In Silico Predictions Driving the Development of 3D-Printed Drug Delivery Systems

Area of Science:

• Pharmaceutical Sciences
• Materials Science
• Computational Chemistry

Background:

• Three-dimensional printing (3DP) offers personalized drug product manufacturing.
• Traditional excipient selection for 3DP relies on inefficient trial-and-error methods.

Purpose of the Study:

• To develop and validate in silico methods for predicting excipient miscibility and dissolution behavior in 3DP formulations.
• To establish a framework for high-throughput excipient screening and accelerate 3DP drug delivery system development.

Main Methods:

• Utilized blend module simulations to calculate miscibility parameters (mixing energy and Flory-Huggins parameter).
• Employed molecular dynamics (MD) simulations to determine cohesive energy density (CED) for dissolution prediction.
• Correlated in silico predictions with experimental data from 51 formulations.

Main Results:

• In silico predicted miscibility parameters strongly correlated with experimental printability.
• Accurate forecasting of printability based on drug-excipient ratios, plasticizer/lipid concentrations, and hot-melt extrusion (HME) temperatures.
• Lower CED values indicated faster drug release from hydrophilic carriers; higher CED values suggested sustained release from hydrophobic carriers.

Conclusions:

• Miscibility parameters and MD-simulated CED values offer a practical framework for early-stage, high-throughput excipient screening.
• In silico prediction provides a viable strategy to model the 3DP workflow, minimizing experimentation and accelerating clinical translation.