Non-destructive dose verification of two drugs within 3D printed polyprintlets

Rise of the (3D printing) machines in healthcare

Three-dimensional printing (3D printing) or "additive manufacturing" first came to prominence in the field of engineering, in particular in the transport sector where the value of its fast and accurate prototyping and manufacture of spare parts was quickly recognised. However, over the last ten years, this revolutionary technology has disrupted established manufacture in an increasingly diverse range of technical areas. Perhaps the most unexpected of these is pharmaceuticals - not merely the manufacture of products such as surgically inserted implants, but also of dosage formulations themselves - now available in all manner of printed delivery forms and vehicles and showing promising control of release properties though 3D printing process choices. This review will provide an overview of how 3D printing technology has developed and expanded across technological boundaries during the past decade, with a closer look at the current opportunities and barriers to its widespread adoption, particularly in the medical and pharmaceutical sectors. Special attention has been paid to patents as a boost and barrier to the expansion of 3D printing in the medical and pharmaceutical sector, with a focus on the patent literature.

Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine

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.

3D printed dispersible efavirenz tablets: A strategy for nasogastric administration in children

Enteral feeding tubes (EFTs) can be placed in children diagnosed with HIV which need nutritional support due to malnutrition. EFTs are the main route for medication administration in these patients, bringing up concerns about off label use of medicines, dose inaccuracy and tube clogging. Here we report for the first time the use of selective laser sintering (SLS) 3D printing to develop efavirenz (EFZ) dispersible printlets for patients with HIV that require EFT administration. Water soluble polymers Parteck® MXP and Kollidon® VA64 were used to obtain both 500 mg (P500 and K500) and 1000 mg printlets (P1000 and K1000) containing 200 mg of EFZ each. The use of SLS 3D printing obtained porous dosage forms with high drug content (20 % and 40 % w/w) and drug amorphization using both polymers. P500, K500 and K1000 printlets reached disintegration in under 230 s in 20 mL of water (25 ± 1 °C), whilst P1000 only partially disintegrated, possibly due to saturation of the polymer in the medium. As a result, the development of dispersible EFZ printlets using hydrophilic polymers can be explored as a potential strategy for drug delivery through EFTs in paediatrics with HIV, paving the way towards the exploration of more rapidly disintegrating polymers and excipients for SLS 3D printing.

Tailor-Made Doses of Pharmaceuticals by Tunable Modular Design: A Case Study on Tapering Antidepressant Medication

An abrupt cessation of antidepressant medication can be challenging due to the appearance of withdrawal symptoms. A slow hyperbolic tapering of an antidepressant, such as citalopram hydrobromide (CHB), can mitigate the withdrawal syndrome. However, there are no viable dosage forms on the market to implement the tapering scheme. A solution using a tunable modular design (TMD) approach to produce flexible and accurate doses of CHB is proposed. This design consists of two parts: 1) a module with a fixed amount of preloaded CHB in a freeze-dried polymer matrix, and 2) fine-tuning the CHB dose by inkjet printing. A noncontact food-grade printer, used for the first time for printing pharmaceuticals, is modified to allow for accurate printing of the highly concentrated CHB ink on the porous CHB-free or CHB-preloaded modules. The produced modules with submilligram precision are bench-marked with commercially available CHB tablets that are manually divided. The TMD covers the entire range of doses needed for the tapering (0.5-23.8 mg). The greatest variance is 13% and 88% when comparing the TMD and self-tapering, respectively. Self-tapering is proven inaccurate and showcases the need for the TMD to make available accurate and personalized doses to wean off treatment with CHB.

Targeting SRD5A1 and MMP-2/NLRP3/TGF-β1 axis alleviates the amlodipine-induced gingival hyperplasia in rats: Emerging role of saw palmetto and folic acid

Saw palmetto (SAW), the herbal drug used to treat prostatic hyperplasia, exerts its antiproliferative effects by blocking steroid 5 alpha-reductase (SRD5A1) activity, that has also been involved in gingival hyperplasia (GH) pathogenesis. Concurrently, folic acid (FA) could reduce GH prevalence via its antioxidant and anti-inflammatory effects. Thus, this study tended to assess the potential therapeutic efficacy of SAW, alone and along with FA, against amlodipine-induced gingival inflammation and overgrowth in rats. Rats were grouped into (CONT, AIGH, SAW, SAW-treated, FA-treated, and SAW + FA-treated). SAW and FA were administered once daily for 4 weeks. Gingival SRD5A1, CTGF, GSK-3β, and NLRP3 expressions, as well as T, DHT, MDA, TAC, ET-1, and MMP2 levels were determined. In addition, histopathological and immunohistochemical analyses of TNF-α, IL-6, TGF-β1, and α-SMA were documented. Results declared that SAW and FA administration markedly ameliorated amlodipine-associated GH and may be presenting a novel therapeutic avenue in the future.

