Interpretable Causal System Optimization Framework for the Advancement of Biological Effect Prediction and Redesign of Nanoparticles.

Nanomedicine has promising applications in disease treatment, given the remarkable safety concerns (e.g., nanotoxicity and inflammation) of nanomaterials, and realizing the trade-off between the immune response and organ burden of NPs and deeply understanding the interactions of the organism-nano systems are crucial to facilitate the biological applications of NPs. Here, we propose an interpretable causal system optimization (ICSO) framework and construct the upstream and downstream tasks of accurate prediction and intelligent NP optimization. ICSO framework screens the key drivers (recovery duration, specific surface area, and nanomaterial size) and potential causal information for immune responses and organ burden, revealing the hidden priming/constraint effects in bionano interactions. ICSO can be used to quantify the thresholds of biological responses to multiple properties (e.g., the specific surface area, diameter, and zeta potential). ICSO provides quantitative information and constraint conditions for the design of highly biocompatible and targeted organ delivery nanomaterials. For example, negative inflammation is reduced by 36.19%, and positive lung accumulation is promoted by 40.14% by optimizing the specific surface areas and shape and increasing the diameter-to-length ratio. ICSO overcomes the limitations of experience-dependent approaches and provides powerful and automated solutions for decision-makers during nanomaterial design.