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Journal of Biotechnology and Biosafety Volume 4 Issue 2 March/April 2016

An International, Open Access, Peer reviewed, Bi-Monthly Journal

Editorial

Editor-in-Chief

Chethana G S [email protected] [email protected]

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Advisory Board

Dr. S.M. Gopinath, Phd HOD, Dept of Biotechnology, Acharya Institute of Technology, Bangalore, INDIA

Dr. Vedamurthy A.B. Phd

Professor, P.G. Department of Studies in Biotechnology and Microbiology, Karnatak University, Dharwad, India

Dr. Hari Venkatesh K Rajaraman MD(Ay), PGDHM Manager, R&D, Sri Sri Ayurveda Trust, Bangalore, INDIA

R. Rajamani, M.Sc.,M.Phil.,B.Ed. Co-Principle Investigator, SSIAR, Bangalore, INDIA

Dr. Pravina Koteshwar, MBBS, MD Director, Academic Programs, ICRI, India

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Dr. Pushpinder Kaur, Phd Research Associate, CSIR-Institute of Microbial Technology Sector,

Chandigarh, INDIA

Dr. Kavita Sharma, Phd Senior Scientist, Research and Development, Pharmacology Division,

Sigma Test and Research Centre, New Delhi, INDIA

Dr. Kasim Sakran Abass, Phd Associate Professor, College of Nursing,

University of Kirkuk, Kirkuk, IRAQ

Dr. Ashutosh Chaturvedi (BAMS, PEC Diabetes care)

Resident & M.D Scholar, Department of Panchakarma, SDMCAH - Hassan

Index – JOBB, Volume 4, Issue 2 - March/April 2016 Biotechnology SIMPLE UV SPECTROPHOTOMETRIC ASSAY OF MONTELUKAST SODIUM Madiha Moid, Safila Naveed 355-358 Biotechnology PREDICTION OF ACTIVITY SPECTRA FOR ANALGESICS DRUGS Azhagu Raj R, Sumathi S.R .,Siva Subramanian.S., Lenin E.A., Ragupathi C, Prakash .A 359-377 Animal Biotechnology EFFECTS OF Lallementia royleana Benth. (Lamiaceae) SEEDS IN ACUTE MID STRESS MODELIN NMRI MALE MICE Noorulain Hyder, Baqir S. Naqvi, Humera Ishaq, Shagufta Usman, Atta Abbas Naqvi, Safila Naveed 378-382

Journal of Biotechnology and Biosafety Volume 4, Issue 2, March-April 2016,355-358

ISSN 2322-0406 Journal of Biotechnology and Biosafety

www.jobb.co.in International, Peer reviewed, Open access, Bimonthly Online Journal

SIMPLE UV SPECTROPHOTOMETRIC ASSAY OF MONTELUKAST SODIUM

Research article

____________________________________________________________

Madiha Moid1, Safila Naveed1* _____________________________________

1Faculty of Pharmacy, Jinnah University for Women, 5C, Nazimabad, Karachi – 74600, Pakistan *Corresponding author e-mail address: [email protected]

ABSTRACT: A simple and accurate quantitative determination method is used for the assay of different concentration of active and sample. This assay mainly based on the UV-Visible spectrophotometric determination. The lambda max analyse in this method was 353 nm. Different concentration of standard and sample was prepared used as distilled water as a solvent. The absorbance of these standard and sample solutions was analyze against Distilled water using as blank. The result showing that absorbance is directly proportional to concentration of the absorbing solution according to Beer and Lambart’s law.

Keywords: Montelukast Sodium, UV-Visible Spectrophotometer, % assay. ____________________________________________________________________________________________________

INTRODUCTION Montelukast sodium, chemically described as 1-[({(R)-m-[(E)-2-(7-chloro-2-quinolyl) vinyl]-α-[o-(1-hydroxyl-1-methylethyl)phenethyl]benzyl}thio) methyl]cyclopropaneacetate sodium is a fast acting, selective and orally active cysteinyl leukotriene receptor antagonist used for chronic asthma and allergic rhinitis in children and adults (Arayne, Sultana 2009; Patil, Pore 2009; Patel Nilam and Pancholi 2011). Leukotrienes derived from 5-lipoxygenase (5-LO) activity (Poeckel and Funk 2010) are most importnat inflammatory mediators for asthma and potently reduce bronchoconstriction by combination with CysLT1, the

major leukotriene receptor (Guan, Zheng et al., 2013). Montelukast sodium blocks the action of leukotriene D4 by binding to the cysteinyl leukotriene receptor Cys LT1 in the lungs and bronchial tubes. This action causes the reduction of broncho constriction, otherwise that caused by the leukotriene, and as a result it reduces inflammation. For the reason of Montelukast sodium mechanism of action, it is not useful for the treatment of acute asthma attacks. Furthermore, because of its very specific locus of operation, its interaction does not happen with other allergic medications such as theophylline (KUMAR, Ramachandran 2010).

Figure: 1 Structure of Montelukast Sodium

Montelukast sodium can be rapidly absorbed following its oral administration and extensively metabolized. Montelukast along with its metabolites are excreted almost exclusively via the bile. As per several studies, The mean plasma half-life range for the montelukast is from 2.7 to 5.5 hours in healthy young adults. The pharmacokinetics of montelukast is nearly linear for oral doses up to 50mg (Shafaati, Zarghi 2011).




UV spectrophotometry, spectrofluorometry, RP-HPLC HPTLC, in biological fluid by HPLC, LC/MS, and stability indicating HPLC methods have been reported for the Montelukast Sodium (Tandulwadkar, More et al., 2012).

METHODOLOGY

a. Instrumentation: A double beam UV-visible spectrophotometer, was used to analysis of spectra. The distilled water is used as a solvent for active and formulations.

b. Wavelength Selection: About 100ppm of Montelukast standard solution was prepared in distilled water. These solutions scanned in 200-700nm UV- Visible regions. The highest wavelength (λmax) was analyzed at 353nm and this wavelength was used for analysis of all concentration of Montelukast Sodium standard and samples.

c. Standard solution of Montelukast Sodium: For 100ppm stock solution, 10mg of Montelukast Sodium active was accurately weighed and transferred to a 100mL volumetric flask and dissolved in distilled, make up the volume with distilled water. From this stock solution dilution of 50ppm, 25ppm, 15ppm, 10ppm and 5ppm was prepared.

d. Sample Preparation of Montelukast Sodium For 100ppm stock solution, accurately weighed 10mg of Montelukast Sodium and transferred to a 100mL volumetric flask and dissolved in distilled, make up the volume with distilled water. From this stock solution dilution of 50ppm, 25ppm, 15ppm, 10ppm and 5ppm was prepared.

e. Procedure: After preparation of standard and sample solutions, analyze all standard and samples using 353nm wavelength absorbance and calculate % assay.

