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

Andrew Leber, Raquel Hontecillas, Vida Abedi, Nuria Tubau-Juni, Victoria Zoccoli-Rodriguez, Caroline Stewart, Josep Bassaganya-Riera

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The current treatment paradigm in Clostridium difficile infection is the administration of antibiotics contributing to the high rates of recurrent infections. Recent alternative strategies, such as fecal microbiome transplantation and anti-toxin antibodies, have shown similar efficacy in the treatment of C. difficile associated disease (CDAD). However, barriers exist for either treatment or other novel treatments to displace antibiotics as the standard of care. To aid in the comparison of these and future treatments in CDAD, we developed an in silico pipeline to predict clinical efficacy with nonclinical results. The pipeline combines an ordinary differential equation (ODE)-based model, describing the immunological and microbial interactions in the gastrointestinal (GI) mucosa, with machine learning algorithms to translate simulated output quantities (i.e. time of clearance, quantity of commensal bacteria, T cell ratios) into clinical predictions based on prior preclinical, translational and clinical trial data. As a use case, we compare the efficacy of lanthionine synthetase C-like 2 (LANCL2), a novel immunoregulatory target with promising efficacy in inflammatory bowel disease (IBD), activation with antibiotics, fecal microbiome transplantation and anti-toxin antibodies in the treatment of CDAD. We further validate the potential of LANCL2 pathway activation, in a mouse model of C. difficile infection in which it displays an ability to decrease weight loss and inflammatory cell types while protecting against mortality. The computational pipeline can serve as an important resource in the development of new treatment modalities.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalArtificial Intelligence in Medicine
Volume78
DOIs
StatePublished - May 1 2017

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Artificial Intelligence

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