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Klein P, Kaminski RM, Koepp M, Löscher W. New epilepsy therapies in development. Nat Rev Drug Discov 2024:10.1038/s41573-024-00981-w. [PMID: 39039153 DOI: 10.1038/s41573-024-00981-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2024] [Indexed: 07/24/2024]
Abstract
Epilepsy is a common brain disorder, characterized by spontaneous recurrent seizures, with associated neuropsychiatric and cognitive comorbidities and increased mortality. Although people at risk can often be identified, interventions to prevent the development of the disorder are not available. Moreover, in at least 30% of patients, epilepsy cannot be controlled by current antiseizure medications (ASMs). As a result of considerable progress in epilepsy genetics and the development of novel disease models, drug screening technologies and innovative therapeutic modalities over the past 10 years, more than 200 novel epilepsy therapies are currently in the preclinical or clinical pipeline, including many treatments that act by new mechanisms. Assisted by diagnostic and predictive biomarkers, the treatment of epilepsy is undergoing paradigm shifts from symptom-only ASMs to disease prevention, and from broad trial-and-error treatments for seizures in general to mechanism-based treatments for specific epilepsy syndromes. In this Review, we assess recent progress in ASM development and outline future directions for the development of new therapies for the treatment and prevention of epilepsy.
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Affiliation(s)
- Pavel Klein
- Mid-Atlantic Epilepsy and Sleep Center, Bethesda, MD, USA.
| | | | - Matthias Koepp
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Wolfgang Löscher
- Translational Neuropharmacology Lab., NIFE, Department of Experimental Otology of the ENT Clinics, Hannover Medical School, Hannover, Germany.
- Center for Systems Neuroscience, Hannover, Germany.
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Kaushik M, Mahajan S, Machahary N, Thakran S, Chopra S, Tomar RV, Kushwaha SS, Agarwal R, Sharma S, Kukreti R, Biswal B. Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population. Epilepsy Res 2024; 205:107404. [PMID: 38996687 DOI: 10.1016/j.eplepsyres.2024.107404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/04/2024] [Accepted: 06/27/2024] [Indexed: 07/14/2024]
Abstract
PURPOSE This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE). METHODS Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into 'good responder (GR)' and 'poor responder (PR)' based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to construct classification models. RESULTS Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chi-square (χ2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furthermore, our surrogate analysis revealed that the null hypothesis for both XGB and SVC was rejected at a 100 % confidence level, underscoring the significance of their predictive performance. These findings underscore the robustness and reliability of XGB and SVC in our predictive modelling framework. SIGNIFICANCE Utilizing XG Boost and SVC-based machine learning classifier, we successfully forecasted the likelihood of a patient's response to ASM treatment, categorizing them as either PR or GR, post-completion of standard epilepsy examinations. The classifier's predictions were found to be statistically significant, suggesting their potential utility in improving treatment strategies, particularly in the personalized selection of ASM regimens for individual epilepsy patients.
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Affiliation(s)
- Mahima Kaushik
- Cluster Innovation Centre, University of Delhi, Delhi, India
| | | | - Nitin Machahary
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Sarita Thakran
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Saransh Chopra
- Cluster Innovation Centre, University of Delhi, Delhi, India
| | | | - Suman S Kushwaha
- Department. of Neurology, Institute of Human Behaviour and Allied Sciences, Dilshad Garden, Delhi, India
| | - Rachna Agarwal
- Department. of Neurology, Institute of Human Behaviour and Allied Sciences, Dilshad Garden, Delhi, India
| | - Sangeeta Sharma
- Department. of Neurology, Institute of Human Behaviour and Allied Sciences, Dilshad Garden, Delhi, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Bibhu Biswal
- Cluster Innovation Centre, University of Delhi, Delhi, India.
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Shariff S, Kantawala B, Xochitun Gopar Franco W, Dejene Ayele N, Munyangaju I, Esam Alzain F, Nazir A, Wojtara M, Uwishema O. Tailoring epilepsy treatment: personalized micro-physiological systems illuminate individual drug responses. Ann Med Surg (Lond) 2024; 86:3557-3567. [PMID: 38846814 PMCID: PMC11152789 DOI: 10.1097/ms9.0000000000002078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 04/09/2024] [Indexed: 06/09/2024] Open
Abstract
Introduction Approximately 50 million people worldwide have epilepsy, with many not achieving seizure freedom. Organ-on-chip technology, which mimics organ-level physiology, could revolutionize drug development for epilepsy by replacing animal models in preclinical studies. The authors' goal is to determine if customized micro-physiological systems can lead to tailored drug treatments for epileptic patients. Materials and methods A comprehensive literature search was conducted utilizing various databases, including PubMed, Ebscohost, Medline, and the National Library of Medicine, using a predetermined search strategy. The authors focused on articles that addressed the role of personalized micro-physiological systems in individual drug responses and articles that discussed different types of epilepsy, diagnosis, and current treatment options. Additionally, articles that explored the components and design considerations of micro-physiological systems were reviewed to identify challenges and opportunities in drug development for challenging epilepsy cases. Results The micro-physiological system offers a more accurate and cost-effective alternative to traditional models for assessing drug effects, toxicities, and disease mechanisms. Nevertheless, designing patient-specific models presents critical considerations, including the integration of analytical biosensors and patient-derived cells, while addressing regulatory, material, and biological complexities. Material selection, standardization, integration of vascular systems, cost efficiency, real-time monitoring, and ethical considerations are also crucial to the successful use of this technology in drug development. Conclusion The future of organ-on-chip technology holds great promise, with the potential to integrate artificial intelligence and machine learning for personalized treatment of epileptic patients.
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Affiliation(s)
- Sanobar Shariff
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- Yerevan State Medical University, Yerevan, Armenia
| | - Burhan Kantawala
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- Yerevan State Medical University, Yerevan, Armenia
| | - William Xochitun Gopar Franco
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- University of Guadalajara, Guadalajara, Mexico
| | - Nitsuh Dejene Ayele
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- Department of Internal Medicine, Faculty of Medicine, Wolkite University, Wolkite, Ethiopia
| | - Isabelle Munyangaju
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- College of Medicine and General Surgery, Sudan University Of Science and Technology, Khartoum, Sudan
| | - Fatima Esam Alzain
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- College of Medicine and General Surgery, Sudan University Of Science and Technology, Khartoum, Sudan
| | - Abubakar Nazir
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- Department of Medicine, King Edward Medical University, Lahore, Pakistan
| | - Madga Wojtara
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
| | - Olivier Uwishema
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- Clinton Global Initiative University, New York, NY
- Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey
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Teixeira PF, Battelino T, Carlsson A, Gudbjörnsdottir S, Hannelius U, von Herrath M, Knip M, Korsgren O, Elding Larsson H, Lindqvist A, Ludvigsson J, Lundgren M, Nowak C, Pettersson P, Pociot F, Sundberg F, Åkesson K, Lernmark Å, Forsander G. Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data. Diabetologia 2024; 67:985-994. [PMID: 38353727 PMCID: PMC11058797 DOI: 10.1007/s00125-024-06089-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 12/06/2023] [Indexed: 04/30/2024]
Abstract
The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.
