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) 3-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. 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, which includes producing filaments by hot melt extrusion (HME), 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/)

Machine learning using Multi-Modal Data Predicts the Production of Selective Laser Sintered 3D Printed Drug Products

Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for its high precision, and compatibility with a wide range of pharmaceutical materials, including low-solubility compounds. However, the full potential of SLS for medicines is yet to be realized, requiring expertise and considerable time-consuming and resource-intensive trial-and-error research. Machine learning (ML), a subset of artificial intelligence, is an in silico tool that is accomplishing remarkable breakthroughs in several sectors for its ability to make highly accurate predictions. Therefore, the present study harnessed ML to predict the printability of SLS formulations. Using a dataset of 170 formulations from 78 materials, ML models were developed from inputs that included the formulation composition and characterization data retrieved from Fourier-transformed infrared spectroscopy (FT-IR), X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). Multiple ML models were explored, including supervised and unsupervised approaches. The results revealed that ML can achieve high accuracies, by using the formulation composition leading to a maximum F1 score of 81.9%. Using the FT-IR, XRPD and DSC data as inputs resulted in an F1 score of 84.2%, 81.3%, and 80.1%, respectively. A subsequent ML pipeline was built to combine the predictions from FT-IR, XRPD and DSC into one consensus model, where the F1 score was found to further increase to 88.9%. Therefore, it was determined for the first time that ML predictions of 3D printability benefit from multi-modal data, combining numeric, spectral, thermogram and diffraction data. The study lays the groundwork for leveraging existing characterization data for developing high-performing computational models to accelerate developments

A review of emerging technologies enabling improved solid oral dosage form manufacturing and processing

Tablets are the most widely utilized solid oral dosage forms because of the advantages of self-administration, stability, ease of handling, transportation, and good patient compliance. Over time, extensive advances have been made in tableting technology. This review aims to provide an insight about the advances in tablet excipients, manufacturing, analytical techniques and deployment of Quality by Design (QbD). Various excipients offering novel functionalities such as solubility enhancement, super-disintegration, taste masking and drug release modifications have been developed. Furthermore, co-processed multifunctional ready-to-use excipients, particularly for tablet dosage forms, have benefitted manufacturing with shorter processing times. Advances in granulation methods, including moist, thermal adhesion, steam, melt, freeze, foam, reverse wet and pneumatic dry granulation, have been proposed to improve product and process performance. Furthermore, methods for particle engineering including hot melt extrusion, extrusion-spheronization, injection molding, spray drying / congealing, co-precipitation and nanotechnology-based approaches have been employed to produce robust tablet formulations. A wide range of tableting technologies including rapidly disintegrating, matrix, tablet-in-tablet, tablet-in-capsule, multilayer tablets and multiparticulate systems have been developed to achieve customized formulation performance. In addition to conventional invasive characterization methods, novel techniques based on laser, tomography, fluorescence, spectroscopy and acoustic approaches have been developed to assess the physical-mechanical attributes of tablet formulations in a non- or minimally invasive manner. Conventional UV-Visible spectroscopy method has been improved (e.g., fiber-optic probes and UV imaging-based approaches) to efficiently record the dissolution profile of tablet formulations. Numerous modifications in tableting presses have also been made to aid machine product changeover, cleaning, and enhance efficiency and productivity. Various process analytical technologies have been employed to track the formulation properties and critical process parameters. These advances will contribute to a strategy for robust tablet dosage forms with excellent performance attributes

Vat photopolymerisation 3D printing of controlled drug delivery devices

Pharmaceutical three-dimensional (3D) printing has led to a paradigm shift in the way medicines are designed and manufactured, moving away from the traditional ‘one-size-fits-all’ approaches and advancing towards personalised medicines. Among different 3D printing techniques, vat photopolymerisation 3D printing affords superior printing resolution, which in turn enables fabrication of micro-structures and smooth finishes. This thesis aims to investigate different vat photopolymerisation 3D printing techniques for the fabrication of personalised drug delivery devices for different routes of administration. Stereolithography (SLA) and digital light processing (DLP) 3D printing was used to manufacture devices with flexible materials for localised delivery of a single drug in the bladder and at the anterior segment of the eye. In vitro release studies demonstrated drug releases from these devices were sustained over weeks. Subsequently, to investigate the feasibility of loading more than one drug in a single dosage form, clinically relevant multi-layer antihypertensive polypills were fabricated using SLA 3D printing. A drug-photopolymer interaction was observed from these polypills, and Michael’s addition reaction was confirmed to have occurred. Despite these studies demonstrating the viable use of vat photopolymerization 3D printing for fabricating drug delivery devices, the bulky nature of current printers could be a barrier to clinical integration. As such, a smartphone-enabled DLP 3D printing system was developed to fabricate personalised oral dosage forms and patient-specific drug delivery devices. The portability of this printer could secure exciting opportunities for manufacturing personalised medicines at point-of-care settings. Overall, this thesis showed the potential of vat photopolymerisation 3D printing in preparing different patient-centric drug delivery devices with tuneable and sustained release profiles as well as advancing traditional treatments towards digital healthcare

