1
|
Nyholm J, Ghazi AN, Ghazi SN, Sanmartin Berglund J. Prediction of dementia based on older adults' sleep disturbances using machine learning. Comput Biol Med 2024; 171:108126. [PMID: 38342045 DOI: 10.1016/j.compbiomed.2024.108126] [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: 11/01/2023] [Revised: 12/14/2023] [Accepted: 02/06/2024] [Indexed: 02/13/2024]
Abstract
BACKGROUND The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia. METHODS This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care - Blekinge (n=4175). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors. RESULTS Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms. CONCLUSION There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.
Collapse
Affiliation(s)
- Joel Nyholm
- Department of Computer Science, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden
| | - Ahmad Nauman Ghazi
- Department of Software Engineering, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden.
| | - Sarah Nauman Ghazi
- Department of Health, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden
| | | |
Collapse
|
2
|
Doherty T, Yao Z, Khleifat AAL, Tantiangco H, Tamburin S, Albertyn C, Thakur L, Llewellyn DJ, Oxtoby NP, Lourida I, Ranson JM, Duce JA. Artificial intelligence for dementia drug discovery and trials optimization. Alzheimers Dement 2023; 19:5922-5933. [PMID: 37587767 DOI: 10.1002/alz.13428] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/26/2023] [Accepted: 07/05/2023] [Indexed: 08/18/2023]
Abstract
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.
Collapse
Affiliation(s)
- Thomas Doherty
- Eisai Europe Ltd, Hatfield, UK
- University of Westminster, London, UK
| | | | - Ahmad A L Khleifat
- Institute of Psychiatry, Psychology & Neuroscience, Department of Basic and Clinical Neuroscience, King's College London, London, UK
| | | | - Stefano Tamburin
- University of Verona, Department of Neurosciences, Biomedicine & Movement Sciences, Verona, Italy
| | - Chris Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | | | - James A Duce
- The ALBORADA Drug Discovery Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| |
Collapse
|
3
|
Alshamlan H, Omar S, Aljurayyad R, Alabduljabbar R. Identifying Effective Feature Selection Methods for Alzheimer's Disease Biomarker Gene Detection Using Machine Learning. Diagnostics (Basel) 2023; 13:diagnostics13101771. [PMID: 37238255 DOI: 10.3390/diagnostics13101771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
Alzheimer's disease (AD) is a complex genetic disorder that affects the brain and has been the focus of many bioinformatics research studies. The primary objective of these studies is to identify and classify genes involved in the progression of AD and to explore the function of these risk genes in the disease process. The aim of this research is to identify the most effective model for detecting biomarker genes associated with AD using several feature selection methods. We compared the efficiency of feature selection methods with an SVM classifier, including mRMR, CFS, the Chi-Square Test, F-score, and GA. We calculated the accuracy of the SVM classifier using validation methods such as 10-fold cross-validation. We applied these feature selection methods with SVM to a benchmark AD gene expression dataset consisting of 696 samples and 200 genes. The results indicate that the mRMR and F-score feature selection methods with SVM classifier achieved a high accuracy of around 84%, with a number of genes between 20 and 40. Furthermore, the mRMR and F-score feature selection methods with SVM classifier outperformed the GA, Chi-Square Test, and CFS methods. Overall, these findings suggest that the mRMR and F-score feature selection methods with SVM classifier are effective in identifying biomarker genes related to AD and could potentially lead to more accurate diagnosis and treatment of the disease.