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Affiliation(s)
| | - Tadej Battelino
- University Medical Center Ljubljana, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Anneli Carlsson
- Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden
| | - Soffia Gudbjörnsdottir
- Swedish National Diabetes Register, Centre of Registers, Gothenburg, Sweden
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | | | - Matthias von Herrath
- Global Chief Medical Office, Novo Nordisk, A/S, Søborg, Denmark
- Diabetes Research Institute, University of Miami, Miami, FL, USA
| | - Mikael Knip
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Olle Korsgren
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Helena Elding Larsson
- Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden
- Department of Pediatrics, Skåne University Hospital, Malmö, Sweden
| | | | - Johnny Ludvigsson
- Crown Princess Victoria Children's Hospital and Division of Pediatrics, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Markus Lundgren
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Department of Paediatrics, Kristianstad Hospital, Kristianstad, Sweden
| | | | - Paul Pettersson
- Division of Networked and Embedded Systems, Mälardalen University, Västerås, Sweden
- MainlyAI AB, Stockholm, Sweden
| | - Flemming Pociot
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Frida Sundberg
- Department of Paediatrics, Institute for Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Queen Silvia Children's Hospital, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Karin Åkesson
- Department of Clinical and Experimental Medicine, Division of Pediatrics and Diabetes Research Center, Linköping University, Linköping, Sweden
- Department of Pediatrics, Ryhov County Hospital, Jönköping, Sweden
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden.
| | - Gun Forsander
- Department of Paediatrics, Institute for Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
- Queen Silvia Children's Hospital, Sahlgrenska University Hospital, Gothenburg, Sweden.
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Zhang J, Zhang J, Jin J, Jiang X, Yang L, Fan S, Zhang Q, Chi M. Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis. Front Cardiovasc Med 2024; 11:1323918. [PMID: 38433757 PMCID: PMC10904648 DOI: 10.3389/fcvm.2024.1323918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/19/2024] [Indexed: 03/05/2024] Open
Abstract
Background With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including cardiovascular disease. Facts have proved that AI has broad application prospects in rapid and accurate diagnosis. Objective This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots. Methods The articles and reviews regarding application of AI in cardiovascular disease between 2000 and 2023 were selected from Web of Science Core Collection on 30 December 2023. Microsoft Excel 2019 was applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 6.2.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field. Results A total of 4,611 articles were selected in this study. AI-related research on cardiovascular disease increased exponentially in recent years, of which the USA was the most productive country with 1,360 publications, and had close cooperation with many countries. The most productive institutions and researchers were the Cedar sinai medical center and Acharya, Ur. However, the cooperation among most institutions or researchers was not close even if the high research outputs. Circulation is the journal with the largest number of publications in this field. The most important keywords are "classification", "diagnosis", and "risk". Meanwhile, the current research hotpots were "late gadolinium enhancement" and "carotid ultrasound". Conclusions AI has broad application prospects in cardiovascular disease, and a growing number of scholars are devoted to AI-related research on cardiovascular disease. Cardiovascular imaging techniques and the selection of appropriate algorithms represent the most extensively studied areas, and a considerable boost in these areas is predicted in the coming years.
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Affiliation(s)
- Jirong Zhang
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Jimei Zhang
- College of Public Health, The University of Sydney, NSW, Sydney, Australia
| | - Juan Jin
- The First Department of Cardiovascular, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, HL, China
| | - Xicheng Jiang
- College of basic medicine, Heilongjiang University of Chinese Medicine, Harbin, HL, China
| | - Linlin Yang
- Cardiovascular Disease Branch, Dalian Second People's Hospital, Dalian, LN, China
| | - Shiqi Fan
- Harbin hospital of traditional Chinese medicine, Harbin, HL, China
| | - Qiao Zhang
- School of Pharmacy, Harbin University of Commerce, Harbin, HL, China
| | - Ming Chi
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
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Chang RSK, Nguyen S, Chen Z, Foster E, Kwan P. Role of machine learning in the management of epilepsy: a systematic review protocol. BMJ Open 2024; 14:e079785. [PMID: 38272549 PMCID: PMC10823996 DOI: 10.1136/bmjopen-2023-079785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
INTRODUCTION Machine learning is a rapidly expanding field and is already incorporated into many aspects of medicine including diagnostics, prognostication and clinical decision-support tools. Epilepsy is a common and disabling neurological disorder, however, management remains challenging in many cases, despite expanding therapeutic options. We present a systematic review protocol to explore the role of machine learning in the management of epilepsy. METHODS AND ANALYSIS This protocol has been drafted with reference to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Protocols. A literature search will be conducted in databases including MEDLINE, Embase, Scopus and Web of Science. A PRISMA flow chart will be constructed to summarise the study workflow. As the scope of this review is the clinical application of machine learning, the selection of papers will be focused on studies directly related to clinical decision-making in management of epilepsy, specifically the prediction of response to antiseizure medications, development of drug-resistant epilepsy, and epilepsy surgery and neuromodulation outcomes. Data will be extracted following the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Prediction model Risk Of Bias ASsessment Tool will be used for the quality assessment of the included studies. Syntheses of quantitative data will be presented in narrative format. ETHICS AND DISSEMINATION As this study is a systematic review which does not involve patients or animals, ethics approval is not required. The results of the systematic review will be submitted to peer-review journals for publication and presented in academic conferences. PROSPERO REGISTRATION NUMBER CRD42023442156.
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Affiliation(s)
- Richard Shek-Kwan Chang
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Shani Nguyen
- Monash University Faculty of Medicine Nursing and Health Sciences, Melbourne, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Emma Foster
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
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Wu J, Singleton SS, Bhuiyan U, Krammer L, Mazumder R. Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning. Front Mol Biosci 2024; 10:1337373. [PMID: 38313584 PMCID: PMC10834744 DOI: 10.3389/fmolb.2023.1337373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/27/2023] [Indexed: 02/06/2024] Open
Abstract
The human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various -omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other -omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health.
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Affiliation(s)
- Jingyue Wu
- Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - Stephanie S. Singleton
- Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - Urnisha Bhuiyan
- Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - Lori Krammer
- Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
- Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Raja Mazumder
- Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
- The McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC, United States
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Amniouel S, Jafri MS. High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data. Front Physiol 2024; 14:1272206. [PMID: 38304289 PMCID: PMC10830836 DOI: 10.3389/fphys.2023.1272206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/26/2023] [Indexed: 02/03/2024] Open
Abstract
Introduction: FOLFOX and FOLFIRI chemotherapy are considered standard first-line treatment options for colorectal cancer (CRC). However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed. Methods: A newly developed machine learning model was applied on several gene expression data from the public repository GEO database to identify molecular signatures predictive of efficacy of 5-FU based combination chemotherapy (FOLFOX and FOLFIRI) in patients with CRC. The model was trained using 5-fold cross validation and multiple feature selection methods including LASSO and VarSelRF methods. Random Forest and support vector machine classifiers were applied to evaluate the performance of the models. Results and Discussion: For the CRC GEO dataset samples from patients who received either FOLFOX or FOLFIRI, validation and test sets were >90% correctly classified (accuracy), with specificity and sensitivity ranging between 85%-95%. In the datasets used from the GEO database, 28.6% of patients who failed the treatment therapy they received are predicted to benefit from the alternative treatment. Analysis of the gene signature suggests the mechanistic difference between colorectal cancers that respond and those that do not respond to FOLFOX and FOLFIRI. Application of this machine learning approach could lead to improvements in treatment outcomes for patients with CRC and other cancers after additional appropriate clinical validation.
