Investigation of the Fused Deposition Modeling Additive Manufacturing I: Influence of Process Temperature on the Quality and Crystallinity of the Dosage Forms

Innovative technologies for the fabrication of 3D/4D smart hydrogels and its biomedical applications - A comprehensive review

Repairing and regenerating damaged tissues or organs, and restoring their functioning has been the ultimate aim of medical innovations. 'Reviving healthcare' blends tissue engineering with alternative techniques such as hydrogels, which have emerged as vital tools in modern medicine. Additive manufacturing (AM) is a practical manufacturing revolution that uses building strategies like molding as a viable solution for precise hydrogel manufacturing. Recent advances in this technology have led to the successful manufacturing of hydrogels with enhanced reproducibility, accuracy, precision, and ease of fabrication. Hydrogels continue to metamorphose as the vital compatible bio-ink matrix for AM. AM hydrogels have paved the way for complex 3D/4D hydrogels that can be loaded with drugs or cells. Bio-mimicking 3D cell cultures designed via hydrogel-based AM is a groundbreaking in-vivo assessment tool in biomedical trials. This brief review focuses on preparations and applications of additively manufactured hydrogels in the biomedical spectrum, such as targeted drug delivery, 3D-cell culture, numerous regenerative strategies, biosensing, bioprinting, and cancer therapies. Prevalent AM techniques like extrusion, inkjet, digital light processing, and stereo-lithography have been explored with their setup and methodology to yield functional hydrogels. The perspectives, limitations, and the possible prospects of AM hydrogels have been critically examined in this study.

Fused Deposition Modelling 3D printing and solubility improvement of BCS II and IV active ingredients - A narrative review

In the field of pharmaceutical research and development, Fused Deposition Modelling (FDM) 3D printing (3DP) has aroused growing interest within the last ten years. The use of thermoplastic polymers, combined with the melting process of the raw materials, offers the possibility of manufacturing amorphous solid dispersions (ASDs). In the pharmaceutical industry, the formulation of an ASD is a widely used strategy to improve the solubility of poorly soluble drugs (classified by the Biopharmaceutical Classification System (BCS) as class II and IV). In this review, an analysis of studies that have developed a FDM printed form containing a BCS class II or IV active substance was performed. The focus has been placed on the evaluation of the solid state of the active molecules (crystalline or amorphous) and on the study of their dissolution profile. Thus, the aim of this work is to highlight the interest of FDM 3DP to induce the amorphisation phenomenon of Class II and IV active substances by forming an ASD, and as result improving their solubility.

Veering to a Continuous Platform of Fused Deposition Modeling Based 3D Printing for Pharmaceutical Dosage Forms: Understanding the Effect of Layer Orientation on Formulation Performance

Three-dimensional (3D) printing of pharmaceuticals has been centered around the idea of personalized patient-based 'on-demand' medication. Fused deposition modeling (FDM)-based 3D printing processes provide the capability to create complex geometrical dosage forms. However, the current FDM-based processes are associated with printing lag time and manual interventions. The current study tried to resolve this issue by utilizing the dynamic z-axis to continuously print drug-loaded printlets. Fenofibrate (FNB) was formulated with hydroxypropyl methylcellulose (HPMC AS LG) into an amorphous solid dispersion using the hot-melt extrusion (HME) process. Thermal and solid-state analyses were used to confirm the amorphous state of the drug in both polymeric filaments and printlets. Printlets with a 25, 50, and 75% infill density were printed using the two printing systems, i.e., continuous, and conventional batch FDM printing methods. Differences between the two methods were observed in the breaking force required to break the printlets, and these differences reduced as the infill density went up. The effect on in vitro release was significant at lower infill densities but reduced at higher infill densities. The results obtained from this study can be used to understand the formulation and process control strategies when switching from conventional FDM to the continuous printing of 3D-printed dosage forms.

3D printing of pharmaceuticals for disease treatment

Three-dimensional (3D) printing or Additive manufacturing has paved the way for developing and manufacturing pharmaceuticals in a personalized manner for patients with high volume and rare diseases. The traditional pharmaceutical manufacturing process involves the utilization of various excipients to facilitate the stages of blending, mixing, pressing, releasing, and packaging. In some cases, these excipients cause serious side effects to the patients. The 3D printing of pharmaceutical manufacturing avoids the need for excessive excipients. The two major components of a 3D printed tablet or dosage form are polymer matrix and drug component alone. Hence the usage of the 3D printed dosage forms for disease treatment will avoid unwanted side effects and provide higher therapeutic efficacy. With respect to the benefits of the 3D printed pharmaceuticals, the present review was constructed by discussing the role of 3D printing in producing formulations of various dosage forms such as fast and slow releasing, buccal delivery, and localized delivery. The dosage forms are polymeric tablets, nanoparticles, scaffolds, and films employed for treating different diseases.

Accelerating 3D printing of pharmaceutical products using machine learning

Three-dimensional printing (3DP) has seen growing interest within the healthcare industry for its ability to fabricate personalized medicines and medical devices. However, it may be burdened by the lengthy empirical process of formulation development. Active research in pharmaceutical 3DP has led to a wealth of data that machine learning could utilize to provide predictions of formulation outcomes. A balanced dataset is critical for optimal predictive performance of machine learning (ML) models, but data available from published literature often only include positive results. In this study, in-house and literature-mined data on hot melt extrusion (HME) and fused deposition modeling (FDM) 3DP formulations were combined to give a more balanced dataset of 1594 formulations. The optimized ML models predicted the printability and filament mechanical characteristics with an accuracy of 84%, and predicted HME and FDM processing temperatures with a mean absolute error of 5.5 °C and 8.4 °C, respectively. The performance of these ML models was better than previous iterations with a smaller and a more imbalanced dataset, highlighting the importance of providing a structured and heterogeneous dataset for optimal ML performance. The optimized models were integrated in an updated web-application, M3DISEEN, that provides predictions on filament characteristics, printability, HME and FDM processing temperatures, and drug release profiles (https://m3diseen.com/predictionsFDM/). By simulating the workflow of preparing FDM-printed pharmaceutical products, the web-application expedites the otherwise empirical process of formulation development, facilitating higher pharmaceutical 3DP research throughput.