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\begin{document}

\title{``Digital by Design'' (DbD) approach to develop a Universal Deep
learning AI architecture for automatic chromatographic peak integration.}



\author[1]{Abhijeet Satwekar}%
\author[2]{Anubhab Panda}%
\author[2]{Phani Nandula}%
\author[2]{Sriharsha Sripada}%
\author[2]{Ramachandiran Govindaraj}%
\author[1]{Mara Rossi}%
\affil[1]{Merck Serono SpA Guidonia Montecelio}%
\affil[2]{Bosch Limited}%


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  \date{May 25, 2024}


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\begin{abstract}
Chromatographic data processing has garnered attention due to multiple
FDA 483 citations and warning letters, highlighting the need for a
robust technological solution. The healthcare industry has the potential
to greatly benefit from the adoption of digital technologies, but the
process of implementing these technologies can be slow and complex. This
article presents a ``Digital by Design'' managerial approach, adapted
from pharmaceutical quality by design principles, for designing and
implementing an artificial intelligence (AI)-based solution for
chromatography peak integration process in the healthcare industry. We
report the use of a convolutional neural network model to predict
analytical variability for integrating chromatography peaks and propose
a potential GxP framework for using artificial intelligence in the
healthcare industry that includes elements on data management, model
management, and human-in-the-loop processes. The component on analytical
variability prediction has a great potential to enable Industry 4.0
objectives on real-time release testing, automated quality control, and
continuous manufacturing.%
\end{abstract}%



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