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Affiliation(s)
- Soukaina Amniouel
- School of Systems Biology, George Mason University, Fairfax, VA, United States
| | - Mohsin Saleet Jafri
- School of Systems Biology, George Mason University, Fairfax, VA, United States
- Center for Biomedical Engineering and Technology, University of Maryland School of Medicine, Baltimore, MD, United States
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Zhang B, Zhang L, Chen Q, Jin Z, Liu S, Zhang S. Harnessing artificial intelligence to improve clinical trial design. COMMUNICATIONS MEDICINE 2023; 3:191. [PMID: 38129570 PMCID: PMC10739942 DOI: 10.1038/s43856-023-00425-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
Zhang et al. discuss how artificial intelligence (AI) can be used to optimize clinical trial design and potentially boost the success rate of clinical trials. AI has unparalleled potential to leverage real-world data and unlock valuable insights for innovative trial design.
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Affiliation(s)
- Bin Zhang
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Lu Zhang
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Qiuying Chen
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Zhe Jin
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Shuyi Liu
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Shuixing Zhang
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China.
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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Auvin S, Galanopoulou AS, Moshé SL, Potschka H, Rocha L, Walker MC. Revisiting the concept of drug-resistant epilepsy: A TASK1 report of the ILAE/AES Joint Translational Task Force. Epilepsia 2023; 64:2891-2908. [PMID: 37676719 PMCID: PMC10836613 DOI: 10.1111/epi.17751] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/08/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023]
Abstract
Despite progress in the development of anti-seizure medications (ASMs), one third of people with epilepsy have drug-resistant epilepsy (DRE). The working definition of DRE, proposed by the International League Against Epilepsy (ILAE) in 2010, helped identify individuals who might benefit from presurgical evaluation early on. As the incidence of DRE remains high, the TASK1 workgroup on DRE of the ILAE/American Epilepsy Society (AES) Joint Translational Task Force discussed the heterogeneity and complexity of its presentation and mechanisms, the confounders in drawing mechanistic insights when testing treatment responses, and barriers in modeling DRE across the lifespan and translating across species. We propose that it is necessary to revisit the current definition of DRE, in order to transform the preclinical and clinical research of mechanisms and biomarkers, to identify novel, effective, precise, pharmacologic treatments, allowing for earlier recognition of drug resistance and individualized therapies.
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Affiliation(s)
- Stéphane Auvin
- Institut Universitaire de France, Paris, France
- Paediatric Neurology, Assistance Publique - Hôpitaux de Paris, EpiCARE ERN Member, Robert-Debré Hospital, Paris, France
- University Paris-Cité, Paris, France
| | - Aristea S Galanopoulou
- Saul R. Korey Department of Neurology, Isabelle Rapin Division of Child Neurology, Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, and Montefiore/Einstein Epilepsy Center, Bronx, New York, USA
| | - Solomon L Moshé
- Saul R. Korey Department of Neurology, Isabelle Rapin Division of Child Neurology, Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, and Montefiore/Einstein Epilepsy Center, Bronx, New York, USA
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Heidrun Potschka
- Institute of Pharmacology, Toxicology, and Pharmacy, Ludwig-Maximilians-University (LMU), Munich, Germany
| | - Luisa Rocha
- Pharmacobiology Department, Center for Research and Advanced Studies (CINVESTAV), Mexico City, Mexico
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
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12
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Almohaish S, Cook AM, Brophy GM, Rhoney DH. Personalized antiseizure medication therapy in critically ill adult patients. Pharmacotherapy 2023; 43:1166-1181. [PMID: 36999346 DOI: 10.1002/phar.2797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/01/2023] [Accepted: 03/08/2023] [Indexed: 04/01/2023]
Abstract
Precision medicine has the potential to have a significant impact on both drug development and patient care. It is crucial to not only provide prompt effective antiseizure treatment for critically ill patients after seizures start but also have a proactive mindset and concentrate on epileptogenesis and the underlying cause of the seizures or seizure disorders. Critical illness presents different treatment issues compared with the ambulatory population, which makes it challenging to choose the best antiseizure medications and to administer them at the right time and at the right dose. Since there is a paucity of information available on antiseizure medication dosing in critically ill patients, therapeutic drug monitoring is a useful tool for defining each patient's personal therapeutic range and assisting clinicians in decision-making. Use of pharmacogenomic information relating to pharmacokinetics, hepatic metabolism, and seizure etiology may improve safety and efficacy by individualizing therapy. Studies evaluating the clinical implementation of pharmacogenomic information at the point-of-care and identification of biomarkers are also needed. These studies may make it possible to avoid adverse drug reactions, maximize drug efficacy, reduce drug-drug interactions, and optimize medications for each individual patient. This review will discuss the available literature and provide future insights on precision medicine use with antiseizure therapy in critically ill adult patients.
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Affiliation(s)
- Sulaiman Almohaish
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia, USA
- Department of Pharmacy Practice, Clinical Pharmacy College, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Aaron M Cook
- Department of Pharmacy Practice and Science, College of Pharmacy, University of Kentucky, Lexington, Kentucky, USA
| | - Gretchen M Brophy
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Denise H Rhoney
- Division of Practice Advancement and Clinical Education, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
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13
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Vijayakumar S, Lee VV, Leong QY, Hong SJ, Blasiak A, Ho D. Physicians' Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform. JMIR Hum Factors 2023; 10:e48476. [PMID: 37902825 PMCID: PMC10644191 DOI: 10.2196/48476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/24/2023] [Accepted: 09/10/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND Physicians play a key role in integrating new clinical technology into care practices through user feedback and growth propositions to developers of the technology. As physicians are stakeholders involved through the technology iteration process, understanding their roles as users can provide nuanced insights into the workings of these technologies that are being explored. Therefore, understanding physicians' perceptions can be critical toward clinical validation, implementation, and downstream adoption. Given the increasing prevalence of clinical decision support systems (CDSSs), there remains a need to gain an in-depth understanding of physicians' perceptions and expectations toward their downstream implementation. This paper explores physicians' perceptions of integrating CURATE.AI, a novel artificial intelligence (AI)-based and clinical stage personalized dosing CDSSs, into clinical practice. OBJECTIVE This study aims to understand physicians' perspectives of integrating CURATE.AI for clinical work and to gather insights on considerations of the implementation of AI-based CDSS tools. METHODS A total of 12 participants completed semistructured interviews examining their knowledge, experience, attitudes, risks, and future course of the personalized combination therapy dosing platform, CURATE.AI. Interviews were audio recorded, transcribed verbatim, and coded manually. The data were thematically analyzed. RESULTS Overall, 3 broad themes and 9 subthemes were identified through thematic analysis. The themes covered considerations that physicians perceived as significant across various stages of new technology development, including trial, clinical implementation, and mass adoption. CONCLUSIONS The study laid out the various ways physicians interpreted an AI-based personalized dosing CDSS, CURATE.AI, for their clinical practice. The research pointed out that physicians' expectations during the different stages of technology exploration can be nuanced and layered with expectations of implementation that are relevant for technology developers and researchers.