Collapse
Affiliation(s)
- Hala Alshamlan
- Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia
| | - Samar Omar
- Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia
| | - Rehab Aljurayyad
- Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia
| | - Reham Alabduljabbar
- Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia
| |
Collapse
|
4
|
Akushevich I, Yashkin A, Ukraintseva S, Yashin AI, Kravchenko J. The Construction of a Multidomain Risk Model of Alzheimer's Disease and Related Dementias. J Alzheimers Dis 2023; 96:535-550. [PMID: 37840484 PMCID: PMC10657690 DOI: 10.3233/jad-221292] [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] [Accepted: 08/28/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) and related dementia (ADRD) risk is affected by multiple dependent risk factors; however, there is no consensus about their relative impact in the development of these disorders. OBJECTIVE To rank the effects of potentially dependent risk factors and identify an optimal parsimonious set of measures for predicting AD/ADRD risk from a larger pool of potentially correlated predictors. METHODS We used diagnosis record, survey, and genetic data from the Health and Retirement Study to assess the relative predictive strength of AD/ADRD risk factors spanning several domains: comorbidities, demographics/socioeconomics, health-related behavior, genetics, and environmental exposure. A modified stepwise-AIC-best-subset blanket algorithm was then used to select an optimal set of predictors. RESULTS The final predictive model was reduced to 10 features for AD and 19 for ADRD; concordance statistics were about 0.85 for one-year and 0.70 for ten-year follow-up. Depression, arterial hypertension, traumatic brain injury, cerebrovascular diseases, and the APOE4 proxy SNP rs769449 had the strongest individual associations with AD/ADRD risk. AD/ADRD risk-related co-morbidities provide predictive power on par with key genetic vulnerabilities. CONCLUSION Results confirm the consensus that circulatory diseases are the main comorbidities associated with AD/ADRD risk and show that clinical diagnosis records outperform comparable self-reported measures in predicting AD/ADRD risk. Model construction algorithms combined with modern data allows researchers to conserve power (especially in the study of disparities where disadvantaged groups are often grossly underrepresented) while accounting for a high proportion of AD/ADRD-risk-related population heterogeneity stemming from multiple domains.
Collapse
Affiliation(s)
- Igor Akushevich
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
| | - Arseniy Yashkin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
| | - Svetlana Ukraintseva
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
| | - Anatoliy I. Yashin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
| | - Julia Kravchenko
- Department of Surgery, Duke University Medical Center, Durham, NC, USA
| |
Collapse
|
5
|
Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity. Commun Biol 2022; 5:125. [PMID: 35149761 PMCID: PMC8837797 DOI: 10.1038/s42003-022-03068-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 01/21/2022] [Indexed: 12/19/2022] Open
Abstract
With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1β-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.
Collapse
|
6
|
Niyasti P, Saberi A, Hatamyain H, Ajamian F, Shirkouhi SG, Mirzanejad L, Andalib S. Association of Insulin Receptor Substrate-1 Gene Polymorphism (rs1801278) with Alzheimer’s Disease. J Alzheimers Dis Rep 2022. [DOI: 10.3233/adr-210060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background: Alzheimer’s disease (AD) is the most common form of dementia. AD is also the leading cause of morbidity and mortality due to dementia worldwide. It has been shown that AD is associated with type 2 diabetes mellitus (T2DM) and brain insulin resistance. Rs1801278 is a polymorphism in insulin receptor substrate-1 (IRS-1) gene which changes the amino acid Arg972. This polymorphism has been found to be associated with susceptibility to AD in some populations. Objective: In the present study, our aim was to investigate the association of Arg972 IRS-1 (rs1801278) gene polymorphism and late-onset Alzheimer’s disease (LOAD) in an Iranian population. Methods: In this case-control study, 150 patients with LOAD and 150 unrelated healthy controls were recruited. Polymerase chain reaction (PCR) was performed to amplify a DNA segment of 263 base-pair (bp) length containing the single nucleotide polymorphism (SNP). The PCR product was then incubated with MvaI restriction enzyme to undergo enzymatic cleavage. Electrophoresis was thereafter carried out using agarose gel and DNA safe stain. The gel was ultimately visualized under a UV trans-illuminator. Allelic and genotypic frequencies were then compared. Results: A allele (mutant) of the gene was significantly associated with the risk of AD after adjustment for sex and age (p = 0.04, adjusted OR:1.77, 95% CI:1.00–3.11). Only AA genotype (mutant homozygote) was significantly associated with the risk of AD after adjustment for sex and age (p = 0.01, adjusted OR:2.39, 95% CI:1.22–4.66). Conclusion: SNP rs1801278 is significantly associated with the risk of developing AD in the studied Iranian population.
