Prediction of dissolution profiles by non-destructive NIR spectroscopy in bilayer tablets

Baranwal, Yukteshwar; Román-Ospino, Andrés D.; Keyvan, Golshid; Ha, Jung Myung; Hong, Eon Pyo; Muzzio, Fernando J.; Ramachandran, Rohit

doi:10.1016/j.ijpharm.2019.05.022

Cited by 24 publications

(5 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using a fundamental property of a formulation, like viscosity, prevents subjectivity, and the model does not need to be re-trained when new materials or grades are used. A more conventional approach employed is to use vibrational spectroscopy data as inputs in developing ML models (Baranwal et al, 2019;Donoso and Ghaly, 2005;Pawar et al, 2016). Recently near-infrared spectroscopy and Raman spectroscopy were individually combined with ML to predict dissolution profiles, where the highest f2 score reported was 90.53 (Galata et al, 2019).…”

Section: Predicting Dissolution Using Rheology and Machine Learningmentioning

confidence: 99%

3D printing tablets: Predicting printability and drug dissolution from rheological data

Elbadawi

Gustaffson

Gaisford

et al. 2020

International Journal of Pharmaceutics

View full text Add to dashboard Cite

Rheology is an indispensable tool for formulation development, which when harnessed, can both predict a material's performance and provide valuable insight regarding the material's macrostructure. However, rheological characterizations are under-utilized in 3D printing of drug formulations. In this study, viscosity measurements were used to establish a mathematical model for predicting the printability of fused deposition modelling 3D printed tablets (Printlets). The formulations were composed of polycaprolactone (PCL) with different amounts of ciprofloxacin and polyethylene glycol (PEG), and different molecular weights of PEG. With all printing parameters kept constant, both binary and ternary blends were found to extrude at nozzle temperatures of 130, 150 and 170 C. In contrast PCL was unextrudable at 130 and 150 C. Three standard rheological models were applied to the experimental viscosity measurements, which revealed an operating viscosity window of between 100-1000 Pa.s at the apparent shear rate of the nozzle. The drug profile of the printlets were experimentally measured over seven days. As a proof-of-concept, machine learning models were developed to predict the dissolution behaviour from the viscosity measurements. The machine learning models were discovered to accurately predict the dissolution profile, with the highest f2 similarity score value of 90.9 recorded. Therefore, the study demonstrated that using only the viscosity measurements can be employed for the simultaneous high-throughput screening of formulations that are printable and with the desired release profile.

show abstract

Section: Predicting Dissolution Using Rheology and Machine Learningmentioning

confidence: 99%

3D printing tablets: Predicting printability and drug dissolution from rheological data

Elbadawi

Gustaffson

Gaisford

et al. 2020

International Journal of Pharmaceutics

View full text Add to dashboard Cite

show abstract

“…Previous studies have highlighted the potential for non-destructive NIR spectroscopic methods to be able to predict the physical characteristics of tablets, including density and porosity ( Donoso et al, 2003 ; Otsuka, 2006 ), hardness ( Kandpal et al, 2017 ; Otsuka and Yamane, 2006 ; Qiushi et al, 2019 ) and drug release ( Ojala et al, 2020 ) due to the sensitivity to surface and internal structural effects. As an example, reflectance NIR spectroscopy has previously been used to quantify drug release in tablets manufactured at different compression levels ( Baranwal et al, 2019 ). The researchers found that denser dosage forms were produced upon increasing compression level, which in turn increased the amount of NIR absorbance due to a reduced scattering effect.…”

Section: Resultsmentioning

confidence: 99%

Releasing fast and slow: Non-destructive prediction of density and drug release from SLS 3D printed tablets using NIR spectroscopy

Trenfield

Goyanes

et al. 2023

International Journal of Pharmaceutics: X

View full text Add to dashboard Cite

“…In the pharmaceutical world, ML has mostly been used to predict and optimise drug release [209][210][211][212][213][214][215][216][217][218][219][220]. Medicines' drug dissolution profiles are a fundamental characterisation technique in pharmaceutics [221].…”

Section: Machine Learning In the Pre-printing Stagementioning

confidence: 99%

Harnessing artificial intelligence for the next generation of 3D printed medicines

Elbadawi

McCoubrey

Gavins

et al. 2021

Advanced Drug Delivery Reviews

103

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Prediction of dissolution profiles by non-destructive NIR spectroscopy in bilayer tablets

Cited by 24 publications

References 33 publications

3D printing tablets: Predicting printability and drug dissolution from rheological data

3D printing tablets: Predicting printability and drug dissolution from rheological data

Releasing fast and slow: Non-destructive prediction of density and drug release from SLS 3D printed tablets using NIR spectroscopy

Harnessing artificial intelligence for the next generation of 3D printed medicines

Contact Info

Product

Resources

About