ISO/TS 9491-1:2023

TECHNICAL ISO/TS
SPECIFICATION 9491-1
First edition
2023-06
Biotechnology — Predictive
computational models in personalized
medicine research —
Part 1:
Constructing, verifying and validating
models
Biotechnologie — Modèles informatiques prédictifs dans la recherche
sur la médecine personnalisée —
Partie 1: Construction, vérification et validation des modèles
Reference number
© ISO 2023
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ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Principles . 4
4.1 General . 4
4.2 Computational models in personalized medicine . 4
4.2.1 General . 4
4.2.2 Cellular systems biology models . 5
4.2.3 Risk prediction for common diseases. 6
4.2.4 Disease course and therapy response prediction . 6
4.2.5 Pharmacokinetic/-dynamic modelling and in silico trial simulations . 7
4.2.6 Artificial intelligence models . 7
4.3 Standardization needs for computational models. 8
4.3.1 General . 8
4.3.2 Challenges . 8
4.3.3 Common standards relevant for personalized medicine . 9
4.4 Data preparation for integration into computer models . 9
4.4.1 General . 9
4.4.2 Sampling data . . 9
4.4.3 Data formatting . 10
4.4.4 Data description . 11
4.4.5 Data annotation (semantics) . 11
4.4.6 Data interoperability requirements across subdomains .12
4.4.7 Data integration .13
4.4.8 Data provenance information . 13
4.4.9 Data access . 14
4.5 Model formatting . . 14
4.6 Model validation . 15
4.6.1 General .15
4.6.2 Specific recommendations for model validation .15
4.7 Model simulation . 17
4.7.1 General . 17
4.7.2 Requirements for capturing and sharing simulation set-ups. 18
4.7.3 Requirements for capturing and sharing simulation results . 19
4.8 Requirements for model storing and sharing . 19
4.9 Application of models in clinical trials and research . 19
4.9.1 General . 19
4.9.2 Specific recommendations . 20
4.10 Ethical requirements for modelling in personalized medicine . 20
Annex A (informative) Common standards relevant for personalized medicine and in silico
approaches .21
Annex B (informative) Information on modelling approaches relevant for personalized
medicine . .24
Bibliography .26
iii
Foreword
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ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters of
electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are
described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the
different types of ISO documents should be noted. This document was drafted in accordance with the
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www.iso.org/iso/foreword.html.
This document was prepared by Technical Committee ISO/TC 276, Biotechnology.
A list of all parts in the ISO 9491 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
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iv
Introduction
The capacity to generate data in life sciences and health research has greatly increased in the last decade.
In combination with patient/personal-derived data, such as electronic health records, patient registries
and databases, as well as lifestyle information, this big data holds an immense potential for clinical
applications, especially for computer-based models with predictive capacities in personalized medicine.
However, and despite the ever-progressing technological advances in producing data, the exploitation of
big data to generate new knowledge for medical benefits, while guaranteeing data privacy and security,
is lacking behind its full potential. A reason for this obstacle is the inherent heterogeneity of big data
and the lack of broadly accepted standards allowing interoperable integration of heterogeneous health
data to perform analysis and interpretation for predictive modelling approaches in health research,
such as personalized medicine.
Common standards lead to a mutual understanding and improve information exchange within and
across research communities and are indispensable for collaborative work. In order to setup computer
models in personalized medicine, data integration from heterogeneous and different sources at different
times plays a key role. Consistent documentation of data, models and simulation results based on basic
guiding principles for data management practices, such as FAIR (findable, accessible, interoperable,
[7]
reusable) or ALCOA (attributable, legible, contemporaneous, original, accurate), and standards can
ensure that the data and the corresponding metadata (data describing the data and its context), as well
as the models, methods and visualizations, are of reliable high quality.
Hence, standards for biomedical and clinical data, simulation models and data exchange are a
prerequisite for reliable integration of health-related data. Such standards, together with harmonized
ways to describe their metadata, ensure the interoperability of tools used for data integration and
modelling, as well as the reproducibility of the simulation results. In this sense, modelling standards are
agreed ways of consistently structuring, describing, and associating models and data, their respective
parts and their graphical visualization, as well as the information about applied methods and the
outcome of model simulations. Such standards also assist in describing how constituent parts interact,
or are linked together, and how they are embedded in their physiological context.
Major challenges in the field of personalized medicine are to:
a) harmonize the standardization efforts that refer to different data types, approaches and
technologies;
b) make the standards interoperable, so that the data can be compared and integrated into models.
An overall goal is to FAIRify data and processes in order to improve data integration and reuse. An
additional challenge is to ensure a legal and ethical framework enabling interoperability.
This document presents modelling requirements and recommendations for research in the field of
personalized medicine, especially
...