Data-Enriched Edible Pharmaceuticals (DEEP) with Bespoke Design, Dose and Drug Release

Customizable orodispersible films: Inkjet printing and data matrix encoding for personalized hydrocortisone dosing

The aim of this study was to exploit the versatility of inkjet printing to develop flexible doses of drug-loaded orodispersible films that encoded information in a data matrix pattern, and to introduce a specialised data matrix-generator software specifically focused on the healthcare sector. Pharma-inks (drug-loaded inks) containing hydrocortisone (HC) were developed and characterised based on their rheological properties and drug content. Different strategies were investigated to improve HC solubility: formation of β-cyclodextrin complexes, Soluplus® based micelles, and the use of co-solvent systems. The software automatically adapted the data matrix size and identified the number of layers for printing. HC content deposited in each film layer was measured, and it was found that the proportion of co-solvent used directly affected the drug solubility and simultaneously played a role in the modification of the viscosity and surface tension of the inks. The formation of β-cyclodextrin complexes improved the drug quantity deposited in each layer. On the contrary, micelle-based inks were not suitable for printing. Orodispersible films containing flexible and low doses of personalised HC were successfully prepared, and the development of a code generator software oriented to medical use provided an additional, innovative, and revolutionary advantage to personalised medicine safety and accessibility.

Inkjet Printing of Pharmaceuticals

Inkjet printing (IJP) is an additive manufacturing process that selectively deposits ink materials, layer-by-layer, to create 3D objects or 2D patterns with precise control over their structure and composition. This technology has emerged as an attractive and versatile approach to address the ever-evolving demands of personalized medicine in the healthcare industry. Although originally developed for nonhealthcare applications, IJP harnesses the potential of pharma-inks, which are meticulously formulated inks containing drugs and pharmaceutical excipients. Delving into the formulation and components of pharma-inks, the key to precise and adaptable material deposition enabled by IJP is unraveled. The review extends its focus to substrate materials, including paper, films, foams, lenses, and 3D-printed materials, showcasing their diverse advantages, while exploring a wide spectrum of therapeutic applications. Additionally, the potential benefits of hardware and software improvements, along with artificial intelligence integration, are discussed to enhance IJP's precision and efficiency. Embracing these advancements, IJP holds immense potential to reshape traditional medicine manufacturing processes, ushering in an era of medical precision. However, further exploration and optimization are needed to fully utilize IJP's healthcare capabilities. As researchers push the boundaries of IJP, the vision of patient-specific treatment is on the horizon of becoming a tangible reality.

Predicting pharmaceutical inkjet printing outcomes using machine learning

Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process makes formulation (e.g., composition, surface tension, and viscosity) and printing parameter optimization (e.g., nozzle diameter, peak voltage, and drop spacing) an empirical and time-consuming endeavour. Instead, given the wealth of publicly available data on pharmaceutical inkjet printing, there is potential for a predictive model for inkjet printing outcomes to be developed. In this study, machine learning (ML) models (random forest, multilayer perceptron, and support vector machine) to predict printability and drug dose were developed using a dataset of 687 formulations, consolidated from in-house and literature-mined data on inkjet-printed formulations. The optimized ML models predicted the printability of formulations with an accuracy of 97.22%, and predicted the quality of the prints with an accuracy of 97.14%. This study demonstrates that ML models can feasibly provide predictive insights to inkjet printing outcomes prior to formulation preparation, affording resource- and time-savings.

Two-dimensional printing of nanoparticles as a promising therapeutic method for personalized drug administration

The necessity for personalized patient treatment has drastically increased since the contribution of genes to the differences in physiological and metabolic state of individuals have been exposed. Different approaches have been considered so far in order to satisfy all of the diversities in patient needs, yet none of them have been fully implemented thus far. In this framework, various types of 2D printing technologies have been identified to offer some potential solutions for personalized medication, which development is increasing rapidly. Accurate drug-on-demand deposition, the possibility of consuming multiple drug substances in one product and adjusting individual drug concentration are just some of the few benefits over existing bulk pharmaceuticals manufacture, which printing technologies brings. With inclusion of nanotechnology by printing nanoparticles from its dispersions some further opportunities such as controlled and stimuli-responsive drug release or targeted and dose depending on drug delivery were highlighted. Yet, there are still some challenges to be solved before such products can reach the pharmaceutical market. In those terms mostly chemical, physical as well as microbiological stability concerns should be answered, with which 2D printing technology could meet the treatment needs of every individual and fulfill some existing drawbacks of large-scale batch production of pharmaceuticals we possess today.

Harnessing personalized tailored medicines to digital-based data-enriched edible pharmaceuticals

Tailoring drug products to personalized medicines poses challenges for conventional dosage forms. The prominent reason is the restricted availability of flexible dosage strengths in the market. Inappropriate dosage strengths lead to adverse drug reactions or compromised therapeutic effects. The situation worsens when the drug has a narrow therapeutic window. To overcome these challenges, data-enriched edible pharmaceuticals (DEEP) are novel concepts for designing solid oral products. DEEP have individualized doses and information embedded in quick response (QR) code form. When data are presented in a QR code, the information is printed with edible ink that contains the drug in tailored doses required for the patients.