Intriguing of pharmaceutical product development processes with the help of artificial intelligence and deep/machine learning or artificial neural network

被引:8
作者
Jariwala, Naitik [1 ]
Putta, Chandra Lekha [1 ]
Gatade, Ketki [1 ]
Umarji, Manasi [1 ]
Rahman, Syed Nazrin Ruhina [1 ]
Pawde, Datta Maroti [1 ]
Sree, Amoolya [1 ]
Kamble, Atul Sayaji [1 ]
Goswami, Abhinab [1 ]
Chakraborty, Payel [1 ]
Shunmugaperumal, Tamilvanan [1 ]
机构
[1] Natl Inst Pharmaceut Educ & Res Guwahati, Dept Pharmaceut, Changsari 781101, Assam, India
关键词
Artificial neural network; Artificial intelligence; Deep learning; Machine learning; Linear and non-linear models; PREDICTION; OPTIMIZATION; FORMULATIONS; DESIGN; MODEL;
D O I
10.1016/j.jddst.2023.104751
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
The objectives of current review are (1) to provide a historical overview of artificial intelligence and deep/ machine learning (AI & D/ML) or Artificial Neural Network (ANN) (2) to update the financial dealings of pharma companies related to the application of AI & D/ML or ANN in drug discovery and development processes and (3) to showcase the application of AI & D/ML or ANN concept for optimization of analytical method conditions and formula of the dosage form. The optimization of analytical method conditions and formula of dosage form started with the employment of linear model such as design of experiment followed by non-linear model like AI & D/ML or ANN. Such type of linear and non-linear models blending in optimization processes nevertheless helped to suitably identify the influence of critical process parameters or critical material attributes on critical quality attributes. However, much of integration and understandable interpretation between the available data arised from clinical trials and the prevalence/progression of pandemic/endemic infections could potentially be ambitioned through the application of AI & D/ML or ANN.
引用
收藏
页数:12
相关论文
共 103 条
[1]  
10xDS Team, 2023, 5 COMM CHALL IMPL AR
[2]   The application of artificial neural network and least square support vector machine methods based on spectrophotometry method for the rapid simultaneous estimation of triamcinolone, neomycin, and nystatin in skin ointment formulation [J].
Abasi, Negar ;
Sohrabi, Mahmoud Reza ;
Motiee, Fereshteh ;
Davallo, Mehran .
OPTIK, 2021, 241
[3]  
Abdullahi H.U., 2020, Methods, V6, P362
[4]  
Abhinav GVKS, 2019, J DRUG DELIVERY THER, V9, P164, DOI DOI 10.22270/JDDT.V9I5-S.3634
[5]   Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research [J].
Agatonovic-Kustrin, S ;
Beresford, R .
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS, 2000, 22 (05) :717-727
[6]   Prediction of Drug Stability Using Deep Learning Approach: Case Study of Esomeprazole 40 mg Freeze-Dried Powder for Solution [J].
Ajdaric, Jovana ;
Ibric, Svetlana ;
Pavlovic, Aleksandar ;
Ignjatovic, Ljubisa ;
Ivkovic, Branka .
PHARMACEUTICS, 2021, 13 (06)
[7]   Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis [J].
Amar, Yehia ;
Schweidtmann, ArturM. ;
Deutsch, Paul ;
Cao, Liwei ;
Lapkin, Alexei .
CHEMICAL SCIENCE, 2019, 10 (27) :6697-6706
[8]  
[Anonymous], Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies
[9]   Using artificial neural network and multivariate calibration methods for simultaneous spectrophotometric analysis of Emtricitabine and Tenofovir alafenamide fumarate in pharmaceutical formulation of HIV drug [J].
Arabzadeh, Valeh ;
Sohrabi, Mahmoud Reza ;
Goudarzi, Nasser ;
Davallo, Mehran .
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2019, 215 :266-275
[10]   Simultaneous spectrophotometric quantitative analysis of velpatasvir and sofosbuvir in recently approved FDA pharmaceutical preparation using artificial neural networks and genetic algorithm artificial neural networks [J].
Attia, Khalid A. M. ;
El-Abasawi, Nasr M. ;
El-Olemy, Ahmed ;
Abdelazim, Ahmed H. ;
Goda, Abdelrahman, I ;
Shahin, Mohammed ;
Zeid, Abdallah M. .
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2021, 251