Nano-Antioxidants, A Next-Generation Therapeutic Strategy

Chauhan, Pallavi; Tayal, Shivam; Jha, Namrata

doi:10.1201/9781003129585-1

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The potential use of NMs to reduce damages related to oxidative stress and prevent diseases is the subject of intense scientific research [20,24]. The potential use of NMs in treatments of degenerative Alzheimer's disease, cancer, metabolic diseases and chronic inflammatory diseases has been reported [25,26]. Ghorbani [12] demonstrated that NMs have stable anti-ox activity with specific pharmacokinetic actions, intrinsic ROSscavenging properties and fewer adverse effects.…”

Section: Introductionmentioning

confidence: 99%

Employing Supervised Algorithms for the Prediction of Nanomaterial’s Antioxidant Efficiency

Mirzaei

Furxhi

Murphy

et al. 2023

IJMS

View full text Add to dashboard Cite

Reactive oxygen species (ROS) are compounds that readily transform into free radicals. Excessive exposure to ROS depletes antioxidant enzymes that protect cells, leading to oxidative stress and cellular damage. Nanomaterials (NMs) exhibit free radical scavenging efficiency representing a potential solution for oxidative stress-induced disorders. This study aims to demonstrate the application of machine learning (ML) algorithms for predicting the antioxidant efficiency of NMs. We manually compiled a comprehensive dataset based on a literature review of 62 in vitro studies. We extracted NMs’ physico-chemical (P-chem) properties, the NMs’ synthesis technique and various experimental conditions as input features to predict the antioxidant efficiency measured by a 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. Following data pre-processing, various regression models were trained and validated. The random forest model showed the highest predictive performance reaching an R2 = 0.83. The attribute importance analysis revealed that the NM’s type, core-size and dosage are the most important attributes influencing the prediction. Our findings corroborate with those of the prior research landscape regarding the importance of P-chem characteristics. This study expands the application of ML in the nano-domain beyond safety-related outcomes by capturing the functional performance. Accordingly, this study has two objectives: (1) to develop a model to forecast the antioxidant efficiency of NMs to complement conventional in vitro assays and (2) to underline the lack of a comprehensive database and the scarcity of relevant data and/or data management practices in the nanotechnology field, especially with regards to functionality assessments.

show abstract