Arju

nolic

acid)

268.0

1Epic

atchin

265.3

5 Catc

hin

261.1

2 (And

rogr

apho

lid)

258.6

8 Lute

olin)

256.1

6 (Elagic

acid)

250.99

(Nuc

iferin

)

211.5

1 (Methy

l Cinn

amate

)

199.70

(l Menth

ol

188.7

0 ( La

wson)

185.02

(Allic

in

168.13

(Cine

ol0

2000

4000

6000

8000

10000

LD50

E-Value of Phyto-Ligands

Ora

l L

D50

25

III 1.ACUTE TOXICITY STUDY RESULTS1.Gross Behavior studyNo 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

26

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

Control group 2 (100mg/kg) group 3(500mg/kg) group 4 (1000mg/kg) group 5 (2000 mg/kg)

256257258259260261262263264265

Day 1Day 7Day 14

wei

ght i

n gm

Figure 8: Mean body weight of rat at different doses of INM-4801-A

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3.Hematological parametersBlood 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

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/kgWBC

(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±0.80HCT % 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±16.54LY (103/mm3) 80.74±4.21 78.35±6.63 81.15±2.85 78.76±4.90 79.35±6.63MO (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

2. DOSE EFFICACY STUDYTable 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± 1.63Day 1 243.33±1.1 106±1.2 10.33±1.24 117.33±2Day 2 245±1.56 103±0.3 8± 1.21 109±6.48Day 3 248.6±1.4 100±0.3 7.33±1.24 106.67±6.18Day 4 252±2.1 99± 0.22 3.67±1.24 105±5.71Day 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± 2Day 1 242.33±2 104±0.9 13±1.24 114.67±2Day 2 247.3±1.24 100±0.3 9±1.54 109±1.63Day 3 248±1.63 98±0.31 3.33±1.24 97±1.63Day 4 253.67±1.24 96± 0.42 0.33±0.47 92.67±2Day 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±1.63Day 1 243±1.34 105±0.4 12±1.63 117.67±1.24Day 2 246± 1.24 103± 0.4 9±1.34 110.33±1.24Day 3 248.7±1.24 101±0.4 7±0.81 105.3±3.09Day 4 252.1±1.24 98±0.11 3±0.81 102±2.44Day 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±2.16Day 1 244±1.90 106±0.2 10±1.21 116±1.24Day 2 246±1.23 104±0.2 8±1.64 109.67±3.29Day 3 249.1±1.24 102±0.31 6±0.81 104.33±2.62Day 4 253.7±1.24 97±0.42 4.33±0.4 102.67±2.05Day 5 257.11±1.24 97±0.12 0.47 90±1.63

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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

0 50 100 150 200 250 300 3507.8

88.28.48.68.8

99.29.49.69.8

Series1f(x) = − 0.00615846994535519 x + 9.7555737704918

Time (min)

Am

ount

of R

adio

activ

ity

(µC

i)

Figure9: In vivo clearance rate of INM-4801-A in Group 1

3.PHARMACOKINETIC STUDYTable 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

0 50 100 150 200 250 300 3507

7.5

8

8.5

9

9.5

10

f(x) = − 0.00539617486338798 x + 9.63893442622951

Time (min)

Am

ount

of R

adio

activ

ity

(µC

i)

Figure10: In vivo clearance rate of INM-4801-A in Group 2

29

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

0 50 100 150 200 250 300 3507.8

8.3

8.8

9.3

9.8

10.3

Series1f(x) = − 0.00587431693989072 x + 10.022131147541

Time (min)

Am

ount

of R

adio

activ

ity

(µC

i)

Figure11: In vivo clearence rate of INM-4801-A in Group3

Table 13: List of Different Pharmacokinetic Parameters calculatedParameter 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

Bioavailabilit

y 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

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

6. CONCLUSION

31

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THANKS

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