Alkaloids from lotus (Nelumbo nucifera): recent advances in biosynthesis, pharmacokinetics, bioactivity, safety, and industrial applications.

Different parts of lotus (Nelumbo nucifera Gaertn.) including the seeds, rhizomes, leaves, and flowers, are used for medicinal purposes with health promoting and illness preventing benefits. The presence of active chemicals such as alkaloids, phenolic acids, flavonoids, and terpenoids (particularly alkaloids) may account for this plant's pharmacological effects. In this review, we provide a comprehensive overview and summarize up-to-date research on the biosynthesis, pharmacokinetics, and bioactivity of lotus alkaloids as well as their safety. Moreover, the potential uses of lotus alkaloids in the food, pharmaceutical, and cosmetic sectors are explored. Current evidence shows that alkaloids, mainly consisting of aporphines, 1-benzylisoquinolines, and bisbenzylisoquinolines, are present in different parts of lotus. The bioavailability of these alkaloids is relatively low in vivo but can be enhanced by technological modification using nanoliposomes, liposomes, microcapsules, and emulsions. Available data highlights their therapeutic and preventive effects on obesity, diabetes, neurodegeneration, cancer, cardiovascular disease, etc. Additionally, industrial applications of lotus alkaloids include their use as food, medical, and cosmetic ingredients in tea, other beverages, and healthcare products; as lipid-lowering, anticancer, and antipsychotic drugs; and in facial masks, toothpastes, and shower gels. However, their clinical efficacy and safety remains unclear; hence, larger and longer human trials are needed to achieve their safe and effective use with minimal side effects.

Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO2 system.

Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on simple linear regression algorithms that are prone to underfitting the training data. These models rely heavily on experimental and expensive computational quantum mechanical descriptors, which significantly limit their practical use. Herein, we present the application of empirical descriptors and complex machine learning algorithms to the development of high-performance quantitative structure-toxicity relationship (QSTR) models of TiO2 hybridized with multi-metallic (Ag, Au, Pt) alloy nanoparticles (multi-metallic NPs/TiO2). To confirm the viability of empirical descriptors as model input, we selected five distinct machine learning algorithms for predicting the toxicity of multi-metallic alloy NPs/TiO2 system in Chinese hamster ovary cell line. Notably, an empirical descriptor-based QSTR model (kernel ridge regression) revealed a predictive performance that is on par with density functional theory (DFT) descriptor-based counterparts. More specifically, the results indicated that model selection is influenced by descriptor choice, such that complex DFT descriptors worked best with a complex algorithm (random forest regression; RMSET = 0.0954, MAET = 0.0811, RT2 = 0.9411), whereas more straightforward empirical descriptors were most suitable with a simpler algorithm (kernel ridge regression; RMSET = 0.1244, MAET = 0.1106, RT2 = 0.8999). Moreover, our model outperforms existing QSAR models built on the same data set. This study offers a new perspective on using empirical features to develop accurate predictive computational models for the rapid discovery and profiling of safe-by-design nanomaterials.