Hybrid modeling for biopharmaceutical processes: advantages, opportunities, and implementation

Process models are mathematical formulations (essentially a set of equations) that try to represent the real system/process in digital or virtual form. These derived either based on fundamental physical laws often combined with empirical assumptions learned data. The former has been existing for several decades chemical and process engineering while latter recently received lot attention emergence artificial intelligence/machine learning techniques. Hybrid modeling is an emerging paradigm explores synergy between these two paradigms, taking advantage knowledge (or know-how) information disseminated by collected Such approach especially suitable systems industries where data generation significantly resource intensive at same time fundamentally not completely deciphered such as processes involved biopharmaceutical pipeline. This technology could, fact, be enabler meeting demands goals initiatives Quality design, Analytical tools, Pharma 4.0. In addition, it can aid different applications throughout development Chemistry, Manufacturing, Control (CMC) make more strategic efficient. article focuses providing step-by-step guide considerations made develop reliable applicable hybrid model. aims highlighting need tools industry summarizes works advocate its implications. Subsequently, key qualities elaborated reference literature demonstrating qualities.