Arthrospira platensis (A. platensis) aqueous extract has massive amounts of natural products that can be used as future drugs, such as C-phycocyanin, allophycocyanin, etc. This extract was chosen because of its high adaptability, which reflects its resolute genetic composition. The proactive roles of cyanobacteria, particularly in the medical field, have been discussed in this review, including the history, previous food and drug administration (FDA) reports, health benefits and the various dose-dependent therapeutic functions that A. platensis possesses, including its role in fighting against lethal diseases such as cancer, SARS-CoV-2/COVID-19, etc. However, the remedy will not present its maximal effect without the proper delivery to the targeted place for deposition. The goal of this research is to maximize the bioavailability and delivery efficiency of A. platensis constituents through selected sites for effective therapeutic outcomes. The solutions reviewed are mainly on parenteral and tablet formulations. Moreover, suggested enteric polymers were discussed with minor composition variations applied for better storage in high humid countries alongside minor variations in the polymer design were suggested to enhance the premature release hindrance of basic drugs in low pH environments. In addition, it will open doors for research in delivering active pharmaceutical ingredients (APIs) in femtoscale with the use of various existing and new formulations.Abbrevations: SDGs; Sustainable Development Goals, IL-4; Interleukin-4, HDL; High-Density Lipoprotein, LDL; Low-Density Lipoprotein, VLDL; Very Low-Density Lipoprotein, C-PC; C-Phycocyanin, APC; Allophycocyanin, PE; Phycoerythrin, COX-2; Cyclooxygenase-2, RCTs; Randomized Control Trials, TNF-α; Tumour Necrosis Factor-alpha, γ-LFA; Gamma-Linolenic Fatty Acid, PGs; Polyglycans, PUFAs: Polyunsaturated Fatty Acids, NK-cell; Natural Killer Cell, FDA; Food and Drug Administration, GRAS; Generally Recognized as Safe, SD; Standard Deviation, API; Active Pharmaceutical Ingredient, DW; Dry Weight, IM; Intramuscular, IV; Intravenous, ID; Intradermal, SC; Subcutaneous, AERs; Adverse Event Reports, DSI-EC; Dietary Supplement Information Executive Committee, cGMP; Current Good Manufacturing Process, A. platensis; Arthrospira platensis, A. maxima; Arthrospira maxima, Spirulina sp.; Spirulina species, Arthrospira; Spirulina, Tecuitlatl; Spirulina, CRC; Colorectal Cancer, HDI; Human Development Index, Tf; Transferrin, TfR; Transferrin Receptor, FR; Flow Rate, CPP; Cell Penetrating Peptide, SUV; Small Unilamenar Vesicle, LUV; Large Unilamenar Vesicle, GUV; Giant Unilamenar Vesicle, MLV; Multilamenar Vesicle, COVID-19; Coronavirus-19, PEGylated; Stealth, PEG; Polyethylene Glycol, OSCEs; Objective Structured Clinical Examinations, GI; Gastrointestinal Tract, CAP; Cellulose Acetate Phthalate, HPMCP, Hydroxypropyl Methyl-Cellulose Phthalate, SR; Sustained Release, DR; Delay Release, Poly(MA-EA); Polymethyl Acrylic Co-Ethyl Acrylate, f-DR L-30 D-55; Femto-Delay Release Methyl Acrylic Acid Co-Ethyl Acrylate Polymer, MW; Molecular Weight, Tg; Glass Transition Temperature, SN2; Nucleophilic Substitution 2, EPR; Enhance Permeability and Retention, VEGF; Vascular Endothelial Growth Factor, RGD; Arginine-Glycine-Aspartic Acid, VCAM-1; Vascular Cell Adhesion Molecule-1, P; Coefficient of Permeability, PES; Polyether Sulfone, pHe; Extracellular pH, ζ-potential; Zeta potential, NTA; Nanoparticle Tracking Analysis, PB; Phosphate Buffer, DLS; Dynamic Light Scattering, AFM; Atomic Force Microscope, Log P; Partition Coefficient, MR; Molar Refractivity, tPSA; Topological Polar Surface Area, C log P; Calculated Partition Coefficient, CMR; Calculated Molar Refractivity, Log S; Solubility Coefficient, pka; Acid Dissociation Constant, DDAB; Dimethyl Dioctadecyl Ammonium Bromide, DOPE; Dioleoylphosphatidylethanolamine, GDP; Good Distribution Practice, RES; Reticuloendothelial System, PKU; Phenylketonuria, MS; Multiple Sclerosis, SLE; Systemic Lupus Erythematous, NASA; National Aeronautics and Space Administration, DOX; Doxorubicin, ADRs; Adverse Drug Reactions, SVM; Support Vector Machine, MDA; Malondialdehyde, TBARS; Thiobarbituric Acid Reactive Substances, CRP; C-Reactive Protein, CK; Creatine Kinase, LDH; Lactated Dehydrogenase, T2D; Type 2 Diabetes, PCB; Phycocyanobilin, PBP; Phycobiliproteins, PEB; Phycoerythrobilin, DPP-4; Dipeptidyl Peptidase-4, MTT; 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide, IL-2; Interleukin-2, IL-6; Interleukin-6, PRISMA; Preferred Reporting Items for Systematic Reviews and Meta-Analyses, STATA; Statistics, HepG2; Hepatoblastoma, HCT116; Colon Cancer Carcinoma, Kasumi-1; Acute Leukaemia, K562; Chronic Leukaemia, Se-PC; Selenium-Phycocyanin, MCF-7; Breast Cancer Adenocarcinoma, A375; Human Melanoma, RAS; Renin-Angiotensin System, IQP; Ile-Gln-Pro, VEP; Val-Glu-Pro, Mpro; Main Protease, PLpro; Papin-Like Protease, BMI; Body Mass Index, IC50; Inhibitory Concentration by 50%, LD50; Lethal Dose by 50%, PC12 Adh; Rat Pheochromocytoma Cells, RNS; Reactive Nitrogen Species, Hb1Ac; hemoglobin A1c.
Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.
The world at large is facing a new threat with the emergence of the Coronavirus Disease 2019 (COVID-19) pandemic. Though imperceptible by the naked eye, the medical, sociological and economical implications caused by this newly discovered virus have been and will continue to be a great impediment to our lives. This health threat has already caused over two million deaths worldwide in the span of a year and its mortality rate is projected to continue rising. In this review, the potential of algae in combating the spread of COVID-19 is investigated since algal compounds have been tested against viruses and algal anti-inflammatory compounds have the potential to treat the severe symptoms of COVID-19. The possible utilization of algae in producing value-added products such as serological test kits, vaccines, and supplements that would either mitigate or hinder the continued health risks caused by the virus is prominent. Many of the characteristics in algae can provide insights on the development of microalgae to fight against SARS-CoV-2 or other viruses and contribute in manufacturing various green and high-value products.