Unmet technological demands in orodispersible films for age-appropriate paediatric drug delivery

Age-appropriateness of a formulation is the ability to deliver variable but accurate doses to the paediatric population in a safe and acceptable manner to improve medical adherence and reduce medication errors. Paediatric drug delivery is a challenging area of formulation research due to the existing gap in knowledge. This includes the unknown safety of excipients in the paediatric population, the need for an age-appropriate formulation, the lack of an effective taste-masking method and the lack of paediatric pharmacokinetic data and patient acceptability. It is equally important to establish methods for predicting the biopharmaceutical performance of a paediatric formulation as a function of age. Overcoming the challenges of existing technologies and providing custom-made solutions for the development of age-appropriate formulation is, therefore, a daunting task. Orodispersible films (ODF) are promising as age-appropriate formulations, an unmet need in paediatric drug delivery. New technological improvements in taste masking, improving solubility and rate of dissolution of insoluble drugs, the flexibility of dosing and extemporaneous preparation of these films in a hospital good manufacturing practises (GMP) setup using 3D printing can increase its acceptance among clinicians, patients and caregivers. The current review discusses the problems and possibilities in ODF technology to address the outstanding issues of age-appropriateness, which is the hallmark of patient acceptance and medical adherence in paediatrics.

Towards Precision Ophthalmology: The Role of 3D Printing and Bioprinting in Oculoplastic Surgery, Retinal, Corneal, and Glaucoma Treatment

In the forefront of ophthalmic innovation, biomimetic 3D printing and bioprinting technologies are redefining patient-specific therapeutic strategies. This critical review systematically evaluates their application spectrum, spanning oculoplastic reconstruction, retinal tissue engineering, corneal transplantation, and targeted glaucoma treatments. It highlights the intricacies of these technologies, including the fundamental principles, advanced materials, and bioinks that facilitate the replication of ocular tissue architecture. The synthesis of primary studies from 2014 to 2023 provides a rigorous analysis of their evolution and current clinical implications. This review is unique in its holistic approach, juxtaposing the scientific underpinnings with clinical realities, thereby delineating the advantages over conventional modalities, and identifying translational barriers. It elucidates persistent knowledge deficits and outlines future research directions. It ultimately accentuates the imperative for multidisciplinary collaboration to enhance the clinical integration of these biotechnologies, culminating in a paradigm shift towards individualized ophthalmic care.

Simultaneous fabrication of multiple tablets within seconds using tomographic volumetric 3D printing

3D printing is driving a shift in patient care away from a generalised model and towards personalised treatments. To complement fast-paced clinical environments, 3D printing technologies must provide sufficiently high throughputs for them to be feasibly implemented. Volumetric printing is an emerging 3D printing technology that affords such speeds, being capable of producing entire objects within seconds. In this study, for the first time, rotatory volumetric printing was used to simultaneously produce two torus- or cylinder-shaped paracetamol-loaded Printlets (3D printed tablets). Six resin formulations comprising paracetamol as the model drug, poly(ethylene glycol) diacrylate (PEGDA) 575 or 700 as photoreactive monomers, water and PEG 300 as non-reactive diluents, and lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP) as the photoinitiator were investigated. Two printlets were successfully printed in 12 to 32 s and exhibited sustained drug release profiles. These results support the use of rotary volumetric printing for efficient and effective manufacturing of various personalised medicines at the same time. With the speed and precision it affords, rotatory volumetric printing has the potential to become one of the most promising alternative manufacturing technologies in the pharmaceutical industry.

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

Quality attributes for printable emulsion gels and 3D-printed tablets: Towards production of personalized dosage forms

3D-printing technology offers a flexible manufacturing platform with the potential to address the need of personalized dosage forms. However, quality aspects of such small-scale, on-demand production of pharmaceutical products intended for personalization is still limited. The aim of this study was therefore to study critical quality control attributes of lipid tablets produced by semi-solid extrusion (SSE) 3D printing from emulsion gels incorporating a poorly water-soluble drug. Quality attributes for both the printable emulsion gel and the printed dosage forms were assessed. The emulsion gel was shown to be printable with accurate dosing for at least one month of storage at 4 °C. Tablets were 3D printed in different sizes and a correlation, R2 value of 0.99, was found between the weight and the drug content. The 3D-printed tablets complied with the mass and drug content uniformity requirements described in the European Pharmacopoeia.. Solid-state characterization of the tablets during short-term storage revealed no signs of crystallinity of the drug. Lastly, the lipid digestion and drug release were unchanged after short-term storage of the tablets. This study demonstrates the potential of SSE 3D printing for personalized dosing of a lipid-based formulation strategy and discusses central quality attributes for the printable formulation and the 3D-printed dosage form.

