Artificial intelligence for neurodegenerative experimental models

Sarah J. Marzi; Brian M. Schilder; Alexi Nott; Carlo Sala Frigerio; Sandrine Willaime‐Morawek; Magda Bucholc; Diane P. Hanger; Charlotte James; Patrick A. Lewis; Ilianna Lourida; Wendy Noble; Francisco Rodriguez‐Algarra; Jalil‐Ahmad Sharif; Maria Tsalenchuk; Laura M. Winchester; Ümran Yaman; Zhi Yao; Janice M. Ranson; David J. Llewellyn

doi:10.1002/alz.13479

Artificial intelligence for neurodegenerative experimental models

, Sarah J. Marzi
, Brian M. Schilder
, Alexi Nott
, Carlo Sala Frigerio
, Sandrine Willaime‐Morawek
, Magda Bucholc
, Diane P. Hanger
, Charlotte James
, Patrick A. Lewis
, Ilianna Lourida
, Wendy Noble
, Francisco Rodriguez‐Algarra
, Jalil‐Ahmad Sharif
, Maria Tsalenchuk
, Laura M. Winchester
, Ümran Yaman
, Zhi Yao
, Janice M. Ranson
, David J. Llewellyn

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

122 Downloads (Pure)

Abstract

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross‐model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross‐species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi‐omics analysis with AI offers exciting future possibilities in drug discovery.

Original language	English
Pages (from-to)	5970-5987
Number of pages	18
Journal	Alzheimer's and Dementia
Volume	19
Issue number	12
Early online date	28 Sept 2023
DOIs	https://doi.org/10.1002/alz.13479
Publication status	Published online - 28 Sept 2023

Bibliographical note

Funding Information:
This manuscript was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed by authors in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, Zhi Yao. This paper was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. JMR and DJL are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). SJM and AN are funded by the Edmond and Lily Safra Early Career Fellowship Program and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. MB is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (SEUPB) INTERREG (ERDF/SEUPB), and HSC R&D (COM/5750/23). PAL acknowledges generous support from the Michael J. Fox Foundation and Parkinson's UK. CJ and LMW are supported by Alzheimer's Research UK.

Publisher Copyright:
© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.

Funding

This manuscript was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed by authors in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, Zhi Yao. This paper was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. JMR and DJL are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). SJM and AN are funded by the Edmond and Lily Safra Early Career Fellowship Program and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. MB is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (SEUPB) INTERREG (ERDF/SEUPB), and HSC R&D (COM/5750/23). PAL acknowledges generous support from the Michael J. Fox Foundation and Parkinson's UK. CJ and LMW are supported by Alzheimer's Research UK. This manuscript was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed by authors in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, Zhi Yao. This paper was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. JMR and DJL are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). SJM and AN are funded by the Edmond and Lily Safra Early Career Fellowship Program and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. MB is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (SEUPB) INTERREG (ERDF/SEUPB), and HSC R&D (COM/5750/23). PAL acknowledges generous support from the Michael J. Fox Foundation and Parkinson's UK. CJ and LMW are supported by Alzheimer's Research UK.

Funders	Funder number
EP/N510129/1
HSC R&D	COM/5750/23
SEUPB
National Institutes of Health	RF1AG055654
National Institute on Aging
Alzheimer's Association
INTERREG
Medical Research Council	MR/X005674/1
Economic and Social Research Council	ES/W010240/1
Parkinson's UK
Alzheimer's Society
European Commission
National Health and Medical Research Council
Alzheimer's Research UK
European Regional Development Fund

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

neurodegeneration
artificial intelligence
reproducibility
in vitro
dementia
machine learning
in vivo
preclinical
comparative biology
FAIR
translation
animal models
iPSC
experimental models
in silico

Access to Document

10.1002/alz.13479

alz.13479.pdfFinal published version, 1.03 MBLicence: CC BY

Cite this

, Marzi, S. J., Schilder, B. M., Nott, A., Frigerio, C. S., Willaime‐Morawek, S., Bucholc, M., Hanger, D. P., James, C., Lewis, P. A., Lourida, I., Noble, W., Rodriguez‐Algarra, F., Sharif, JA., Tsalenchuk, M., Winchester, L. M., Yaman, Ü., Yao, Z., & Ranson, J. M., & Llewellyn, D. J. (2023). Artificial intelligence for neurodegenerative experimental models. Alzheimer's and Dementia, 19(12), 5970-5987. Advance online publication. https://doi.org/10.1002/alz.13479