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Affiliation(s)
- Smrithi Vijayakumar
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - V Vien Lee
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Qiao Ying Leong
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Soo Jung Hong
- Department of Communications and New Media, National University of Singapore, Singapore, Singapore
| | - Agata Blasiak
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dean Ho
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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14
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Gunasekera CL, Sirven JI, Feyissa AM. The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time? J Cent Nerv Syst Dis 2023; 15:11795735231209209. [PMID: 37868934 PMCID: PMC10586013 DOI: 10.1177/11795735231209209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/05/2023] [Indexed: 10/24/2023] Open
Abstract
Antiseizure medications (ASMs) are the mainstay of symptomatic epilepsy treatment. The primary goal of pharmacotherapy with ASMs in epilepsy is to achieve complete seizure remission while minimizing therapy-related adverse events. Over the years, more ASMs have been introduced, with approximately 30 now in everyday use. With such a wide variety, much guidance is needed in choosing ASMs for initial therapy, subsequent replacement monotherapy, or adjunctive therapy. The specific ASMs are typically tailored by the patient's related factors, including epilepsy syndrome, age, sex, comorbidities, and ASM characteristics, including the spectrum of efficacy, pharmacokinetic properties, safety, and tolerability. Weighing these key clinical variables requires experience and expertise that may be limited. Furthermore, with this approach, patients may endure multiple trials of ineffective treatments before the most appropriate ASM is found. A more reliable way to predict response to different ASMs is needed so that the most effective and tolerated ASM can be selected. Soon, alternative approaches, such as deep machine learning (ML), could aid the individualized selection of the first and subsequent ASMs. The recognition of epilepsy as a network disorder and the integration of personalized epilepsy networks in future ML platforms can also facilitate the prediction of ASM response. Augmenting the conventional approach with artificial intelligence (AI) opens the door to personalized pharmacotherapy in epilepsy. However, more work is needed before these models are ready for primetime clinical practice.
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15
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Bhatnagar A, Murray G, Ray S. Circadian biology to advance therapeutics for mood disorders. Trends Pharmacol Sci 2023; 44:689-704. [PMID: 37648611 DOI: 10.1016/j.tips.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/30/2023] [Accepted: 07/30/2023] [Indexed: 09/01/2023]
Abstract
Mood disorders account for a significant global disease burden, and pharmacological innovation is needed as existing medications are suboptimal. A wide range of evidence implicates circadian and sleep dysfunction in the pathogenesis of mood disorders, and there is growing interest in these chronobiological pathways as a focus for treatment innovation. We review contemporary evidence in three promising areas in circadian-clock-based therapeutics in mood disorders: targeting the circadian system informed by mechanistic molecular advances; time-tailoring of medications; and personalizing treatment using circadian parameters. We also consider the limitations and challenges in accelerating the development of new circadian-informed pharmacotherapies for mood disorders.
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Affiliation(s)
- Apoorva Bhatnagar
- Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, 502284, Telangana, India; Centre for Mental Health, Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Greg Murray
- Centre for Mental Health, Swinburne University of Technology, Melbourne, Victoria, Australia.
| | - Sandipan Ray
- Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, 502284, Telangana, India.
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16
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Kerr WT, Reddy AS, Seo SH, Kok N, Stacey WC, Stern JM, Pennell PB, French JA. Increasing challenges to trial recruitment and conduct over time. Epilepsia 2023; 64:2625-2634. [PMID: 37440282 PMCID: PMC10592378 DOI: 10.1111/epi.17716] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/10/2023] [Accepted: 07/11/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVE This study was undertaken to evaluate how the challenges in the recruitment and retention of participants in clinical trials for focal onset epilepsy have changed over time. METHODS In this systematic analysis of randomized clinical trials of adjunct antiseizure medications for medication-resistant focal onset epilepsy, we evaluated how the numbers of participants, sites, and countries have changed since the first such trial in 1990. We also evaluated the proportion of participants who completed each trial phase and their reasons for early trial exit. We analyzed these trends using mixed effects generalized linear models accounting for the influence of the number of trial sites and trial-specific variability. RESULTS The number of participants per site has steadily decreased over decades, with recent trials recruiting fewer than five participants per site (reduction by .16 participants/site/year, p < .0001). Fewer participants also progressed from recruitment to randomization over time (odds ratio = .94/year, p = .014). Concurrently, there has been an increase in the placebo response over time (increase in median percent reduction of .4%/year, p = .02; odds ratio of increase in 50% responder rate of 1.03/year, p = .02), which was not directly associated with the number of sites per trial (p > .20). SIGNIFICANCE This historical analysis highlights the increasing challenges with participant recruitment and retention, as well as increasing placebo response. It serves as a call to action to change clinical trial design to address these challenges.
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Affiliation(s)
- Wesley T. Kerr
- Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Advith S. Reddy
- Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA
| | - Sung Hyun Seo
- Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA
| | - Neo Kok
- Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA
| | - William C. Stacey
- Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - John M. Stern
- Department of Neurology, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California, USA
| | - Page B. Pennell
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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17
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Zeibich R, Kwan P, J. O’Brien T, Perucca P, Ge Z, Anderson A. Applications for Deep Learning in Epilepsy Genetic Research. Int J Mol Sci 2023; 24:14645. [PMID: 37834093 PMCID: PMC10572791 DOI: 10.3390/ijms241914645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/11/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.
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Affiliation(s)
- Robert Zeibich
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
- Department of Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Terence J. O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
- Department of Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Piero Perucca
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
- Department of Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
- Epilepsy Research Centre, Department of Medicine, Austin Health, The University of Melbourne, Melbourne, VIC 3084, Australia
- Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, The University of Melbourne, Melbourne, VIC 3084, Australia
| | - Zongyuan Ge
- Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia;
- Monash-Airdoc Research, Monash University, Melbourne, VIC 3800, Australia
| | - Alison Anderson
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
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18
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Perucca E, Perucca P, White HS, Wirrell EC. Drug resistance in epilepsy. Lancet Neurol 2023:S1474-4422(23)00151-5. [PMID: 37352888 DOI: 10.1016/s1474-4422(23)00151-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/23/2023] [Accepted: 04/06/2023] [Indexed: 06/25/2023]
Abstract
Drug resistance is estimated to affect about a third of individuals with epilepsy, but its prevalence differs in relation to the epilepsy syndrome, the cause of epilepsy, and other factors such as age of seizure onset and presence of associated neurological deficits. Although drug-resistant epilepsy is not synonymous with unresponsiveness to any drug treatment, the probability of achieving seizure freedom on a newly tried medication decreases with increasing number of previously failed treatments. After two appropriately used antiseizure medications have failed to control seizures, individuals should be referred whenever possible to a comprehensive epilepsy centre for diagnostic re-evaluation and targeted management. The feasibility of epilepsy surgery and other treatments, including those targeting the cause of epilepsy, should be considered early after diagnosis. Substantial evidence indicates that a delay in identifying an effective treatment can adversely affect ultimate outcome and carry an increased risk of cognitive disability, other comorbidities, and premature mortality. Research on mechanisms of drug resistance and novel therapeutics is progressing rapidly, and potentially improved treatments, including those targeting disease modification, are on the horizon.
