  The use of synthetic data is recognized as a crucial step in the development
of neural network-based Artificial Intelligence (AI) systems. While the methods
for generating synthetic data for AI applications in other domains have a role
in certain biomedical AI systems, primarily related to image processing, there
is a critical gap in the generation of time series data for AI tasks where it
is necessary to know how the system works. This is most pronounced in the
ability to generate synthetic multi-dimensional molecular time series data
(SMMTSD); this is the type of data that underpins research into biomarkers and
mediator signatures for forecasting various diseases and is an essential
component of the drug development pipeline. We argue the insufficiency of
statistical and data-centric machine learning (ML) means of generating this
type of synthetic data is due to a combination of factors: perpetual data
sparsity due to the Curse of Dimensionality, the inapplicability of the Central
Limit Theorem, and the limits imposed by the Causal Hierarchy Theorem.
Alternatively, we present a rationale for using complex multi-scale
mechanism-based simulation models, constructed and operated on to account for
epistemic incompleteness and the need to provide maximal expansiveness in
concordance with the Principle of Maximal Entropy. These procedures provide for
the generation of SMMTD that minimizes the known shortcomings associated with
neural network AI systems, namely overfitting and lack of generalizability. The
generation of synthetic data that accounts for the identified factors of
multi-dimensional time series data is an essential capability for the
development of mediator-biomarker based AI forecasting systems, and therapeutic
control development and optimization through systems like Drug Development
Digital Twins.