@article{a248f2973fb0488da47177b258f56991,

title = "Artificial intelligence for neurodegenerative experimental models",

abstract = "INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross‐model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross‐species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi‐omics analysis with AI offers exciting future possibilities in drug discovery.",

keywords = "neurodegeneration, artificial intelligence, reproducibility, in vitro, dementia, machine learning, in vivo, preclinical, comparative biology, FAIR, translation, animal models, iPSC, experimental models, in silico",

author = "Marzi, \{Sarah J.\} and Schilder, \{Brian M.\} and Alexi Nott and Frigerio, \{Carlo Sala\} and Sandrine Willaime‐Morawek and Magda Bucholc and Hanger, \{Diane P.\} and Charlotte James and Lewis, \{Patrick A.\} and Ilianna Lourida and Wendy Noble and Francisco Rodriguez‐Algarra and Jalil‐Ahmad Sharif and Maria Tsalenchuk and Winchester, \{Laura M.\} and {\"U}mran Yaman and Zhi Yao and Ranson, \{Janice M.\} and Llewellyn, \{David J.\}",

note = "Funding Information: This manuscript was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed by authors in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, Zhi Yao. This paper was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. JMR and DJL are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). SJM and AN are funded by the Edmond and Lily Safra Early Career Fellowship Program and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. MB is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (SEUPB) INTERREG (ERDF/SEUPB), and HSC R\&D (COM/5750/23). PAL acknowledges generous support from the Michael J. Fox Foundation and Parkinson's UK. CJ and LMW are supported by Alzheimer's Research UK. Publisher Copyright: {\textcopyright} 2023 The Authors. Alzheimer's \& Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.",

year = "2023",

month = sep,

day = "28",

doi = "10.1002/alz.13479",

language = "English",

volume = "19",

pages = "5970--5987",

journal = "Alzheimer's and Dementia",

issn = "1552-5260",

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Marzi, SJ, Schilder, BM, Nott, A, Frigerio, CS, Willaime‐Morawek, S, Bucholc, M, Hanger, DP, James, C, Lewis, PA, Lourida, I, Noble, W, Rodriguez‐Algarra, F, Sharif, JA, Tsalenchuk, M, Winchester, LM, Yaman, Ü, Yao, Z & Ranson, JM & Llewellyn, DJ 2023, 'Artificial intelligence for neurodegenerative experimental models', Alzheimer's and Dementia, vol. 19, no. 12, pp. 5970-5987. https://doi.org/10.1002/alz.13479

TY - JOUR

T1 - Artificial intelligence for neurodegenerative experimental models

AU - Marzi, Sarah J.

AU - Schilder, Brian M.

AU - Nott, Alexi

AU - Frigerio, Carlo Sala

AU - Willaime‐Morawek, Sandrine

AU - Bucholc, Magda

AU - Hanger, Diane P.

AU - James, Charlotte

AU - Lewis, Patrick A.

AU - Lourida, Ilianna

AU - Noble, Wendy

AU - Rodriguez‐Algarra, Francisco

AU - Sharif, Jalil‐Ahmad

AU - Tsalenchuk, Maria

AU - Winchester, Laura M.

AU - Yaman, Ümran

AU - Yao, Zhi

AU - Ranson, Janice M.

AU - Llewellyn, David J.

N1 - Funding Information: This manuscript was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed by authors in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, Zhi Yao. This paper was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. JMR and DJL are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). SJM and AN are funded by the Edmond and Lily Safra Early Career Fellowship Program and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. MB is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (SEUPB) INTERREG (ERDF/SEUPB), and HSC R&D (COM/5750/23). PAL acknowledges generous support from the Michael J. Fox Foundation and Parkinson's UK. CJ and LMW are supported by Alzheimer's Research UK. Publisher Copyright: © 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.

PY - 2023/9/28

Y1 - 2023/9/28

N2 - INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross‐model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross‐species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi‐omics analysis with AI offers exciting future possibilities in drug discovery.

AB - INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross‐model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross‐species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi‐omics analysis with AI offers exciting future possibilities in drug discovery.

KW - neurodegeneration

KW - artificial intelligence

KW - reproducibility

KW - in vitro

KW - dementia

KW - machine learning

KW - in vivo

KW - preclinical

KW - comparative biology

KW - FAIR

KW - translation

KW - animal models

KW - iPSC

KW - experimental models

KW - in silico

UR - https://www.scopus.com/pages/publications/85173098553

U2 - 10.1002/alz.13479

DO - 10.1002/alz.13479

M3 - Article

C2 - 37768001

SN - 1552-5260

VL - 19

SP - 5970

EP - 5987

JO - Alzheimer's and Dementia

JF - Alzheimer's and Dementia

IS - 12

ER -

Artificial intelligence for neurodegenerative experimental models

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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Cite this