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Affiliation(s)
- Emilio Perucca
- Department of Medicine, Austin Health, University of Melbourne, Melbourne, VIC, Australia; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia.
| | - Piero Perucca
- Department of Medicine, Austin Health, University of Melbourne, Melbourne, VIC, Australia; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia; Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Melbourne, VIC, Australia; Department of Neurology, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Neurology, Alfred Health, Melbourne, VIC, Australia
| | - H Steve White
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Elaine C Wirrell
- Divisions of Child and Adolescent Neurology and Epilepsy, Department of Neurology, Mayo Clinic, Rochester, MN, USA
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19
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Chong D, Jones NC, Schittenhelm RB, Anderson A, Casillas-Espinosa PM. Multi-omics Integration and Epilepsy: Towards a Better Understanding of Biological Mechanisms. Prog Neurobiol 2023:102480. [PMID: 37286031 DOI: 10.1016/j.pneurobio.2023.102480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/09/2023] [Accepted: 06/03/2023] [Indexed: 06/09/2023]
Abstract
The epilepsies are a group of complex neurological disorders characterised by recurrent seizures. Approximately 30% of patients fail to respond to anti-seizure medications, despite the recent introduction of many new drugs. The molecular processes underlying epilepsy development are not well understood and this knowledge gap impedes efforts to identify effective targets and develop novel therapies against epilepsy. Omics studies allow a comprehensive characterisation of a class of molecules. Omics-based biomarkers have led to clinically validated diagnostic and prognostic tests for personalised oncology, and more recently for non-cancer diseases. We believe that, in epilepsy, the full potential of multi-omics research is yet to be realised and we envisage that this review will serve as a guide to researchers planning to undertake omics-based mechanistic studies.
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Affiliation(s)
- Debbie Chong
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia
| | - Nigel C Jones
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia; Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Ralf B Schittenhelm
- Monash Proteomics & Metabolomics Facility and Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, 3800, Australia
| | - Alison Anderson
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia; Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Pablo M Casillas-Espinosa
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia; Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
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20
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Potschka H, Fischer A, Löscher W, Volk HA. Pathophysiology of drug-resistant canine epilepsy. Vet J 2023; 296-297:105990. [PMID: 37150317 DOI: 10.1016/j.tvjl.2023.105990] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
Drug resistance continues to be a major clinical problem in the therapeutic management of canine epilepsies with substantial implications for quality of life and survival times. Experimental and clinical data from human medicine provided evidence for relevant contributions of intrinsic severity of the disease as well as alterations in pharmacokinetics and -dynamics to failure to respond to antiseizure medications. In addition, several modulatory factors have been identified that can be associated with the level of therapeutic responses. Among others, the list of potential modulatory factors comprises genetic and epigenetic factors, inflammatory mediators, and metabolites. Regarding data from dogs, there are obvious gaps in knowledge when it comes to our understanding of the clinical patterns and the mechanisms of drug-resistant canine epilepsy. So far, seizure density and the occurrence of cluster seizures have been linked with a poor response to antiseizure medications. Moreover, evidence exists that the genetic background and alterations in epigenetic mechanisms might influence the efficacy of antiseizure medications in dogs with epilepsy. Further molecular, cellular, and network alterations that may affect intrinsic severity, pharmacokinetics, and -dynamics have been reported. However, the association with drug responsiveness has not yet been studied in detail. In summary, there is an urgent need to strengthen clinical and experimental research efforts exploring the mechanisms of resistance as well as their association with different etiologies, epilepsy types, and clinical courses.
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Affiliation(s)
- Heidrun Potschka
- Institute of Pharmacology, Toxicology, and Pharmacy, Ludwig-Maximilians-University, Munich, Germany.
| | - Andrea Fischer
- Clinic of Small Animal Medicine, Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-University, Munich, Germany
| | - Wolfgang Löscher
- Department of Pharmacology, Toxicology, and Pharmacy, University of Veterinary Medicine, Hannover, Germany; Center for Systems Neuroscience, Hannover, Germany
| | - Holger A Volk
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine, Hannover, Germany
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21
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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22
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Hakeem H, Alsfouk BAA, Kwan P, Brodie MJ, Chen Z. Should substitution monotherapy or combination therapy be used after failure of the first antiseizure medication? Observations from a 30-year cohort study. Epilepsia 2023; 64:1248-1258. [PMID: 36869855 DOI: 10.1111/epi.17573] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 03/05/2023]
Abstract
OBJECTIVES To assess the temporal trends in the use of second antiseizure (ASM) regimens and compare the efficacy of substitution monotherapy and combination therapy after failure of initial monotherapy in people with epilepsy. METHODS This was a longitudinal observational cohort study conducted at the Epilepsy Unit of the Western Infirmary in Glasgow, Scotland. We included patients who were newly treated for epilepsy with ASMs between July 1982, and October 2012. All patients were followed up for a minimum of 2 years. Seizure freedom was defined as no seizure for at least 1 year on unchanged medication at the last follow up. RESULTS During the study period, 498 patients were treated with a second ASM regimen after failure of the initial ASM monotherapy, of whom 346 (69%) were prescribed combination therapy and 152 (31%) were given substitution monotherapy. The proportion of patients receiving second regimen as combination therapy increased during the study period from 46% in first epoch (1985-1994) to 78% in the last (2005-2015) (RR = 1.66, 95% CI: 1.17-2.36, corrected-p = .010). Overall, 21% (104/498) of the patients achieved seizure freedom on the second ASM regimen, which was less than half of the seizure-free rate on the initial ASM monotherapy (45%, p < .001). Patients who received substitution monotherapy had similar seizure-free rate compared with those who received combination therapy (RR = 1.17, 95% CI: 0.81-1.69, p = .41). Individual ASMs used, either alone or in combination, had similar efficacy. However, the subgroup analysis was limited by small sample sizes. SIGNIFICANCE The choice of second regimen used based on clinical judgment was not associated with treatment outcome in patients whose initial monotherapy failed due to poor seizure control. Alternative approaches such as machine learning should be explored to aid individualized selection of the second ASM regimen.
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Affiliation(s)
- Haris Hakeem
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Bshra Ali A Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
- University of Glasgow, Glasgow, UK
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Clinical Epidemiology, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Huang Z, Ma Y, Wang R, Yuan B, Jiang R, Yang Q, Li W, Sun J. DSCNN-LSTMs: A Lightweight and Efficient Model for Epilepsy Recognition. Brain Sci 2022; 12:brainsci12121672. [PMID: 36552132 PMCID: PMC9775067 DOI: 10.3390/brainsci12121672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Epilepsy is the second most common disease of the nervous system. Because of its high disability rate and the long course of the disease, it is a worldwide medical problem and social public health problem. Therefore, the timely detection and treatment of epilepsy are very important. Currently, medical professionals use their own diagnostic experience to identify seizures by visual inspection of the electroencephalogram (EEG). Not only does it require a lot of time and effort, but the process is also very cumbersome. Machine learning-based methods have recently been proposed for epilepsy detection, which can help clinicians make rapid and correct diagnoses. However, these methods often require extracting the features of EEG signals before using the data. In addition, the selection of features often requires domain knowledge, and feature types also have a significant impact on the performance of the classifier. In this paper, a one-dimensional depthwise separable convolutional neural network and long short-term memory networks (1D DSCNN-LSTMs) model is proposed to identify epileptic seizures by autonomously extracting the features of raw EEG. On the UCI dataset, the performance of the proposed 1D DSCNN-LSTMs model is verified by cross-validation and time complexity comparison. Compared with other previous models, the experimental results show that the highest recognition rates of binary and quintuple classification are 99.57% and 81.30%, respectively. It can be concluded that the 1D DSCNN-LSTMs model proposed in this paper is an effective method to identify seizures based on EEG signals.