RESULT & DISCUSSION: The table 1 showing the absorbance value for the different concentration of standard and sample. The concentration prepared was 100ppm, 50ppm, 25pm, 15ppm, 10ppm and 5ppm. Absorbance for 100ppm standard is 2.85 and for sample 2.776, for 50ppm standard and sample absorbance is 1.552 and 0.772, for 25ppm standard and sample dilution, absorbance value is 0.804 and 0.345, for 15ppm dilution of standard and sample absorbance value is 0.443 and 0.345, for 10ppm dilution of standard and sample absorbance value is 0.137 and 0.196 and for 5ppm dilution absorbance value is 0.282 and 0.107. % assay value for 100ppm sock solution is 97.403 which ids within the range according to USP and BP. Linear regression equation for standard is y = 0.028x + 0.035 and for sample y = 0.027x - 0.177. The regression R2 value for standard and absorbance are 0.989 and 0.942 which (Figure 2 and 3) showing that absorbance is directly proportional to concentration of the absorbing solution according to Beer and Lamberts law. In this paper, simple rapid quantitative determination method (UV-Visible Spectrophotometric method is used for the analysis different concentration of montelukast sodium. In spectrum maximum spectrum achieved at 353nm. Our result full fill USP/BP requirement an Beer and Lamberts law.

Table 1: Specification of Standard and Sample Conc ppm Absorbance of Standard Absorbance of sample 100 2.85 2.776 50 1.552 0.772 25 0.804 0.345 15 0.443 0.353 10 0.137 0.196 5 0.282 0.107 Regression equation y = 0.028x + 0.035 y = 0.027x - 0.177 R2 0.989 0.942 % Assay of 100 ppm solution 97.403




Figure 2: Linearity of Standard

Figure 3: Linearity of Sample

CONCLUSION A good linear relationship was observed for six different concentrations ranges of 100ppm, 50ppm, 25ppm, 15ppm, 10ppm and 25ppm for standard and samples. Furthermore, it can be concluded that assay method performed is quite simple, accurate, precise and easy to perform without any use of expensive chemicals.

REFERENCES Arayne M. S., N. Sultan (2009). "Spectrophotometric method for quantitative determination of montelukast in bulk, pharmaceutical formulations and human serum." Journal of analytical Chemistry 64(7): 690-695.

Guan, W.-J., J.-P. Zheng, (2013). "Responsiveness to leukotriene D4 and methacholine for predicting efficacy of montelukast in asthma." Journal of thoracic disease 5(3): 298.




KUMAR, J. S., D. Ramachandran (2010). "Visible spectrophotometric methods for estimation of Montelukast sodium in bulk dosage forms and formulations." Oriental Journal of Chemistry 26(1): 293-296. Patel Nilam, K. and S. Pancholi (2011). "Spectrophotometric determination of Montelukast sodium and Levocetirizine dihydrochloride in tablet dosage form by AUC curve method." Der Pharma Chemica 3(5): 135-140. Patil, S., Y. Pore (2009). "Determination of montelukast sodium and bambuterol hydrochloride in tablets using RP HPLC." Indian journal of pharmaceutical sciences 71(1): 58.

Poeckel, D. and C. D. Funk (2010). "The 5-lipoxygenase/leukotriene pathway in preclinical models of cardiovascular disease." Cardiovascular research 86(2): 243-253. Shafaati, A., A. Zarghi (2011). "Rapid and sensitive determination of Montelukast in human plasma by high performance liquid chromatographic method using monolithic column: application to pharmacokinetic studies." Journal of bioequivalence & bioavailability 2010. Tandulwadkar, S. S., S. J. More (2012). "Method development and validation for the simultaneous determination of fexofenadine hydrochloride and montelukast sodium in drug formulation using normal phase high-performance thin-layer chromatography." ISRN Analytical Chemistry 2012.

Citation of this article: Madiha Moid and Safila Naveed. SIMPLE UV SPECTROPHOTOMETRIC ASSAY OF MONTELUKAST SODIUM. Journal of Biotechnology

and Biosafety. 4(2): 355-358.

Source of Support: Nil Conflict of Interest: None Declared


ISSN 2322-0406

Journal of Biotechnology and Biosafety


PREDICTION OF ACTIVITY SPECTRA FOR ANALGESICS DRUGS

Research article ________________________________________________ Azhagu Raj R1*, Sumathi S.R1 .,Siva Subramanian.S2 ., Lenin E.A3., Ragupathi C4, Prakash .A5

_________________________________________________________________________ 1P.G. Department of Zoology, Pachaiyappa’s College for Men, Kanchipuram, 631 501,Tamilnadu, India. 1*Department of Animal Science, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India-627012 2Department of Plant Science, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India-627012 3PHM Division, National Institute of Plant Health Management, Rajendra Nagar, Hyderabad, Telangana, India 4Department of Chemistry, Sri Krishna Engineering College, Panapakkam, Chennai, 601301, India 5P.G. Department of Physics, Pachaiyappa’s College for Men, Kanchipuram, 631 501 Tamilnadu, India. *Corresponding author email id: [email protected]

ABSTRACT

This study aims to evaluate the biological activity (In-Silico approach) of analgesics drugs by using Prediction of Activity Spectra for Substances (PASS) tool. The biological activity profile of the analgesic drugs such as Aceclofenac, Dicyclomycin, Condrointin, Diclofenac Sodium, Dioxathion, Famotidine, Combifina, Gentamicin, Ibuprofen and Mafmaftal Spasan were analysed. The results showed the various biological activities (side effects) such as hypertension, eye irritation, hypotension, glaucoma, diarrhea, weakness, sweating, hyperthermic, irritation, skin irritation, neurotoxic, allergic dermatitis, nausea, headache and sleep disturbance (Probability of active and Probability of inactive). Hence, the application of computerized system PASS could be used in toxicoinformatics and cheminformatics.

Key words: PASS, Drugs side effects, Toxicoinformatics, Pharmacoinformatics and Biological activity _________________________________________________________________________________________________

INTRODUCTION Bioinformatics is progressing from the mere

analysis of nucleic acid and amino acid sequences for the search of new targets ligands and new drugs. The computational chemistry tools have become very important to ascertain the targets for different ligands (Richon, 1994). It generates new knowledge that is useful in such fields as drug design and develops new software tools to create that knowledge. Experimental determination of drug efficacy and safety is a time and cost consuming procedure. There exist standard tests for drug safety assessment and different strategies of search for new lead compound (Filimonov and Poroikov, 1996; Stepanchikova, et al., 2003 and Pramely and Leon Stephan Raj, 2012).

Prediction of Activity Spectra for Substances (PASS) is a software product designed as a tool for

evaluating the general biological potential of an organic drug-like molecule. PASS provides simultaneous predictions of many types of biological activity based effects exclusively on the structural organic compounds. Thus the program PASS can be used to estimate the biological activity profiles for virtual molecules, prior to their chemical synthesis and biological testing (Filimonov, et al., 1995; Poroikov et al., 1996;Filimonov and Poroikov, 1996; Poroikov, 2001 and Stepanchikova, et al., 2003).

Its includes main and side pharmacological effects, (antihypertensive, hepatoprotective, sedative, etc.), molecular mechanisms of action, (5-hydroxytryptamine agonist, acetylcholine esterase inhibitor, adenosine uptake inhibitor, etc.), specific toxicities (mutagenicity, carcinogenicity, teratogenecity, etc.), drug metabolism (CYP1A substrate, CYP1A1 human substrate, CYP3A4 substrate, etc.), transport of drugs (P-glycoprotein


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substrate, P-glycoprotein inhibitor, P-glycoprotein inductor, etc.) and gene expression (APOA1 expression enhancer, ErbB-2 expression inhibitor, etc.) (Poroikov et al., 1996; Filimonov and Poroikov, 1996; Poroikov, 2001;Stepanchikova, et al., 2003; Anzali, et al., 2001 and Sadym, et al., 2003).

