Liver Microtissue Histopathology: How AI Image Analysis Can Enhance Research Efficiency in Drug Development

Imagine that a promising new drug for chronic pain is stopped during clinical trials. Not because it is ineffective, but because it unexpectedly causes liver damage.
This scenario of hepatotoxicity is often a bitter reality in the pharmaceutical industry, as the liver plays a crucial role in drug metabolism. Hepatotoxicity is a common cause of drug candidate failure.¹ Therefore, understanding liver health and drug-induced hepatotoxicity is not only a scientific endeavor; it can also be a matter of life or death for patients who urgently need a drug that is not approved because it causes liver damage.
Innovative technologies such as AI (artificial intelligence), driving image analysis, are becoming an important tool in ensuring the drug safety and efficacy. Researchers are trying to bridge the gap between in vitro models and human responses in drug development, toxicology, and disease modelling. One such researcher is Simon Plummer.
 

The process of liver microtissue analysis begins with fixing the liver microtissue, followed by the preparation of microTMA. Once the microarrays are prepared, the next step is to section the microtissues, followed by H&E staining to visualize the tissue structures.

After staining, the samples are scanned using a slide scanner, in conjunction with ZEN microscopy software. This allows high-resolution imaging of the stained sections.

Finally, the images are annotated and analyzed using ZEISS arivis Cloud, which allows for training of AI models with no need to code, to facilitate advanced image processing and data interpretation.
 

Using microscopy and AI analysis, Simon Plummer and his team were able to measure hypertrophy and demonstrate that this response could be recapitulated in both rat and human liver microtissues. The results showed a significant increase in the cytoplasmic area of the hepatocytes, and the induction of phase 1 and phase 2 enzymes was confirmed by proteomics analysis. Interestingly, the sensitivity of the AI algorithm in measuring this response differed between species.

It was observed that the specificity of the AI algorithm was lower in images of rat liver microtissues, compared to those of human liver microtissues. This discrepancy was attributed to the cell membrane being more clearly defined in human samples due to qualitative differences in the H&E staining.

Thus, the MicroMatrices team addressed questions related to the recapitulation of a key event – liver hepatocyte hypertrophy – in the mechanism of liver carcinogenesis in rats. They used 3D microtissues to model this response and assess, whether it also occurs in human liver microtissues.
 

Early exploration of AI-driven image analysis has revealed several advantages for its application in microscopy. Simon Plummer’s team has found that AI-driven analysis is efficient for measuring distance and morphological parameters, providing accurate and reliable data.

For identifying variations in diffuse staining intensity within images, threshold-based image analysis is a suitable method, while AI-driven approaches may serve different analytical purposes. This highlights the importance of selecting the appropriate technique based on specific research needs, as different methods can be tailored to address diverse analytical challenges.

The next steps in the research are to use AI to identify morphological changes in other microtissues, including those from the brain, heart, and kidney.