Pistoia Alliance: drug discovery in health and microbiomes
It is widely recognized that individuals with the same disease may have varying responses to the same drug. Some patients benefit from the drug without toxicity, others suffer from the toxicity but benefit from the drug. Other patients experience neither benefit nor toxicity, while an unlucky group will experience toxicity without benefit.
Although there has long been research into the cause of different drug responses, a contributing factor is finally being recognized – the microbiome. A bustling community of trillions of bacteria, archaea, viruses and fungi, the microbiome resides throughout the body, making up 99% of our genetic makeup. Scientific interest in the microbiome is growing, with more than $1.7 billion spent on microbiome research in the past decade alone. Once we recognize the role of the microbiome and put in place an effective framework for the data around it, we have the potential to transform drug discovery and development.
The focus in drug discovery has long been on human genomes, yet the genome represents only 1% of our genetic makeup. An individual has 23,000 human genes, and of these, 30 protein families are involved in drug metabolism. These numbers are dwarfed by the microbiome. Each individual has two million microbial genes and an unknown number of protein families involved in drug metabolism.
Over the past decade, research has begun to demonstrate the interactions between a patient’s microbiome and the metabolism of a given drug. Take paracetamol, for example, an over-the-counter pain reliever that many patients take several times a week. Patients with a relatively high proportion of a chemical called p-cresol in their urine metabolize paracetamol differently than those with a low proportion of the same chemical. This microbial variation can also lead to more severe drug reactions, especially in other indications. For example, an anti-cancer drug causes delayed bacterial activity, causing intense, sometimes fatal diarrhea.
There is growing awareness of the role the microbiome plays in drug metabolism, but there is still no systemic, global, standardized map or system in which microbiome-derived metabolism data can be stored. . Without such a system, we are unable to predict, and possibly modify, interactions between the microbiome and drug pharmacokinetics (“what the body does to the drug”) or pharmacodynamics (“what the drug does to the body” ).
There is also no standardized methodology or regulatory requirements for microbiome-drug testing. As a result, drug discovery and clinical experiments take longer than necessary and consume more valuable resources. These problems are carried up to the prescription. When prescribing a drug, a clinician has no system to predict (and therefore prevent) a negative interaction with a patient’s microbiome.
Because the microbiome is so vast and complex, yet has the potential to offer such rich information, industry needs to both compile this wide range of dispersed data into a single holistic data set, but also develop a standardized format for this data for the subsequent tool. development.
The ideal solution involves the development of a common central repository of microbiome data with annotation and standardization agreed upon by major industry players. With this “atlas” of microbiome-derived metabolism data, we can then begin to incorporate microbiome data into development processes, creating safer and more effective drugs, and in some cases harnessing the microbiome to create new drugs.
The Pistoia Alliance has launched a new project to do just that. The project will organize existing data on the metabolism of drugs and other microbiome-derived compounds, from existing publications, public data repositories, and companies involved in microbiome research. This data will be standardized to create a machine-readable atlas, allowing the data to be easily searched and accessed. The second step of the project will be to identify specific chemical groups of interest, which will allow building a prediction tool and integrating it into future development.
With a standardized “atlas” of microbiome-derived metabolism, we hope to finally enable microbiome data to become a standard part of the drug discovery process, creating safer and more effective drugs. By incorporating machine learning, we also have the ability to predict the interaction of a drug, dietary supplement or even chemical fragments used in fields such as crop science with the microbiome.
This project is now supported by several major pharmaceutical organizations, and we will be looking to launch it in early 2022, we encourage any other research-led organizations that would like to participate to get involved.