Addressing drug–microbiome interactions: the role of healthcare professionals

Growing evidence has highlighted the potentially significant impact of drug–microbiome interactions on patient care. It is possible that hundreds of drugs alter the composition of the microbiome, including many drugs with non-microbial targets. Drug-induced alteration of the microbiome could increase patients’ risk of dysbiosis, a state of microbiome unbalance that increases the chance of disease. Further, a drug’s pharmacokinetics and pharmacodynamics can be altered by the microbiome via direct (e.g. biotransformation or bioaccumulation) and indirect processes. Although these interactions are potentially important for patient health and therapeutic success, they are rarely considered during drug development or in clinical practice. Healthcare professionals working across sectors should consider drug–microbiome interactions to improve patient outcomes. This review provides an overview of the current evidence relating to drug–microbiome interactions, and describes how healthcare professionals working in clinical settings, academia, policy and drug development can immediately begin to address drug–microbiome interactions as an integral part of their roles

Suffering and Redemption in the Works of Fyodor Dostoevsky

Thesis advisor: Peter KreeftIn The Brothers Karamazov, Ivan Karamazov was convinced it is not right that there is so much suffering in the world, and was convinced nothing could make it right. As a result he was left with no choice but to reject the ticket for this world, or to be indignant toward the world, which means he was indignant toward life in it. If we listen closely to what Fyodor Dostoevksy had to say in five of his works, The Brothers Karamazov, Crime and Punishment, The Idiot, The Insulted and Injured, and Notes from the Underground, we will find a way in which we can accept the ticket, which is to say that we will find a way to love life.Thesis (BA) — Boston College, 2004.Submitted to: Boston College. College of Arts and Sciences.Discipline: Philosophy.Discipline: College Honors Program

Predicting drug-microbiome interactions with machine learning

Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care

Harnessing machine learning for development of microbiome therapeutics

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field

Still waiting for disruption: Final report

Advances in legal tech are not currently disrupting Canada’s access to justice crisis. In this project we sought to understand the barriers and opportunities for disruptive legal technology to address people’s everyday legal problems. The application of the discipline of Strategic Foresight to the justice system allowed us to examine the forces of change in the current environment affecting A2J actors and identify key levers that could be pulled now to support transformative change. The project has resulted in a se of practical tools for A2J actors

Machine learning uncovers adverse drug effects on intestinal bacteria

The human gut microbiome, composed of trillions of microorganisms, plays an essential role in human health. Many factors shape gut microbiome composition over the life span, including changes to diet, lifestyle, and medication use. Though not routinely tested during drug development, drugs can exert profound effects on the gut microbiome, potentially altering its functions and promoting disease. This study develops a machine learning (ML) model to predict whether drugs will impair the growth of 40 gut bacterial strains. Trained on over 18,600 drug–bacteria interactions, 13 distinct ML models are built and compared, including tree-based, ensemble, and artificial neural network techniques. Following hyperparameter tuning and multi-metric evaluation, a lead ML model is selected: a tuned extra trees algorithm with performances of AUROC: 0.857 (±0.014), recall: 0.587 (±0.063), precision: 0.800 (±0.053), and f1: 0.666 (±0.042). This model can be used by the pharmaceutical industry during drug development and could even be adapted for use in clinical settings

Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota

Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug–microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs’ susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug–microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients