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Machine Learning for Pharmacogenomics and Precision Medicine
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National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA
MAQC Society, 4301 W Markham St. 522-3, Little Rock, AR 72205, USA
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Machine Learning for Pharmacogenomics and Precision Medicine

Abstract submission deadline
closed (30 September 2023)
Manuscript submission deadline
closed (31 December 2023)
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5541

Topic Information

Dear Colleagues,

Delivering the correct drug to the correct patient at the right time while avoiding toxic side effects remains a major limitation of modern medicine across disease modalities. The corpus of available disease-relevant omics data is ever-expanding alongside the increasing panoply of imaging and other relevant data types. Concurrent with this expansion, various machine learning algorithms are becoming increasingly suitable for application using these diverse data types and are even able to integrate multiple types. Together, these advances present the opportunity to ask and answer important questions around pharmacogenomics and precision medicine.

This Topic serves as a compendium for global leaders to present their most recent findings to educate and enhance the work of the broader community. In order to be relevant to experts and newcomers alike, both review and original research articles are invited to this Topic.

Prof. Dr. Weida Tong
Dr. Rebecca Kusko
Topic Editors

Keywords

  • machine learning
  • artificial intelligence
  • genomics and genetics
  • pharmacogenomics
  • drug response
  • diagnosis and prognosis
  • precision and personalized medicine
  • biomarker development
  • translational informatics
  • image analysis

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Biomedicines
biomedicines 		3.9 5.2 2013 15.3 Days CHF 2600
International Journal of Molecular Sciences
ijms 		4.9 8.1 2000 18.1 Days CHF 2900
Journal of Clinical Medicine
jcm 		3.0 5.7 2012 17.3 Days CHF 2600
Journal of Personalized Medicine
jpm 		3.0 4.1 2011 16.7 Days CHF 2600
Pharmaceuticals
pharmaceuticals 		4.3 6.1 2004 12.8 Days CHF 2900
Pharmacy
pharmacy 		2.0 - 2013 22.9 Days CHF 1800
Pharmaceutics
pharmaceutics 		4.9 7.9 2009 14.9 Days CHF 2900

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Published Papers (2 papers)

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13 pages, 870 KiB  
Article
Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma
by Mei-Sing Ong, Joanne E. Sordillo, Amber Dahlin, Michael McGeachie, Kelan Tantisira, Alberta L. Wang, Jessica Lasky-Su, Murray Brilliant, Terrie Kitchner, Dan M. Roden, Scott T. Weiss and Ann Chen Wu
J. Pers. Med. 2024, 14(3), 246; https://doi.org/10.3390/jpm14030246 - 25 Feb 2024
Viewed by 1675
Abstract
Background: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma. Methods: The subjects included asthma patients of European ancestry [...] Read more.
Background: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma. Methods: The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest. Results: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67–0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70–0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others. Conclusions: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction. Full article
(This article belongs to the Topic Machine Learning for Pharmacogenomics and Precision Medicine)
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14 pages, 757 KiB  
Review
Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics
by Yoonjeong Cha, Mohamedi N. Kagalwala and Jermaine Ross
Pharmaceuticals 2024, 17(2), 158; https://doi.org/10.3390/ph17020158 - 25 Jan 2024
Viewed by 3266
Abstract
Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In [...] Read more.
Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In this review, we will discuss the latest developments linking machine learning and CNS drug discovery. While machine learning has aided our understanding of chronic diseases like Alzheimer’s disease and Parkinson’s disease, only modest effective therapies currently exist. We highlight promising new efforts led by academia and emerging biotech companies to leverage machine learning for exploring new therapies. These approaches aim to not only accelerate drug development but to improve the detection and treatment of neurodegenerative diseases. Full article
(This article belongs to the Topic Machine Learning for Pharmacogenomics and Precision Medicine)
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