Accelerating Pharma Innovation by Ai with Computational Models - Tapas Kumar Mohapatra; Priyanka Behera; Aditi Sharma; Subham Biswal (International Journal of Scientific Research in Science and Technology, 2025)
Complex biological activity is the straightforward explanation for the pharmaceutical industry's notable innovation drop in recent decades. Drug discovery, as we all know, is a complex, unsafe, expensive and time-consuming process. It typically takes 10- 12 years to get a medicine onto the market. However, with the use of computer-aided drug development, computational chemistry, and artificial intelligence (AI), we can now speed up this process. The entire process of finding and developing new drugs could become more efficient with the use of artificial intelligence (AI). Here Most marketed drugs are administered orally, despite the complex process of oral absorption that is difficult to predict. The interaction of numerous processes that rely on both chemical and physiological features determines oral bioavailability. In an effort to mechanistically depict the process of oral absorption, computational oral physiologically-based pharmacokinetic (PBPK) models, such as the Advanced Compartmental Absorption and Transit Model (ACAT), have become a useful tool for integrating these variables. These models forecast the pharmacokinetic behaviour of medications in the human body using data from in vitro tests. This review focusses on the rapid development of new drug discovery utilising the Advanced Compartmental Absorption and Transit Model (ACAT) for oral absorption in order to reduce the number of phase II and phase III clinical trials.
Keywords: Drug discovery; Artificial Intelligence (AI); In Silico model; ACAT model