Visualizing disintegration of 3D printed tablets in humans using MRI and comparison with in vitro data

Three-dimensional (3D) printing is revolutionising the way that medicines are manufactured today, paving the way towards more personalised medicine. However, there is limited in vivo data on 3D printed dosage forms, and no studies to date have been performed investigating the intestinal behaviour of these drug products in humans, hindering the complete translation of 3D printed medications into clinical practice. Furthermore, it is unknown whether conventional in vitro release tests can accurately predict the in vivo performance of 3D printed formulations in humans. In this study, selective laser sintering (SLS) 3D printing technology has been used to produce two placebo torus-shaped tablets (printlets) using different laser scanning speeds. The printlets were administered to 6 human volunteers, and in vivo disintegration times were assessed using magnetic resonance imaging (MRI). In vitro disintegration tests were performed using a standard USP disintegration apparatus, as well as an alternative method based on the use of reduced media volume and minimal agitation. Printlets fabricated at a laser scanning speed of 90 mm/s exhibited an average in vitro disintegration time of 7.2 ± 1 min (measured using the USP apparatus) and 25.5 ± 4.1 min (measured using the alternative method). In contrast, printlets manufactured at a higher laser scanning speed of 130 mm/s had an in vitro disintegration time of 2.8 ± 0.8 min (USP apparatus) and 18.8 ± 1.9 min (alternative method). When tested in humans, printlets fabricated at a laser scanning speed of 90 mm/s showed an average disintegration time of 17.3 ± 7.2 min, while those manufactured at a laser scanning speed of 130 mm/s exhibited a shorter disintegration time of 12.7 ± 6.8 min. Although the disintegration times obtained using the alternative method more closely resembled those obtained in vivo, no clear correlation was observed between the in vitro and in vivo disintegration times, highlighting the need to develop better in vitro methodology for 3D printed drug products

Fabrication, Modeling and Characterization of Multi-Crosslinked Methacrylate Copolymeric Nanoparticles for Oral Drug Delivery

Nanotechnology remains the field to explore in the quest to enhance therapeutic efficacies of existing drugs. Fabrication of a methacrylate copolymer-lipid nanoparticulate (MCN) system was explored in this study for oral drug delivery of levodopa. The nanoparticles were fabricated employing multicrosslinking technology and characterized for particle size, zeta potential, morphology, structural modification, drug entrapment efficiency and in vitro drug release. Chemometric Computational (CC) modeling was conducted to deduce the mechanism of nanoparticle synthesis as well as to corroborate the experimental findings. The CC modeling deduced that the nanoparticles synthesis may have followed the mixed triangular formations or the mixed patterns. They were found to be hollow nanocapsules with a size ranging from 152 nm (methacrylate copolymer) to 321 nm (methacrylate copolymer blend) and a zeta potential range of 15.8–43.3 mV. The nanoparticles were directly compressible and it was found that the desired rate of drug release could be achieved by formulating the nanoparticles as a nanosuspension, and then directly compressing them into tablet matrices or incorporating the nanoparticles directly into polymer tablet matrices. However, sustained release of MCNs was achieved only when it was incorporated into a polymer matrix. The experimental results were well corroborated by the CC modeling. The developed technology may be potentially useful for the fabrication of multi-crosslinked polymer blend nanoparticles for oral drug delivery

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Seeing is believing: using optical diagnostics to investigate MDI sprays and DPI fluidization

Inhaler device modifications directly influence aerosol generation mechanisms, so the development of inhalation products could be advanced effectively if the relevant processes can be studied in situ. However, the ability to visualize, in real time, atomization in a metered dose inhaler (MDI) or fluidization in a dry powder inhaler (DPI) remains challenging. This article reviews high-speed imaging, fluorescence imaging, particle image velocimetry (PIV) and phase-doppler anemometry (PDA) and demonstrates how these techniques can be used to characterize aerosol clouds whilst they are being generated and reveal the nature of aerosol production processes as they unfold inside inhaler devices. The different optical diagnostics generate complementary data including aerosol droplet size, velocity and overall aerosol/spray attributes. Understanding the details of transient events during metering of a drug dose into the inhaled airflow at the appropriate temporal and spatial resolution creates opportunities for targeted interventions to solve problems e.g. excessive device deposition. Moreover, the images generated by optical techniques represent a readily accessible form of information which supports multi-disciplinary collaboration, so, when using optical diagnostics, we can legitimately claim that “Seeing is Believing”