Sciences of Pharmacy Archives (Volume 3 Issue 4)

Maria Elvina Tresia Butar-Butar, Muh Taufiqurrahman, Adhe Septa Ryant Agus, Dwi Retno Sari, Selvina Selvina, Santa Eufrasia Carolin Tokan. Optimization of Cream Formulation with Borneo Tallow Nut, Almond Oil, and Olive Oil Using the Simplex Lattice Design Method. Sciences of Pharmacy. 2024; 3(4):212-219.

Abstract: Dry skin, a common dermatological issue affecting individuals across all age groups, often disrupts the skin's protective barrier, leading to discomfort and increased sensitivity. Addressing this condition involves the use of moisturizers, which play a vital role in restoring skin hydration. In Show more...

Abstract: Dry skin, a common dermatological issue affecting individuals across all age groups, often disrupts the skin's protective barrier, leading to discomfort and increased sensitivity. Addressing this condition involves the use of moisturizers, which play a vital role in restoring skin hydration. In this study, a cream preparation was successfully developed using vegetable oils, including Borneo Tallow Nut, Almond Oil, and Olive Oil. The cream formula was optimized using the Design-Expert software with the Simplex Lattice Design (SLD) method to evaluate the impact of different concentrations of these oils on the cream's pH, viscosity, spreadability, and adhesion. The results demonstrated that the cream exhibited excellent thermo-physical stability, with optimum values of Borneo Tallow Nut at 1.349% w/w, Almond Oil at 3.598% w/w, and Olive Oil at 4.051% w/w. The cream achieved a pH value of 5.702, viscosity of 16.851 Cp, spreadability of 8.147 cm, and adhesion of 63.682 s, with a desirability score of 0.718. This research confirms that Borneo Tallow Nut, Olive Oil, and Almond Oil have significant potential as excipients in cream formulations. Show less...

Vegetable oils Cream spreadability Dry skin cream Cream excipients

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Madhuchandra Lahan. Formulation and Characterization of Resveratrol-Loaded Nanostructured Lipid Carriers (NLC) with Mesua ferrea Seed Oil as Liquid Lipid. Sciences of Pharmacy. 2024; 3(4):203-211.

Abstract: Nanostructured Lipid Carriers (NLCs) are colloidal drug delivery systems composed of both solid and liquid lipids. They enhance drug loading capacity, regulate the release of poorly water-soluble drugs, and are suitable for targeted delivery. Resveratrol, a polyphenol with promising anticancer p Show more...

Abstract: Nanostructured Lipid Carriers (NLCs) are colloidal drug delivery systems composed of both solid and liquid lipids. They enhance drug loading capacity, regulate the release of poorly water-soluble drugs, and are suitable for targeted delivery. Resveratrol, a polyphenol with promising anticancer properties, faces challenges due to its low water solubility, poor bioavailability, and chemical instability, resulting in rapid metabolism and excretion. Therefore, it is crucial to develop a delivery system that safeguards resveratrol during its transit through the body. This study aimed to develop and characterize resveratrol-loaded NLCs using the nano-precipitation method followed by ultrasonication, incorporating Mesua ferrea seed oil as the liquid lipid. The NLCs were evaluated for particle size, morphology (TEM), zeta potential, drug entrapment efficiency, drug loading, and in vitro drug release. The resulting NLCs demonstrated stability and homogeneity, with a particle size of 181.6 ± 12.4 nm, a polydispersity index (PDI) of 0.135 ± 0.09, drug entrapment efficiency of 82.76 ± 12.2%, and drug loading capacity of 42.94 ± 7.5%. They exhibited sustained drug release, achieving 84.56% release within 24 h. These findings suggest that the developed NLCs can effectively enhance the incorporation and controlled release of poorly water-soluble drugs like resveratrol, offering potential advantages over conventional delivery systems. Show less...

Nanostructured Lipid Carrier Resveratrol Mesua ferrea Nanoparticles

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Samukelisiwe Nhlapho, Musawenkosi Hope Lotriet Nyathi, Brendeline Linah Ngwenya, Thabile Dube, Arnesh Telukdarie, Inderasan Munien, Andre Vermeulen, Uche A. K Chude-Okonkwo. Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning. Sciences of Pharmacy. 2024; 3(4):177-192.