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Chiang S, Rao VR. Choosing the Best Antiseizure Medication-Can Artificial Intelligence Help? JAMA Neurol 2022; 79:970-972. [PMID: 36036914 PMCID: PMC11163946 DOI: 10.1001/jamaneurol.2022.2441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- Sharon Chiang
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco
| | - Vikram R Rao
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco
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Hakeem H, Feng W, Chen Z, Choong J, Brodie MJ, Fong SL, Lim KS, Wu J, Wang X, Lawn N, Ni G, Gao X, Luo M, Chen Z, Ge Z, Kwan P. Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy. JAMA Neurol 2022; 79:986-996. [PMID: 36036923 PMCID: PMC9425285 DOI: 10.1001/jamaneurol.2022.2514] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/17/2022] [Indexed: 11/14/2022]
Abstract
Importance Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed. Objective To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients. Design, Setting, and Participants This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables. Exposures One of 7 antiseizure medications. Main Outcomes and Measures With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models. Results The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC. Conclusions and Relevance In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.
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Affiliation(s)
- Haris Hakeem
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Wei Feng
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
- Monash-Airdoc Research, Monash University, Melbourne, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Jiun Choong
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
| | - Martin J. Brodie
- Department of Medicine and Clinical Pharmacology, University of Glasgow, Glasgow, Scotland
| | - Si-Lei Fong
- Neurology Division, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kheng-Seang Lim
- Neurology Division, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Junhong Wu
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing, China
| | - Xuefeng Wang
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing, China
| | - Nicholas Lawn
- WA Adult Epilepsy Service, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Guanzhong Ni
- Department of Neurology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiang Gao
- Department of Pharmacy, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Mijuan Luo
- Department of Pharmacy, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ziyi Chen
- Department of Neurology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zongyuan Ge
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
- Monash-Airdoc Research, Monash University, Melbourne, Victoria, Australia
- Monash eResearch Centre, Monash University, Melbourne, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing, China
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Gong Y, Xu C, Wang S, Wang Y, Chen Z. Computerized application for epilepsy in China: Does the era of artificial intelligence comes? Acta Neurol Scand 2022; 146:732-742. [PMID: 36156212 DOI: 10.1111/ane.13711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 12/01/2022]
Abstract
Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, infections, etc. This heterogeneity partly hampered the accurate diagnosis and choice of appropriate treatments. Encouragingly, great achievements have been achieved in computational science, making it become a key player in medical fields gradually and bringing new hope for rapid and accurate diagnosis as well as targeted therapies in epilepsy. Here, we historically review the advances of computerized applications in epilepsy-especially those tremendous findings achieved in China-for different purposes including seizure prediction, localization of epileptogenic zone, post-surgical prognosis, etc. Special attentions are paid to the great progress based on artificial intelligence (AI), which is more "sensitive", "smart" and "in-depth" than human capacities. At last, we give a comprehensive discussion about the disadvantages and limitations of current computerized applications for epilepsy and propose some future directions as further stepping stones to embrace "the era of AI" in epilepsy.
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Affiliation(s)
- Yiwei Gong
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuang Wang
- Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Wang
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
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Deqi Sensation to Predict Acupuncture Effect on Functional Dyspepsia: A Machine Learning Study. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:4824575. [PMID: 36159564 PMCID: PMC9492368 DOI: 10.1155/2022/4824575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 08/22/2022] [Accepted: 08/26/2022] [Indexed: 11/18/2022]
Abstract
Objectives The aim of the study was to predict the effect of acupuncture for treating functional dyspepsia (FD) using the support vector machine (SVM) techniques based on initial deqi sensations of patients. Methods This retrospective study involved 90 FD patients who had received four weeks of acupuncture treatment. The support vector classification model was used to distinguish higher responders (patients with Symptom Index of Dyspepsia improvement score ≥ 2) from lower responders (patients with Symptom Index of Dyspepsia improvement score < 2). A support vector regression model was used to predict the change in the Symptom Index of Dyspepsia at the end of acupuncture treatment. Deqi sensations of patients in the first acupuncture treatment of a 20-session acupuncture intervention were defined as features and used to train models. Models were validated by 10-fold cross-validation and evaluated by accuracy, specificity, sensitivity, the area under the receive-operating curve, the coefficient of determination (R2), and the mean squared error. Results The two models could predict the efficacy of acupuncture successfully. These models had an accuracy of 0.84 in predicting acupuncture response, and an R2 of 0.16 in the prediction of symptom improvements, respectively. The presence or absence of deqi sensation, the duration of deqi sensation, distention, and pain were finally selected as significant predicting features. Conclusion Based on the SVM algorithms and deqi sensation, the current study successfully predicted the acupuncture response as well as clinical symptom improvement in FD patients at the end of treatment. Our prediction models are expected to promote the clinical efficacy of acupuncture treatment for FD, reduce medical expenditures, and optimize the allocation of medical resources.
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Janmohamed M, Nhu D, Kuhlmann L, Gilligan A, Tan CW, Perucca P, O’Brien TJ, Kwan P. Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning—clinical application perspectives. Brain Commun 2022; 4:fcac218. [PMID: 36092304 PMCID: PMC9453433 DOI: 10.1093/braincomms/fcac218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 05/25/2022] [Accepted: 08/25/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
The application of deep learning approaches for the detection of interictal epileptiform discharges is a nascent field, with most studies published in the past 5 years. Although many recent models have been published demonstrating promising results, deficiencies in descriptions of data sets, unstandardized methods, variation in performance evaluation and lack of demonstrable generalizability have made it difficult for these algorithms to be compared and progress to clinical validity. A few recent publications have provided a detailed breakdown of data sets and relevant performance metrics to exemplify the potential of deep learning in epileptiform discharge detection. This review provides an overview of the field and equips computer and data scientists with a synopsis of EEG data sets, background and epileptiform variation, model evaluation parameters and an awareness of the performance metrics of high impact and interest to the trained clinical and neuroscientist EEG end user. The gold standard and inter-rater disagreements in defining epileptiform abnormalities remain a challenge in the field, and a hierarchical proposal for epileptiform discharge labelling options is recommended. Standardized descriptions of data sets and reporting metrics are a priority. Source code-sharing and accessibility to public EEG data sets will increase the rigour, quality and progress in the field and allow validation and real-world clinical translation.
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Affiliation(s)
- Mubeen Janmohamed
- Department of Neuroscience, Central Clinical School, Monash University , Melbourne, VIC 3004 , Australia
- Department of Neurology, Alfred Health , Melbourne, VIC 3004 , Australia
- Department of Neurology, The Royal Melbourne Hospital , Melbourne, VIC 3050 , Australia
| | - Duong Nhu
- Department of Data Science and AI, Faculty of IT, Monash University , Clayton, VIC 3800 , Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University , Clayton, VIC 3800 , Australia
| | - Amanda Gilligan
- Neurosciences Clinical Institute, Epworth Healthcare Hospital , Melbourne, VIC 3121 , Australia
| | - Chang Wei Tan
- Department of Data Science and AI, Faculty of IT, Monash University , Clayton, VIC 3800 , Australia
| | - Piero Perucca
- Department of Neuroscience, Central Clinical School, Monash University , Melbourne, VIC 3004 , Australia
- Department of Neurology, Alfred Health , Melbourne, VIC 3004 , Australia
- Department of Medicine, Austin Health, The University of Melbourne , Melbourne, VIC 3084 , Australia
- Comprehensive Epilepsy Program, Department of Neurology, Austin Health , Melbourne, VIC 3084 , Australia
| | - Terence J O’Brien
- Department of Neuroscience, Central Clinical School, Monash University , Melbourne, VIC 3004 , Australia
- Department of Neurology, Alfred Health , Melbourne, VIC 3004 , Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University , Melbourne, VIC 3004 , Australia
- Department of Neurology, Alfred Health , Melbourne, VIC 3004 , Australia
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Traynor BJ, Al-Chalabi A. The Neurogenetics Collection: emerging themes and future considerations for the field in Brain. Brain 2022; 145:e31-e35. [PMID: 35403674 PMCID: PMC9630880 DOI: 10.1093/brain/awac120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/05/2022] [Indexed: 11/13/2022] Open
Abstract
Genomics has emerged over the last two decades as a fundamental approach to understanding the molecular basis of human diseases. This Collection brings together some recent articles published in Brain, selected to illustrate the impact of genomics on neurology and to highlight emerging themes in the neurogenetics space.