PASS inlet predicates biological activity spectrum on the basis of structural formula of the compound. Establishing quantitative relationship between molecular structure and broad biological effects, that has been long standing challenge in science (Poroikov et al., 1996 and Poroikov, 2001). Pa and Pi are the estimates of probability for the compound to be active and inactive respectively for each type of activity from the biological activity spectrum, their values vary from 0.000 to 1.000. It is reasonable that only those types of activities may be revealed by the compound, where Pa >Pi and so they are put into the biological activity spectrum. If Pa> 0.7 the compound is very likely to reveal this activity in experiments, but in this case the chance of being the analogue of the known pharmaceutical agents for this compound is also high, if 0.5 < Pa< 0.7 the compound is likely to reveal this activity in experiments, but this probability is less, and the compound is not so similar to the known pharmaceutical agents (Poroikov et al., 1996; Poroikov, 2001; Anzali, et al., 2001 and Sadym, et al., 2003).

Experimental determination of drug efficacy and safety is a time and cost consuming procedure. Biological testing is organized taking into account the similarity and dissimilarity of new compounds to the known biologically active substances, several similarity and dissimilarity suggestions are used both in drug design and screening to determine if particular tests are necessary and sufficient for comprehensive estimation of new compound activity (Sadym, et al., 2003).

Biological activity is a result of a chemical compounds interaction with a biological entity. In clinical study, a human organism represents a biological entity. In preclinical testing, it is the experimental animals (in vivo) and experimental models (in vitro). Biological activity depends on the peculiarities of a compound (structure and physic- chemicals properties), biological entity (species, sex, age, etc.), mode of treatment (dose, route, etc) (Anzali, et al., 2001 and Sadym, et al., 2003).

Any biologically active compound reveals a wide spectrum of different effects. Some of them are useful in the treatment of definite diseases, but the others cause various side and toxic effects (Gillett, et al., 1998; Ghose, et al., 1999; Bender and Glen, 2004). Total complex of activities caused by the compound in biological entities is called the biological activity spectrum of the substance. Such evaluation can be done using internet with the software PASS, which estimates that the probabilities of 900 types of biological activity on the basis of structural formulae of compounds with the accuracy of 85 %. PASS

predictions are based on the analysis of structure-activity relationship (SAR) for the training set of about 46000 biologically active compounds (Anzali, et al., 2001 and Sadym, et al., 2003).

Therefore, PASS once trained is able to predict simultaneously all biological activities that are included in the training set. To provide the best quality of predication new information about biologically active compounds is collected from the literature, electronic sources and after the expert’s evaluation, is regularly added to the training set (Poroikov et al., 1996; Filimonov and Poroikov, 1996; Poroikov, 2001 and Stepanchikova, et al., 2003).

PASS has been used to predict the Antimalarial activity (John de Britto, 2008), Anti-HIV activity (Maridass et al., 2008), activities of plant secondary metabolites (Maridass 2008), Activities of essential oils (Abiya Chelliah, 2008), Antitumor activity (Azhaguraj et al., 2010) and Phenazine derivatives (Azhaguraj et al., 2012). Currently no method exists for forecasting broad biological activity profiles of medicinal agents; even narrow boundaries of structurally similar molecules (Poroikov et al., 1996; Filimonov and Poroikov, 1996; Poroikov, 2001 and Stepanchikova, et al., 2003). Pain is a common condition where sore and aching muscles can be related to tension or stress, overuse, or muscle injury from exercise or physically demanding work. An analgesic, or painkiller, is any member of the group of drugs used to achieve analgesia - relief from pain. Analgesic drugs act in various ways on the Peripheral and Central Nervous System (Ajay Bemis and Murcko, 1999). A side effect is usually regarded as an undesirable secondary effect that occurs in addition to the desired therapeutic effect of a drug or medication (Blake, 2000).

Side effects may vary for each individual depending on the person's disease state, age, weight, gender, ethnicity and general health. Side effects (severe allergic reactions rash; hives; itching; difficulty in breathing; tightness in the chest; swelling of the mouth, face, lips, or tongue; dark urine or pale stools; severe or persistent stomach pain; unusual fatigue; yellowing of the skin or eyes) can occur when commencing, decreasing and increasing dosages, or ending a drug or medication regimen. Side effects may also lead to non-compliance with prescribed treatment (Ajay Walters and Murcko, 1998; Sadowski and Kubinyi, 1998; Ajay Bemis and Murcko, 1999; Frimurer, et al., 2000; Wagener and Van Geerestein, 2000). In this study the PASS (Prediction of Activity Spectra for Substances) computer program, which is able to simultaneously predict more than one thousand biological and toxicological activities from only the structural formulas of the chemicals, was used to predict the biological activity profile of the Analgesic (pain killer) drugs.


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MATERIALS AND METHODS The chemical structures of the Aceclofenac,

Dicyclomycin, Condrointin, Diclofenac Sodium, Dioxathion, Famotidine, Combifina, Gentamicin, Ibuprofen and Mafmaftal Spasan were obtained from Pubchem compound (NCBI) repository (http://www.ncbi.nlm.nih.gov/pubchemcompound). The structures were drawn using the Chem Sketch package 11.0 belonging to the ACD Chem Laboratory. The Analgesic drugs were selected on the basis of commonly available drugs in the common drug stores (medical Shop) in Kanchipuram, Tamil nadu, India. The biological activity (Poroikov et al., 1996; Filimonov and Poroikov, 1996 and Poroikov, 2001) of analgesic drugs was predicated by PASS.

RESULTS AND DISCUSSION The biological activity spectra of these analgesic

drugs obtained by PASS online (http://www.pharmaexpert.ru/PASSOnline/index.php) estimates the predicted activity spectrum of a compound as probable activity (Pa) and probable inactivity (Pi). The PASS prediction tool will predict the Pa: Pi (active: inactive ratio) at prediction threshold of Pa > 70%, 30% < Pa < 70%, Pa < 30%. If Pa > 0.7, the substance is very likely to exhibit the activity in experiment, but the chance of the substance being the analogue of a known pharmaceutical agent is also high. If 0.3 < Pa < 0.7, the substance is likely to exhibit the activity experimentally but the probability is less and the substance is unlike known pharmaceutical agents. If Pa < 0.3, the substance is unlikely to exhibit the activity experimentally, however if the presence of such substance is confirmed in the experiment the substance might be a new entity (Poroikov et al., 1996;Filimonov and Poroikov, 1996 Poroikov, 2001and Pramely and Leon Stephan Raj, 2012).

