Search for: All records

Abstract The pharmaceutical manufacturing sector needs to rapidly evolve to absorb the next wave of disruptive industrial innovations—Industry 4.0. This involves incorporating technologies like artificial intelligence and 3D printing (3DP) to automate and personalize the drug production processes. This study aims to build a formulation and process design (FPD) framework for a pharmaceutical 3DP platform that recommends operating (formulation and process) conditions at which consistent drop printing can be obtained. The platform used in this study is a displacement‐based drop‐on‐demand 3D printer that manufactures dosages by additively depositing the drug formulation as droplets on a substrate. The FPD framework is built in two parts: the first part involves building a machine learning model to simulate the forward problem—predicting printer operation for given operating conditions and the second part seeks to solve and experimentally validate the inverse problem—predicting operating conditions that can yield desired printer operation. 

Providing drug products for pediatric patients is a challenging problem for the pharmaceutical industry. Children often require flexible low-dose medication with features like taste-masking and ease of swallowing. In recent years, mini-tablets have emerged as an attractive dosing solution that can meet these requirements. They are small form oral dosages around 2-4 mm in diameter that can be dispensed individually or in combination. Conventionally, they are made using methods like direct compression and hot melt extrusion. This study introduces a new technique to make mini-tablets: drop-on-demand 3D printing. Here the active ingredient is suspended in a liquid excipient, the formulation is printed as droplets and each drop is solidified to yield a mini-tablet. An optimal solvent bath that can uniformly capture mini-tablets is designed and dosages of Atorvastatin (active ingredient) are produced as a test case. Quality of these dosages is determined by measuring their content uniformity.