Collapse
Affiliation(s)
- Parham Niyasti
- School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | - Alia Saberi
- Neuroscience Research Center, Department of Neurology, Poursina Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | - Hamidreza Hatamyain
- Neuroscience Research Center, Department of Neurology, Poursina Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | - Farzam Ajamian
- Department of Biology, Faculty of Sciences, University of Guilan, Rasht, Iran
| | - Samaneh Ghorbani Shirkouhi
- School of Medicine, Shahroud University of Medical Sciences, Shahroud, Iran
- Neuroscience Research Center, Poursina Hospital, Guilan University of MedicalSciences, Rasht, Iran
| | - Laleh Mirzanejad
- Department of Biology, Faculty of Sciences, University of Guilan, Rasht, Iran
| | - Sasan Andalib
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Research Unit of Clinical Physiology and Nuclear Medicine, Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
- Neuroscience Research Center, Poursina Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| |
Collapse
|
7
|
Mahendran N, Vincent PMDR, Srinivasan K, Chang CY. Improving the Classification of Alzheimer's Disease Using Hybrid Gene Selection Pipeline and Deep Learning. Front Genet 2021; 12:784814. [PMID: 34868275 PMCID: PMC8632950 DOI: 10.3389/fgene.2021.784814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.
Collapse
Affiliation(s)
- Nivedhitha Mahendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - P M Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
| |
Collapse
|
8
|
Lin CX, Li HD, Deng C, Guan Y, Wang J. TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data. Nucleic Acids Res 2021; 50:D710-D718. [PMID: 34850130 PMCID: PMC8728275 DOI: 10.1093/nar/gkab1133] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/10/2021] [Accepted: 11/18/2021] [Indexed: 01/02/2023] Open
Abstract
Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.
Collapse
Affiliation(s)
- Cui-Xiang Lin
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Hong-Dong Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Chao Deng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| |
Collapse
|
9
|
Tan MS, Cheah PL, Chin AV, Looi LM, Chang SW. A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach. Comput Biol Med 2021; 139:104947. [PMID: 34678481 DOI: 10.1016/j.compbiomed.2021.104947] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/12/2021] [Accepted: 10/12/2021] [Indexed: 12/26/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.
Collapse
Affiliation(s)
- Mei Sze Tan
- Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Phaik-Leng Cheah
- Department of Pathology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Ai-Vyrn Chin
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Lai-Meng Looi
- Department of Pathology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Siow-Wee Chang
- Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia.
| |
Collapse
|
10
|
Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
Collapse
Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
| |
Collapse
|
11
|
Aromolaran O, Aromolaran D, Isewon I, Oyelade J. Machine learning approach to gene essentiality prediction: a review. Brief Bioinform 2021; 22:6219158. [PMID: 33842944 DOI: 10.1093/bib/bbab128] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/04/2021] [Accepted: 03/17/2021] [Indexed: 12/17/2022] Open
Abstract
Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions. SHORT ABSTRACT Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.
Collapse
Affiliation(s)
- Olufemi Aromolaran
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Damilare Aromolaran
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Itunuoluwa Isewon
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Jelili Oyelade
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| |
Collapse
|
12
|
Qorri B, Tsay M, Agrawal A, Au R, Gracie J. Using machine intelligence to uncover Alzheimer’s disease progression heterogeneity. EXPLORATION OF MEDICINE 2020. [DOI: 10.37349/emed.2020.00026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Aim: Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Through a proprietary interactive ML system, several underlying biological mechanisms associated with AD pathology were uncovered. This paper is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD.
Methods: A public AD data set (GSE84422) consisting of transcriptomic data of postmortem brain samples from healthy controls (n = 121) and AD (n = 380) subjects was analyzed. Data were processed by an artificial intelligence platform designed to discover potential drug repurposing candidates, followed by an interactive augmented intelligence program.