Analgesic (Pain killer) drugs Aceclofenac, Dicyclomycin, Condrointin, Diclofenac - Sodium, Dioxathion, Famotidine, Combifina, Gentamicin, Ibuprofen, Mafmaftal-Spasan biological activity, toxic/ side effects were predicted (Figure 1-2, Table 2-12). The biological activity of analgesic drug Acelofenac shows that the Probability of active (Pa) maximum value is 0.990; Probability of inactive (Pi) maximum value is 0.003 and Molecular Weight (354.18) was recorded. The analgesic drug acelofenac side-effects such as anemia, hepatitis, ulcer, hepatotoxic, asthma, necrosis, coma, headache, pain, teratogen, nausea, inflammation, hypertensive, eye irritation, hypotension, glaucoma, diarrhea, weakness, sweating, hperthermic, irritation, skin irritation, carcinogenic, paralysis depression, Edema, hypogltcemic etc., were predicated (Table 3, Figure 1.a).

The biological activity of analgesic drug Combifina shows that the probability of active (pa) maximum value is 0.659; probability of inactive (pi)

0.041; and molecular weight (193.17) was recorded. The analgesic drug combifina side-effects such as irritation, ulcer, neurotoxic , sweating, optic neuropathy, hepatotoxic, inflammation, ocular toxicity, nephritis, anemia, hypotension, toxic, respiration, weakness, eilaucoma, asthma, skin irritation, teratogen, muscle weakness, weight loss, hypertensive, skin irritation, eye irritation, pain, nausea, anemia, anemia, mutagenic, weight gain, dystonia etc., were predicated (Table 4, Figure 1.g)

The biological activity of analgesic drug Condrointin shows that the probability of active (pa) maximum value is (0.874); probability of inactive (pi) maximum value is (0.041); and molecular weight (463.36) was recorded. The analgesic drug Condrointin side effects such as asthma, gastrointestinal, weight loss, necrosis, nausea, inflammation, pain, anemia, headache, weakness, acidosis, respiratory failure, hyperthermic, sleep disturbance, carcinogenic, diarrhea,etc., were predicated (Table 5, Figure 3.c)

The biological activity of analgesic drug Diclofenac sodium shows that the probability of active (pa) maximum value is (0.984); probability of inactive (pi) maximum value is (0.004) and molecular weight (318.13) was recorded. The analgesic drug diclofenac sodium shows side-effects such as ulcer, anemia, allergic dermatitis, coma, hypatotoxic, necrosis, asthma, respiratory failure, drowsiness, inflammation, nausea, teratogen, weight gain, eye irritation, weakness, edema, skin irritation, depression irritation, eye irritation, mutagenic, etc., were predicted (Table 6, Figure 1.d)

The biological activity of analgesic drug Dicyclomycin shows that the probability of active (pa) maximum value is (0); probability of inactive (pi) maximum value is (0); molecular weight (302.28) was recorded. The analgesic drug Dicyclomycin side effects such as pulmonary edema, hypercalcaemic, hyperglycemic, acidosis, lactic, pneumotoxic, fatty liver, hematemesis etc., predicted (Table 8, Figure 3.c). The biological activity of analgesic drug dioxathin shows that the probability of active (pa) maximum value is (0.929); probability of inactive (pi) maximum value-is (0.008), molecular weight (456.53) was recorded. The analgesic drug dioxathin side-effects were mutagenic, ataxia, anemia, diarrhea, eye irritation, teratogen, nausea, skin irritation, coma, weakness, hyperglycemic, pulmonary edema, edema, dermatitis, weight loss, anemia, multiple organ failure, hypertensive, cleft palate, muscle weakness etc., were predicted (Table 7, Figure 1.b)

The biological activity of analgesic drug Famotdine shows that the probability of active (pa) maximum value (0.671); probability of inactive (pi) maximum value (0.039) and molecular weight (337.44) was recorded. The analgesic Famotidine drug side-effects such as hepatotoxic, inflammation, teratogen, embryotoxic, hepatitis, nephrotoxic, emetic, pain,


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headache, hyperthermic, nausea etc., were predicted (Table 9, Figure 1.f)

The biological activity of analgesic drug Gentamycin shows that the probability of active (pa) maximum value (0.957) probability of inactive (pi) maximum value (0.004) and molecular weight (477.59) was recorded. The analgesic drug Gentamycin side-effects such as nephrotoxic, embryotoxic, neurotoxic, teratogen, stomatitism, nausea, necrosis, optic neuropathy, excitability, dyskinesia, conjunctivitis, inflammation, sleep disturbance, skin irritation, allergic dermatitis etc., were predicted (Table 10, Figure 1.i)

The biological activity of analgesic drug Ibuprofen shows that the probability of active (pa) maximum value is (0.985) probability of inactive (pi) maximum value is (0.004) and molecular weight (206.28) was recorded. The analgesic drug Ibuprofen side-effects such as hematemesis, edema, sweating, coma, anemia, hepatitis, asthma, stomatitis, optic neurotherpy, optic neuritis, ulcer, inflammation, nausea, pain, headache, hypotension, glaucoma, diarrhea, cyanosis, irritation, weight gain, weakness, teratogen, skin irritation, fatty liver, skin irritation, salorrhea were predicted etc., (Table 11, Figure 1.j)

The biological activity of analgesic drug mafmaftalspasmen shows that the probability of active (pa) maximum value is (0.885); probability of inactive (pi) maximum value is (0.019) and molecular weight (275.81) was recorded. The analgesic drug mafmaftalspasmen side-effects such as euphoria, panic, dystonia, coma, hepatitis, hypertensive, glaucoma, coma, hepatitis, hypertensive, glaucoma, muscle weakness, hypothermic, asthma, disturbance, inflammation, pain, sleep disturbance, inflammation, pain, sleep disturbance, weight loss head ache, eye irritation, ulceration irritation, anemia, skin irritation were predicated (Table 12, Figure 1.e)

PASS was used to predict the biological activity profile of secondary metabolites like Taxol, Vinblastine, Vincristine Topotecan, Irinotecan, Etoposide and Teniposide. Secondary metabolites are also well known for their effectiveness on living species (9). Biological activity of major flavanoids from a medicinal herb, Boesenbergia pandurata Holtt (Zingiberaceae) was predicted through PASS (Maridass, et al., 2008). Principal anti-HIV and other biological activities of pinostrobin, pinocembrin, cardamonin and alpinetin were predicted through PASS, their similarity and difference in the mechanisms of action with reference to accessory biological activities have been compared and verified with the available data on pharmacological and toxicological activity of these compounds. All the four flavanoids observed to have multi potential activities as hepatoprotectant, antipruritic, allergic, anti-inflammatory, neuroprotector other than their principal anti-HIV activity (Maridass, 2008 and Maridass, et al., 2008).

The biological activity of marine algae phytochemicals such as 8α-11-dihydroxypachydictyolA, Bis–prenylatedquinons, Cyckozonarone, Terpeniod C, Styoplactone, 12-hydroxygeranylgeraniol, Dictyone acetate, Ethyl cholesta-diene 3-one, 24-ethylcholestal, Diterpene ,xenicane., Zonaral, Zonarone, Isozonaral., leptosin., Turbinaric acid and Ribofuranosides were reported (Azhaguraj et al., 2010).

