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Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease

With the approval of aducanumab on the “Accelerated Approval Pathway” and the recognition of amyloid load as a surrogate marker, new successful therapeutic approaches will be driven by combination therapy as was the case in oncology after the launch of immune checkpoint inhibitors. However, the shee...

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Autores principales: Geerts, Hugo, van der Graaf, Piet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673549/
https://www.ncbi.nlm.nih.gov/pubmed/34966890
http://dx.doi.org/10.3233/ADR-210039
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author Geerts, Hugo
van der Graaf, Piet
author_facet Geerts, Hugo
van der Graaf, Piet
author_sort Geerts, Hugo
collection PubMed
description With the approval of aducanumab on the “Accelerated Approval Pathway” and the recognition of amyloid load as a surrogate marker, new successful therapeutic approaches will be driven by combination therapy as was the case in oncology after the launch of immune checkpoint inhibitors. However, the sheer number of therapeutic combinations substantially complicates the search for optimal combinations. Data-driven approaches based on large databases or electronic health records can identify optimal combinations and often using artificial intelligence or machine learning to crunch through many possible combinations but are limited to the pharmacology of existing marketed drugs and are highly dependent upon the quality of the training sets. Knowledge-driven in silico modeling approaches use multi-scale biophysically realistic models of neuroanatomy, physiology, and pathology and can be personalized with individual patient comedications, disease state, and genotypes to create ‘virtual twin patients’. Such models simulate effects on action potential dynamics of anatomically informed neuronal circuits driving functional clinical readouts. Informed by data-driven approaches this knowledge-driven modeling could systematically and quantitatively simulate all possible target combinations for a maximal synergistic effect on a clinically relevant functional outcome. This approach seamlessly integrates pharmacokinetic modeling of different therapeutic modalities. A crucial requirement to constrain the parameters is the access to preferably anonymized individual patient data from completed clinical trials with various selective compounds. We believe that the combination of data- and knowledge driven modeling could be a game changer to find a cure for this devastating disease that affects the most complex organ of the universe.
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spelling pubmed-86735492021-12-28 Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease Geerts, Hugo van der Graaf, Piet J Alzheimers Dis Rep Review With the approval of aducanumab on the “Accelerated Approval Pathway” and the recognition of amyloid load as a surrogate marker, new successful therapeutic approaches will be driven by combination therapy as was the case in oncology after the launch of immune checkpoint inhibitors. However, the sheer number of therapeutic combinations substantially complicates the search for optimal combinations. Data-driven approaches based on large databases or electronic health records can identify optimal combinations and often using artificial intelligence or machine learning to crunch through many possible combinations but are limited to the pharmacology of existing marketed drugs and are highly dependent upon the quality of the training sets. Knowledge-driven in silico modeling approaches use multi-scale biophysically realistic models of neuroanatomy, physiology, and pathology and can be personalized with individual patient comedications, disease state, and genotypes to create ‘virtual twin patients’. Such models simulate effects on action potential dynamics of anatomically informed neuronal circuits driving functional clinical readouts. Informed by data-driven approaches this knowledge-driven modeling could systematically and quantitatively simulate all possible target combinations for a maximal synergistic effect on a clinically relevant functional outcome. This approach seamlessly integrates pharmacokinetic modeling of different therapeutic modalities. A crucial requirement to constrain the parameters is the access to preferably anonymized individual patient data from completed clinical trials with various selective compounds. We believe that the combination of data- and knowledge driven modeling could be a game changer to find a cure for this devastating disease that affects the most complex organ of the universe. IOS Press 2021-11-23 /pmc/articles/PMC8673549/ /pubmed/34966890 http://dx.doi.org/10.3233/ADR-210039 Text en © 2021 – The authors. Published by IOS Press https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Geerts, Hugo
van der Graaf, Piet
Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease
title Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease
title_full Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease
title_fullStr Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease
title_full_unstemmed Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease
title_short Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease
title_sort computational approaches for supporting combination therapy in the post-aducanumab era in alzheimer’s disease
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673549/
https://www.ncbi.nlm.nih.gov/pubmed/34966890
http://dx.doi.org/10.3233/ADR-210039
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