Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection

Treating Autoimmune Diseases With LANCL2 Therapeutics: A Novel Immunoregulatory Mechanism for Patients With Ulcerative Colitis and Crohn's Disease

Lanthionine synthetase C-like 2 (LANCL2) therapeutics have gained increasing recognition as a novel treatment modality for a wide range of autoimmune diseases. Genetic ablation of LANCL2 in mice results in severe inflammatory phenotypes in inflammatory bowel disease (IBD) and lupus. Pharmacological activation of LANCL2 provides therapeutic efficacy in mouse models of intestinal inflammation, systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, and psoriasis. Mechanistically, LANCL2 activation enhances regulatory CD4 + T cell (Treg) responses and downregulates effector responses in the gut. The stability and suppressive capacities of Treg cells are enhanced by LANCL2 activation through engagement of immunoregulatory mechanisms that favor mitochondrial metabolism and amplify IL-2/CD25 signaling. Omilancor, the most advanced LANCL2 immunoregulatory therapeutic in late-stage clinical development, is a phase 3 ready, first-in-class, gut-restricted, oral, once-daily, small-molecule therapeutic in clinical development for the treatment of UC and CD. In this review, we discuss this novel mechanism of mucosal immunoregulation and how LANCL2-targeting therapeutics could help address the unmet clinical needs of patients with autoimmune diseases, starting with IBD.

Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm

OBJECTIVE

Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whether the proposed pose estimation model and the skeleton-based action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants.

METHODS

This observational study prospectively assessed 30 full-term infants and 54 preterm infants using the Alberta Infant Motor Scale (58 movements) from 4 to 18 months of age with their movements recorded by 5 video cameras simultaneously in a standardized clinical setup. The movement videos were annotated for the start/end times and presence of movements by 3 pediatric physical therapists. The annotated videos were used for the development and testing of an AI algorithm that consisted of a 17-point human pose estimation model and a skeleton-based action recognition model.

RESULTS

The infants contributed 153 sessions of Alberta Infant Motor Scale assessment that yielded 13,139 videos of movements for data processing. The intra and interrater reliabilities for movement annotation of videos by the therapists showed high agreements (88%-100%). Thirty-one of the 58 movements were selected for machine learning because of sufficient data samples and developmental significance. Using the annotated results as the standards, the AI algorithm showed satisfactory agreement in classifying the 31 movements (accuracy = 0.91, recall = 0.91, precision = 0.91, and F1 score = 0.91).

CONCLUSION

The AI algorithm was accurate in classifying 31 movements in full-term and preterm infants from 4 to 18 months of age in a standardized clinical setup.

IMPACT

The findings provide the basis for future refinement and validation of the algorithm on home videos to be a remote infant movement assessment.

Oral Omilancor Treatment Ameliorates Clostridioides difficile Infection During IBD Through Novel Immunoregulatory Mechanisms Mediated by LANCL2 Activation

BACKGROUND

Clostridioides difficile infection (CDI) is an opportunistic infection of the gastrointestinal tract, commonly associated with antibiotic administration, that afflicts almost 500 000 people yearly only in the United States. CDI incidence and recurrence is increased in inflammatory bowel disease (IBD) patients. Omilancor is an oral, once daily, first-in-class, gut-restricted, immunoregulatory therapeutic in clinical development for the treatment of IBD.

METHODS

Acute and recurrent murine models of CDI and the dextran sulfate sodium-induced concomitant model of IBD and CDI were utilized to determine the therapeutic efficacy of oral omilancor. To evaluate the protective effects against C. difficile toxins, in vitro studies with T84 cells were also conducted. 16S sequencing was employed to characterize microbiome composition.

RESULTS

Activation of the LANCL2 pathway by oral omilancor and its downstream host immunoregulatory changes decreased disease severity and inflammation in the acute and recurrence models of CDI and the concomitant model of IBD/CDI. Immunologically, omilancor treatment increased mucosal regulatory T cell and decreased pathogenic T helper 17 cell responses. These immunological changes resulted in increased abundance and diversity of tolerogenic gut commensal bacterial strains in omilancor-treated mice. Oral omilancor also resulted in accelerated C. difficile clearance in an antimicrobial-free manner. Furthermore, omilancor provided protection from toxin damage, while preventing the metabolic burst observed in intoxicated epithelial cells.

