An experimental technique created a model that could kick-start the development process for new drugs targeting Clostridium difficile infections (C. Diff).
Scientists based at Virginia Tech’s Biocomplexity Institute harnessed a mix of algorithms, simulations, and machine learning to test and predict the efficacy of novel treatments for infectious and immune-mediated diseases.
A modeling system of this nature could be particularly important when it comes to predicting the progression and treatment response to C. Diff. Antibiotics are the standard form of treatment for C. Diff, but they run the risk of perpetuating drug-resistant bacterial strains.
High rates of recurrence can lead to an uptick in healthcare costs and mortality.
The research team used this model to identify a potential alternative treatment for C. Diff, which is a protein called lanthionine synthetase c-like or LANCL2.
“Our modeling shows that we do not need to remove the pathogen nor directly influence inflammation in the case of CDI to have an effective treatment,” said one of the study author’s Andrew Leber, scientific director at BioTherapeutics, in a statement. Simply restoring immune tolerance in the gut through an LANCL2, or similar immunoregulatory pathway, or boosting the gut microbiome to allow it to naturally outcompete pathogenic C. difficile strains is effective in the absence of antibiotics.”
This new model could be the first step in constructing a personalized disease treatment process for these conditions by translating preclinical results in animal models to clinical outcomes, pinpointing effective treatments, analyzing dosage effects, and forecasting patient reactions to combination therapies.
“The convergence of advanced data analytics, modeling, and artificial intelligence systems with high resolution, large-scale patient data creates an opportunity to fundamentally transform how medicine will be practiced,” said Josep Bassaganya-Riera, director of the Nutritional Immunology and Molecular Medicine Laboratory and CEO of BioTherapeutics, in a statement. In this study and in our continuing efforts, we aim to be a leader in this developing field of precision, personalized medicine in infectious and autoimmune diseases.”
Refining this model could ultimately minimize undesirable side effects and enable maximal efficacy of treatment in response to C. Diff and similar conditions.
The study appeared in the journal Artificial Intelligence in Medicine.
Filed Under: Drug Discovery