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Disease phenotype prediction in multiple sclerosis
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.ORCID iD: 0000-0001-9382-3273
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0001-6709-7116
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Experimental neurology.ORCID iD: 0000-0003-0934-4478
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm, Sweden.ORCID iD: 0000-0002-4137-5517
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2023 (English)In: iScience, E-ISSN 2589-0042, Vol. 26, no 6, article id 106906Article in journal (Refereed) Published
Abstract [en]

Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.

Place, publisher, year, edition, pages
Cell Press, 2023. Vol. 26, no 6, article id 106906
National Category
Neurosciences Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-415212DOI: 10.1016/j.isci.2023.106906ISI: 001022775300001PubMedID: 37332601OAI: oai:DiVA.org:uu-415212DiVA, id: diva2:1455173
Funder
Uppsala UniversityÅke Wiberg FoundationSwedish Association of Persons with Neurological DisabilitiesSwedish Society for Medical Research (SSMF)Swedish Research Council Formas, 2020-01267Swedish Research Council, 2021-02189Swedish Research Council, 2021-02814Knut and Alice Wallenberg FoundationAvailable from: 2020-07-22 Created: 2020-07-22 Last updated: 2024-04-23Bibliographically approved
In thesis
1. Towards an Earlier Detection of Progressive Multiple Sclerosis using Metabolomics and Machine Learning
Open this publication in new window or tab >>Towards an Earlier Detection of Progressive Multiple Sclerosis using Metabolomics and Machine Learning
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Decision-making guided by advanced analytics is becoming increasingly common in many fields. Implementing computationally driven healthcare solutions does, however, pose ethical dilemmas as it involves human health. Therefore, augmenting clinical expertise with advanced analytical insights to support decision-making in healthcare is probably a more feasible strategy.

Multiple sclerosis is a debilitating neurological disease with two subtypes; relapsing-remitting multiple sclerosis (RRMS) and the typically late-stage progressive subtype (PMS). Progressive multiple sclerosis is a neurodegenerative phenotype, with a vague functional definition, that currently is diagnosed retrospectively. The challenge of diagnosing PMS earlier is a great example where data-driven insights might prove useful.

This thesis addresses the need for an earlier detection of patients developing the progressive and neurodegenerative subtype of multiple sclerosis, using primarily metabolomics and machine learning approaches. In Paper I, the biochemical differences in cerebrospinal fluid (CSF) from RRMS and PMS patients were characterised, leading to the conclusion that it is possible to distinguish PMS patients based on biochemical alterations. In addition, pathway analysis revealed several metabolic pathways that were affected in the transition to PMS, including tryptophan metabolism and pyrimidine metabolism. In Paper II and III, the possibility of generating a concise PMS signature based on solely low-molecular measurements (III) or in combination with radiological and protein measures (II) was explored. In both cases, it was concluded that it is plausible to generate a condensed set of highly informative markers that can distinguish PMS patients from RRMS patients. In Paper III, the classifier was complemented with conformal prediction that enabled an estimate of confidence in single patient predictions and a personalised evaluation of current disease state. Finally, in Paper IV, the extracted low-molecular marker candidates were characterised in isolation, revealing that several metabolites were distinctively altered in the CSF of PMS patients, including increased levels of 4-acetamidobutanoate, 4-hydroxybenzoate and thymine.

Overall, the results from this work indicate that it is possible to detect PMS at an earlier stage and that advanced analytical algorithms can support healthcare.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2020. p. 56
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1674
Keywords
bioinformatics, biomarkers, progressive multiple sclerosis, metabolomics, machine learning, advanced analytics, mass spectrometry
National Category
Bioinformatics (Computational Biology) Neurology
Identifiers
urn:nbn:se:uu:diva-416655 (URN)978-91-513-0981-1 (ISBN)
Public defence
2020-09-17, H:son-Holmdahlsalen, Akademiska sjukhuset, Ing 100, 2 tr, Dag Hammarskjölds Väg 8, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2020-08-26 Created: 2020-07-29 Last updated: 2020-09-02
2. Confidence Predictions in Pharmaceutical Sciences
Open this publication in new window or tab >>Confidence Predictions in Pharmaceutical Sciences
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The main focus of this thesis has been on Quantitative Structure Activity Relationship (QSAR) modeling using methods producing valid measures of uncertainty. The goal of QSAR is to prospectively predict the outcome from assays, such as ADMET (Absorption, Distribution, Metabolism, Excretion), toxicity and on- and off-target interactions, for novel compounds. QSAR modeling offers an appealing alternative to laboratory work, which is both costly and time-consuming, and can be applied earlier in the development process as candidate drugs can be tested in silico without requiring to synthesize them first. A common theme across the presented papers is the application of conformal and probabilistic prediction models, which are used in order to associate predictions with a level of their reliability – a desirable property that is essential in the stage of decision making. In Paper I we studied approaches on how to utilize biological assay data from legacy systems, in order to improve predictive models. This is otherwise problematic since mixing data from separate systems will cause issues for most machine learning algorithms. We demonstrated that old data could be used to augment the proper training set of a conformal predictor to yield more efficient predictions while preserving model calibration. In Paper II we studied a new approach of predicting metabolic transformations of small molecules based on transformations encoded in SMIRKS format. In this work use used the probabilistic Cross-Venn-ABERS predictor which overall worked well, but had difficulty in modeling the minority class of imbalanced datasets. In Paper III we studied metabolomics data from patients diagnosed with Multiple Sclerosis and found a set of 15 discriminatory metabolites that could be used to classify patients from a validation cohort into one of two sub types of the disease with high accuracy. We further demonstrated that conformal prediction could be useful for tracking the progression of the disease for individual patients, which we exemplified using data from a clinical trial. In Paper IV we introduced CPSign – a software for cheminformatics modeling using conformal and probabilistic methods. CPSign was compared against other regularly used methods for this task, using 32 benchmark datasets, demonstrating that CPSign produces predictive accuracy on par with the best performing methods.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2023. p. 42
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 337
Keywords
machine learning, QSAR, Conformal prediction, Venn-ABERS, Probabilistic machine learning
National Category
Pharmaceutical Sciences
Research subject
Machine learning; Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-508930 (URN)978-91-513-1868-4 (ISBN)
Public defence
2023-09-29, Room A1:111, BMC, Husargatan 3, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2023-09-06 Created: 2023-08-14 Last updated: 2023-09-06

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Herman, StephanieArvidsson McShane, StaffanZhukovsky, ChristinaEmami, PayamBurman, JoachimSpjuth, OlaKultima, Kim
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