TRIPLE OBJECTIVE OPTIMIZATION OF CYTOTOXIC POTENCY OF HUMAN CARCINOMA CELL LINES OF A MARINE MACROALGAE USING NON-SORTING GENETIC ALGORITHM–A THEORETICAL STUDY

Objective: The main purpose of our work was to arrive at an acceptable model for optimizing the cytotoxic potency of Ulva fasciata Delile extract on human carcinoma cell lines of which can provide believable indications as compared to experimental results.Methods: The experimental result for cytotoxic potency of a methanolic extract of the Ulva fasciata Delile (MEUF) with a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay against human colon carcinoma (HT-29), human hepatocyte carcinoma (Hep-G2), and human breast carcinoma (MCF-7) cell lines was used to carry out a multi-objective (triple objective) optimization. Thirty non-dominating solutions were considered for analyses of absorbance (y1), % cell survival (y2) and% cell inhibition (y3) data.Results: The model developed using non-dominated sorting genetic algorithm (NSGA) was compared with data obtained experimentally and the results were found to be significant. This method has distinct advantages over other methods which relied heavily on statistical-regression-models, in the sense that it does triple-objective optimization. The resulted in obtaining solutions which were not only significant or believable, but it also corroborated well with experimental results. Thus the solutions obtained during optimization provided the necessary data for generating a successful model.Conclusion: The solutions obtained by NSGA method helped to build an acceptable model for optimizing the cytotoxic potency of Ulva fasciata Delile on human carcinoma cell lines

Evolution of modern age drug discovery of lipopeptides and computer-aided drug discovery in India

503-508The endeavor has been attempted to present a review on the evolution of modern age drug discovery in India. The contribution of next generation therapeutics options microbial metabolites and the computational drug discovery aspects to the global market from India have been represented. Microbial metabolites such as lipopeptides and peptide therapeutics are gaining worldwide importance due to their multiple applications as broad-spectrum antimicrobial, antiviral, anticancer properties etc. Due to the surge of microbial resistance, tumor resistance, and ongoing pandemic due to constantly mutating corona virus, there is a need to develop next-generation therapeutics options from natural origin, less toxic to the environment, and have higher specificity towards target. Small molecule therapeutics are certainly less specific towards cancer targets hence the cytotoxicity is a major issue in cancer treatment while drug resistance due to the mutations are coming as challenges every day for drug discovery researchers. Microbial lipopeptide reserves a sweet spot in between the small molecule inhibitors and peptide therapeutics because of their amphiphilic compounds consist of a fatty acid side chain and a cyclic peptide moiety of hydrophilic nature. The computational drug discovery approach accelerates the drug discovery process due to the advancement in supercomputer facilities provided by various funding agencies such as the Department of Biotechnology (DBT) and the Department of Science and Technology (DST) in India. The current review article is focusing light on the research contribution of Indian Scientists and Govt. of India in the field of lipopeptide-based research and applications of Computer-aided drug discovery

Ultrasonication mode for the expedition of extraction process of chitin from the maritime shrimp shell waste

431-438Worldwide, marine crustacean waste is a major problem for environmental pollution, and it is a severe risk to the coastline area. Shellfish wastes consist of some commercially valuable products, mainly the chitin. The extraction of chitin from the shellfish waste is very complicated and required a successive pretreatment process. Sonication can improve the process of extraction of chitin from the shrimp shell waste. In this study, the conventional and ultrasonication method of pretreatment was applied and compared for the extraction of chitin. By the conventional method, 12 h was required for the removal of calcium and proteins each. In contrast, only 6 h was required for the complete removal of calcium and proteins each, by the ultrasonication assisted method. After pretreatment, the results were analyzed and compared by the already purified commercial chitin using Fourier-transform infrared spectroscopy. Ultrasonication improves the rate of reaction of the pretreatment by the process of cavitation. By this work, the ultrasonication technique was proved to be much faster than the conventional method for the pretreatment process

Multiobjective simultaneous optimization of biosurfactant process medium by integrating differential evolution with artificial neural networks

A method of differential evolution (DE) integrated with artificial neural networks (ANN) is derived for modelling and optimization of a biosurfactant process producing rhamnolipid by Pseudomonas aeruginosa. A central composite rotatable design (CCRD) data is used to develop multiple regression and ANN response surface models in order to integrate them with DE for optimizing the medium compositions. The DE with global search operators explores the search space of the response surface models and finds the optimum medium compositions that maximize the rhamnolipid productivity. A multiobjective simultaneous optimization strategy that integrates ANN model with DE search is found to compromise for biomass concentration and maximize the rhamnolipid activity as 55.9 mg/L (R2 = 0.914) with an optimized medium compositions of glucose=24.079; NH4NO3=3.28; KH2PO4=0.24; yeast extract=7.95 and MgSO4.7H2O=2.69. The experimental rhamnolipid activity of 56 mg/L obtained using the optimized medium compositions are close to the predicted rhamnolipid activity. These findings demonstrate that the ANN-DE integrated multi objective optimization strategy is quite effective for simultaneous optimization of biochemical and biotechnological processes

Multiobjective simultaneous optimization of biosurfactant process medium by integrating differential evolution with artificial neural networks

335-344A method of differential evolution (DE) integrated with artificial neural networks (ANN) is derived for modelling and optimization of a biosurfactant process producing rhamnolipid by Pseudomonas aeruginosa. A central composite rotatable design (CCRD) data is used to develop multiple regression and ANN response surface models in order to integrate them with DE for optimizing the medium compositions. The DE with global search operators explores the search space of the response surface models and finds the optimum medium compositions that maximize the rhamnolipid productivity. A multiobjective simultaneous optimization strategy that integrates ANN model with DE search is found to compromise for biomass concentration and maximize the rhamnolipid activity as 55.9 mg/L (R2 = 0.914) with an optimized medium compositions of glucose=24.079; NH4NO3=3.28; KH2PO4=0.24; yeast extract=7.95 and MgSO4.7H2O=2.69. The experimental rhamnolipid activity of 56 mg/L obtained using the optimized medium compositions are close to the predicted rhamnolipid activity. These findings demonstrate that the ANN-DE integrated multi objective optimization strategy is quite effective for simultaneous optimization of biochemical and biotechnological processes

Evolution of modern age drug discovery of lipopeptides and computer-aided drug discovery in India

The endeavor has been attempted to present a review on the evolution of modern age drug discovery in India. The contribution of next generation therapeutics options microbial metabolites and the computational drug discovery aspects to the global market from India have been represented. Microbial metabolites such as lipopeptides and peptide therapeutics are gaining worldwide importance due to their multiple applications as broad-spectrum antimicrobial, antiviral, anticancer properties etc. Due to the surge of microbial resistance, tumor resistance, and ongoing pandemic due to constantly mutating corona virus, there is a need to develop next-generation therapeutics options from natural origin, less toxic to the environment, and have higher specificity towards target. Small molecule therapeutics are certainly less specific towards cancer targets hence the cytotoxicity is a major issue in cancer treatment while drug resistance due to the mutations are coming as challenges every day for drug discovery researchers. Microbial lipopeptide reserves a sweet spot in between the small molecule inhibitors and peptide therapeutics because of their amphiphilic compounds consist of a fatty acid side chain and a cyclic peptide moiety of hydrophilic nature. The computational drug discovery approach accelerates the drug discovery process due to the advancement in supercomputer facilities provided by various funding agencies such as the Department of Biotechnology (DBT) and the Department of Science and Technology (DST) in India. The current review article is focusing light on the research contribution of Indian Scientists and Govt. of India in the field of lipopeptide-based research and applications of Computer-aided drug discovery