CONCLUSIONS

These data support the development of omilancor as a novel host-targeted, antimicrobial-free immunoregulatory therapeutic for the treatment of IBD patients with C. difficile-associated disease and pathology with the potential to address the unmet clinical needs of ulcerative colitis and Crohn's disease patients with concomitant CDI.

In Silico Characterization of Uncharacterized Proteins From Multiple Strains of Clostridium Difficile

Clostridium difficile (C. difficile) is a multi-strain, spore-forming, Gram-positive, opportunistic enteropathogen bacteria, majorly associated with nosocomial infections, resulting in severe diarrhoea and colon inflammation. Several antibiotics including penicillin, tetracycline, and clindamycin have been employed to control C. difficile infection, but studies have suggested that injudicious use of antibiotics has led to the development of resistance in C. difficile strains. However, many proteins from its genome are still considered uncharacterized proteins that might serve crucial functions and assist in the biological understanding of the organism. In this study, we aimed to annotate and characterise the 6 C. difficile strains using in silico approaches. We first analysed the complete genome of 6 C. difficile strains using standardised approaches and analysed hypothetical proteins (HPs) employing various bioinformatics approaches coalescing, including identifying contigs, coding sequences, phage sequences, CRISPR-Cas9 systems, antimicrobial resistance determination, membrane helices, instability index, secretory nature, conserved domain, and vaccine target properties like comparative homology analysis, allergenicity, antigenicity determination along with structure prediction and binding-site analysis. This study provides crucial supporting information about the functional characterization of the HPs involved in the pathophysiology of the disease. Moreover, this information also aims to assist in mechanisms associated with bacterial pathogenesis and further design candidate inhibitors and bona fide pharmaceutical targets.

A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice

Simple Summary

Computational modeling of bacterial infection is an attractive way to simulate infection scenarios. In the long-term, such models could be used to identify factors that make individuals more susceptible to infection, or how interference with bacterial growth influences the course of bacterial infection. This study used different mouse infection models (immunocompetent, lacking a microbiota, and immunodeficient models) to develop a basic mathematical model of a Yersinia enterocolitica gastrointestinal infection. We showed that our model can reflect our findings derived from mouse infections, and we demonstrated how crucial the exact knowledge about parameters influencing the population dynamics is. Still, we think that computational models will be of great value in the future; however, to foster the development of more complex models, we propose the broad implementation of the interdisciplinary training of mathematicians and biologists.

Abstract

The complex interplay of a pathogen with its virulence and fitness factors, the host’s immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.

Artificial intelligence and synthetic biology approaches for human gut microbiome

The gut microbiome comprises a variety of microorganisms whose genes encode proteins to carry out crucial metabolic functions that are responsible for the majority of health-related issues in human beings. The advent of the technological revolution in artificial intelligence (AI) assisted synthetic biology (SB) approaches will play a vital role in the modulating the therapeutic and nutritive potential of probiotics. This can turn human gut as a reservoir of beneficial bacterial colonies having an immense role in immunity, digestion, brain function, and other health benefits. Hence, in the present review, we have discussed the role of several gene editing tools and approaches in synthetic biology that have equipped us with novel tools like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR-Cas) systems to precisely engineer probiotics for diagnostic, therapeutic and nutritive value. A brief discussion over the AI techniques to understand the metagenomic data from the healthy and diseased gut microbiome is also presented. Further, the role of AI in potentially impacting the pace of developments in SB and its current challenges is also discussed. The review also describes the health benefits conferred by engineered microbes through the production of biochemicals, nutraceuticals, drugs or biotherapeutics molecules etc. Finally, the review concludes with the challenges and regulatory concerns in adopting synthetic biology engineered microbes for clinical applications. Thus, the review presents a synergistic approach of AI and SB toward human gut microbiome for better health which will provide interesting clues to researchers working in the area of rapidly evolving food and nutrition science.

Artificial Intelligence Transforms the Future of Health Care

Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. AI focuses on how computers learn from data and mimic human thought processes. AI increases learning capacity and provides decision support system at scales that are transforming the future of health care. This article is a review of applications for machine learning in health care with a focus on clinical, translational, and public health applications with an overview of the important role of privacy, data sharing, and genetic information.

