Active Machine learning for formulation of precision probiotics DOI Creative Commons
Laura E. McCoubrey,

Nidhi Seegobin,

Moe Elbadawi

et al.

International Journal of Pharmaceutics, Journal Year: 2022, Volume and Issue: 616, P. 121568 - 121568

Published: Feb. 9, 2022

It is becoming clear that the human gut microbiome critical to health and well-being, with increasing evidence demonstrating dysbiosis can promote disease. Increasingly, precision probiotics are being investigated as investigational drug products for restoration of healthy balance. To reach distal alive where density microbiota highest, oral should be protected from harsh conditions during transit through stomach small intestines. At present, few probiotic formulations designed this delivery strategy in mind. This study employs an emerging machine learning (ML) technique, known active ML, predict how excipients at pharmaceutically relevant concentrations affect intestinal proliferation a common probiotic, Lactobacillus paracasei. Starting labelled dataset just 6 bacteria-excipient interactions, ML was able effects further 111 using uncertainty sampling. The average certainty final model 67.70% experimental validation demonstrated 3/4 excipient-probiotic interactions could correctly predicted. used enable superior maximise vivo marks first use science.

Language: Английский

Prebiotics Beyond the Gut: Omics Insights, Artificial Intelligence, and Clinical Trials in Organ-Specific Applications DOI
I.S.I. Al-Adham, Ahmed S.A. Ali Agha, Faisal Al‐Akayleh

et al.

Probiotics and Antimicrobial Proteins, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

Language: Английский

Citations

1
Influence of probiotic bacteria on gut microbiota composition and gut wall function in an in-vitro model in patients with Parkinson's disease DOI Creative Commons

Jonas Ghyselinck,

Lynn Verstrepen,

Frédéric Moens

et al.

International Journal of Pharmaceutics X, Journal Year: 2021, Volume and Issue: 3, P. 100087 - 100087

Published: July 2, 2021

We report here the potential role of a 4-strain probiotic suspension for use with patients Parkinson's disease (PD). Stool samples from group three diagnosed PD were used to create microbiotas in an

Language: Английский

Citations

47
Optical biosensors - Illuminating the path to personalized drug dosing DOI
Jun Jie Ong, Thomas D. Pollard, Álvaro Goyanes

et al.

Biosensors and Bioelectronics, Journal Year: 2021, Volume and Issue: 188, P. 113331 - 113331

Published: May 13, 2021

Language: Английский

Citations

46
The intestinal and biliary microbiome in autoimmune liver disease—current evidence and concepts DOI Creative Commons
Timur Liwinski, Melina Heinemann, Christoph Schramm

et al.

Seminars in Immunopathology, Journal Year: 2022, Volume and Issue: 44(4), P. 485 - 507

Published: May 10, 2022

Autoimmune liver diseases are a group of immune-mediated with three distinct entities, including autoimmune hepatitis, primary biliary cholangitis, and sclerosing cholangitis. The interplay genetic environmental factors leads to the breakdown self-tolerance, resulting in hyper-responsiveness, auto-aggressive immune activation. Emerging evidence links alterations commensal microbiome configuration aberrant system activation by microbial signals, mainly via gut-liver axis. Thus, is new frontier deepen pathogenetic understanding, uncover biomarkers, inspire innovative treatments. Herein, we review current on role from both clinical basic research. We highlight recent achievements also bottlenecks limitations. Moreover, give an outlook future developments potential for applications.

Language: Английский

Citations

35
Microbiome as an immune regulator in health, disease, and therapeutics DOI Creative Commons
Valeria M. Juarez,

Alyssa N. Montalbine,

Ankur Singh

et al.

Advanced Drug Delivery Reviews, Journal Year: 2022, Volume and Issue: 188, P. 114400 - 114400

Published: June 16, 2022

Language: Английский

Citations

29
Gut microbiome immaturity and childhood acute lymphoblastic leukaemia DOI Open Access
Ioannis Peppas, Anthony M. Ford, Caroline L. Furness

et al.

Nature reviews. Cancer, Journal Year: 2023, Volume and Issue: 23(8), P. 565 - 576

Published: June 6, 2023

Language: Английский

Citations

21
Recent advances in gut microbiota-associated natural products: structures, bioactivities, and mechanisms DOI
Huanqin Dai, Junjie Han, Tao Wang

et al.

