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

• 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.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Keyword

Three-dimensional printing

3D Printed drug products

Fused Deposition Modeling (FDM)

Oral drug delivery systems

Artificial intelligence

Personalized pharmaceuticals and medicines

Prediction models

Reglerteknik

Control Engineering

Publication and Content Type

ref (subject category)

art (subject category)