The current study describes the synthesis, physicochemical characterization and cytotoxicity evaluation of a new series of pyrrole derivatives in order to identify new bioactive molecules. The new pyrroles were obtained by reaction of benzimidazolium bromide derivatives with asymmetrical acetylenes in 1,2-epoxybutane under reflux through the Huisgen [3 + 2] cycloaddition of several ylide intermediates to the corresponding dipolarophiles. The intermediates salts were obtained from corresponding benzimidazole with bromoacetonitrile. The structures of the newly synthesized compounds were confirmed by elemental analysis, spectral techniques (i.e., IR, ¹H-NMR and ¹³C-NMR) and single-crystal X-ray analysis. The cytotoxicity of the synthesized compounds was evaluated on plant cells (i.e., Triticum aestivum L.) and animal cells using aquatic crustaceans (i.e., Artemia franciscana Kellogg and Daphnia magna Straus). The potential antitumor activity of several of the pyrrole derivatives was studied by performing in vitro cytotoxicity assays on human adenocarcinoma-derived cell lines (i.e., LoVo (colon), MCF-7 (breast), and SK-OV-3 (ovary)) and normal human umbilical vein endothelial cells (HUVECs). The obtained results of the cytotoxicity assessment indicated that the tested compounds had nontoxic activity on Triticum aestivum L., while on Artemia franciscana Kellogg nauplii, only compounds 2c and 4c had moderate toxicity. On Daphnia magna, 4b and 4c showed high toxicity; 2a, 2b, and 2c moderate to high toxicity; only 4a and 4d were nontoxic. The compound-mediated cytotoxicity assays showed that several pyrrole compounds demonstrated dose- and time-dependent cytotoxic activity against all tested tumor cell lines, the highest antitumor properties being achieved by 4a and its homologue 4d, especially against LoVo colon cells.

Solidago virgaurea L. (European goldenrod, Woundwort), Asteraceae, is a familiar medicinal plant in Europe and other parts of the world, widely used and among the most researched species from its genus. The aerial parts of European goldenrod have long been used for urinary tract conditions and as an anti-inflammatory agent in the traditional medicine of different peoples. Its main chemical constituents are flavonoids (mainly derived from quercetin and kaempferol), C6-C1 and C6-C3 compounds, terpenes (mostly from the essential oil), and a large number of saponin molecules (mainly virgaureasaponins and solidagosaponins). Published research on its potential activities is critically reviewed here: antioxidant, anti-inflammatory, analgesic, spasmolitic, antihypertensive, diuretic, antibacterial, antifungal, antiparasite, cytotoxic and antitumor, antimutagenic, antiadipogenic, antidiabetic, cardioprotective, and antisenescence. The evidence concerning its potential benefits is mainly derived from non-clinical studies, some effects are rather modest, whereas others are more promising, but need more confirmation in both non-clinical models and clinical trials.

The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling.

With a widespread distribution throughout the Northern Hemisphere and 11 genera, Pinaceae is the largest family of Gymnosperms in the world. Essential oils are an important chemotaxonomic marker for the species of this family, although the degree of chemical and biological investigation has not been the same for all genera. Essential oils from Abies and Cedrus (from the abietoid clade) or Pinus and Picea (from the pinoid clade) have been more extensively investigated with respect to their chemical composition and biological or pharmacological properties, including their antioxidant effects. Instead, essential oils from the other genera of the family have been less explored in this respect or even have not been investigated at all. This is a narrative review looking into the knowledge acquired up to date, the variability and limitations of the current methods used to estimate antioxidant effects, and multiple comparisons between EOs obtained from different genera, species, and plant parts, as well as potential applications and future directions of research and utilization of essential oils derived from Pinaceae species.

(1) Background: The mussel (Mytilus edulis, Mytilus galloprovincialis) is the most widespread lamellibranch mollusk, being fished on all coasts of the European seas. Mussels are also widely grown in Japan, China, and Spain, especially for food purposes. This paper shows an original technique for mussel shell processing for preparation of calcium salts, such as calcium levulinate. This process involves synthesis of calcium levulinate by treatment of Mytilus galloprovincialis shells with levulinic acid. The advantage of mussel shell utilization results in more straightforward qualitative composition. Thus, the weight of the mineral component lies with calcium carbonate, which can be used for extraction of pharmaceutical preparations. (2) Methods: Shell powder was first deproteinized by calcination, then the mineral part was treated with levulinic acid. The problem of shells generally resulting from the industrialization of marine molluscs creates enough shortcomings, if one only mentions storage and handling. One of the solutions proposed by us is the capitalization of calcium from shells in the pharmaceutical industry. (3) Results: The toxicity of calcium levulinate synthesized from the mussel shells was evaluated by the method known in the scientific literature as the Constantinescu phytobiological method (using wheat kernels, Triticum vulgare Mill). Acute toxicity of calcium levulinate was evaluated; the experiments showed the low toxicity of calcium levulinate. (4) Conclusion: The experimental results highlighted calcium as the predominant element in the composition of mussel shells, which strengthens the argument of capitalizing the shells as an important natural source of calcium.

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.