4.7 Review

Emerging Technologies for In Vitro Inhalation Toxicology

Journal

ADVANCED HEALTHCARE MATERIALS
Volume 10, Issue 18, Pages -

Publisher

WILEY
DOI: 10.1002/adhm.202100633

Keywords

air-liquid-interfaces; inhalation; lungs-on-chip; machine learning; toxicology

Funding

  1. BfR SFP [1322-735, 1322-725]
  2. HMC

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Respiratory toxicology is a major research focus in the 21st century due to the increased annual morbidity from airborne viral infections and pollutant inhalation. Research connecting human exposure to air contaminants with adverse pulmonary health outcomes is crucial, and different strategies are being used in inhalation toxicity studies to assess a variety of inhalable substances. The integration of bioengineering, machine learning, and artificial intelligence in systems inhalation toxicology approaches is discussed as a future model for toxicology research.
Respiratory toxicology remains a major research area in the 21st century since current scenario of airborne viral infection transmission and pollutant inhalation is expected to raise the annual morbidity beyond 2 million. Clinical and epidemiological research connecting human exposure to air contaminants to understand adverse pulmonary health outcomes is, therefore, an immediate subject of human health assessment. Important observations in defining systemic effects of environmental contaminants on inhalation metabolic dysfunction, liver health, and gastrointestinal tract have been well explored with in vivo models. In this review, a framework is provided, a paradigm is established about inhalation toxicity testing in vitro, and a brief overview of breathing Lungs-on-Chip (LoC) as design concepts is given. The optimized bioengineering approaches and microfluidics with their fundamental pros, and cons are presented. There are different strategies that researchers apply to inhalation toxicity studies to assess a variety of inhalable substances and relevant LoC approaches. A case study from published literature and frame arguments about reproducibility as well as in vitro/in vivo correlations are discussed. Finally, the opportunities and challenges in soft robotics, systems inhalation toxicology approach integrating bioengineering, machine learning, and artificial intelligence to address a multitude model for future toxicology are discussed.

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