The PASS result shows that the phytochemical present in the marine algae could possess several pharmacological activities such as Anti inflammatory, Antineoplastic (nonsmall cell lung cancer),Antineoplastic (lung cancer), Anticarcinogenic,Choleretic,Antioxidant ,Antipruritic ,Cardiovascular analeptic ,Hypercholesterolemic,Antiseborrheic, Antimetastatic, Phosphatase inhibitor, Neurotoxic.,Hyperthermic,Antiviral (Arbovirus), Apoptosis agonist, TERT expression inhibitor, Chemopreventive, Hypokalemia, Antithrombotic. Azhaguraj et al., (2012) reported the biological activity of marine algae Phenazine derivatives, Caulerpin, β-Sitosterol, Taraxerol and Palmtic acid. The results show biological activities like pharmacological (Kinase inhibitor, Neuroprotector and Antiviral), Effects (Oxidoreductase inhibitor, Acid Phosphatase inhibitor) and toxicological activity (Teratogen) of these compounds. The PASS software was useful for the study of biological activity of anti tumor compounds isolated from the brown algae.

Biological activity for compounds present in five major spices namely, cinnamon (Cinnamom umverum), nutmeg (Myristica fragrans), garcinia (Garcinia cambogia), all spice (Pimenta dioica) and black pepper (Piper nigrum), for their biological activity as promising therapeutic compounds (Riju et al., 2009). Out of 328 compounds analyzed, ascorbic acid, nonaldehyde, delphinidin, malabaricone-B, malabaricone- C, isoquercitrin, quercitrin, bisabolol, cis-nerolidol, eudesmol, hexan-1-ol and n-octanal were reported as non-carcinogenic and non-mutagenic phytochemicals (Riju et al., 2009). Biological activity such as anti-inflammatory, antioxidant, antiviral (HIV), antitoxic, free radical scavenging, cardio protectant, hepatoprotectant, antitussive and anti-hemorrhagic etc., (Riju et al., 2009).

PASS was used to predict the biological activity of the compound 2-substituted, 3-mercapto-1, 4-naphthoquinones having wide spectrum activity. It is possible to expect higher probability of antiviral, anti-inflammatory, antiallergic, antipruritic activity for the represented compounds (Stasevych et al., 2006).

Coumarin-4-acetic acids are evaluated for their potential using the computer program, prediction of activity spectra for substances (PASS). The results revealed the correlation between the observed and predicted anti-inflammatory activity of Coumarin-4-acetic acid (Basanagouda et al., 2011). Prediction of activity spectra for substances (PASS) is the computer program


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used to predict the anti-inflammatory activity, mechanism of action, pharmacological activity and toxic or side effects of 3-substituted-4-hydroxy-6- methyl-2H-Pyran-2-ones (Rajendra Prasad et al., 2010 and Rajendra Prasad et al., 2011). Rajendra Prasad et al., (2011) studied the mechanism of action, pharmacological activity and toxic and side effects of 1,3,5-Trisubstituted-2-Pyrazoline derivatives.

In this study the PASS (Prediction of activity spectra for substances) computer program, PASS predicted results show the available information on the pharmacological and toxicological activity of these

compounds and they are corroborative with the previous reports (John de Britto, 2008; Abiya Chelliah, 2008; Maridass, 2008; Maridass, et al., 2008; Pramely and Leon Stephan Raj, 2012; and Azhaguraj, et al., 2012). Certainly, in this present study, such a vital approach has been made to take into an account the particular interest in some kind of toxic and side effects .The accuracy of biological activity prediction through PASS with reference to analgesic drugs constituents is about 90%. Now a bandwidth of drugs for the pain killer drugs and its side effects were analyzed.

Figure 1: Structure of analgesic drugs

Figure 2: Analgesic drugs toxic effects


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0

0.2

0.4

0.6

0.8

1

1.2

Tox

ic e

ffec

ts

COMPOUND STRUCTURE

PA-MAX

PI-MIN

PA-Probability of active; Pi-probability of inactive

Table 1: Analgesic drugs molecular weight (MW)

Sl. No Drugs Molecular weight (mw) 1 Aceclofenac 354.1847 2 Diclomycin 302.2805 3 Condrointin 463.3685 4 Diclofenac sodium 318.1304 5 Dioxathin 456.5387 6 Famotidine 337.4454 7 Combifina 193.1760 8 Gentamycin 477.5954 9 Ibubrofen 206.2808 10 Mafmaftal Spasman 275.8162

Table 2: Biological activity of analgesic drug toxic effects

Sl. No Drugs

Toxic Effects Maximum Minimum Pa Pi Pa Pi

1 Acelofenac 0.990 0.003 0.647 0.054 2 Combifina 0.659 0.041 0.484 0.102 3 Condrointin 0.874 0.015 0.402 0.129 4 Diclofenac sodium 0.984 0.004 0.509 0.078 5 Dicycllomycin 0.0000 0.0000 0.000 0.000 6 Dioxathion 0.929 0.008 0.597 0.050 7 Famotidine 0.671 0.039 0.539 0.072 8 Gentamicin 0.957 0.004 0.000 0.000 9 Ibuprofen 0.985 0.004 0.533 0.073

10 Mafmaftal 0.885 0.019 0.282 0.083


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Pa-Probability of active; Pi-Probability of inactive

Table 3: Biological activity profile of analgesic drug Acelofenac

Pa Pi Activity

0,990 0,003 Toxic, respiration

0,966 0,004 Anemia

0,958 0,001 Gastrointestinal hemorrhage

0,951 0,002 Allergic contact dermatitis

0,936 0,003 Allergic dermatitis

0,935 0,005 Hepatitis

0,921 0,002 Ulcer, peptic

0,910 0,003 Nephrotic syndrome

0,901 0,002 Photoallergy dermatitis

0,895 0,001 Papillary necrosis

0,900 0,011 Conjunctivitis

0,889 0,002 Ulceration

0,891 0,010 Hepatotoxic

0,880 0,004 Gastrointestinal disturbance

0,876 0,007 Toxic, vascular

0,875 0,012 Drowsiness

0,863 0,003 Nephritis

0,837 0,010 Asthma

0,824 0,003 Ulcer, gastric

0,819 0,015 Thrombocytopenia

0,818 0,018 Dizziness

0,813 0,016 Necrosis

0,801 0,009 Endocrine disruptor

0,807 0,015 Excitability

0,807 0,022 Dermatitis

0,805 0,023 Sleep disturbance

0,802 0,021 Ocular toxicity

0,804 0,025 Hematotoxic

0,789 0,010 Coma

0,796 0,022 Sensory disturbance

0,772 0,004 Interstitial nephritis

0,786 0,019 Stomatitis


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0,764 0,009 Hypomagnesemia

0,778 0,023 Headache

0,767 0,026 Pain

0,759 0,021 Respiratory failure

0,768 0,031 Toxic, gastrointestinal

0,753 0,017 Teratogen

0,744 0,011 Keratopathy

0,757 0,031 Nausea

0,745 0,021 Nephrotoxic

0,747 0,024 Xerostomia

0,749 0,026 Reproductive dysfunction

0,739 0,021 Agranulocytosis

0,733 0,019 Embryotoxic

0,729 0,020 Occult bleeding

0,728 0,025 Consciousness alteration

0,725 0,026 Tachycardiac

0,715 0,026 Hyperglycemic

Table 4: Biological activity of analgesic drug Combifina.