Results: Using perspective analytics, six perspective classes were identified: Class I is defined by TUBB1, ASB4, and PDE5A; Class II by NRG2 and ZNF3; Class III by IGF1, ASB4, and GTSE1; Class IV is defined by cDNA FLJ39269, ITGA1, and CPM; Class V is defined by PDE5A, PSEN1, and NDUFS8; and Class VI is defined by DCAF17, cDNA FLJ75819, and SLC33A1. It is hypothesized that these classes represent biological mechanisms that may act alone or in any combination to manifest an Alzheimer’s pathology.
Conclusions: Using a limited transcriptomic public database, six different classes that drive AD were uncovered, supporting the premise that AD is a heterogeneously complex disorder. The perspective classes highlighted genetic pathways associated with vasculogenesis, cellular signaling and differentiation, metabolic function, mitochondrial function, nitric oxide, and metal ion metabolism. The interplay among these genetic factors reveals a more profound underlying complexity of AD that may be responsible for the confluence of several biological factors. These results are not exhaustive; instead, they demonstrate that even within a relatively small study sample, next-generation machine intelligence can uncover multiple genetically driven subtypes. The models and the underlying hypotheses generated using novel analytic methods may translate into potential treatment pathways.
Collapse
Affiliation(s)
- Bessi Qorri
- Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Mike Tsay
- NetraMark Corp, Toronto, ON M4E 1G8, Canada
| | | | - Rhoda Au
- Department of Anatomy & Neurobiology, Neurology and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA 02218, USA
| | - Joseph Gracie
- NetraMark Corp, Toronto, ON M4E 1G8, Canada 5Department of Pathology and Molecular Medicine, Queen’s University, Kingston, ON K7L 3N6, Canada
| |
Collapse
|
13
|
Mishra R, Li B. The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease. Aging Dis 2020; 11:1567-1584. [PMID: 33269107 PMCID: PMC7673858 DOI: 10.14336/ad.2020.0312] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/12/2020] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.
Collapse
Affiliation(s)
- Rohan Mishra
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
| | - Bin Li
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
- Georgetown University Medical Center, Washington D.C. 20057, USA
| |
Collapse
|
14
|
Arzouni N, Matloff W, Zhao L, Ning K, Toga AW. Identification of Dysregulated Genes for Late-Onset Alzheimer's Disease Using Gene Expression Data in Brain. JOURNAL OF ALZHEIMER'S DISEASE & PARKINSONISM 2020; 10:498. [PMID: 33282526 PMCID: PMC7717689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
BACKGROUND Alzheimer's Disease (AD) is a neurodegenerative complex brain disease that represents a public health concern. AD is considered the fifth leading cause of death in Americans who are older than 65 years which prioritizes the importance of understanding the etiology of AD in its early stages before the onset of symptoms. This study attempted to further understand Alzheimer's disease (AD) etiology by investigating the dysregulated genes using gene expression data from multiple brain regions. METHODS A linear mixed-effects model for differential gene expression analysis was used in a sample of 15 AD and 30 control subjects, each with data from four different brain regions, in order to deal with the hierarchical multilevel data. Post-hoc Gene Ontology and pathway enrichment analyses provided insights on the biological implications in AD progression. Supervised machine learning algorithms were used to assess the discriminative power of the top 10 candidate genes in distinguishing between the two groups. RESULTS Enrichment analyses revealed biological processes and pathways that are related to structural constituents and organization of the axons and synapses. These biological processes and pathways imply dysfunctional axon and synaptic transmission between neuronal cells in AD. Random Forest classification algorithm gave the best accuracy on the test data with F1-score of 0.88. CONCLUSION The differentially expressed genes were associated with axon and synaptic transmissions which affect the neuronal connectivity in cognitive systems involved in AD pathophysiology. These genes may open ways to explore new effective treatments and early diagnosis before the onset of clinical symptoms.
