Interested, who to contact?

The innovation team: projectenquiries@pistoiaalliance.org

Originator: Manuela Pausan, Bayer

Algorithmic prediction of the influence of the gut microbiome on therapeutics 

Background:

The Microbiome has become a hot topic over the last decade. Researchers are working on understanding how the microbiome influences our lives and health and how we can manipulate the microbiome to prevent and fight diseases. Such microbiome manipulation has been in focus for many companies. Identifying and building strategies to translate human microbiome health/disease associations into therapeutics and other chemical compounds is one aspect of how we could harness the power of the microbiome.

The Problem:

We should consider the importance of the microbiome on drug metabolism. For some time it was believed that different patients responded differently to the same drug as a result of their different genomes. However, there is increasing evidence to suggest that the gut microbiome also has an affect on patients' response to oral medication.

Although the potential influence of the microbiome on drug efficacy is known, pharma companies are not universally studying the microbiome-mediated metabolism as part of the new drug development pipeline. One reason could be the lack of a systematic, standardised map of microbiome-derived metabolism. This hinders the industry to predict and eventually target the microbiome effect on drug pharmacokinetics and/or pharmacodynamics.

Microbiome research has quickly developed in part fuelled by the improvements and developments of technologies including sequencing. The tools and methods to study the microbiome vary widely, consequently, results produced by different laboratories may not be comparable. As such, standards and validated protocols are essential for a common and comparable understanding of scientific findings and their translation into diagnostic and therapeutic strategies. 

The Proposal:

Part 1: Establish a database for the microbiome-derived metabolism. The data would be pooled from published literature, from pharma industry sources, and future prospective experiments. 

Part 2: Use the database as source material for developing machine learning algorithms that could help predict the influence of the microbiome and identify the key enzymes both of which contribute to drug metabolism in the gastrointestinal tract. 

Part 3: Identify data required to augment the performance of the algorithms and undertake the experiments necessary to provide that data.  A consistent standardised methodology would need to be applied to these experiments.  

Value:

Mapping the microbiome-derived metabolism and creating a common database will help in developing safer and better solutions to identify and treat diseases, especially for the development of new therapeutic drugs with reduced side effects. Furthermore, this will bring the entire microbiome-focused research and industry to more quickly develop new products aimed at manipulating the microbiome for increased health benefits.

Implementing standards and creating a common methodological framework across industry and academia would lead to generating comparable data across laboratories and enable faster and more accurate translation of the microbiome research into new diagnostics and therapeutics.