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Affiliation(s)
| | - Ammar Al-Chalabi
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Kings College London, London, SE5 8AF, UK
- King's College Hospital, London, SE5 9RS, UK
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State-dependent effects of neural stimulation on brain function and cognition. Nat Rev Neurosci 2022; 23:459-475. [PMID: 35577959 DOI: 10.1038/s41583-022-00598-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2022] [Indexed: 01/02/2023]
Abstract
Invasive and non-invasive brain stimulation methods are widely used in neuroscience to establish causal relationships between distinct brain regions and the sensory, cognitive and motor functions they subserve. When combined with concurrent brain imaging, such stimulation methods can reveal patterns of neuronal activity responsible for regulating simple and complex behaviours at the level of local circuits and across widespread networks. Understanding how fluctuations in physiological states and task demands might influence the effects of brain stimulation on neural activity and behaviour is at the heart of how we use these tools to understand cognition. Here we review the concept of such 'state-dependent' changes in brain activity in response to neural stimulation, and consider examples from research on altered states of consciousness (for example, sleep and anaesthesia) and from task-based manipulations of selective attention and working memory. We relate relevant findings from non-invasive methods used in humans to those obtained from direct electrical and optogenetic stimulation of neuronal ensembles in animal models. Given the widespread use of brain stimulation as a research tool in the laboratory and as a means of augmenting or restoring brain function, consideration of the influence of changing physiological and cognitive states is crucial for increasing the reliability of these interventions.
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Terman SW. Rise of the Machines? Predicting Brivaracetam Response Using Machine Learning. Epilepsy Curr 2022; 22:111-113. [PMID: 35444508 PMCID: PMC8988725 DOI: 10.1177/15357597211049052] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Li S, Deng L, Zhang X, Chen L, Yang T, Qi Y, Jiang T. Deep Phenotyping on Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives with a Sequence Motif Discovery Tool: Algorithm Development and Validation (Preprint). J Med Internet Res 2022; 24:e37213. [PMID: 35657661 PMCID: PMC9206202 DOI: 10.2196/37213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/21/2022] [Accepted: 05/12/2022] [Indexed: 11/23/2022] Open
Abstract
Background Phenotype information in electronic health records (EHRs) is mainly recorded in unstructured free text, which cannot be directly used for clinical research. EHR-based deep-phenotyping methods can structure phenotype information in EHRs with high fidelity, making it the focus of medical informatics. However, developing a deep-phenotyping method for non-English EHRs (ie, Chinese EHRs) is challenging. Although numerous EHR resources exist in China, fine-grained annotation data that are suitable for developing deep-phenotyping methods are limited. It is challenging to develop a deep-phenotyping method for Chinese EHRs in such a low-resource scenario. Objective In this study, we aimed to develop a deep-phenotyping method with good generalization ability for Chinese EHRs based on limited fine-grained annotation data. Methods The core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70%) and a testing set (n=300, 30%). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool—MEME (Multiple Expectation Maximums for Motif Elicitation)—was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning–based method for named entity recognition and a pattern recognition–based method for attribute prediction. Results In total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers–bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern–based method. Conclusions We developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non–English-speaking countries.
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Affiliation(s)
- Shicheng Li
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Lizong Deng
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Xu Zhang
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Luming Chen
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
- Guangzhou Laboratory, Guangzhou, China
| | - Tao Yang
- Guangzhou Laboratory, Guangzhou, China
- Guangzhou Medical University, Guangzhou, China
| | - Yifan Qi
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Taijiao Jiang
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
- Guangzhou Laboratory, Guangzhou, China
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Wang B, Han X, Zhao Z, Wang N, Zhao P, Li M, Zhang Y, Zhao T, Chen Y, Ren Z, Hong Y. EEG-Driven Prediction Model of Oxcarbazepine Treatment Outcomes in Patients With Newly-Diagnosed Focal Epilepsy. Front Med (Lausanne) 2022; 8:781937. [PMID: 35047529 PMCID: PMC8761908 DOI: 10.3389/fmed.2021.781937] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 12/06/2021] [Indexed: 11/27/2022] Open
Abstract
Objective: Antiseizure medicine (ASM) is the first choice for patients with epilepsy. The choice of ASM is determined by the type of epilepsy or epileptic syndrome, which may not be suitable for certain patients. This initial choice of a particular drug affects the long-term prognosis of patients, so it is critical to select the appropriate ASMs based on the individual characteristics of a patient at the early stage of the disease. The purpose of this study is to develop a personalized prediction model to predict the probability of achieving seizure control in patients with focal epilepsy, which will help in providing a more precise initial medication to patients. Methods: Based on response to oxcarbazepine (OXC), enrolled patients were divided into two groups: seizure-free (52 patients), not seizure-free (NSF) (22 patients). We created models to predict patients' response to OXC monotherapy by combining Electroencephalogram (EEG) complexities and 15 clinical features. The prediction models were gradient boosting decision tree-Kolmogorov complexity (GBDT-KC) and gradient boosting decision tree-Lempel-Ziv complexity (GBDT-LZC). We also constructed two additional prediction models, support vector machine-Kolmogorov complexity (SVM-KC) and SVM-LZC, and these two models were compared with the GBDT models. The performance of the models was evaluated by calculating the accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the curve (AUC) of these models. Results: The mean accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-LZC model after five-fold cross-validation were 81%, 84%, 91%, 87%, 91%, 64%, 81%, respectively. The average accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-KC model with five-fold cross-validation were 82%, 84%, 92%, 88%, 83%, 92%, 83%, respectively. We used the rank of absolute weights to separately calculate the features that have the most significant impact on the classification of the two models. Conclusion: (1) The GBDT-KC model has the potential to be used in the clinic to predict seizure-free with OXC monotherapy. (2). Electroencephalogram complexity, especially Kolmogorov complexity (KC) may be a potential biomarker in predicting the treatment efficacy of OXC in newly diagnosed patients with focal epilepsy.