Pa Pi Activity

0,839 0,007 Urine discoloration

0,819 0,004 Irritation

0,812 0,004 Hypercholesterolemic



0,809 0,016 Ulcer, aphthous

0,803 0,036 Shivering

0,772 0,011 Non mutagenic, Salmonella


0,756 0,019 Hematemesis

0,757 0,031 Diarrhea

0,707 0,017 Panic

0,713 0,026 Postural (orthostatic) hypotension


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Table 5: Biological activity profile of analgesic drug-Condrointin

Pa Pi Activity

0,954 0,004 Asthma



0,932 0,004 Weight loss

0,928 0,003 Cyanosis



0,887 0,011 Nausea


0,871 0,011 Emetic

0,874 0,015 Toxic



0,831 0,012 Inflammation

0,813 0,018 Pain

0,793 0,025 Behavioral disturbance


0,662 0,021 Ototoxicity

0,663 0,033 Anemia



Table. 6: Biological activity profile of analgesic drug-Diclofenac sodium

Pa Pi Activity





0,930 0,005 Anemia



0,909 0,003 Allergic dermatitis




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0,865 0,007 Coma




0,840 0,009 Asthma










0,781 0,008 Hypothermic



0,777 0,010 Endocrine disruptor

0,772 0,005 Laryngospasm


0,788 0,026 Pure red cell aplasia

0,757 0,009 Withdrawal

0,748 0,011 Hypomagnesemia



0,745 0,016 Bradycardic



0,727 0,005 Bullous pemphigoid

0,731 0,010 Respiratory impairment

0,740 0,023 Leukopenia

0,746 0,029 Sweating


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0,735 0,024 Neurotoxic


0,714 0,010 Pseudoporphyria






0,718 0,036 Pain

Table 7: Biological activity profile of analgesic drug Dicyclomycin

Pa Pi Activity

0,269 0,140 Pulmonary edema

0,201 0,105 Hypercalcaemic

0,301 0,223 Hyperglycemic

0,270 0,194 Acidosis, lactic

0,132 0,085 Pneumotoxic

0,252 0,221 Fatty liver


Table 8: Biological activity of analgesic drug-Dioxathion

Pa Pi Activity


0,929 0,008 Toxic

0,904 0,003 Mutagenic

0,896 0,003 Mutagenic, Salmonella

0,806 0,010 Ataxia

0,805 0,015 Anemia

0,796 0,013 Dyspnea

0,794 0,025 Behavioral disturbance

0,787 0,027 Diarrhea


0,744 0,005 Eye irritation, moderate

0,739 0,005 Skin irritative effect

0,743 0,013 Hyperthermic

0,736 0,021 Dyskinesia


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0,700 0,017 Non mutagenic, Salmonella



0,677 0,007 Lacrimal secretion stimulant

Table 9: Biological activity of analgesic drug-Famotidine

Pa Pi Activity





0,620 0,012 Sneezing



0,603 0,038 Bronchoconstrictor



0,539 0,072 Toxic






0,362 0,170 Emetic



0,327 0,194 Pain






0,266 0,249 Nausea


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Table 10: Biological activity of analgesic drug- Gentamycin

Pa Pi Activity






0,957 0,004 Toxic





0,853 0,000 Vestibular dysfunction

0,847 0,016 Nausea

0,844 0,016 Emetic






0,782 0,023 Pain



Table 11: Biological activity of analgesic drug- Ibuprofen

Pa Pi Activity





0,970 0,003 Acidosis, metabolic


0,932 0,001 Bullous pemphigoid


0,927 0,004 Edema


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0,915 0,002 Pseudoporphyria


0,914 0,008 Acidosis



0,896 0,005 Coma

0,891 0,006 Dyspnea

0,892 0,007 Anemia








0,867 0,008 Asthma


0,849 0,008 Urinary retention


0,837 0,004 Psychoses




0,828 0,005 Optic neuropathy

0,837 0,017 Euphoria

0,825 0,007 Depression


0,817 0,005 Dysarthria

0,812 0,004 Hematuria

0,809 0,004 Optic neuritis




0,792 0,009 Apnea


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0,784 0,007 Hypothermic






0,764 0,005 Dysphoria

0,763 0,005 Parkinsonism






0,756 0,009 Hypercholesterolemic

0,771 0,028 Sleep disturbance


0,755 0,015 Tremor


0,765 0,028 Emetic

0,764 0,030 Nausea



0,739 0,008 Cholestasis

0,734 0,005 Skin irritation, corrosive


0,733 0,005 Porphyria

0,735 0,008 Cataract


0,756 0,032 Pure red cell aplasia

0,750 0,030 Pain




0,726 0,014 Bronchoconstrictor


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0,722 0,010 Anorexiant

0,729 0,022 Dyskinesia

0,726 0,023 Hypertensive




Table 12: Biological activity of analgesic drugs-Mafmaftal spasman

Pa Pi Activity

0,937 0,005 Euphoria

0,924 0,004 Postural (orthostatic) hypotension

0,907 0,004 Twitching


0,884 0,004 Multiple organ failure



0,870 0,005 Fibrillation, atrial

0,849 0,009 Psychomotor impairment

0,795 0,004 Parkinsonism

0,793 0,008 Weight gain

0,781 0,007 Withdrawal


0,772 0,003 Toxic, respiratory center

0,776 0,007 Cyanosis

0,773 0,007 Chorea

0,772 0,008 Dependence

0,766 0,004 Fatty liver

0,764 0,006 Psychoses

0,769 0,011 Paralysis

0,764 0,012 Apnea

0,763 0,012 Galactorrhea

0,778 0,029 Neutrophilicdermatosis (Sweet's syndrome)

0,764 0,015 Extrapyramidal effect

0,758 0,013 Delirium


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0,758 0,014 Edema

0,740 0,005 Yawning


0,749 0,018 Hypotonia

0,744 0,015 Akathisia

0,730 0,007 Laryngospasm

0,736 0,014 Panic

0,729 0,009 QT interval prolongation

0,741 0,025 Weakness


0,718 0,013 Dystonia



0,716 0,024 Urinary retention

0,708 0,017 Depression

0,716 0,027 Nail discoloration

0,708 0,019 Coma

0,694 0,007 Choreoathetosis



0,703 0,020 Dysarthria

0,704 0,023 Acidosis, metabolic

CONCLUSION

The application of computerized system PASS online tool could be used in toxico-informatics and chem-informatics. PASS used in drugs Research and Development in many cases provide the possibilities to select drugs/compounds with desirable spectra of therapeutic effects and side effects/ actions. PASS online is able to give an accurate prediction as they are based on 2Dstructure of the molecule. The present study concludes that the analgesic drugs could possess several pharmacological side effects such as Carcinogenic, Choleretic, Neurotoxic Asthma, Anemia, Eye irritation, Skin irritation, Hyperglycemic, Nausea, Inflammation, Pain, Necrosis, Diarrhea, Coma, Ulcer, Edema, Respiratory failure, Weakness, Optic neuropathy, Embryotoxic, Headache, Nausea, Allergic dermatitis, etc. The PASS software is useful for the study of biological activity of pain killer drugs. From this current studies, it can be concluded that PASS predictions of biological activity spectrum gives a fair approach for the corresponding reported activities of analgesic drugs and determining the other valuable insights of other side effects. ACKNOWLEDGEMENT

The authors would like to thank the Principal, Pachaiyappa’s College for Men, Kanchipuram for providing basic facilities and all the drug stores (pharmacy) in Kanchipuram for their support.


