Drugs and chemicals
All chemicals, drugs and reagents used in the study were analytical grade; methanol (Carlo erba group reagents, Italy), morphine (EPHARM, Ethiopia), acetyl salicylic acid (EPHARM, Ethiopia), distilled water (EPHARM, Ethiopia), and glacial acetic acid and diethyl ether (sigma- Aldrich laborachemikalin, Germany) obtained and used from the respective vendors.
Plant material
The leaf of Otostegia integrifolia was collected in December 2017 from a town called Tulu Dimtu, which is located in North West Shewa, Oromiya, its geographical coordinates are 9° 41’ 0” North, 38° 40’ 0” East, about 29 km southeast of Addis Ababa, Ethiopia (figure 1). Identification & authentication of the plant specimen was done by a taxonomist and a voucher specimen (AD001) was deposited at the National Herbarium, College of Natural and Computational Sciences, Addis Ababa University, for future reference.
Extraction
Five hundred gram of air-dried and powdered plant material was extracted with 80% methanol by cold maceration technique for three consecutive days at room temperature to get the crude hydroalcoholic extract. The resulting liquid extract was then filtered with Whatman no 1 filter paper & concentrated using rotavapor (Buchilabortechnik AG, Switzerland) at 40oC under reduced pressure and the concentrated extract was freeze-dried using a lyophilizer (Heto Power Dry LL3000 freeze-dryer;, USA). A yellowish brown, particularly of apricot type, hygroscopic shiny powder with percentage yield of 16.6% (w/w) was obtained.
Phytochemical analysis
The 80% methanol extract of the seeds of Otostegia integrifolia was screened for the possible presence of secondary metabolites, including alkaloids, tannins, flavonoids, terpenoids and saponins, phenols using qualitative phytochemical screening procedures described elsewhere [16]
Experimental animals
A total of 50 healthy swiss albino mice (25–35 g, 6–8 weeks of age) of either sex were obtained from the animal house of school of pharmacy, Addis Ababa University. They were provided with standard pellet and water ad libtum under a controlled environment (12 h light– dark cycle and temperature of 23–25°C). Animals were acclimatized for one week before commencement of the experiment and after completion of the study the animals were anesthetized with diethyl ether and euthanized. The care and handling of animals were in line with international guidelines [17] and the protocol was approved by institutional review board of the School of Pharmacy (Reference no. ERB/SOP/120/11/2017).
Grouping and dosing of animals
A total of 60 male Swiss albino mice were used for all the experiments. Mice were randomly divided into five groups with each group consisting of 6 mice. Group I served as negative control were administered with distilled water. Group II, Group III and Group IV were given 100mg/kg, 200mg/kg and 400mg/kg of the extract, respectively as per the acute toxicity study result conduct elsewhere. Group V served as positive control received standard drug morphine for hot plate method, 150 mg/kg aspirin for acetic acid induced writhing test. Administration of all agents was performed via an oral route.
Analgesic activity
Hot Plate method
In this method the animals were tested for analgesia screening and mice showing greater than 15 s of latency time on a hot plate maintained at −50 ± 0.1 °C were excluded. The induction of analgesia (time in seconds for which mice-remained on the hot plate without licking or flicking of hind-limb or jumping) was recorded at 0, 30, 60 and 120 min after the administration of the plant samples and drug. To avoid tissue-damage, cut off-time of 15 s was set-for all animals. Percent analgesia-was calculated using the following formula [18].
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)
Acetic acid- induced writhing method
For the writhing test overnight fasted mice were grouped into control and three experimental groups of six mice each and administered with respective doses of the extract and aspirin. To assess analgesic activity of various groups, the number of writhes that was indicated by stretching of the abdomen with simultaneous stretching of at least one hind limb was counted for each mouse for 20 min using a latency period of 5 min, and the percentage was calculated using the formula described below[19].
![](data:image/png;base64,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)
Statistical Analysis
All data found from the research were expressed as mean ± standard error of the mean (SEM). Data was analyzed by one way analysis of variance (ANOVA) followed by Tukey post-hoc test to determine statistical significance using statistical package for social science (SPSS) version 25. P values less than 0.05 were taken as statistically significant.