Collapse
Affiliation(s)
- Nibal Arzouni
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, USA
- Computational Biology and Bioinformatics Program, University of Southern California, USA
| | - Will Matloff
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, USA
- Neuroscience Graduate Program, University of Southern California, USA
| | - Lu Zhao
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, USA
| | - Kaida Ning
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, USA
- Computational Biology and Bioinformatics Program, University of Southern California, USA
| | - Arthur W Toga
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, USA
| |
Collapse
|
15
|
Kaur H, Singh Y, Singh S, Singh RB. Gut microbiome-mediated epigenetic regulation of brain disorder and application of machine learning for multi-omics data analysis. Genome 2020; 64:355-371. [PMID: 33031715 DOI: 10.1139/gen-2020-0136] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The gut-brain axis (GBA) is a biochemical link that connects the central nervous system (CNS) and enteric nervous system (ENS). Clinical and experimental evidence suggests gut microbiota as a key regulator of the GBA. Microbes living in the gut not only interact locally with intestinal cells and the ENS but have also been found to modulate the CNS through neuroendocrine and metabolic pathways. Studies have also explored the involvement of gut microbiota dysbiosis in depression, anxiety, autism, stroke, and pathophysiology of other neurodegenerative diseases. Recent reports suggest that microbe-derived metabolites can influence host metabolism by acting as epigenetic regulators. Butyrate, an intestinal bacterial metabolite, is a known histone deacetylase inhibitor that has shown to improve learning and memory in animal models. Due to high disease variability amongst the population, a multi-omics approach that utilizes artificial intelligence and machine learning to analyze and integrate omics data is necessary to better understand the role of the GBA in pathogenesis of neurological disorders, to generate predictive models, and to develop precise and personalized therapeutics. This review examines our current understanding of epigenetic regulation of the GBA and proposes a framework to integrate multi-omics data for prediction, prevention, and development of precision health approaches to treat brain disorders.
Collapse
Affiliation(s)
- Harpreet Kaur
- Department of Biomedical Sciences, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, USA
| | - Yuvraj Singh
- Department of Biological Sciences, Faculty of Science, University of Calgary, Calgary, AB, Canada
| | - Surjeet Singh
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada
| | - Raja B Singh
- Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.,Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
16
|
Badhwar A, McFall GP, Sapkota S, Black SE, Chertkow H, Duchesne S, Masellis M, Li L, Dixon RA, Bellec P. A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap. Brain 2020; 143:1315-1331. [PMID: 31891371 PMCID: PMC7241959 DOI: 10.1093/brain/awz384] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 10/04/2019] [Accepted: 10/07/2019] [Indexed: 11/14/2022] Open
Abstract
Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer's disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput 'omics' are unbiased data-driven techniques that probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer's disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer's disease.
Collapse
Affiliation(s)
- AmanPreet Badhwar
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, Canada
- Université de Montréal, Montreal, Canada
| | - G Peggy McFall
- Department of Psychology, University of Alberta, Edmonton, Canada
| | - Shraddha Sapkota
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Sandra E Black
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Howard Chertkow
- Baycrest Health Sciences and the Rotman Research Institute, University of Toronto, Toronto, Canada
| | - Simon Duchesne
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Quebec City, Canada
- Department of Radiology, Faculty of Medicine, Université Laval, Quebec City, Canada
| | - Mario Masellis
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Liang Li
- Department of Chemistry, University of Alberta, Edmonton, Canada
| | - Roger A Dixon
- Department of Psychology, University of Alberta, Edmonton, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, Canada
- Université de Montréal, Montreal, Canada
| |
Collapse
|
17
|
Golriz Khatami S, Mubeen S, Hofmann-Apitius M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Curr Opin Neurol 2020; 33:249-254. [PMID: 32073441 PMCID: PMC7077964 DOI: 10.1097/wco.0000000000000795] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
