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      Why the way we define diseases prevents innovation and precision medicine

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            Abstract

            Noncommunicable diseases (NCDs) have become globally abundant, yet the therapeutics we use for them are imprecise. In parallel, identifying new treatments has become more costly than ever due to the ever-aggravating efficacy crisis drug discovery faces. What unites these failures is our ontological classification of diseases, primarily based on descriptive terms. To achieve precision diagnosis and precision therapy in clinical practice, NCDs need to be redefined and subdivided based on their causal molecular mechanisms. However, the inconsistency and incompatibility of the current disease classification systems hinder data integration and analysis towards the characterization of such mechanisms. Here, we explain flaws in the current disease definitions and the dispersion among existing ontologies with the aim of establishing a mechanism-based classification of diseases hence, precision medicine.

            Content

            Author and article information

            Journal
            DrugRxiv
            REPO4EU
            1 February 2023
            Affiliations
            [1 ] Department of Pharmacology & Personalised Medicine, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands ( https://ror.org/02jz4aj89)
            [2 ] Department of Pharmacology & Toxicology, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt ( https://ror.org/053g6we49)
            [3 ] Interdisciplinary Computing and Complex BioSystems (ICOS) Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom ( https://ror.org/01kj2bm70)
            [4 ] School of Life Sciences, University of Warwick, Coventry, United Kingdom ( https://ror.org/01a77tt86)
            [5 ] Department of Pharmacology & Personalised Medicine, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands ( https://ror.org/02d9ce178)
            [6 ] Centro de Tecnología Biomédica / ETS Ingenieros Informáticos. Universidad Politécnica de Madrid. Pozuelo de Alarcón, Madrid, Spain;
            [7 ] Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA ( https://ror.org/04b6nzv94)
            [8 ] Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Department of Neurology, University Hospital Essen, Essen, Germany ( https://ror.org/02na8dn90)
            [9 ] STALICLA, Barcelona, Catalonia, Spain;
            Author notes
            Author information
            https://orcid.org/0000-0002-4201-7973
            Article
            10.14293/S2199-1006.1.SOR-.PPCFYDY.v1
            4bfe23d9-d2f6-4e69-b86e-c6965ff9639e

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            History
            : 1 February 2023
            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/100018696, HORIZON EUROPE Health;
            Award ID: 101057619
            Funded by: funder-id http://dx.doi.org/10.13039/501100007601, Horizon 2020;
            Award ID: 777111
            Funded by: funder-id http://dx.doi.org/10.13039/501100003008, Ministry of Higher Education, Egypt;
            Award ID: 40463/2019
            Funded by: funder-id http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
            Award ID: 01EJ2205D
            Categories

            All data generated or analysed during this study are included in this published article (and its supplementary information files).
            Medicine,Computer science,Life sciences
            Network medicine,Systems medicine,Network pharmacology,Endotype,Endophenotype,Taxonomy,Ontology,Classification

            References

            1. Sollie Annet, Sijmons Rolf H., Lindhout Dick, van der Ploeg Ans T., Rubio Gozalbo M. Estela, Smit G. Peter A., Verheijen Frans, Waterham Hans R., van Weely Sonja, Wijburg Frits A., Wijburg Rudolph, Visser Gepke. A New Coding System for Metabolic Disorders Demonstrates Gaps in the International Disease Classifications ICD-10 and SNOMED-CT, Which Can Be Barriers to Genotype-Phenotype Data Sharing. Human Mutation. Vol. 34(7):967–973. 2013. Hindawi Limited. [Cross Ref]

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