Helicopredict

Helicobacter pylori (H. pylori) infection is one of the most prevalent bacterial infections worldwide. Chronic infection leads to chronic active gastritis and can result in the development of other complications such as ulcers or gastric cancer. Indeed, approximately 90% of all gastric cancers are associated with H. pylori. Failure of standard eradication therapies is rising dramatically due to the increased development of resistant bacterial strains. Since two antibiotics are needed for successful eradication, the usage of only one antibiotic in other indications such as respiratory diseases will render the (mostly yet undetected) H. pylori strain in these patients resistant. Nowadays, it is estimated that already 10-20% of H. pylori strains are multiresistant. However, culture-based resistance testing, which is currently only recommended after unsuccessful second-line therapy, is a lengthy process; in vitro growth of H. pylori takes 5-7 days after isolation from gastric tissue and further resistance tests last between 3 to 5 days. Taking this into account, a rapid method to determine whether an isolated strain will be prone to antibiotic resistance would be a tremendous help to choose the appropriate therapeutic regimen.To address this challenge, we plan to develop an algorithm for prediction of antibiotic resistance, which will primarily be based on H. pylori genome sequencing data that can be obtained quickly. The algorithm will be made publicly available to physicians and serve as a way to select the optimal therapy. This approach will help to optimize therapeutic efficacy and counteract further resistance development.

The aim of the research consortium is the development of a genotypic resistance testing database for the prediction of antibiotic susceptibility of H. pylori. Based on data from more than 2000 patients, we will build a database including genotypic and phenotypic bacterial data, host inflammation-associated parameters as well as microbiota signatures. We will make genetic resistance testing available based on this database in the sense of a “genotype to phenotype concept”. To this end, we will develop an algorithm for prediction of antibiotic resistance that will be made publicly available to physicians and serve as a way to select the optimal therapy. The algorithm will be based on whole genome sequencing of H. pylori but also include other parameters putatively influencing resistance in the stomach such as local inflammation and the gut microbiome, and use machine learning to continuously improve the accuracy.

According to the WHO, antimicrobial resistances such as the ones we find in tuberculosis currently pose the greatest long-term threat to human health and wellbeing. This project builds on novel, data science approaches within basic research to address and counteract the development and spread of resistance within this infectious disease.