Natural Product Reports, Journal Year: 2023, Volume and Issue: 40(6), P. 1078 - 1093

Published: Jan. 1, 2023

Covering: 2014 to June 2022The gut microbiota has attracted increasing attention from researchers due its critical role in regulating human physiology and pathophysiology. Natural products (NPs) produced or transformed by microbes are key signalling mediators for a variety of physiological functions. On the other hand, NPs ethnomedicines have also been found generate health benefits through modulation microbiota. In this highlight, we review most recent studies related microbiota-derived bioactive that regulate pathological processes via microbiota-associated mechanisms. We outline strategies discovery methodologies how elucidate crosstalk between

Language: Английский

Citations

20
Machine Learning Uncovers Adverse Drug Effects on Intestinal Bacteria DOI Creative Commons
Laura E. McCoubrey, Moe Elbadawi, Mine Orlu

et al.

Pharmaceutics, Journal Year: 2021, Volume and Issue: 13(7), P. 1026 - 1026

Published: July 6, 2021

The human gut microbiome, composed of trillions microorganisms, plays an essential role in health. Many factors shape 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 potentially altering its functions promoting disease. This study develops a machine learning (ML) model predict whether will impair growth 40 bacterial strains. Trained 18,600 drug-bacteria interactions, 13 distinct ML models are built compared, tree-based, ensemble, artificial neural network techniques. Following hyperparameter tuning multi-metric evaluation, lead is selected: tuned extra trees algorithm with performances AUROC: 0.857 (±0.014), recall: 0.587 (±0.063), precision: 0.800 (±0.053), f1: 0.666 (±0.042). be used by pharmaceutical industry development could even adapted for use clinical settings.

Language: Английский

Citations

37
Vegetable waste and by-products to feed a healthy gut microbiota: Current evidence, machine learning and computational tools to design novel microbiome-targeted foods DOI Creative Commons
Carlos Sabater, Inés Calvete‐Torre, Mar Villamiel

et al.

Trends in Food Science & Technology, Journal Year: 2021, Volume and Issue: 118, P. 399 - 417

Published: Oct. 7, 2021

Food waste management is a key issue to global food security and friendly environmental governance. Worldwide, one-third of produced for human consumption lost or wasted along the supply chain, primary production processing representing most significant loses. Therefore, need achieve zero schemes becoming priority meet Sustainable Development Goals. Increasing evidence points towards vegetable as rich source wide array carbohydrate structures fibres providing opportunity identify develop alternative approaches valorize agro-food waste. This review describes valorization by-products via (novel) substrates targeted gut microbiota modulation, emphasizing importance raw materials structural-functional properties carbohydrates. Furthermore, we propose novel framework rational selection sources with potential prebiotic activity, based on machine learning other computational tools applied available literature public database information. Integration body knowledge within field valorization, from different perspectives, allows carbohydrate-based promising activities. By exploring interactions among dietary fibre microbial ecosystems using fed structural, functional genomic data, can selectively stimulate commensals, in agreement experimental evidence. Our approach establishes new that be extended range commensal microbes structures.

Language: Английский

Citations

36
Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development DOI Creative Commons
Colm S. O’Reilly, Moe Elbadawi, Neel Desai

et al.

Pharmaceutics, Journal Year: 2021, Volume and Issue: 13(12), P. 2187 - 2187

Published: Dec. 17, 2021

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical rewards. However, the manufacturing ODFs does not adhere to contemporary paradigms personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, three-dimensional (3D) printing machine learning (ML) were employed provide quality control checks ODFs. Direct ink writing (DIW) was able fabricate complex ODF shapes, with thicknesses less than 100 µm. ML algorithms explored classify according their active ingredient, by using near-infrared (NIR) spectrums. A supervised model linear discriminant analysis found 100% accuracy in classifying subsequent partial least square algorithm applied verify dose, where coefficient determination 0.96, 0.99 0.98 obtained paracetamol, caffeine, theophylline, respectively. Therefore, it concluded that combination 3D printing, NIR can result rapid production verification Additionally, vision tool used automate vitro testing. These collective digital technologies demonstrate potential workflow.

Language: Английский

Citations

36