develop a digital twin for research investigations and broad exploration. However, in
an operational environment, the possibilities of a twin can quickly just become
information overload and create a lack of trust which is fatal for efficient usage.
Therefore, the type of work of proactively defining which decisions are aimed at
being enhanced and only focusing on them is a major success factor. If there are too
many options, the decisions quickly get evaluated based on the risk one might take
versus standard operations without a digital twin instead of the expected operation
upside.
Finally, we mainly targeted a modeling approach focusing sequentially on each
production step and using combinations of mechanistic and heuristic modeling.
While this has the benefits of targeted modeling activities that remain more easily
scoped and manageable, it has several caveats. The main issue is probably that as we
optimize each step sub-model, we are targeting an endpoint outcome that may
actually not be optimal for the end-to-end process. What we optimize for at each
step is only as good as our understanding of the overall process.
We explored end-to-end model development using machine learning approaches
and while promising it once again becomes apparent that higher volumes of data and
better quality are absolutely required for this approach.
8 Conclusion and Outlook
The digital twin approach is still in the emerging phase of its use within the
Biopharma industry. We have seen examples of use from process development to
manufacturing and supply chain. However, that is only the start and fascinating
opportunities also exist in other areas beyond industrial operations. A lot of ink has
already been spent around the notions of quantified self and biohacking, investigating possibilities where we take twin approaches to human individuals.
While raising many ethical questions there are also areas such as clinical trials
where such approaches would deliver unquestionable benefits. All levels of twins
can apply from just having real-time sensors continuously updating a digital replica
to the most sophisticated twin where we could do simulations, prediction of therapeutic impact.
The ultimate end goal for digital twin applications within Biopharma and
Healthcare would be to no longer have any need for conducting clinical trials on
living beings but already a first step would be to shift from an approach of doing
experiments and then looking for a model that would explain results to an approach
where we execute simulations on the twin to define a predicted best scenario and
then verify whether actual therapeutic conditions follow the parameters of the
predicted model.
In short, using experiments not to experiment but to validate the hypothesis
produced by twin simulations. While radical, one should remember this is already
the shift that has taken place in areas such as automotive where nearly all crash tests
are now simulated instead of being actually conducted with a prototype vehicle.
Digital Twins: A General Overview of the Biopharma Industry
183
an operational environment, the possibilities of a twin can quickly just become
information overload and create a lack of trust which is fatal for efficient usage.
Therefore, the type of work of proactively defining which decisions are aimed at
being enhanced and only focusing on them is a major success factor. If there are too
many options, the decisions quickly get evaluated based on the risk one might take
versus standard operations without a digital twin instead of the expected operation
upside.
Finally, we mainly targeted a modeling approach focusing sequentially on each
production step and using combinations of mechanistic and heuristic modeling.
While this has the benefits of targeted modeling activities that remain more easily
scoped and manageable, it has several caveats. The main issue is probably that as we
optimize each step sub-model, we are targeting an endpoint outcome that may
actually not be optimal for the end-to-end process. What we optimize for at each
step is only as good as our understanding of the overall process.
We explored end-to-end model development using machine learning approaches
and while promising it once again becomes apparent that higher volumes of data and
better quality are absolutely required for this approach.
8 Conclusion and Outlook
The digital twin approach is still in the emerging phase of its use within the
Biopharma industry. We have seen examples of use from process development to
manufacturing and supply chain. However, that is only the start and fascinating
opportunities also exist in other areas beyond industrial operations. A lot of ink has
already been spent around the notions of quantified self and biohacking, investigating possibilities where we take twin approaches to human individuals.
While raising many ethical questions there are also areas such as clinical trials
where such approaches would deliver unquestionable benefits. All levels of twins
can apply from just having real-time sensors continuously updating a digital replica
to the most sophisticated twin where we could do simulations, prediction of therapeutic impact.
The ultimate end goal for digital twin applications within Biopharma and
Healthcare would be to no longer have any need for conducting clinical trials on
living beings but already a first step would be to shift from an approach of doing
experiments and then looking for a model that would explain results to an approach
where we execute simulations on the twin to define a predicted best scenario and
then verify whether actual therapeutic conditions follow the parameters of the
predicted model.
In short, using experiments not to experiment but to validate the hypothesis
produced by twin simulations. While radical, one should remember this is already
the shift that has taken place in areas such as automotive where nearly all crash tests
are now simulated instead of being actually conducted with a prototype vehicle.
Digital Twins: A General Overview of the Biopharma Industry
183