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Citation of this article: Azhagu Raj R, Sumathi S.R., Siva Subramanian.S., Lenin E.A., Ragupathi C, Prakash A. PREDICTION OF ACTIVITY SPECTRA FOR ANALGESICS DRUGS

Journal of Biotechnology and Biosafety. 4(2):359-377. Source of Support: Nil Conflict of Interest: None Declared




EFFECTS OF Lallementia royleana Benth. (Lamiaceae) SEEDS USING ACUTE MILD STRESS MODEL IN NMRI MALE MICE

Research article _____________________________________ Noorulain Hyder1*, Baqir S. Naqvi2, Humera Ishaq1, Shagufta Usman2, Atta Abbas Naqvi3, Safila Naveed4

______________________________________________________

1Department of Pharmacology, Faculty of Pharmacy, Hamdard University, Karachi-74600, Pakistan 2Department of Pharmaceutics, Faculty of Pharmacy, Hamdard University, Karachi-74600, Pakistan 3Department of Pharmacy Practice, College of Clinical Pharmacy, University of Dammam, Saudia Arabia 4 Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Jinnah University for Women *Corresponding author email id: [email protected]

ABSTRACT The objective of this study is to investigate the potential effects of methanolic extract of Lallemantia royleana (Benth) seeds (MeLRS) in male mice with acute mild stress (AMS) model of depression. Medicinal properties of Lallemantia royleanaare not much examined on scientific merits. So far, not a single study has been reported with reference to its anti-depressant potential. Modified forced swimming test (MFST) was used for the evaluation of anti-depressant activity. Since conventional forced swimming test (CFST) is not sensitive than modified FST in elucidating the behavioral effects of antidepressant drugs. Immobility behavior was evaluated in male NMRI mice administered with different doses of the methanolic extract of L. royleana (25, 50, 75mg/kg, p.o) or vehicle (Tween 80, 0.9% NS). Fluoxetine (20mg/kg) and imipramine (15mg/kg) were used as reference drugs. All doses of the methanolic extract of Lallemantia royleana (Benth) seeds (MeLRS) used in this study, produced a significant reduction in % immobility. However, results demonstrated that percentage of immobility time is significantly reduced at 50 mg/kg, (56.67%) p<0.01. The outcome of this study supported Lallemantia royleana traditional usage as an antidepressant and data obtained was comparable to reference drugs. We may conclude that Lallemantia royleana might contain potential active compound that may be of clinical value for treatment of depression.

Keywords: Lallemantia royleana; Modified forced swimming test; Antidepressant; Methanolic seed extract ____________________________________________________________________________________________________

INTRODUCTION Lallemantia royleana is a mucilaginous annual plant associated to the family Lamiaceae from kingdom plantae. The seeds of Lallemantia vernacularly known as “Tukhume-malanga” and Black psyllium (English). Lamiaceae belongs to major and most distinguishing flowering plants, having 236 genera and just about 7173 species distributed worldwide (Dınç, Pınar et al., 2009). These biennial herbs are covered with dense, short pubescence. Leaves are simple leaves; thyrsoid, ovoid, often discontinuous inflorescence, ovate to rotund; lower

leaves petiolate, 1.5-5cm long; Stems simple or branched, 5 -30cm tall, chaetophorous bracteoles up to 1.5cm long and oblong, triangular, smooth and mucilaginous nutlets (Harley, Atkins et al., 2004). Geographically it is grown all over in the Western Asia, India, northern regions of Iraq and Pakistan (Abdulrasool, Abdulmuttalib et al., 2011). The seeds are about 3 millimeter long, 1 millimeter wide and blackish brown in color (Abdulrasool, Abdulmuttalib et al., 2011). These excessively mucilaginous seeds are most commonly used in Indo-Pak subcontinent as an added soothing ingredient in




beverages. In traditional uses it is commonly used for abscesses, gastrointestinal inflammatory diseases, constipation flatulence, rheumatic pain and osteoarthritis (Abdulrasool, Abdulmuttalib et al., 2011; Saxena 2011; Abbas and Hasan 2013). Additionally, folkloric use of these plants reported to possess capability to treat infectious diseases, and renal disorder (Saxena 2011; Mahmood, Hayat et al., 2013). Moreover seeds have also been used in as diuretic, tonic, aphrodisiac, and antitussive remedy and in treatment of various nervous, hepatic, and renal disorders (Ghannadi and Zolfaghari 2003). However, pharmacologically balangu seeds also reported for its hypolipidemic and hypo-cholesterolemic effects (Ghannadi, Movahedian et al., 2015). L. royleana seeds contains carbohydrates, fiber, oil, protein and tannins (Razavi and Moghaddam 2011). Besides previously it is reported that essential oil of Lallemantia contains terpenes (Ghannadi and Zolfaghari 2003). Ethno-botanical studies are previously conducted however very limited medicinal properties of L. royleana are evaluated. In spite of previous reported data, no scientific investigation has been accompanied to verify the use of L. royleana as an anti-depressant. Therefore, this study was conducted in an attempt to assess the methanolic extract of L. royleana seeds in modified forced swimming test (MFST) in mice.

MATERIAL METHODS Plant Extraction For crude extraction 2kg seeds were grounded to fine powder, which was then macerated in 2000 mL of methanol (1:1) for 72 h, after which the liquid was decanted and filtered twice through filter paper to remove all debris. By the help of rotary evaporator the extract was then concentrated in a water bath under reduced pressure at 45 °C. The concentrated residue (17.76g) obtained this is to yield (0.8 %w/w), which was then kept at 4°C in airtight jars until required. The residue was further dissolved in 5 % Tween 80 for final suitable concentrations as required for oral dose.

Laboratory Animals NMRI Male mice (18-25g) procured from Dow University of Health Sciences, Karachi, Sindh Pakistan, were kept in the Animal House of HMI Research lab Hamdard University. They were housed in cages with controlled environment 12 h light and dark cycle, 22–28 ± 2°C temperature and 50 to 60% humidity. Standard food and water were available adlibitum. All the behavioral

experiments were carried out in between 09:00 am to 15:00 pm to keep control of diurnal variations in activity. All animals were housed and all experiments were carried out according to the NIH Guide for the Care and Use of Laboratory Animals (2011) (Kulkarni and Dhir 2007). Research protocol was approved by Institutional Animal Ethics Committee (Ref # FOP-16-04).

Experimental protocol and treatment regimen All the drugs and dilutions were prepared immediately before use and administered at constant volume of 10mL/kg body weight. Methanolic extract of L. royleana seed (MELRS) dissolved in vehicle (5 % tween 80 and 0.9% NaCl) administered orally by orogastric tube at doses of 25, 50 and 75mg/kg. Fluoxetine (FLX) 20mg/kg and imipramine (IMI) 15mg/kg were used as positive control (standard) administered at TDS dosing (three times a day): at 1, 5 and 24 hour before the forced swimming test. All animals were handled in the laboratory environment to avoid any variation in handling, transportation and environmental factors. Thirty six mice were randomly divided into six groups and each group consisted of 6 mice. Group I-III received 25, 50 and 75mg/kg of MeLRS respectively. Group IV wasadministered vehicle (Veh) in same volume i.e. (10mL/kg body weight). Whereas, group V-VI received standard control drugs i.e. fluoxetine 20mg/kg and imipramine 15mg/kg.

