Periodic Reporting for period 3 - BugTheDrug (Predicting the effects of gut microbiota and diet on an individual’s drug response and safety)
Berichtszeitraum: 2020-08-01 bis 2022-01-31
The response to many commonly prescribed drugs varies substantially between individuals, which poses a burden on the individual, caregivers, and the overall health system. Drugs are designed such that they are absorbed, metabolised, transported, and finally eliminated by the human body, thereby providing the desired effect. These pharmacokinetic properties are assessed during the development of a drug, also through the use of computational models, and lead, for example, to the definition of the optimal dosage for achieving the drug's desired effect. However, variations in the genetic make-up of an individual, that is its genes, can lead to varied drug response, as they may be, for example, slower or faster metabolised or less efficiently eliminated. Similarly, lifestyle factors, such as diet and exercise, can influence the drug response. More recently, the microbes inhabiting the gut have also been found to metabolise commonly prescribed drugs. Thus, these microbes should be considered when assessing the pharmacokinetic properties of drugs. Importantly, the gut microbial composition differs not only between individuals but also with diet and age. Given the complexity of factors involved in ensuring optimal drug response of an individual, novel approaches are required. This research project, BugTheDrug, tackles this important medical challenge. Therefore, researchers will develop a novel, innovative computer model that takes into account genetic make-up, diet, and microbial make-up. This computer model will permit the prediction of an individual's drug response before the treatment. Ultimately, the project's outcome may guide medical doctors and clinician to provide an optimal, tailored, and personalised treatment strategy. The project's goal will be achieved through several objectives:
- Researchers will develop a novel, innovative computer model consisting of a highly curated database of human, microbial, dietary, and drug metabolism information and a software that allows the prediction of a person's drug response based on customizable database queries and input data (such as diet, genetic make-up, and gut microbe composition).
- Researchers will use the computer model to predict metabolites in the blood that can identify colon cancer patients that will have severe side-effects to certain chemotherapy and validate these metabolites in a small group of colon cancer patients.
- Researchers will use the computer model to predict specific food items for Parkinson's disease patients treated with levodopa, a drug is commonly used to reduce the motor symptoms of Parkinson's disease patients, to reduce treatment side effects. These predictions will be tested in collaboration with neurologists and nutritionists in a small number of Parkinson's patient. The study results will help to improve the computer model further.
- Researchers will develop a novel, innovative computer model consisting of a highly curated database of human, microbial, dietary, and drug metabolism information and a software that allows the prediction of a person's drug response based on customizable database queries and input data (such as diet, genetic make-up, and gut microbe composition).
- Researchers will use the computer model to predict metabolites in the blood that can identify colon cancer patients that will have severe side-effects to certain chemotherapy and validate these metabolites in a small group of colon cancer patients.
- Researchers will use the computer model to predict specific food items for Parkinson's disease patients treated with levodopa, a drug is commonly used to reduce the motor symptoms of Parkinson's disease patients, to reduce treatment side effects. These predictions will be tested in collaboration with neurologists and nutritionists in a small number of Parkinson's patient. The study results will help to improve the computer model further.
Researchers have developed the first web resource that combines highly curated biochemical knowledge of human and microbial metabolism with disease and nutritional information. This freely available database is called Virtual Metabolic Human (VMH, https://vmh.life) provides the research community with important biochemical information, collected from thousands of peer-reviewed scientific publications and books, and connects this information with other scientific databases and resources. Importantly, comprehensive computer models underpin the VMH. This means that the information that can be queried in the online database can also be used to simulate healthy and diseased human metabolism, alone or together with gut microbial metabolism. Similarly, the nutritional information can be used to develop defined diets and use them directly when simulating human metabolism to assess how different foods may influence, for example, the fat accumulation in humans, an important aspect in obesity.
The content of the VMH database is continuously maintained and expanded within this research project. For instance, researchers have recently added a detailed biochemical description of gut microbial bile acid metabolism, which may play a role in many human diseases, including Parkinson's disease.
Researchers are now expanding the information included in the VMH with human and microbial metabolism of over 100 commonly prescribed drugs. This information is again collected from peer-reviewed scientific studies and requires the detailed investigation of gut microbial genomes to identify a microbe's potential to modify drugs and their derivatives. This step is crucial as experimental data for many gut microbes are not available, despite recent advances in understanding the role of gut microbes in human drug metabolism.