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Affiliation(s)
- Bin Wang
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China.,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Xiong Han
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China.,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
| | - Na Wang
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Pan Zhao
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Mingmin Li
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Yue Zhang
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Ting Zhao
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Yanan Chen
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Zhe Ren
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Yang Hong
- Department of Neurology, Henan University People's Hospital, Zhengzhou, China
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34
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Marini C, Giardino M. Novel treatments in epilepsy guided by genetic diagnosis. Br J Clin Pharmacol 2021; 88:2539-2551. [PMID: 34778987 DOI: 10.1111/bcp.15139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 10/22/2021] [Accepted: 11/04/2021] [Indexed: 12/21/2022] Open
Abstract
In recent years, precision medicine has emerged as a new paradigm for improved and more individualized patient care. Its key objective is to provide the right treatment, to the right patient at the right time, by basing medical decisions on individual characteristics, including specific genetic biomarkers. In order to realize this objective researchers and physicians must first identify the underlying genetic cause; over the last 10 years, advances in genetics have made this possible for several monogenic epilepsies. Through next generation techniques, a precise genetic aetiology is attainable in 30-50% of genetic epilepsies beginning in the paediatric age. While committed in such search for novel genes carrying disease-causing variants, progress in the study of experimental models of epilepsy has also provided a better understanding of the mechanisms underlying the condition. Such advances are already being translated into improving care, management and treatment of some patients. Identification of a precise genetic aetiology can already direct physicians to prescribe treatments correcting specific metabolic defects, avoid antiseizure medicines that might aggravate functional consequences of the disease-causing variant or select the drugs that counteract the underlying, genetically determined, functional disturbance. Personalized, tailored treatments should not just focus on how to stop seizures but possibly prevent their onset and cure the disorder, often consisting of seizures and its comorbidities including cognitive, motor and behaviour deficiencies. This review discusses the therapeutic implications following a specific genetic diagnosis and the correlation between genetic findings, pathophysiological mechanisms and tailored seizure treatment, emphasizing the impact on current clinical practice.
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Affiliation(s)
- Carla Marini
- Child Neurology and Psychiatric Unit, Pediatric Hospital G. Salesi, United Hospitals of Ancona, Ancona, Italy
| | - Maria Giardino
- Child Neurology and Psychiatric Unit, Pediatric Hospital G. Salesi, United Hospitals of Ancona, Ancona, Italy
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Xu C, Gong Y, Wang Y, Chen Z. New advances in pharmacoresistant epilepsy towards precise management-from prognosis to treatments. Pharmacol Ther 2021; 233:108026. [PMID: 34718071 DOI: 10.1016/j.pharmthera.2021.108026] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/15/2021] [Accepted: 10/25/2021] [Indexed: 12/13/2022]
Abstract
Epilepsy, one of the most severe neurological diseases, is characterized by abrupt recurrent seizures. Despite great progress in the development of antiseizure drugs (ASDs) based on diverse molecular targets, more than one third of epilepsy patients still show resistance to ASDs, a condition termed pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy involves serious challenges. In the past decade, promising advances have been made in the use of interdisciplinary techniques involving biophysics, bioinformatics, biomaterials and biochemistry, which allow more precise prognosis and development of drug target for pharmacoresistant epilepsy. Notably, novel experimental tools such as viral vector gene delivery, optogenetics and chemogenetics have provided a framework for promising approaches to the precise treatment of pharmacoresistant epilepsy. In this review, historical achievements especially recent advances of the past decade in the prognosis and treatment of pharmacoresistant epilepsy from both clinical and laboratory settings are presented and summarized. We propose that the further development of novel experimental tools at cellular or molecular levels with both temporal and spatial precision are necessary to make improve the management and drug development for pharmacoresistant epilepsy in the clinical arena.
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Affiliation(s)
- Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yiwei Gong
- Institute of Pharmacology & Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yi Wang
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Institute of Pharmacology & Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Institute of Pharmacology & Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China; Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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36
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Haga SB. Revisiting Secondary Information Related to Pharmacogenetic Testing. Front Genet 2021; 12:741395. [PMID: 34659361 PMCID: PMC8517135 DOI: 10.3389/fgene.2021.741395] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 09/13/2021] [Indexed: 12/22/2022] Open
Abstract
Incidental or secondary findings have been a major part of the discussion of genomic medicine research and clinical applications. For pharmacogenetic (PGx) testing, secondary findings arise due to the pleiotropic effects of pharmacogenes, often related to their endogenous functions. Unlike the guidelines that have been developed for whole exome or genome sequencing applications for management of secondary findings (though slightly different from PGx testing in that these refer to detection of variants in multiple genes, some with clinical significance and actionability), no corresponding guidelines have been developed for PGx clinical laboratories. Nonetheless, patient and provider education will remain key components of any PGx testing program to minimize adverse responses related to secondary findings.
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Affiliation(s)
- Susanne B Haga
- Center for Applied Genomic and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
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37
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Perucca E. The pharmacological treatment of epilepsy: recent advances and future perspectives. ACTA EPILEPTOLOGICA 2021. [DOI: 10.1186/s42494-021-00055-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
AbstractThe pharmacological armamentarium against epilepsy has expanded considerably over the last three decades, and currently includes over 30 different antiseizure medications. Despite this large armamentarium, about one third of people with epilepsy fail to achieve sustained seizure freedom with currently available medications. This sobering fact, however, is mitigated by evidence that clinical outcomes for many people with epilepsy have improved over the years. In particular, physicians now have unprecedented opportunities to tailor treatment choices to the characteristics of the individual, in order to maximize efficacy and tolerability. The present article discusses advances in the drug treatment of epilepsy in the last 5 years, focusing in particular on comparative effectiveness trials of second-generation drugs, the introduction of new pharmaceutical formulations for emergency use, and the results achieved with the newest medications. The article also includes a discussion of potential future developments, including those derived from advances in information technology, the development of novel precision treatments, the introduction of disease modifying agents, and the discovery of biomarkers to facilitate conduction of clinical trials as well as routine clinical management.
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38
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Prediction of antischistosomal small molecules using machine learning in the era of big data. Mol Divers 2021; 26:1597-1607. [PMID: 34351547 DOI: 10.1007/s11030-021-10288-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 07/24/2021] [Indexed: 12/13/2022]
Abstract
Schistosomiasis is a neglected tropical disease caused by helminths of the Schistosoma genus. Despite its high morbidity and socio-economic burden, therapeutics are just a handful with praziquantel being the main drug. Praziquantel is an old drug registered for human use in 1982 and has since been administered en masse for chemotherapy, risking the development of resistance, thus the need for new drugs with different mechanisms of action. This review examines the use of machine learning (ML) in this era of big data to aid in the prediction of novel antischistosomal molecules. It first discusses the challenges of drug discovery in schistosomiasis. Explanations are then offered for big data, its characteristics and then, some open databases where large biochemical data on schistosomiasis can be obtained for ML model development are examined. The concepts of artificial intelligence, ML, and deep learning and their drug applications are explored in schistosomiasis. The use of binary classification in predicting antischistosomal compounds and some algorithms that have been applied including random forest and naive Bayesian are discussed. For this review, some deep learning algorithms (deep neural networks) are proposed as novel algorithms for predicting antischistosomal molecules via binary classification. Databases specifically designed for housing bioactivity data on antischistosomal molecules enriched with functional genomic datasets and ontologies are thus urgently needed for developing predictive ML models. This shows the application of machine learning techniques for the discovery of novel antischistosomal small molecules via binary classification in the era of big data.
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Chen Z, Anderson A, Ge Z, Kwan P. One step closer towards personalized epilepsy management. Brain 2021; 144:1624-1626. [PMID: 34061164 DOI: 10.1093/brain/awab199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
This scientific commentary refers to ‘Towards realizing the vision of precision medicine: AI based prediction of clinical drug response’ by De Jong et al. (doi:10.1093/brain/awab108).
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Affiliation(s)
- Zhibin Chen
- Chongqing Key Laboratory of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Clinical Epidemiology, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Alison Anderson
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Zongyuan Ge
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
| | - Patrick Kwan
- Chongqing Key Laboratory of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Epilepsy Unit, Brain Program, Alfred Hospital, Melbourne, Victoria, Australia.,Departments of Medicine and Neurology, University of Melbourne, Royal Melbourne Hospital, Parkville, Victoria, Australia
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