Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine
Abstract
Medicines remain ineffective for over 50% of patients due to conventional mass production methods with fixed drug dosages. Three-dimensional (3D) printing, specifically selective laser sintering (SLS), offers a potential solution to this challenge, allowing the manufacturing of small, personalized batches of medication. Despite its simplicity and suitability for upscaling to large-scale production, SLS was not designed for pharmaceutical manufacturing and necessitates a time-consuming, trial-and-error adaptation process. In response, this study introduces a deep learning model trained on a variety of features to identify the best feature set to represent drugs and polymeric materials for the prediction of the printability of drug-loaded formulations using SLS. The proposed model demonstrates success by achieving 90% accuracy in predicting printability. Furthermore, explainability analysis unveils materials that facilitate SLS printability, offering invaluable insights for scientists to optimize SLS formulations, which can be expanded to other disciplines. This represents the first study in the field to develop an interpretable, uncertainty-optimized deep learning model for predicting the printability of drug-loaded formulations. This paves the way for accelerating formulation development, propelling us into a future of personalized medicine with unprecedented manufacturing precision.
Introduction
Traditional medicines, which often adopt a one-size-fits-all approach, are effective in only 30–50 % of patients (Lancet, 2018). This has driven the pharmaceutical industry’s push towards personalized medicine, where medications are tailored to individual needs in terms of dosage and composition (Nørfeldt et al., 2019). However, the pursuit of personalized medicine brings challenges. A major challenge is that manufacturing personalized medicines using traditional methods is very expensive and inefficient (Seoane-Viaño et al., 2021). Two state-of-the-art technologies offer promising solutions to these problems: three-dimensional (3D) printing and Machine Learning (ML) (Trenfield et al., 2022).
3D printing is a term that describes additive manufacturing technologies that develop 3D objects from computer-aided designs (CAD), layer by layer (Andreadis et al., 2022, Krueger et al., 2024). This advanced technology enables the seamless tailoring of medicines and has been successfully used to develop a range of drug delivery systems (Awad et al., 2022, Funk et al., 2024). Selective laser sintering (SLS) is a 3D printing powder bed fusion technology that has attracted attention for pharmaceutical applications owing to its suitability for large-scale production and simplicity (Hettesheimer et al., 2018, Seoane-Viaño et al., 2024). Utilizing primarily carbon dioxide lasers, this method fuses powder particles. The key advantages of SLS include the ability to produce intricate 3D objects without the need for support structures and the use of powder feedstock material without requiring solvents (Charoo et al., 2020). This technology also allows for the recycling of feedstock material and is adaptable to large-scale production. However, SLS printing was not initially designed to produce medicines. Therefore, developing drug formulations for 3D printing, known as pharma-inks, is an iterative process that is difficult to streamline since it relies on user expertise to ensure successful printing outcomes (Carou-Senra et al., 2024). This presents a significant barrier to its implementation in the clinic (Awad et al., 2021). Predicting printability before printing medicines could save costs, and resources and eliminate the need for an expert in the clinic.
ML leverages data to learn patterns from data, instead of explicit programming, it has proven to be effective in making predictions in pharmaceutics (Gavins et al., 2022, Suryavanshi et al., 2023). In recent years, there has been a surge of interest in the potential ML applications within the field of 3D-printed pharmaceuticals, with studies exploring different 3D printing technologies. ML has been employed to optimize various aspects of 3D printing (Goh et al., 2021), including process parameter optimization (Gan et al., 2019), quality control (Scime and Beuth, 2019), and the CAD of 3D printed products (Bin Maidin et al., 2012). Among these, fused deposition modelling (FDM) has commonly emerged, with several successful attempts at predicting printed medicines’ printability and mechanical properties (Elbadawi et al., 2020, Ong et al., 2022). ML has also shown promise in predicting outcomes for pharmaceuticals printed using inkjet technology (Carou-Senra et al., 2023). Previous work demonstrated the feasibility of using a decision tree to predict the effect of energy density and particle size distribution on the SLS printability of irbesartan tablets (Madžarević et al., 2021) and the use of multi-modal data to predict the printability of SLS formulations (Abdalla et al., 2023). While there has been research exploring the use of ML in other fields using SLS printing and neural networks (NN) for other 3D printing technologies (Mahmood et al., 2021), there has been limited research on the use of Deep Learning (DL), a subset of ML which mimics human neural circuitry, for SLS printing (Azizi, 2023). Furthermore, none of the existing studies have explored explaining or addressing the trade-off between accuracy and prediction confidence, a frequent problem within the application of ML in healthcare (An et al., 2023) and pharmaceutics (Bannigan et al., 2023). Moreover, the vast array of factors influencing the printability of drug formulations — including molecular structure, mechanical properties, particle size, melting point, and glass transition temperature, among others − has resulted in an inconsistency in the features employed to characterize medicines for ML. Evaluating these features and developing calibrated models is crucial for enabling accurate and confident predictions of the 3D printability of medicines.
To this end, this study aimed to develop an interpretable, uncertainty calibrated DL model to predict the printability of SLS formulations. Therefore, a Deep Ensemble was employed, which uses multiple NNs in parallel to make predictions, based on the state-of-the-art method developed by Lakshminarayanan et al. (Lakshminarayanan et al., 2017) for uncertainty quantification (UQ). The Deep Ensemble was supplemented with explainability analysis and utilized to predict the printability of SLS formulations. Multiple features underwent experimentation, revealing that the Morgan fingerprint (MFP) features offered the best approach for training the ensemble NN and yielded the best trade-off between confidence and accuracy, achieving 90 % accuracy and high confidence. Further explainability analysis revealed materials that either contribute positively or negatively to SLS printability, offering insights scientists can use to optimize SLS formulations.
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Excipients mentioned in the study: Kollicoat® IR, Eudragit® RSPO, mannitol, and Eudragit® L100-55
Youssef Abdalla, Martin Ferianc, Atheer Awad, Jeesu Kim, Moe Elbadawi, Abdul W. Basit, Mine Orlu, Miguel Rodrigues, Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine, International Journal of Pharmaceutics, Volume 661, 2024, 124440, ISSN 0378-5173, https://doi.org/10.1016/j.ijpharm.2024.124440.
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