PURPOSE OF REVIEW With the advancement of computational approaches and abundance of biomedical data, a broad range of neurodegenerative disease models have been developed. In this review, we argue that computational models can be both relevant and useful in neurodegenerative disease research and although the current established models have limitations in clinical practice, artificial intelligence has the potential to overcome deficiencies encountered by these models, which in turn can improve our understanding of disease. RECENT FINDINGS In recent years, diverse computational approaches have been used to shed light on different aspects of neurodegenerative disease models. For example, linear and nonlinear mixed models, self-modeling regression, differential equation models, and event-based models have been applied to provide a better understanding of disease progression patterns and biomarker trajectories. Additionally, the Cox-regression technique, Bayesian network models, and deep-learning-based approaches have been used to predict the probability of future incidence of disease, whereas nonnegative matrix factorization, nonhierarchical cluster analysis, hierarchical agglomerative clustering, and deep-learning-based approaches have been employed to stratify patients based on their disease subtypes. Furthermore, the interpretation of neurodegenerative disease data is possible through knowledge-based models which use prior knowledge to complement data-driven analyses. These knowledge-based models can include pathway-centric approaches to establish pathways perturbed in a given condition, as well as disease-specific knowledge maps, which elucidate the mechanisms involved in a given disease. Collectively, these established models have revealed high granular details and insights into neurodegenerative disease models. SUMMARY In conjunction with increasingly advanced computational approaches, a wide spectrum of neurodegenerative disease models, which can be broadly categorized into data-driven and knowledge-driven, have been developed. We review the state of the art data and knowledge-driven models and discuss the necessary steps which are vital to bring them into clinical application.
Collapse
Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| |
Collapse
|
18
|
Dagan H, Flashner-Abramson E, Vasudevan S, Jubran MR, Cohen E, Kravchenko-Balasha N. Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures. Biomolecules 2020; 10:biom10040503. [PMID: 32225014 PMCID: PMC7226317 DOI: 10.3390/biom10040503] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/23/2020] [Accepted: 03/24/2020] [Indexed: 12/27/2022] Open
Abstract
Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1–3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.
Collapse
Affiliation(s)
- Hila Dagan
- The Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University, Jerusalem 9190416, Israel;
| | - Efrat Flashner-Abramson
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
| | - Swetha Vasudevan
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
| | - Maria R. Jubran
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
| | - Ehud Cohen
- Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel—Canada, The Hebrew University School of Medicine, Jerusalem 9112102, Israel;
| | - Nataly Kravchenko-Balasha
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
- Correspondence:
| |
Collapse
|
19
|
Predicting bipolar disorder and schizophrenia based on non-overlapping genetic phenotypes using deep neural network. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-019-00346-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
20
|
SP-BRAIN: scalable and reliable implementations of a supervised relevance-based machine learning algorithm. Soft comput 2019. [DOI: 10.1007/s00500-019-04366-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
21
|
Mauricio R, Benn C, Davis J, Dawson G, Dawson LA, Evans A, Fox N, Gallacher J, Hutton M, Isaac J, Jones DN, Jones L, Lalli G, Libri V, Lovestone S, Moody C, Noble W, Perry H, Pickett J, Reynolds D, Ritchie C, Rohrer JD, Routledge C, Rowe J, Snyder H, Spires-Jones T, Swartz J, Truyen L, Whiting P. Tackling gaps in developing life-changing treatments for dementia. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2019; 5:241-253. [PMID: 31297438 PMCID: PMC6597931 DOI: 10.1016/j.trci.2019.05.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Since the G8 dementia summit in 2013, a number of initiatives have been established with the aim of facilitating the discovery of a disease-modifying treatment for dementia by 2025. This report is a summary of the findings and recommendations of a meeting titled "Tackling gaps in developing life-changing treatments for dementia", hosted by Alzheimer's Research UK in May 2018. The aim of the meeting was to identify, review, and highlight the areas in dementia research that are not currently being addressed by existing initiatives. It reflects the views of leading experts in the field of neurodegeneration research challenged with developing a strategic action plan to address these gaps and make recommendations on how to achieve the G8 dementia summit goals. The plan calls for significant advances in (1) translating newly identified genetic risk factors into a better understanding of the impacted biological processes; (2) enhanced understanding of selective neuronal resilience to inform novel drug targets; (3) facilitating robust and reproducible drug-target validation; (4) appropriate and evidence-based selection of appropriate subjects for proof-of-concept clinical trials; (5) improving approaches to assess drug-target engagement in humans; and (6) innovative approaches in conducting clinical trials if we are able to detect disease 10-15 years earlier than we currently do today.
