How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry

Elise Koch; Antonio F. Pardiñas; Kevin S. O'Connell; Pierluigi Selvaggi; José Camacho Collados; Aleksandar Babic; Serena E. Marshall; Erik Van der Eycken; Cecilia Angulo; Yi Lu; Patrick F. Sullivan; Anders M. Dale; Espen Molden; Danielle Posthuma; Nathan White; Alexander Schubert; Srdjan Djurovic; Hakon Heimer; Hreinn Stefánsson; Kári Stefánsson; Thomas Werge; Ida Sønderby; Michael C. O'Donovan; James T.R. Walters; Lili Milani; Ole A. Andreassen

doi:10.1016/j.biopsych.2024.01.001

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry

Elise Koch^*, Antonio F. Pardiñas, Kevin S. O'Connell, Pierluigi Selvaggi, José Camacho Collados, Aleksandar Babic, Serena E. Marshall, Erik Van der Eycken, Cecilia Angulo, Yi Lu, Patrick F. Sullivan, Anders M. Dale, Espen Molden, Danielle Posthuma, Nathan White, Alexander Schubert, Srdjan Djurovic, Hakon Heimer, Hreinn Stefánsson, Kári StefánssonThomas Werge, Ida Sønderby, Michael C. O'Donovan, James T.R. Walters, Lili Milani, Ole A. Andreassen

^*Corresponding author for this work

Research output: Contribution to Journal › Review article › Academic › peer-review

Abstract

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements—well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms—to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

Original language	English
Pages (from-to)	543-551
Number of pages	9
Journal	Biological psychiatry
Volume	96
Issue number	7
Early online date	5 Jan 2024
DOIs	https://doi.org/10.1016/j.biopsych.2024.01.001
Publication status	Published - 1 Oct 2024

Bibliographical note

Publisher Copyright:
© 2024 Society of Biological Psychiatry

Funding

This work was supported by the European Union's Horizon 2020 research and innovation program (Grant No. 964874 [to OAA]). We also acknowledge support from the Research Council of Norway (Grant Nos. 296030 and 223273 [to OAA]) and grants from South-Eastern Norway Regional Health Authority (Grant No. 2020060 [to IS]), the Estonian Research Council (Grant No. PRG184 [to LM]), the National Institute of Mental Health (Grant No. R01 MH123724 [to PFS]), and the European Research Council (Grant No. 101042183 [to YL]). OAA reported grants from Stiftelsen Kristian Gerhard Jebsen, South-East Regional Health Authority, Research Council of Norway, and European Union's Horizon 2020 during the conduct of the study and personal fees from cortechs.ai (stock options), Lundbeck (speaker's honorarium), and Sunovion (speaker's honorarium) and Janssen (speaker's honorarium) outside of the submitted work. JTRW and MCO have received grant funding from Takeda for work unrelated to this paper and from the Medical Research Council (United Kingdom), European Union's Horizon 2020, and Akrivia Health to develop linked genomic and electronic health resources. PFS is a shareholder and scientific advisory board member for Neumora Therapeutics. All other authors report no biomedical financial interests or potential conflicts of interest. Dr. Andreassen reported grants from Stiftelsen Kristian Gerhard Jebsen, South-East Regional Health Authority, Research Council of Norway, and European Union\u2019s Horizon 2020 during the conduct of the study; personal fees from cortechs.ai (stock options), Lundbeck (speaker\u2019s honorarium), and Sunovion (speaker\u2019s honorarium) and Janssen (speaker\u2019s honorarium) outside the submitted work. Drs. Walters and O\u2019Donovan have received grant funding from Takeda for work unrelated to this paper and from the Medical research Council (UK), from European Union\u2019s Horizon 2020, and Akrivia Health to develop linked genomic and electronic health resources. Dr. Sullivan is a shareholder and SAB member for Neumora Therapeutics. All other authors report no biomedical financial interests or potential conflicts of interest. This project has received funding from the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 964874. We also acknowledge support from the Research Council of Norway (296030, 223273), grants from South-Eastern Norway Regional Health Authority (2020060), and the Estonian Research Council (PRG184).

Funders	Funder number
Medical Research Council
Sunovion
Takeda Pharmaceutical Company
Stiftelsen Kristian Gerhard Jebsen
European Commission
European Union’s Horizon 2020, and Akrivia Health
European Union's Horizon 2020, and Akrivia Health
Horizon 2020
South East Regional Health Authority
Eesti Teadusagentuur	PRG184
Norges forskningsråd	296030, 223273
Helse Sør-Øst RHF	2020060
Horizon 2020 Framework Programme	964874
European Research Council	101042183
National Institute of Mental Health	R01 MH123724

Keywords

Drug treatment outcomes
Genomics
Precision medicine
Prediction algorithms
Psychiatry
Real-world data

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10.1016/j.biopsych.2024.01.001

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Koch, E., Pardiñas, A. F., O'Connell, K. S., Selvaggi, P., Camacho Collados, J., Babic, A., Marshall, S. E., Van der Eycken, E., Angulo, C., Lu, Y., Sullivan, P. F., Dale, A. M., Molden, E., Posthuma, D., White, N., Schubert, A., Djurovic, S., Heimer, H., Stefánsson, H., ... Andreassen, O. A. (2024). How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry. Biological psychiatry, 96(7), 543-551. https://doi.org/10.1016/j.biopsych.2024.01.001

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abstract = "Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements—well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms—to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.",

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Koch, E, Pardiñas, AF, O'Connell, KS, Selvaggi, P, Camacho Collados, J, Babic, A, Marshall, SE, Van der Eycken, E, Angulo, C, Lu, Y, Sullivan, PF, Dale, AM, Molden, E, Posthuma, D, White, N, Schubert, A, Djurovic, S, Heimer, H, Stefánsson, H, Stefánsson, K, Werge, T, Sønderby, I, O'Donovan, MC, Walters, JTR, Milani, L & Andreassen, OA 2024, 'How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry', Biological psychiatry, vol. 96, no. 7, pp. 543-551. https://doi.org/10.1016/j.biopsych.2024.01.001

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AU - Koch, Elise

AU - Pardiñas, Antonio F.

AU - O'Connell, Kevin S.

AU - Selvaggi, Pierluigi

AU - Camacho Collados, José

AU - Babic, Aleksandar

AU - Marshall, Serena E.

AU - Van der Eycken, Erik

AU - Angulo, Cecilia

AU - Lu, Yi

AU - Sullivan, Patrick F.

AU - Dale, Anders M.

AU - Molden, Espen

AU - Posthuma, Danielle

AU - White, Nathan

AU - Schubert, Alexander

AU - Djurovic, Srdjan

AU - Heimer, Hakon

AU - Stefánsson, Hreinn

AU - Stefánsson, Kári

AU - Werge, Thomas

AU - Sønderby, Ida

AU - O'Donovan, Michael C.

AU - Walters, James T.R.

AU - Milani, Lili

AU - Andreassen, Ole A.

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KW - Prediction algorithms

KW - Psychiatry

KW - Real-world data

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How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry

Abstract

Bibliographical note

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Keywords

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