PMID- 33508347
OWN - NLM
STAT- MEDLINE
DCOM- 20210617
LR  - 20210617
IS  - 1873-3476 (Electronic)
IS  - 0378-5173 (Linking)
VI  - 596
DP  - 2021 Mar 1
TI  - Probabilistic modeling of an injectable aqueous crystalline suspension using 
      influence networks.
PG  - 120283
LID - S0378-5173(21)00087-9 [pii]
LID - 10.1016/j.ijpharm.2021.120283 [doi]
AB  - Probabilistic modeling using influence networks is an efficient, intuitive, and 
      easy to communicate strategy in the development of complex pharmaceutical 
      products. This study was aimed to use a risk-based approach to explore the 
      complex interactions between product and process design parameters affecting size 
      and shape of the particles in injectable aqueous crystalline suspensions (ACS). 
      Based on a risk assessment, a design of experiments (DOE) was applied to evaluate 
      the most important parameters, i.e., four critical material attributes and two 
      critical process parameters. A model hydrophobic drug (carbamazepine) was milled 
      and homogenized in a multistep process (dispersion and milling steps). The final 
      formulations were characterized with automated at-line image analysis of 
      thousands of individual particles. The particle size and shape distributions were 
      summarized with descriptive parameters, and the relationship of these parameters 
      and the DOE was modeled using influence networks (INs). This approach was 
      compared and contrasted with a classical modeling approach based on multivariate 
      linear regression (MVLR). INs had a superior visual interpretation capability of 
      the complex and multivariate ACS system making the risk-based decision making 
      more accessible. The probability and causality were included in the IN, i.e., the 
      relationships between size and shape. Moreover, IN allowed to incorporate prior 
      knowledge in a systematic way by implementing a 'black and white list'. An IN 
      based model was created with the following model performance: a mean absolute 
      percentage error of 1.7% and 1.1% for the size and 6.2% and 5.0% for the shape, 
      respectively for dispersed and milled ACS. Parameters with the highest and lowest 
      probability to control the critical quality attributes of ACS could be 
      identified. Consequently, the parameter settings giving the optimum particle size 
      and shape could be predicted using a Monte Carlo simulation to calculate the 
      probability of success including the uncertainty of the model. The cubic MVLR 
      model for the size of milled ACS was comparable to the IN in terms of the mean 
      absolute percentage error, i.e., 1.1%. However, IN was more efficient in 
      visualizing the complex and multivariate data set, including all the critical 
      quality attributes and formulation/process parameters of the ACS at the same 
      time. Moreover, the prior knowledge used in probabilistic modeling of IN could be 
      systematically documented.
CI  - Copyright (c) 2021 Elsevier B.V. All rights reserved.
FAU - Sekulovic, Andrea
AU  - Sekulovic A
AD  - University of Copenhagen, Department of Pharmacy, Denmark; Dr. Reddy's R&D, 
      Leiden, the Netherlands.
FAU - Petit, Marion
AU  - Petit M
AD  - Dr. Reddy's R&D, Leiden, the Netherlands.
FAU - Verrijk, Ruud
AU  - Verrijk R
AD  - Dr. Reddy's R&D, Leiden, the Netherlands.
FAU - Rades, Thomas
AU  - Rades T
AD  - University of Copenhagen, Department of Pharmacy, Denmark.
FAU - Rantanen, Jukka
AU  - Rantanen J
AD  - University of Copenhagen, Department of Pharmacy, Denmark. Electronic address: 
      jukka.rantanen@sund.ku.dk.
LA  - eng
PT  - Journal Article
DEP - 20210126
PL  - Netherlands
TA  - Int J Pharm
JT  - International journal of pharmaceutics
JID - 7804127
RN  - 0 (Suspensions)
SB  - IM
MH  - Particle Size
MH  - *Suspensions
OTO - NOTNLM
OT  - Aqueous crystalline suspension
OT  - Data analysis
OT  - Design of experiments
OT  - Influence network
OT  - Particle shape
OT  - Particle size
OT  - Probabilistic modeling
EDAT- 2021/01/29 06:00
MHDA- 2021/06/22 06:00
CRDT- 2021/01/28 20:10
PHST- 2020/11/18 00:00 [received]
PHST- 2021/01/11 00:00 [revised]
PHST- 2021/01/12 00:00 [accepted]
PHST- 2021/01/29 06:00 [pubmed]
PHST- 2021/06/22 06:00 [medline]
PHST- 2021/01/28 20:10 [entrez]
AID - S0378-5173(21)00087-9 [pii]
AID - 10.1016/j.ijpharm.2021.120283 [doi]
PST - ppublish
SO  - Int J Pharm. 2021 Mar 1;596:120283. doi: 10.1016/j.ijpharm.2021.120283. Epub 2021 
      Jan 26.