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.

M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines.

Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) three-dimensional printing (3DP) has made significant advancements in the field of oral drug delivery with personalized drug-loaded formulations being designed, developed and dispensed for the needs of the patient. The FDM 3DP process begins with the production of drug-loaded filaments by hot melt extrusion (HME), followed by the printing of a drug product using a FDM 3D printer. However, the optimization of the fabrication parameters is a time-consuming, empirical trial approach, requiring expert knowledge. Here, M3DISEEN, a web-based pharmaceutical software, was developed to accelerate FDM 3D printing using AI machine learning techniques (MLTs). In total, 614 drug-loaded formulations were designed from a comprehensive list of 145 different pharmaceutical excipients, 3D printed and assessed in-house. To build the predictive tool, a dataset was constructed and models were trained and tested at a ratio of 75:25. Significantly, the AI models predicted key fabrication parameters with accuracies of 76% and 67% for the printability and the filament characteristics, respectively. Furthermore, the AI models predicted the HME and FDM processing temperatures with a mean absolute error of 8.9 °C and 8.3 °C, respectively. Strikingly, the AI models achieved high levels of accuracy by solely inputting the pharmaceutical excipient trade names. Therefore, AI provides an effective holistic modeling technology and software to streamline and advance 3DP as a significant technology within drug development. M3DISEEN is available at (http://m3diseen.com/predictions/).

Automated therapy preparation of isoleucine formulations using 3D printing for the treatment of MSUD: First single-centre, prospective, crossover study in patients.

Maple syrup urine disease (MSUD) is a rare metabolic disorder with a worldwide prevalence of 1 in every 185,000 live births. However, certain populations display a significant overexpression of the disorder where incidence is reported to be 1 in every 52,541 new-borns. The first-line therapy for MSUD involves a strict dietary leucine restriction and oral supplementation of isoleucine and valine. The dose administered to patients requires strict tailoring according to age, weight and blood levels. In current clinical practice, however, practitioners still have to prepare extemporaneous formulations due to the lack of suitable oral treatments for MSUD. Herein, we evaluate the first time use of 3D printing in a hospital setting for the preparation of personalised therapies with the aim of improving safety and acceptability to isoleucine supplementation in paediatric patients suffering from MSUD. This investigation was a single-centre, prospective crossover experimental study. Four paediatric patients with MSUD (aged 3-16 years) were treated at the Clinic University Hospital in Santiago de Compostela, Spain which is a MSUD reference hospital in Europe. The primary objective was to evaluate isoleucine blood levels after six months of treatment with two types of formulations; conventional capsules prepared by manual compounding and personalised chewable formulations prepared by automated 3D printing. A secondary investigation was to evaluate patient acceptability of 3D printed formulations prepared with different flavours and colours. Isoleucine blood levels in patients were well controlled using both types of formulations, however, the 3D printed therapy showed mean levels closer to the target value and with less variability (200-400 µM). The 3D printed formulations were well accepted by patients regarding flavour and colour. The study demonstrates for the first time that 3D printing offers a feasible, rapid and automated approach to prepare oral tailored-dose therapies in a hospital setting. 3D printing has shown to be an effective manufacturing technology in producing chewable isoleucine printlets as a treatment of MSUD with good acceptability.