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Metabolites
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Metabolomics and Machine Learning for Improved Diagnostics and as a...
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Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Metabolomic Profiling Technology".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 1631

Special Issue Editor

Dr. Elizabeth O’Day

E-Mail Website
Guest Editor
Olaris, Inc. 175 Crossing Boulevard Suite 410, Framingham, MA 01702, USA
Interests: metabolomics; machine learning; biomarkers; diagnostics; drug development; NMR; MS; precision medicine; personalized medicine; omics

Special Issue Information

Dear Colleagues,

Altered metabolism has been linked to nearly every disease, including cancer, neurodegeneration, cardiovascular disease, transplantation, aging and many more. Not unexpectedly, altered metabolites already provide powerful clinical biomarkers to diagnose diseases and guide treatments. However, due to challenges in analytical measurements, most clinical assays to date only measure a limited number of metabolites, leaving the true potential largely untapped.

With advancements in technology in NMR and MS, coupled with machine learning and a deeper understanding of biology, full metabolomics studies are on the horizon. The ability to measure thousands of metabolites in a single sample is now feasible. This should usher in a new era of clinical insights driving diagnostic innovation and accelerating drug development. However, reproducibility concerns, sample logistics, metabolite annotations and questions around complex statistics must be addressed. In this Special Issue, we highlight technical advancements (and lingering limitations) driving the field. Furthermore, we provide use cases in which metabolomics and machine learning are changing our ability to diagnose and treat disease. Finally, we provide tangible best practices and considerations for those looking to apply metabolomics and ML to their research.      

Dr. Elizabeth O’Day
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • machine learning
  • biomarkers
  • diagnostics
  • drug development
  • NMR
  • MS
  • precision medicine
  • personalized medicine
  • omics

Published Papers (2 papers)

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Research

15 pages, 1576 KiB  
Article
Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning
by Tanvir Sajed, Zinat Sayeeda, Brian L. Lee, Mark Berjanskii, Fei Wang, Vasuk Gautam and David S. Wishart
Metabolites 2024, 14(5), 290; https://doi.org/10.3390/metabo14050290 - 19 May 2024
Viewed by 400
Abstract
NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound [...] Read more.
NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, “solvent-aware” experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced “prosper”) has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases. Full article
(This article belongs to the Special Issue Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development)
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18 pages, 5080 KiB  
Article
Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery
by Masoumeh Dorrani, Jifang Zhao, Nihel Bekhti, Alessia Trimigno, Sangil Min, Jongwon Ha, Ahram Han, Elizabeth O’Day and Jurre J. Kamphorst
Metabolites 2024, 14(5), 280; https://doi.org/10.3390/metabo14050280 - 11 May 2024
Viewed by 661
Abstract
Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity [...] Read more.
Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set. Full article
(This article belongs to the Special Issue Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development)
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