Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges

Area of Science:

• Environmental Science
• Toxicology
• Nanotechnology

Background:

• Assessing the ecotoxicity of nanomaterials (NMs) necessitates understanding their bioaccumulation in organisms.
• Experimental bioaccumulation determination is challenging, driving the development of predictive models for NMs.
• Conventional models (BLM, QSAR) may yield inaccurate results due to NMs' unique uptake behaviors.

Purpose of the Study:

• To critically review existing models for predicting NM bioaccumulation.
• To analyze the feasibility and limitations of current bioaccumulation models for NMs.
• To explore the potential of machine learning (ML) approaches for improved NM bioaccumulation prediction.

Main Methods:

• Literature review of established and emerging models for NM bioaccumulation.
• Critical analysis of model applicability, considering NM-specific uptake mechanisms.
• Evaluation of machine learning-based models for *in silico* prediction of NM biological behavior.

Main Results:

• Conventional models face limitations due to differing uptake patterns of NMs compared to traditional substances.
• Machine learning models show promise in predicting NM biological interactions and bioaccumulation.
• ML approaches offer new insights into bioaccumulation modeling and identification of critical features.

Conclusions:

• Current predictive models for NM bioaccumulation require critical evaluation regarding their limitations.
• Machine learning offers a promising data-driven approach for enhancing the accuracy of NM bioaccumulation predictions.
• Further research into ML-driven models can improve our understanding of NM behavior in organisms and identify key factors influencing bioaccumulation.