Advancements in Predictive Medicine: NLRP3 Inflammasome Inhibitors and AI-Driven Predictive Health Analytics

Abstract

Recent advancements in predictive medicine are significantly reshaping the field, primarily through developing novel NLRP3 inflammasome inhibitors and applying AI-driven predictive health analytics. NLRP3 inflammasome inhibitors offer new therapeutic strategies for treating inflammatory and neurodegenerative diseases. Concurrently, AI’s role in predictive health analytics marks a transformative shift in disease management and personalized healthcare. By analyzing complex biomarker data, AI provides crucial insights into individual health trajectories, enabling early interventions and customized treatment plans. This convergence of cutting-edge therapies and AI technology heralds a new era in precision medicine and personalized healthcare strategies.

Important Compound Classes

Titles

NLRP3 Inflammasome Inhibitors and Compositions and Uses Thereof; Method for Prediction of Survival Rate and Its Use

Patent Publication Numbers

WO 2023/204967 A1 (URL: https://patents.google.com/patent/WO2023204967A1/en?oq=WO+2023%2f204967+A1);

WO 2023/247674 A1 (URL: https://patents.google.com/patent/WO2023247674A1/en?oq=WO+2023%2f247674+A1)

Publication Dates

October 26, 2023; December 28, 2023

Priority Applications

US 63/333,209; EP 22180465.1

Priority Dates

April 21, 2022; June 22, 2022

Inventors

Zhang, S.; Xu, Y. (WO 2023/204967 A1); Mischak, H. (WO 2023/247674 A1)

Assignee Companies

Virginia Commonwealth University [US/US], 800 East Leigh Street, Suite 3000, Richmond, VA 23298, USA (WO 2023/204967 A1); Mosaiques Diagnostics and Therapeutics AG [DE/DE], Rotenburger Straße 20, 30629 Hannover, Germany (WO 2023/247674 A1)

Disease Area

Cancer and neurodegenerative diseases

Biological Target

NLRP3

Summary

The landscape of predictive medicine is rapidly evolving with ground-breaking discoveries and the integration of advanced technologies. The development of novel NLRP3 (NOD-, LRR-, and pyrin domain-containing protein 3) inflammasome inhibitors exemplifies the evolution of combating inflammatory diseases, while AI-driven analytics play a pivotal role in predicting individual health outcomes.

NLRP3 is a vital component of a complex in the immune system known as the NLRP3 inflammasome. The NLRP3 inflammasome plays a crucial role in innate immunity, the body’s first line of defense against infections and various stressors. When activated, the NLRP3 inflammasome triggers an inflammatory response by activating caspase-1, which promotes the maturation and release of pro-inflammatory cytokines like interleukin-1 beta (IL-1β) and interleukin-18 (IL-18). These cytokines are vital for the immune system’s response to infection and injury. However, overactivation or dysregulation of the NLRP3 inflammasome can contribute to a variety of inflammatory diseases, including autoinflammatory disorders, metabolic disorders like type 2 diabetes, gout, atherosclerosis, and neurodegenerative diseases such as Alzheimer’s disease.

The patent application WO 2023/204967 A1 introduces alkyl 4-(1-(substituted benzyl)-triazol-4-yl)benzoate-type analogs as novel NLRP3-selective inhibitors, offering therapeutic potential for various conditions like Alzheimer’s disease, multiple sclerosis, and COVID-19. Recent research on NLRP3 inflammasome inhibitors has shown significant progress, particularly in their application to aging-related diseases.

The clinical approach to treating NLRP3 inflammasome-related diseases has been using anti-IL-1β antibodies. However, specific inhibitors targeting NLRP3 may offer better therapeutic options. Some promising small-molecule inhibitors of NLRP3 inflammasome that have shown excellent pharmacological effects and good pharmacokinetics include ZYIL1, DFV890, and OLT1177. There is an ongoing effort to discover, optimize, and understand the biological properties of NLRP3 inhibitors from a medicinal chemistry perspective.

AI-Driven Predictive Health Analytics

The patent application WO 2023/247674 A1 presents an innovative AI-based method for predicting an individual’s health risks by analyzing urinary peptidome/proteome. This technique significantly advances personalized medicine, enabling early interventions and tailored treatment strategies. Integrating AI in predictive health analytics is a significant advancement in medicine, enhancing the ability to predict and manage diseases. AI can process complex biomarker data using machine-learning and deep-learning techniques, offering vital insights into an individual’s health risks and trajectory. This approach is particularly transformative in clinical settings, where predictive models assist physicians in identifying and treating patients at a higher risk of serious illnesses. These algorithms provide customized advice and guide clinical practice by analyzing various factors unique to each patient.

One notable application of AI in health analytics is the HAIM (holistic artificial intelligence in medicine) framework, which has demonstrated its effectiveness in handling multimodal clinical datasets. This framework includes scalable patient-centric data pre-processing and enables standardized feature extraction, allowing for rapid prototyping, testing, and deployment of predictive models. The HAIM framework has consistently improved the average AUROC (area under the receiver operating characteristic curve) across all models as data modalities and sources increase. It also suggests that AI can consistently enhance predictive analytics for various healthcare applications compared to single-modality analytics. In particular, vision data contributed significantly to model performance for chest pathology diagnosis tasks. At the same time, historical time-series records of patients were more relevant for predicting length-of-stay and 48-h mortality.

AI’s role in predictive health analytics illustrates a paradigm shift in disease prediction and management. By effectively integrating multiple data sources and modalities, AI helps provide a more comprehensive view of patient health, enabling more accurate predictions and personalized healthcare strategies.

As such, the discovery of NLRP3 inhibitors opens new avenues for treating diseases where inflammation is a key driver. These small-molecule inhibitors target the NLRP3 inflammasome pathway, offering a novel approach to modulate immune responses and alleviate disease symptoms. Integrating AI in predictive health analytics represents a paradigm shift in disease prediction and management. By processing complex biomarker data, AI provides actionable insights into an individual’s health trajectory, facilitating personalized healthcare.

Key Structures

Biological Assay

The biological assay involves testing NLRP3-selective inhibitors (NSIs) for their ability to inhibit the NLRP3 inflammasome in cultured mouse macrophages. The process includes treating macrophages with lipopolysaccharide (LPS) and ATP to induce inflammasome formation, then introducing NSIs to observe their inhibitory effect. The assay measures the release of mature IL-1β to assess NLRP3 inflammasome activity and tests the selectivity of NSIs by measuring IL-1β release after different stimulations. This helps in determining the specific targeting of NSIs toward the NLRP3 inflammasome.

Biological Data

The table below provides exemplary binding affinities of NSIs to human recombinant NLRP3.

Recent Review Articles

See refs (1−6).

The author declares no competing financial interest.

Special Issue

Published as part of ACS Medicinal Chemistry Lettersvirtual special issue “Exploring the Use of AI/ML Technologies in Medicinal Chemistry and Drug Discovery”.