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Keynote Talk: An in silico approach for Monoclonal Antibody (mAb) Process Research & Early Development

Source: AIChE
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    Individuals

    AIChE Member Credits 0.5
    AIChE Members $19.00
    AIChE Graduate Student Members Free
    AIChE Undergraduate Student Members Free
    Non-Members $29.00
  • Type:
    Conference Presentation
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    November 10, 2021
  • Duration:
    35 minutes
  • Skill Level:
    Intermediate
  • PDHs:
    0.50

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Monoclonal antibodies (mAbs) are a type of biopharmaceutical that can be used to treat various diseases, for example, hemophilia and other serious diseases (Wolf Pérez et al., 2019). Improvements for the development of mAbs can be categorized as follows, product related improvements, for example, expression, solubility etc. and process related improvements, for example, novel flowsheets, new technologies (unit operations) etc. The synergistic effect of product-process decisions is also of importance because it addresses the integrated problem, that is, how can process design decisions be accounted for in the product design phase. Consider the example, given a specified mAb, purify the mAb using a chromatography column and selected eluent at a predetermined concentration. If the resulting aggregated pool volume for a finite time horizon for a pilot and/or industrial process requires a non-standard tank size that increases capital investment, how can this be a priori mitigated? If product and process design is performed sequentially then the design decision is unchanged however, if product-process design is performed integrated then, additional technologies can be evaluated for up-concentration. Therefore, the process is sized to the required scale and not oversized which has both capital investment and time-to-market implications.

Production planning-scheduling (PPS) is a field within operations research that solves the dynamic problem as to what to produce when (Harjunkoski et al., 2014). It can be performed using optimization-based methods and simulation-based methods, for example, discrete event simulation. The former differs from the latter because it provides an optimal solution for a set of decisions however, the latter provides the opportunity to deep dive into the details of the plan-schedule. Therefore, the symbiosis between the two approaches can be explained as follows. The former generates the optimal plan-schedule and the latter simulates how this is executed. In other words, the latter acts as a digital version of the process, a so-called digital twin. Therefore, we can use discrete event simulation to also evaluate the impact of different technologies and various design decisions on pharmaceutical processes, evaluate economic and sustainability impacts and improve decision making a priori to final investment (Yang et la., 2020).

The objective of this presentation is to present an in silico, systematic framework for multiscale process design and idea evaluation for biopharmaceutical products. A case study applied to mAbs will be presented. The method will be presented, and its application exemplified through idea evaluation related to new technologies for mAb production. Finally, the screening of the evaluation space using a combination of economic and sustainability metrics will be presented.

References

Harjunkoski I, Maravelias CT, Bongers P, Castro PM, Engell S, Grossmann IE, Hooker J, Méndez C, Sand G and Wassick J. 2014. Scope for industrial applications of production scheduling models and solution methods. Computers and Chemical Engineering, 62, 161–193.

Wolf Pérez A. M., Sormanni P., Andersen J. S., Sakhnini L. I., Rodriguez-Leon I., Bjelke J. R., Gajhede A. J., De Maria L., Otzen D. E., Vendruscolo M. and Lorenzen N. 2014. In vitro and in silico assessment of the developability of a designed monoclonal antibody library. mAbs, 11(2), 388–400.

Yang, O., Qadan, M. & Ierapetritou, M. 2019. Economic Analysis of Batch and Continuous Biopharmaceutical Antibody Production: a Review. Journal of pharmaceutical innovation, 15, 182–200.

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AIChE Member Credits 0.5
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AIChE Undergraduate Student Members Free
Non-Members $29.00
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