The industrial processing of coconut to produce valuable foods, such as water and milk, generates large volumes of waste, especially the fruit shell. Despite this, material can be used in bioprocess applications, e.g., the production of enzymes, its recalcitrance hinders the cultivation of microorganisms, and low productivity is usually achieved. In this study, the production of cellulolytic enzymes through solid-state fermentation (SSF) and their extraction was investigated using the green coconut fiber pretreated by steam explosion, followed by alkali. The fungus Trichoderma reesei CCT-2768 was cultivated, using an experimental design, to study the effect of the water activity and the amount of biomass in the reactor. The combination of the pretreatment strategies yielded more porous biomass, with less hemicellulose (5.38%, compared to 10.15% of the raw biomass) and more cellulose (47.77% and 33.96% in the pretreated and raw biomasses, respectively). The water activity significantly affected the production of cellulases, with maximum activity yielded at the highest investigated value (0.995). Lastly, the extraction of the enzymes from the cultivation medium was studied, and a 9 g/L NaCl solution recovered the highest CMCase and FPase activities (5.19 and 1.19 U/g, respectively). This study provides an important contribution to the valorization of the coconut residue through (i) the application of the steam explosion technology to optimize the production of cellulases using the SSF technology and (ii) their extraction using different solvents.

Commonly known as marijuana or hemp, Cannabis sativa L. (Cannabaceae), contains numerous active compounds, particularly cannabinoids, which have been extensively studied for their biological activities. Among these, cannabidiol (CBD) stands out for its therapeutic potential, especially given its non-psychotropic effects. This review evaluates the antitumor properties of CBD, highlighting its various mechanisms of action, including the induction of apoptosis, autophagy, and necrosis. By synthesizing findings from in vitro studies on the cell death mechanisms and signaling pathways activated by CBD in various human tumor cell lines, this literature review emphasizes the therapeutic promise of this natural antineoplastic agent. We conducted a comprehensive search of articles in PubMed, Scopus, Springer, Medline, Lilacs, and Scielo databases from 1984 to February 2022. Of the forty-three articles included, the majority (68.18%) reported that CBD activates apoptosis, while 18.18% observed simultaneous apoptosis and autophagy, 9.09% focused on autophagy alone, and 4.54% indicated necrosis. The antitumor effects of CBD appear to be mediated by transient receptor potential cation channels (TRPVs) in endometrial cancer, glioma, bladder cancer, and myeloma, with TRPV1, TRPV2, and TRPV4 playing key roles in activating apoptosis. This knowledge paves the way for innovative therapeutic strategies that may enhance cancer treatment outcomes while minimizing the toxicity and side effects associated with conventional therapies.

Cancer remains one of the leading causes of death worldwide and continues to pose a serious public health challenge. The limited success of many current treatments—often due to toxicity, poor selectivity, and the development of drug resistance—highlights the need for new and more effective therapeutic options. Phytochemicals have emerged as a valuable source of anticancer agents, offering rich structural diversity and a wide range of biological activities. However, identifying promising compounds from the vast chemical space of natural products remains difficult using conventional screening methods, which are typically slow, costly, and inefficient. In recent years, artificial intelligence (AI) has begun to transform phytochemical-based drug discovery. Machine learning and deep learning approaches are now used to support key steps in the discovery process, including metabolite identification, virtual screening, target prediction, and toxicity assessment. By integrating chemical, biological, and multi-omics data, AI enables a more systematic and data-driven exploration of natural product diversity. Despite these advances, challenges persist, particularly the scarcity of high-quality experimental data, the structural complexity of phytochemicals, and their limited representation in public databases. This review critically examines current AI applications in phytochemical-based anticancer drug discovery and discusses emerging strategies aimed at overcoming these limitations. Overall, AI-driven phytochemical screening represents a promising path toward accelerating the development of next-generation cancer therapies. Graphical Abstract