Machine learning techniques accelerate development of anti-inflammatory biomaterials

(Nanowerk News) Biocompatibility is a crucial goal in medical device development. For years, scientists have worked to create implant coatings and components that integrate well with the body's immune systems. Despite thorough efforts, most prosthetics and implants still cause some inflammation, reducing their therapeutic value. The challenging pursuit of developing biomaterials that effectively control inflammation with minimal side effects may now benefit from an unexpected ally: artificial intelligence.
When any foreign material enters the body, resident immune cells called macrophages trigger inflammation to contain potential pathogens. But chronic inflammation damages tissues and causes implanted devices to be rejected. Polymers are commonly used as coatings and hydrogels to make implants more biocompatible, with properties that mitigate inflammation. However, designing effective anti-inflammatory polymers is challenging. Researchers must screen countless polymer combinations over months of expensive trial-and-error experiments.
Recent advances in machine learning offer shortcuts by predicting polymer anti-inflammatory potential. In a new study published in Advanced NanoBiomed Research ("Machine Learning-Based Prediction of Immunomodulatory Properties of Polymers: Toward a Faster and Easier Development of Anti-Inflammatory Biomaterials"), scientists demonstrate how even limited cellular testing data combined with Bayesian analysis techniques can train predictive models. These models accurately classify which polymers will curb inflammation and by how much.
Schematic presentation of the work on prediction of anti-inflammatory properties of the polymers using machine learning
Schematic presentation of the work on prediction of anti-inflammatory properties of the polymers using ML. (© Wiley-VCH Verlag)
“Our work opens completely new prospects for how functional biomaterials are developed,” said study co-author Dr. Varvara Gribova of the University of Strasbourg. “Instead of lengthy trial-and-error screening, models can select the most promising polymer candidates in advance, making development faster and cheaper.”

The Complex Biology of Inflammation

When a biomaterial implant triggers inflammation, macrophages surround the site and secrete signaling proteins called cytokines to recruit other immune cells. Key cytokines promoting inflammation include TNF-alpha and compounds spurring nitric oxide (NO) production. The researchers focused modeling efforts on predicting polymers’ suppression of these two inflammation markers.
“We defined the initial immune response to implants in a simplified way, as macrophages’ capacity to produce inflammatory molecules,” said principal investigator Dr. Nihal Engin Vrana. “While inflammation is actually more complex, TNF-alpha and NO are relatively easy to quantify experimentally as initial indicators of anti-inflammatory potential.”

Limited Data Yields Powerful Predictions

The team performed assays monitoring 50 unique polymers’ effects on mouse macrophage inflammation. They tracked TNF-alpha and NO levels along with cell mortality rates and other polymer characteristics. Though a small dataset, they hypothesized machine learning techniques designed for limited information could still extract useful relationships.
They first built a Bayesian logistic regression model correlating different test measurements with overall anti-inflammatory activity, defined as TNF-alpha suppression. Analysis revealed the probability of a polymer having anti-inflammatory effects is tripled if positively charged. Unit molecular weight also positively correlated with reduced inflammation. These insights can directly inform designs of new polymers.
The team trained and tested two types of classifiers—K-nearest neighbors (KNN) and Naive Bayes models—to categorize polymers as either anti-inflammatory or proinflammatory based on metrics like nitric oxide secretion profiles. Both models performed with a high degree of accuracy, with the KNN algorithm correctly predicting anti-inflammatory versus proinflammatory polymers in 94% of cases.
The analysis also revealed substantial correlations between certain polymer characteristics and inflammation-mediating effects; positively charged polymers were found to be three times more likely to exhibit anti-inflammatory capabilities. Molecular weight additionally tracked closely with immunosuppressive properties.
“Our study suggests that even modest yet thoughtfully designed datasets combined with Bayesian techniques can provide valuable insights into functional polymer properties,” said Dr. Vrana. “This enables selecting the most promising polymer candidates to progress to further development.”

Accelerating the Iterative Design Process

The researchers emphasize their methodology serves for early-phase screening rather than final inflammation assessment. Top predicted polymer candidates would still undergo more rigorous in vitro and in vivo validation. However, their approach shortcuts the initial trial-and-error process to pick best prospects upfront.
The scientists aim to expand capabilities of their inflammation prediction models going forward. They will incorporate more diverse training data, additional polymer traits and refined definitions of anti-inflammatory effects. They believe integrating machine learning into the materials design cycle can revolutionize development of implants and devices with targeted therapeutic properties.
“Instead of lucky accidental findings or lengthy screening, models can provide an ‘express test’ selecting optimal polymers for a particular biomedical application,” said Dr. Vrana. “This will contribute to quality-of-life for patients by getting new multifunctional solutions onto the market faster.”
Source: Nanowerk (Note: Content may be edited for style and length)