Researchers are developing novel computational methods to enable the direct analysis of gut microbe data as well as nutritional and physiological information. These computer methods will be directly connected with the VMH database to allow for cross-reference of computer simulations and currently biochemical knowledge. In addition to including microbial and nutritional information in the computer models, the new computer methods will enable to simulate human and microbial metabolism as well as pharmacokinetic properties of one or more drugs.
The content of the VMH database is continuously maintained and expanded within this research project. For instance, researchers have recently added a detailed biochemical description of gut microbial bile acid metabolism, which may play a role in many human diseases, including Parkinson's disease.
Researchers are now expanding the information included in the VMH with human and microbial metabolism of over 100 commonly prescribed drugs. This information is again collected from peer-reviewed scientific studies and requires the detailed investigation of gut microbial genomes to identify a microbe's potential to modify drugs and their derivatives. This step is crucial as experimental data for many gut microbes are not available, despite recent advances in understanding the role of gut microbes in human drug metabolism.
Researchers are developing novel computational methods to enable the direct analysis of gut microbe data as well as nutritional and physiological information. These computer methods will be directly connected with the VMH database to allow for cross-reference of computer simulations and currently biochemical knowledge. In addition to including microbial and nutritional information in the computer models, the new computer methods will enable to simulate human and microbial metabolism as well as pharmacokinetic properties of one or more drugs.
The VMH is a unique resource that connects a variety of biochemical knowledge, which has not been previously connected and hence, represents an ideal starting point for other research programs. Accordingly, the scientific publication has been selected to feature on the front page of the publishing journal and as a "breakthrough article" by the journal editor. In fact, since its launch in its current format on October 2018, the VMH database has been visited by over 35,000 users from 115 different countries, highlighting its growing importance for the biomedical research community.
Microbial drug metabolism remains understudied despite recent discoveries in microbiology. Subsequently, the genomic annotation and the detailed description of drug metabolism that has been carried out during the first 18 months will provide valuable insight into microbial capabilities to modulate commonly prescribed drugs. Once, publically made available, this resource will enable researchers to estimate the influence of microbial drug metabolism on an individual's drug response and allow other researchers to formulate novel, experimentally testable hypotheses.
The combination of biochemistry-based computer models and pharmacokinetic models has been described in the scientific literature with a few examples. However, the computer models generated within this ERC project will go beyond these published efforts both on the level of captured biochemistry, most notably the microbial metabolism, but also with the extent to a variety of the drugs that will be covered.
Consequently, the computer model will be used for hitherto unpreceded applications, such as the prediction of metabolites that could help to identify colon cancer patients that would develop severe side-effects to certain chemotherapy drugs. Similarly, the prediction of dietary supplementations for Parkinson's disease patients based on their gut microbial make-up. If the anticipated proof-of-concept study is successful, this application may provide an improved, personalised treatment strategy for Parkinson's disease patients.
Microbial drug metabolism remains understudied despite recent discoveries in microbiology. Subsequently, the genomic annotation and the detailed description of drug metabolism that has been carried out during the first 18 months will provide valuable insight into microbial capabilities to modulate commonly prescribed drugs. Once, publically made available, this resource will enable researchers to estimate the influence of microbial drug metabolism on an individual's drug response and allow other researchers to formulate novel, experimentally testable hypotheses.
The combination of biochemistry-based computer models and pharmacokinetic models has been described in the scientific literature with a few examples. However, the computer models generated within this ERC project will go beyond these published efforts both on the level of captured biochemistry, most notably the microbial metabolism, but also with the extent to a variety of the drugs that will be covered.
Consequently, the computer model will be used for hitherto unpreceded applications, such as the prediction of metabolites that could help to identify colon cancer patients that would develop severe side-effects to certain chemotherapy drugs. Similarly, the prediction of dietary supplementations for Parkinson's disease patients based on their gut microbial make-up. If the anticipated proof-of-concept study is successful, this application may provide an improved, personalised treatment strategy for Parkinson's disease patients.