Collapse
Affiliation(s)
| | | | - John Davis
- Alzheimer's Research UK Oxford Drug Discovery Institute, University of Oxford, Oxford, UK
| | - Gerry Dawson
- P1 Vital, Howbery Business Park, Wallingford, Oxfordshire, UK
| | - Lee A. Dawson
- Cerevance Ltd, Cambridge Science Park, Cambridge, UK
| | | | - Nick Fox
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - John Isaac
- Neuroscience External Innovation, Neuroscience Therapeutic Area, Johnson & Johnson Innovation, London, UK
| | - Declan N.C. Jones
- Neuroscience External Innovation, Neuroscience Therapeutic Area, Johnson & Johnson Innovation, London, UK
| | - Lesley Jones
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, Wales, UK
| | | | - Vincenzo Libri
- Institute of Neurology, University College London, London, UK
| | | | | | - Wendy Noble
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Hugh Perry
- Faculty of Natural and Environmental Sciences, University of Southampton, Southampton, UK
| | | | | | - Craig Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Jonathan D. Rohrer
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - James Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Tara Spires-Jones
- UK Dementia Research Institute, The University of Edinburgh, Edinburgh, UK
| | - Jina Swartz
- European Innovation Hub, Merck Sharp and Dohme, London, UK
| | - Luc Truyen
- Janssen Research & Development LLC, Titusville, NJ, USA
| | - Paul Whiting
- Dementia Research Institute, UCL, London, UK
- ARUK Drug Discovery Institute, Institute of Neurology, University College London, London, UK
| | | |
Collapse
|
22
|
Benedetti A, Khoo J, Sharma S, Facco P, Barolo M, Zomer S. Data analytics on raw material properties to accelerate pharmaceutical drug development. Int J Pharm 2019; 563:122-134. [PMID: 30951857 DOI: 10.1016/j.ijpharm.2019.04.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 03/29/2019] [Accepted: 04/01/2019] [Indexed: 12/19/2022]
Abstract
Manufacturability of active pharmaceutical ingredients (APIs) is often evaluated by an empirical approach during development due to limited material availability. This brings challenges in designing flexible yet robust manufacturing processes under highly accelerated timelines. Hence, good utilisation of a limited material dataset is key to accelerate the delivery of high quality final drug product into the market at minimum cost and maximum process capacity. In this study, we present a data-driven method to investigate a raw materials database where the integration of multivariate analysis and machine learning modelling aids the selection of new incoming materials based on their manufacturability. The procedure was applied to an industrial representative database of thirty-four APIs and seven excipients where eight measurements relevant to flow properties for each of those forty-one materials were collected. The models identified four clusters of materials with different flow properties. These models can serve as a risk assessment tool for new API in early product development phases based on the nearest surrogate material which behave similarly, as well as to identify targeted and material sparring experiments to address key risks during secondary process selection.
Collapse
Affiliation(s)
- Antonio Benedetti
- Product Development and Supply, GlaxoSmithKline Research & Development, Park Road, SG12 0DP Ware, UK.
| | - Jiyi Khoo
- Product Development and Supply, GlaxoSmithKline Research & Development, Park Road, SG12 0DP Ware, UK
| | - Sandeep Sharma
- Product Development and Supply, GlaxoSmithKline Research & Development, Park Road, SG12 0DP Ware, UK
| | - Pierantonio Facco
- CAPE-Lab, Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova, Italy
| | - Massimiliano Barolo
- CAPE-Lab, Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova, Italy
| | - Simeone Zomer
- Product Development and Supply, GlaxoSmithKline Research & Development, Park Road, SG12 0DP Ware, UK
| |
Collapse
|