FORMULATION OF ATOVAQUONE TABLET USING BIOSURFACTANT IN A TERNARY SOLID DISPERSION SYSTEM: IN VITRO AND IN VIVO EVALUATION

Objective: The goal of the present investigation was to improve the solubility and bioavailability of atovaquone tablet, using in-house biosynthesized biosurfactant in the ternary system of solid dispersion containing hydrophilic polymers with varying concentrations of biosurfactant. Atovaquone is an anti-malarial agent and belongs to biopharmaceutical classification system class IV. Methods: The solid dispersion of binary and ternary mixture was prepared using hydroxyl propyl methyl cellulose (HPMC) and biosurfactant respectively by a solvent evaporation method. All the atovaquone tablet formulations were prepared by incorporation of physical mixture, binary and ternary solid dispersed products with excipients by direct compression method. Pre-compression and post-compression parameters of atovaquone tablets were evaluated. In vivo bioavailability study was performed using female albino rabbits. Results: In vitro dissolution profile of binary and ternary system of solid dispersion products showed 8.65% and 34.64% respectively. Precompression and post-compression values of all atovaquone tablets formulations were within the specified limits. In vitro dissolution efficiency of F2 and F5 were 1.44 fold and 6.62 fold respectively, in accordance to the F1. In vivo study revealed that bioavailability of optimized formulation F5 was increased by 2.5 times and time to reach peak concentration was reduced to 1.4 h, in accordance to pure atovaquone suspension. Conclusion: Potential application of biosurfactant in the solid dosage form of atovaquone tablet was proved for enhanced dissolution rate and bioavailability of atovaquone for malaria treatment

Physiological characterization of Jasmine flower (Jasminum sambac) senescence during storage

The aim of this work was to identify metabolic differences and hormonal profiles in jasmine flower (Jasminum sambac) and to investigate the possibility that experimental promotion of retardation of the senescence of jasmine flower may mediated by abscisic acid (ABA) and phenolic content. Determinations of ABA and phenols were made in flower senescing under different conditions using two different packaging materials such as polyethylene (PE) and polypropylene (PP) of 200 gauge micron thickness with no ventilation. Pre-treatment of 4 % boric acid for jasmine flowers was selected. Abscisic acid levels in petals also increased during senescence 91.27 pmol g-1, but much less in boric acid-treated jasmine flower 34.16 pmol g-1. However, the lowest content of total phenolics was measured in buds and partially opened flowers 50.90 μg/g but highest in fully opened 61.80 μg/g on the fourth day of storage, respectively. It was concluded that boric acid prevented the early rise in ethylene production and considerably improved jasmine flower shelf-life

Scaling Law for Criticality Conditions in Heterogeneous Energetic Materials under Shock Loading

Initiation in heterogeneous energetic material (HEM) subjected to shock loading occurs due to the formation of hot spots. The criticality of the hot spots governs the initiation and sensitivity of HEMs. In porous energetic materials, collapse of pores under impact leads to the formation of hot spots. Depending on the size and strength of the hot spots chemical reaction can initiate. The criticality of the hot spots is dependent on the imposed shock load, void morphology and the type of energetic material. This work evaluates the relative importance of material constitutive and reactive properties on the criticality condition of spots. Using a scaling-based approach, the criticality criterion for cylindrical voids as a function of shock pressure, Ps and void diameter, Dvoid is obtained for two different energetic material HMX and TATB. It is shown that the criticality of different energetic materials is significantly dependent on their reactive properties

Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i.e., shape descriptors), structure-property-performance (S-P-P) linkage estimation, and optimization/design exploration. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials-by-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research