Abstract
Background: Conventional approach of formulating a new dosage form is a comprehensive task and uses various sources like man, money, time and experimental efforts. The use of AI can help to obtain optimized pharmaceutical formulation with desired (best) attributes. AI minimizes the use of resources and increases the understanding of impact, of independent variable over desired dependent responses/variables.
Objective: Thus, the aim of present work is to explore the use of Artificial intelligence in designing pharmaceutical products as well as the manufacturing process to get the pharmaceutical product of desired attributes with ease. The review is presenting various aspects of Artificial intelligence like Quality by Design (QbD) & Design of Experiment (DoE) to confirm the quality profile of drug product, reduce interactions among the input variables for the optimization, modelization and various simulation tools used in pharmaceutical manufacturing (scale up and production).
Conclusion: Hence, the use of QbD approach in Artificial intelligence is not only useful in understanding the products or process but also helps in building an excellent and economical pharmaceutical product.
Keywords: Artificial Intelligence, product quality profile, quality by design, design of experiment, critical quality attributes, current uses of artificial intelligence.
Graphical Abstract
[http://dx.doi.org/10.1177/0141076818815510] [PMID: 30507284]
[http://dx.doi.org/10.1007/s11095-007-9511-1] [PMID: 18185986]
[http://dx.doi.org/10.1016/j.ijpharm.2013.08.074] [PMID: 24016743]
[http://dx.doi.org/10.1016/j.ijheatfluidflow.2004.07.005]
[http://dx.doi.org/10.1002/aic.11876]
[http://dx.doi.org/10.1021/i160024a007]
[http://dx.doi.org/10.1016/0009-2509(64)85047-8]
[http://dx.doi.org/10.1016/j.ejps.2012.09.020] [PMID: 23046836]
[http://dx.doi.org/10.3389/fphar.2017.00298] [PMID: 28588497]
[http://dx.doi.org/10.2174/2210303104666141112220253]
[http://dx.doi.org/10.1007/s10822-017-0085-7] [PMID: 29134430]
[http://dx.doi.org/10.18203/2349-3259.ijct20163955]
[http://dx.doi.org/10.1002/pat.4375]
[http://dx.doi.org/10.1002/jbm.a.36145] [PMID: 28639394]
[http://dx.doi.org/10.1016/j.ijpharm.2016.01.040] [PMID: 26802496]
[http://dx.doi.org/10.1016/j.jconrel.2015.03.020] [PMID: 25797561]
[http://dx.doi.org/10.1002/pat.3381]
[http://dx.doi.org/10.2174/157488612800492753] [PMID: 22663957]
[http://dx.doi.org/10.3109/10837450.2012.705294] [PMID: 22881350]
[http://dx.doi.org/10.1081/DDC-100100347] [PMID: 10697759]
[http://dx.doi.org/10.1208/pt040226] [PMID: 12916908]
[http://dx.doi.org/10.4274/tjps.97720]
[http://dx.doi.org/10.4314/tjpr.v13i9.1]