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Journal : Pharmacon

Optimasi Masker Gel Peel-Off Ekstrak Etanolik Daun Sirih (Piper Betle L.) dengan Kombinasi Carbomer dan Polivinil Alkohol Setiyadi, Gunawan; Qonitah, Annisa
Pharmacon: Jurnal Farmasi Indonesia Vol 17, No 2 (2020)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/pharmacon.v17i2.11976

Abstract

Betel leaf (Piper betle L.) is a plant that has potential as a source of natural antioxidants. The purpose of this study was to determine the effect of the combination of carbomer and PVA on the homogeneity, dispersibility, adhesion, viscosity, dry time, elasticity and pH of the gel peel-off mask preparation; obtain the optimum formula and determine the antioxidant activity of the optimum formula. Extraction of betel leaf was carried out using the maceration method with 96% ethanol as a solvent. The experimental design was made using Design Expert software version 11 with the Simplex Lattice Design (SLD) method with the optimized components being carbomer and PVA. The response of physical properties in the form of spreadability, adhesion, pH, viscosity, dry time, and elasticity of each formula is measured and the measurement data is processed with Design Expert software to determine the optimum formula based on predetermined criteria. The verification dosage form is made based on the optimum formula composition produced. The response of the optimum physical properties of the prediction formula was compared with the results of the verification preparation with the One sample t-test. The yield of thick betel leaf extract in this study was 20.26%. The optimum formula has homogeneous characteristics with a distinctive odor of dark green betel leaf and semi-solid form, spreadability 49.25 ± 0.29 cm2, adhesion 4.77 ± 0.22 seconds, pH 7.26 ± 0.119, viscosity 763 ± 12.47 dPas, drying time 29.68 ± 0.52 minutes, elasticity 1.05 ± 0.04%. The antioxidant activity of betel leaf ethanolic extract has an IC50 value of 7.62 µg / mL and the optimum formula has an IC50 value of 111.25 µg / mL.
Application Of Factorial Design To Optimize Lubricant Concentration And Granule Mixing Time In The Formulation Of Sour Star Fruit (Averrhoa bilimbi L) Ethanolic Extract Tablet Setiyadi, Gunawan; Putri, Yola Veranita
Pharmacon: Jurnal Farmasi Indonesia Vol 21, Special Issue 1, 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/pharmacon.v21i0.23590

Abstract

The use of lubricant in tablet compaction can reduce both intergranular and granule to die-wall friction that in turen improve granule flow properties and reduce adhesion of granule mass to the die wall. However, the concentration and method of adding lubricants into granule mass has also been known to influence the physical properties of tablets, such as hardness, friability, disintegration time and dissolution. This study aimed to apply 2-factor-2-level-factorial (22) design to determine the effect of lubricant concentration in granule mass (factor A) and the granule mixing time (factor B) on the physical properties of granules and tablets of starfruit leaves (Averrhoa bilimbi L.) ethanolic extract and to obtain the optimum setting of both factors that results in responses that satisfy most of the predefined criteria. The factorial design was set as follows: (l) = 2.5%, 5 minutes, a = 5%, 5 minutes, b = 2.5%, 15 minutes, and ab = 5%, 15 minutes. The lubricant used was a mixture of magnesium stearate and talc in a ratio of 1:9. Experimental design and optimization were carried out using Design Expert 13.0 software with granule flowability, angle of repose, and compressibility, as well as tablet weight uniformity (%RSD), hardness, friability, and disintegration time as responses. Optimum results were obtained with 2.5% lubricant and 5 minutes factor combination. The verification test to the optimum parameters showed that the granule flowability, angle of repose, tablet hardness and tablet friability are within the prediction interval range (PI 95%).