Personalized Medicine – Methods for Production Planning

Personalized therapies and medicine are new and promising trends for the treatment of many diseases. They have been proven exceptionally effective in low scale tests. However, in order to succeed economically, the production processes need to be scaled to an industrial level. They need to be secure, cost efficient and last, but not least, fast. After all, long waits will be unacceptable for patients – especially in life and death situations. Therefore, it is of the essence that the production is planned and executed optimally.


Challenges of Bio-Processes

Bio-processes show characteristics that complicate the optimal design and efficient management of industrialized processes and thus make the production of personalized medicin more expensive:

  • High quality standards often call for re-executing process phases for individual patients
  • Heterogeneous processing times complicate the development of a periodic production flow
  • The probabilistic nature of processing times and error occurrence prevent a structured, predictable workflow
Pipettieren einer DNA-Lösung
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Pipetting a DNA solution

Approaches for Process Optimization

One measure for the optimal design of industrial processes is to carefully analyze capacities of subprocesses with high error rates: With what time interval should people who need personalized medicine ideally arrive so that there are no long waiting times? On the basis of this, we coordinate the subprocesses and decide where and how much additional capacity should be kept available to mitigate workload peaks. In addition, investments in additional equipment can be secured.

To avoid frequent adjustments, we plan buffers between process steps. This way, delays only have a local impact and a periodic roadmap for the production of personalized medicines is created: For each process step, we determine when it will be started for how many patients. Who and how many there actually are depends on the repetitions.

We analyze the individual challenges of bioprocesses and develop concepts for controlling and optimizing the processes. To evaluate the concepts, we create digital twins of the processes and simulate the interaction of the various aspects.