Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Molecular mechanisms of silver nanoparticle-induced neurotoxic injury and new perspectives for its neurotoxicity studies: A critical review

Silver nanoparticles (AgNPs) garnered significant attention and applications in the field of nanotechnology due to their unique physicochemical properties. However, with the increasing exposure of AgNPs in the environment and biological systems, concerns about their potential neurotoxicity have also risen. Recent studies on the neurotoxic effects and mechanisms of AgNPs have often relied on traditional toxicological research methods and perspectives. This reliance has limited the extrapolation of these findings to the human brain environment and hindered a deep understanding of the neurotoxicity of AgNPs. This review first outlines the molecular mechanisms of AgNPs-induced neurotoxic injury from a traditional research perspective, identifying oxidative stress, inflammatory responses, and autophagy disorders as key areas of current research. Related molecular signaling pathways, including the nuclear transcription factor-κB (NF-κB) signaling pathway, the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway, and the calcium signaling pathway, have been implicated in the neurotoxic injury process induced by AgNPs. Subsequently, we elucidated the unique advantages of the 3D brain organoids applied to the neurotoxicity study of AgNPs by drawing on relevant studies in the same field. We also emphasize that establishing a standardized 3D brain organoids construction platform is a crucial prerequisite for its widespread application. Furthermore, we suggest that future studies should explore the neurotoxicity mechanisms of AgNPs through the lenses of "adaptive homeostasis" and "structure-activity relationship analysis". In conclusion, the neurotoxicity of AgNPs should be comprehensively evaluated by integrating new research techniques and perspectives, ultimately allowing these nanoparticles to better serve human society.

Prediction of Dynamic Toxicity of Nanoparticles Using Machine Learning

Predicting the toxicity of nanoparticles plays an important role in biomedical nanotechnologies, in particular in the creation of new drugs. Safety analysis of nanoparticles can identify potentially harmful effects on living organisms and the environment. Advanced machine learning models are used to predict the toxicity of nanoparticles in a nutrient solution. In this article, we performed a comparative analysis of the current state of research in the field of nanoparticle toxicity analysis using machine learning methods; we trained a regression model for predicting the quantitative toxicity of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of MSE = 2.19 and RMSE = 1.48; we trained a multi-class classification model for predicting the toxicity class of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of Accuracy = 0.9756, Recall = 0.9623, F1-Score = 0.9640, and Log Loss = 0.1855. As a result of the analysis, we concluded the good predictive ability of the trained models. The optimal dosages for the nanoparticles under study were determined as follows: ZnO = 9.5 × 10-5 mg/mL; Fe3O4 = 0.1 mg/mL; SiO2 = 1 mg/mL. The most significant features of predictive models are the diameter of the nanoparticle and the nanoparticle concentration in the nutrient solution.

Nanotechnology's frontier in combatting infectious and inflammatory diseases: prevention and treatment

Inflammation-associated diseases encompass a range of infectious diseases and non-infectious inflammatory diseases, which continuously pose one of the most serious threats to human health, attributed to factors such as the emergence of new pathogens, increasing drug resistance, changes in living environments and lifestyles, and the aging population. Despite rapid advancements in mechanistic research and drug development for these diseases, current treatments often have limited efficacy and notable side effects, necessitating the development of more effective and targeted anti-inflammatory therapies. In recent years, the rapid development of nanotechnology has provided crucial technological support for the prevention, treatment, and detection of inflammation-associated diseases. Various types of nanoparticles (NPs) play significant roles, serving as vaccine vehicles to enhance immunogenicity and as drug carriers to improve targeting and bioavailability. NPs can also directly combat pathogens and inflammation. In addition, nanotechnology has facilitated the development of biosensors for pathogen detection and imaging techniques for inflammatory diseases. This review categorizes and characterizes different types of NPs, summarizes their applications in the prevention, treatment, and detection of infectious and inflammatory diseases. It also discusses the challenges associated with clinical translation in this field and explores the latest developments and prospects. In conclusion, nanotechnology opens up new possibilities for the comprehensive management of infectious and inflammatory diseases.

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.

Two-dimensional printing of nanoparticles as a promising therapeutic method for personalized drug administration

The necessity for personalized patient treatment has drastically increased since the contribution of genes to the differences in physiological and metabolic state of individuals have been exposed. Different approaches have been considered so far in order to satisfy all of the diversities in patient needs, yet none of them have been fully implemented thus far. In this framework, various types of 2D printing technologies have been identified to offer some potential solutions for personalized medication, which development is increasing rapidly. Accurate drug-on-demand deposition, the possibility of consuming multiple drug substances in one product and adjusting individual drug concentration are just some of the few benefits over existing bulk pharmaceuticals manufacture, which printing technologies brings. With inclusion of nanotechnology by printing nanoparticles from its dispersions some further opportunities such as controlled and stimuli-responsive drug release or targeted and dose depending on drug delivery were highlighted. Yet, there are still some challenges to be solved before such products can reach the pharmaceutical market. In those terms mostly chemical, physical as well as microbiological stability concerns should be answered, with which 2D printing technology could meet the treatment needs of every individual and fulfill some existing drawbacks of large-scale batch production of pharmaceuticals we possess today.