Artificial intelligence in nanomedicine

The field of nanomedicine has made substantial strides in the areas of therapeutic and diagnostic development. For example, nanoparticle-modified drug compounds and imaging agents have resulted in markedly enhanced treatment outcomes and contrast efficiency. In recent years, investigational nanomedicine platforms have also been taken into the clinic, with regulatory approval for Abraxane® and other products being awarded. As the nanomedicine field has continued to evolve, multifunctional approaches have been explored to simultaneously integrate therapeutic and diagnostic agents onto a single particle, or deliver multiple nanomedicine-functionalized therapies in unison. Similar to the objectives of conventional combination therapy, these strategies may further improve treatment outcomes through targeted, multi-agent delivery that preserves drug synergy. Also, similar to conventional/unmodified combination therapy, nanomedicine-based drug delivery is often explored at fixed doses. A persistent challenge in all forms of drug administration is that drug synergy is time-dependent, dose-dependent and patient-specific at any given point of treatment. To overcome this challenge, the evolution towards nanomedicine-mediated co-delivery of multiple therapies has made the potential of interfacing artificial intelligence (AI) with nanomedicine to sustain optimization in combinatorial nanotherapy a reality. Specifically, optimizing drug and dose parameters in combinatorial nanomedicine administration is a specific area where AI can actionably realize the full potential of nanomedicine. To this end, this review will examine the role that AI can have in substantially improving nanomedicine-based treatment outcomes, particularly in the context of combination nanotherapy for both N-of-1 and population-optimized treatment.

Nonclinical Toxicology and Toxicokinetic Profile of an Oral Lanthionine Synthetase C-Like 2 (LANCL2) Agonist, BT-11

BT-11 is an orally active, gut-restricted investigational therapeutic targeting the lanthionine synthetase C-like 2 pathway with lead indications in ulcerative colitis (UC) and Crohn disease (CD), 2 manifestations of inflammatory bowel disease (IBD). In 5 mouse models of IBD, BT-11 is effective at oral doses of 8 mg/kg. BT-11 was also efficacious at nanomolar concentrations in primary human samples from patients with UC and CD. BT-11 was tested under Good Laboratory Practice conditions in 90-day repeat-dose general toxicity studies in rats and dogs, toxicokinetics, respiratory, cardiovascular and central nervous system safety pharmacology, and genotoxicity studies. Oral BT-11 did not cause any clinical signs of toxicity, biochemical or hematological changes, or macroscopic or microscopic changes to organs in 90-day repeat-dose toxicity studies in rats and dogs at doses up to 1,000 mg/kg/d. Oral BT-11 resulted in low systemic exposure in both rats (area under the curve exposure from t = 0 to t = 8 hours [AUC_0-8] of 216 h × ng/mL) and dogs (650 h × ng/mL) and rapid clearance with an average half-life of 3 hours. BT-11 did not induce changes in respiratory function, electrocardiogram parameters, or behavior with single oral doses of 1,000 mg/kg/d. There was no evidence of mutagenic or genotoxic potential for BT-11 up to tested limit doses using an Ames test, chromosomal aberration assay in human peripheral blood lymphocytes, or micronucleus assay in rats. Therefore, nonclinical studies show BT-11 to be a safe and well-tolerated oral therapeutic with potential as a potent immunometabolic therapy for UC and CD with no-observed adverse effect level >1,000 mg/kg in in vivo studies.

Clostridium Difficile Infection: An Immunological Conundrum

The lack of comprehensive understanding of the way immunity backfires on incidence and complications has made Clostridium difficile infection (CDI), the infectious disease of our times, as evidenced by in the parallel course it follows along epidemic of chronic degenerative diseases. Within these ailments, if as suspected the main effect of Clostridium difficile A and B toxins depends on inflammation, then aberrant immune function due to antibiotics would explain IBD triggering after treatment but also, the higher incidence and mortality surrounding disorders that are inflammatory and/or that show abatement of neutrophils. This review will discuss severity of the disease in terms of challenges to immunity during the progression of acute illness. We will identify the common signals in the communication between microbiota and inflammatory cells, as well as the sequestration of the regulatory network by Clostridium difficile, which leads to tissue damage and prevents its elimination from intestinal lumen.

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.