Abstract: In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of com Show more...

Abstract: In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of computational techniques in drug discovery in its early stages has significantly transformed the pharmaceutical industry, enabling further efficient screening and selection of possible drug candidates. Therefore, this study explores RO5 using algorithms of Machine Learning (ML), offering a comprehensive method to predict the druggability of pharmaceutical compounds. The study developed, evaluated, and validated the performance metrics of multiple supervised machine learning models. The best model was used to build an application that can predict and classify potential drug candidates. The findings revealed promising capabilities across all models for drug classification. Among all the explored models, Random Forest (RF), Extreme Gradient Boost (XGBoost), and Decision Tree (DT) classifiers demonstrated exceptional performance, achieving near-perfect accuracy of 99.94%, 99.81% and 99.87% respectively. This highlights the robustness of ensemble learning methods in classifying compounds based on RO5 adherence. The comparative analysis of these models underscores the importance of considering balanced accuracy, precision, F1-score, recall, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) score, interpretability, and computational efficiency when choosing between ML algorithms in drug discovery. The DrugCheckMaster application was subsequently developed using the most predictive model and is now available on Render (https://capstone-project-dc7w.onrender.com/). Show less...

Drug discovery Machine learning models Molecular descriptors Rule of five (RO5)

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Mubarak Muhammad Dahiru, James Danga, Abdulhasib Oluwatobi Oni, Hesper Alex Zoaka, Rejoice Daniel Peter, Usanye Zira, Patience Christopher, Hauwa Yahaya Alkasim, Muhammad Zainab. Phytoconstituents and In Vitro Free Radical Scavenging Potential of n-Hexane and Aqueous Fractions of Cucurbita maxima and Leptadenia hastata. Sciences of Pharmacy. 2024; 3(4):193-202.

Abstract: The present study explored the phytoconstituents and radical scavenging activity of the respective n-hexane and aqueous fractions of Cucurbita maxima (CMHF and CMAF) and Leptadenia hastata (LHHF and LHAF) for potential application in oxidative stress-related ailments. The phytoconstituents were qual Show more...

Abstract: The present study explored the phytoconstituents and radical scavenging activity of the respective n-hexane and aqueous fractions of Cucurbita maxima (CMHF and CMAF) and Leptadenia hastata (LHHF and LHAF) for potential application in oxidative stress-related ailments. The phytoconstituents were qualitatively determined and characterized using Fourier-transform Infrared (FTIR), while the antioxidant activity was determined in vitro. Alkaloids were present in only the aqueous fractions of C. maxima and L. hastata, while saponins, steroids, and flavonoids were detected in all the fractions. The FTIR revealed the presence of functional groups, including alcohols, sulfonates, alkenes, alkanes, amines, and aromatics in both plant fractions. The LHHF (35.53 ±2.11 ascorbic acid (AA) equivalent µg/mL) exhibited a significantly (p<0.05) higher total reducing power (TRP) than all the other fractions. The CMHF (69.11 ±2.56 AAE µg/mL) demonstrated a significantly (p<0.05) higher total antioxidant capacity (TAC) than all the other fractions. For the ferric thiocyanate (FTC) assay, the highest inhibition was exhibited by LHHF (79.78 ± 3.24%), significantly (p<0.05) higher than AA (26.46 ± 2.12%), CMHF (69.77 ± 3.16%), and CMAF (43.80 ± 2.12%). In the thiobarbituric acid assay, the lowest MDA concentration was exhibited by the CMHF (0.07 ±0.01 nmol/mL), significantly (p<0.05) lower than all the other fractions and ascorbic acid. Conclusively, the n-hexane fraction of both plants presents potential sources of novel antioxidant compounds with significant free radical scavenging and anti-lipid peroxidation activities, applicable in ailments linked to oxidative stress. Show less...

Antioxidants Functional groups Lipid peroxidation Malonaldehyde concentration

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