Modified forced swim test (MFST) Originally test was proposed by Porsolt (Porsolt, Bertin et al., 1977). Later it was modified (Cryan, Valentino et al., 2005). In the pretest session, mice were allowed to swim for 10 min and returned back to their home cages. Randomly grouped mice received the drug treatments or control via TDS dosing (i.e. 1, 5 and 24 hour). Next day one hour after the last dose mice were again allowed to swim for 5 mins and their behavioral activity was recorded with a video camera. A behavioral sampling method was adopted for scoring i.e. 5 mins duration of the swimming session calculated as 300 secs. The active behavioral actions were recorded such as swimming, climbing and immobility as reported (Cryan, Valentino et al., 2005). Immobility was the behavior when no further motion was observed except when required to keep the mice afloat.




Statistical analysis Data were expressed as mean ± SEM and analyzed by one-way analysis of variance (ANOVA) for multiple comparisons followed by post hoc Tukey's HSD. The accepted level of significance was p<0.05.

RESULTS Effects of the methanolic extract from L. royleana in the forced swimming test for immobility in one-day treatment with acute mild stress (AMS): Statistical analysis revealed significant differences in immobility time in seconds between groups treated with MeLRS at 25, 50 and 75mg/kg [150.3±9; 122±3; 140±2.8; F (5, 30)=373.221; p <0.05- p < 0.01] as compared to standard anti-depressants [IMI 110±2.7 and FLX132.83±2.3; F (5, 30) =373.221; p < 0.01] (Table1). Similarly, percentage inhibition of immobility was most prominently reduced at 50mg/kg of crude extract i.e., 48% decreased 112.67 sec vs. vehicle control, which was 265.67 sec (Fig.1). However, the effect of SSRI antidepressant; fluoxetineless significantly (p>0.05) reduced immobility time i.e. 101.84 sec as compared to MeLRS 50mg/kg (Table1 and Fig.1). Post hoc analysis also revealed that MeLRSat 25 and 50mg/kg induced significant diminution in immobility duration (p< 0.01) at a dose-dependent manner (84.37 sec and 112.67 sec) as compared tovehicle control (234.4 sec) as shown in Table 1. Hence, the most significant dose of MeLRS observed for immobility time reduction was 50mg/kg.

DISCUSSION The research of medicinal plants for antidepressants has recently increased its concern. Because therapy of depression with synthetic antidepressants (i.e. monoamine oxidase inhibitors, selective serotonin reuptake inhibitors, noradrenaline reuptake inhibitors and tricyclics) delivers only 50 percent of alleviation with serious side effects. Black psyllium seeds (L. royleana Benth) from Lamiaceae are commonly used as a nutritional ingredient in traditional use of medical discipline. In spite of the traditional use of L. royleana for treating depression, there is no scientific report to study its anti-depressant potential. In light of results it was demonstrated that acute (one day) oral treatments with methanolic extract of L. royleana Benth. Seeds elicit significant anti-depressant like activity in MFST. These effects are congruent with established reports of medicinal plants from Lamiaceae family and their constituents including Rosmarinus officinalis, Salvia

elegans, Ocimum sanctum, Alafia multiflora, Nepetacataria, Melissa officinalis, Eremostachys laciniata, Ocimum basilicum exhibited antidepressant activity in behavioral models of depression (Herrera-Ruiz, García-Beltrán et al., 2006; Machado, Bettio et al., 2009). The MFST is extensively used pharmacological paradigm for assessment of possible antidepressant activity in mice following acute or sub-acute treatment. The test is based upon immobilization behavior in mice when exposed to inescapable strain which consequently contemplate depression in humans (Foyet, Tsala et al., 2012). MFST is sensitive and comparatively precise to all main forms of antidepressants including tricyclics, serotonin selective reuptake inhibitors, and MAO inhibitors (Slattery and Cryan 2012). The observed effect of MeLRS on immobility time was highly significant compared to control and anti-depressant FLX (20mg/kg) administration. However, comparable effects have been demonstrated when immobility time was observed with IMI (15mg/kg). However, it was also revealed that percentage inhibition of immobility was reduced most significantly at 56.67% at the dose of 50mg/kg, with reduction in immobility time 150.5 sec after acute treatment with MeLRS in acute mild stress. It is worth mentioning that this effect produced by the MeLRS at 50mg/kg p.o was more pronounced than the one produced by the antidepressant fluoxetine (20 mg/kg, p.o.). Results attained in this study effectively suggested the inference of the serotoninergic and catecholaminergic pathway in the antidepressant effect of MeLRS. Furthermore, doses below 25mg/kg of MeLRS haven’t showed any pharmacological effect (data not shown). The exact molecular mechanism of antidepressant effect of MeLRSis not studied in the present study. However, behavioral pattern achieved by MFST is comparable to imipramine, which implies that Lallemantia crude extract acts probably by improvement of serotonergic and nor-adrenergic neurotransmission. The presence of terpenes in Lamiaceae are reported to contain anti-depressant activity (Naghibi, Mosaddegh et al., 2010). However, further research should be carried out to investigate the molecular mechanism of action of L. royleana extracts, as well as the impact on the central nervous system compounds.




CONCLUSION This study showed that methanolic extract of seeds of L. royleana possess antidepressant effects as reported in folkloric medicine and gives an intimation to develop new antidepressant agents from well-known traditional remedies. ACKNOWLEDMENTS We acknowledge the recourse sharing of Dr HMI Research Institute of Pharmacology herbal sciences to perform behavioral experiments.

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Fig. 1: Effects of MeLRS (25, 50 and 75 mg/kg, p.o), FLX (20 mg/kg, p.o) and IMI (15 mg/kg, p.o) after one day acute treatment on total percent inhibition of immobility time exposed to FST for 5 min. P<0.05, P<0.01, P<0.001 vs. vehicle (Veh). Results are expressed as mean ± SEM. (n=6). MeLRS = Methanolic extract of Lallementiaroyleanaseed, FLX= Fluoxetine, IMI=Imipramine, Veh = Vehicle and FST=Forced swimming test Table 1: Effect of crude Methanolic extract of L. royleana on immobility time during AMS forced swimming test (MFST) in male mice in AMS

Treatment (n=6) Dose mg/kg p.o Duration of immobility (sec)

for 5 mins mean± SEM Decrease in

immobility (s) Percentage decrease in

immobility Vehicle 10mL 234.4±1.9 0 0

Imipramine 15mg 110±2.7 124.67 53.1 Fluoxetine 20mg 132.83±2.3 102.84 43.3

MeLRS 25mg 150.3±4 84.37 35.9 MeLRS 50mg 122±3 112.67 48 MeLRS 75mg 140±2.8 94.67 40.3

Citation of this article: NoorulainHyder, Baqir S. Naqvi, Humera Ishaq, ShaguftaUsman, Atta Abbas Naqvi, Safila Naveed. Effects of LallementiaroyleanaBenth. (Lamiaceae) seeds

in acute mid stress modelin NMRI male mice. Journal of Biotechnology and Biosafety. 4(2): 378-382.

Source of Support: Nil Conflict of Interest: None Declared

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