Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease

Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease

Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials.

Challenges in IBD Research 2024: Precision Medicine

Precision medicine is part of 5 focus areas of the Challenges in IBD Research 2024 research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. Building on Challenges in IBD Research 2019, the current Challenges aims to provide a comprehensive overview of current gaps in inflammatory bowel diseases (IBDs) research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient-centric research prioritization. In particular, the precision medicine section is focused on the main research gaps in elucidating how to bring the best care to the individual patient in IBD. Research gaps were identified in biomarker discovery and validation for predicting disease progression and choosing the most appropriate treatment for each patient. Other gaps were identified in making the best use of existing patient biosamples and clinical data, developing new technologies to analyze large datasets, and overcoming regulatory and payer hurdles to enable clinical use of biomarkers. To address these gaps, the Workgroup suggests focusing on thoroughly validating existing candidate biomarkers, using best-in-class data generation and analysis tools, and establishing cross-disciplinary teams to tackle regulatory hurdles as early as possible. Altogether, the precision medicine group recognizes the importance of bringing basic scientific biomarker discovery and translating it into the clinic to help improve the lives of IBD patients.

The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis

BACKGROUND AND OBJECTIVES

This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition.

METHOD

A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English.

RESULTS

Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis.

CONCLUSIONS

When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

Therapeutic effects of genistein in experimentally induced ulcerative colitis in rats via affecting mitochondrial biogenesis

Ulcerative colitis (UC) is an inflammatory bowel disease that affects the mucosa of the colon, resulting in severe inflammation and ulcers. Genistein is a polyphenolic isoflavone present in several vegetables, such as soybeans and fava beans. Therefore, we conducted the following study to determine the therapeutic effects of genistein on UC in rats by influencing antioxidant activity and mitochondrial biogenesis and the subsequent effects on the apoptotic pathway. UC was induced in rats by single intracolonic administration of 2 ml of 4% acetic acid. Then, UC rats were treated with 25-mg/kg genistein. Colon samples were obtained to assess the gene and protein expression of nuclear factor erythroid 2-related factor-2 (Nrf2), heme oxygenase-1 (HO-1), peroxisome proliferator-activated receptor-gamma coactivator (PGC-1), mitochondrial transcription factor A (TFAM), B-cell lymphoma 2 (BCL2), BCL2-associated X (BAX), caspase-3, caspase-8, and caspase-9. In addition, colon sections were stained with hematoxylin/eosin to investigate the cell structure. The microimages of UC rats revealed inflammatory cell infiltration, hemorrhage, and the destruction of intestinal glands, and these effects were improved by treatment with genistein. Finally, treatment with genistein significantly increased the expression of PGC-1, TFAM, Nrf2, HO-1, and BCL2 and reduced the expression of BAX, caspase-3, caspase-8, and caspase-9. In conclusion, genistein exerted therapeutic effects against UC in rats. This therapeutic activity involved enhancing antioxidant activity and increasing mitochondrial biogenesis, which reduced cell apoptosis.

Linking gene expression to clinical outcomes in pediatric Crohn's disease using machine learning

Pediatric Crohn's disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPE tissue support the importance of colonic gene expression and the potential for machine learning-based models in predicting outcomes for pediatric CD.

Omics and Multi-Omics in IBD: No Integration, No Breakthroughs

The recent advent of sophisticated technologies like sequencing and mass spectroscopy platforms combined with artificial intelligence-powered analytic tools has initiated a new era of "big data" research in various complex diseases of still-undetermined cause and mechanisms. The investigation of these diseases was, until recently, limited to traditional in vitro and in vivo biological experimentation, but a clear switch to in silico methodologies is now under way. This review tries to provide a comprehensive assessment of state-of-the-art knowledge on omes, omics and multi-omics in inflammatory bowel disease (IBD). The notion and importance of omes, omics and multi-omics in both health and complex diseases like IBD is introduced, followed by a discussion of the various omics believed to be relevant to IBD pathogenesis, and how multi-omics "big data" can generate new insights translatable into useful clinical tools in IBD such as biomarker identification, prediction of remission and relapse, response to therapy, and precision medicine. The pitfalls and limitations of current IBD multi-omics studies are critically analyzed, revealing that, regardless of the types of omes being analyzed, the majority of current reports are still based on simple associations of descriptive retrospective data from cross-sectional patient cohorts rather than more powerful longitudinally collected prospective datasets. Given this limitation, some suggestions are provided on how IBD multi-omics data may be optimized for greater clinical and therapeutic benefit. The review concludes by forecasting the upcoming incorporation of multi-omics analyses in the routine management of IBD.

Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach

Inflammatory bowel disease (IBD) is an illness with increasing prevalence, particularly in emerging countries, which can have a substantial impact on the quality of life of the patient. The illness is rather heterogeneous with different evolution among patients. A machine learning approach is followed in this paper to identify potential genes that are related to IBD. This is done by following a Monte Carlo simulation approach. In total, 23 different machine learning techniques were tested (in addition to a base level obtained using artificial neural networks). The best model identified 74 genes selected by the algorithm as being potentially involved in IBD. IBD seems to be a polygenic illness, in which environmental factors might play an important role. Following a machine learning approach, it was possible to obtain a classification accuracy of 84.2% differentiating between patients with IBD and control cases in a large cohort of 2490 total cases. The sensitivity and specificity of the model were 82.6% and 84.4%, respectively. It was also possible to distinguish between the two main types of IBD: (1) Crohn's disease and (2) ulcerative colitis.

Curative effects of crocin in ulcerative colitis via modulating apoptosis and inflammation

Ulcerative colitis (UC) is an inflammatory bowel disease with characteristic inflammation to mucosal cells in rectum and colon leading to lesions in mucosa and submucosa. Moreover, crocin is a carotenoid compound among active constituents of saffron with many pharmacological effects as antioxidant, anti-inflammatory and anticancer activities. Therefore, we aimed to investigate therapeutic effects of crocin against UC through affecting the inflammatory and apoptotic pathways. For induction of UC in rats, intracolonic 2 ml of 4% acetic acid was used. After induction of UC, part of rats was treated with 20 mg/kg crocin. cAMP was measured using ELISA. Moreover, we measured gene and protein expression of B-cell lymphoma 2 (BCL2), BCL2-associated X (BAX), caspase-3/8/9, NF-κB, tumor necrosis factor (TNF)-α and IL-1β/4/6/10. Colon sections were stained with hematoxylin-eosin and Alcian blue or immune-stained with anti-TNF-α antibodies. Microscopic images of colon sections in UC group revealed destruction of intestinal glands associated with infiltration of inflammatory cell and severe hemorrhage. While images stained with Alcian blue showed damaged and almost absent intestinal glands. Crocin treatment ameliorated morphological changes. Finally, crocin significantly reduced expression levels of BAX, caspase-3/8/9, NF-κB, TNF-α, IL-1β and IL-6, associated with increased levels of cAMP and expression of BCL2, IL-4 and IL-10. In conclusion, protective of action of crocin in UC is proved by restoration of normal weight and length of colon as well as improvement of morphological structure of colon cells. The mechanism of action of crocin in UC is indicated by activation of anti-apoptotic and anti-inflammatory effects.

Mise au point de l’utilisation de l’intelligence artificielle dans la prise en charge des maladies inflammatoires chroniques de l’intestin

Complexity of inflammatory bowel diseases (IBD) lies on their management and their biology. Clinics, blood and fecal samples tests, endoscopy and histology are the main tools guiding IBD treatment, but they generate a large amount of data, difficult to analyze by clinicians. Because of its capacity to analyze large number of data, artificial intelligence is currently generating enthusiasm in medicine, and this technology could be used to improve IBD management. In this review, after a short summary on IBD management and artificial intelligence, we will report pragmatic examples of artificial intelligence utilisation in IBD. Lastly, we will discuss the limitations of this technology.

A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer

Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.

Protective effects of flavonoids isolated from <i>Agrocybe aegirita</i> on dextran sodium sulfate-induced colitis

Mushroom derived phytochemical has become the promising agent to treat inflammatory bowel disease (IBD). Here, we investigated the effect of flavonoids from <i>Agrocybe aegirita</i> (AAF) on dextran sodium sulfate-induced colitis. Our results showed that flavonoids from <i>Agrocybe aegirita</i> had a certain effect on physical signs in mice (improving the weight loss of mice, reducing reducing the DAI index and the spleen index of mice). AAF could also significantly reduce the shortening of the colon, and improve the level of tissue damage and colon inflammation. Besides, AAF could alleviate the colon inflammatory status including reducing the levels of TNF-α and IL-1β and increasing the levels of IL-10. In addition, AAF significantly promoted the growth of goblet cells and enhance the intestinal barrier function (the secretion of mucin in the colon were increased). In conclusion, flavonoids from <i>Agrocybe aegirita</i> has the potential to relieve the DSS-induced colitis in mice and could be a novel therapy for combating with IBD.