Investigations of hybrid infill pattern in additive manufactured tablets: A novel approach towards tunable drug release

The significance of 3D printing has risen exponentially in biomedical and pharmaceutical applications. Its potential in the field of fabricating drug delivery systems, by virtue of processing biocompatible polymers, has been very lucrative. This work aims to tap the interstitial drug delivery kinetics that are often inaccessible through machine-specific infill patterns in additive manufactured tablets fabricated using PVA biopolymer as an excipient. In this regard, a myo-inositol containing tablet has been printed using Fused Deposition Modeling preceded by Hot Melt Extrusion drug loading route. Two machine-specific infill patterns were taken, namely straight and grid. Later, these two distinct patterns were juxtaposed to obtain novel hybrid infill patterns in the tablets. Then, these tablets and their filament were subjected to various thermal, mechanical, imaging and pharmaceutical characterization tests to assess the feasibility of the research attempt. Finally, dissolution tests were conducted to evaluate their dissolution behavior over a time period. The characterization tests proved the scientific viability of this attempt along with amorphous existence of drug in the polymeric filament. The dissolution results showed favorable drug release by achieving interstitial dissolution timings with surface area/volume (SA/V) ratio being found to be the principal factor.

Enhancing antioxidant delivery through 3D printing: a pathway to advanced therapeutic strategies

The rapid advancement of 3D printing has transformed industries, including medicine and pharmaceuticals. Integrating antioxidants into 3D-printed structures offers promising therapeutic strategies for enhanced antioxidant delivery. This review explores the synergistic relationship between 3D printing and antioxidants, focusing on the design and fabrication of antioxidant-loaded constructs. Incorporating antioxidants into 3D-printed matrices enables controlled release and localized delivery, improving efficacy while minimizing side effects. Customization of physical and chemical properties allows tailoring of antioxidant release kinetics, distribution, and degradation profiles. Encapsulation techniques such as direct mixing, coating, and encapsulation are discussed. Material selection, printing parameters, and post-processing methods significantly influence antioxidant release kinetics and stability. Applications include wound healing, tissue regeneration, drug delivery, and personalized medicine. This comprehensive review aims to provide insights into 3D printing-assisted antioxidant delivery systems, facilitating advancements in medicine and improved patient outcomes for oxidative stress-related disorders.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.

Portable NIR spectroscopy: the route to green analytical chemistry

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

Towards Point-of-Care Manufacturing and Analysis of Immediate-Release 3D Printed Hydrocortisone Tablets for The Treatment of Congenital Adrenal Hyperplasia

Hydrocortisone (HC) is the preferred drug in children with congenital adrenal hyperplasia due to its lower potency as well as fewer reports of side effects. Fused deposition modelling (FDM) 3D printing holds the potential to produce low-cost personalised doses for children at the point of care. However, the compatibility of the thermal process to produce immediate-release bespoke tablets for this thermally labile active is yet to be established. This work aims to develop immediate-release HC tablets using FDM 3D printing and assess drug contents as a critical quality attribute (CQA) using a compact, low-cost near-infrared (NIR) spectroscopy as a process analytical technology (PAT). The FDM 3D printing temperature (140 °C) and drug concentration in the filament (10%-15% w/w) were critical parameters to meet the compendial criteria for drug contents and impurities. Using a compact low-cost NIR spectral device over a wavelength of 900-1700 nm, the drug contents of 3D printed tablets were assessed. Partial least squares (PLS) regression was used to develop individual calibration models to detect HC content in 3D printed tablets of lower drug contents, small caplet design, and relatively complex formula. The models demonstrated the ability to predict HC concentrations over a wide concentration range (0-15% w/w), which was confirmed by HPLC as a reference method. Ultimately, the capability of the NIR model had preceding dose verification performance on HC tablets, with linearity (R² = 0.981) and accuracy (RMSECV = 0.46%). In the future, the integration of 3DP technology with non-destructive PAT techniques will accelerate the adoption of on-demand, individualised dosing in a clinical setting.

The Use of Near-infrared as Process Analytical Technology (PAT) during 3D Printing Tablets at the Point-of-Care

Fused deposition modelling (FDM) is one of the most researched 3D printing technologies that holds great potential for low-cost manufacturing of personalised medicine. To achieve real-time release, timely quality control is a major challenge for applying 3D printing technologies as a point-of-care (PoC) manufacturing approach. This work proposes the use of a low-cost and compact near-infrared (NIR) spectroscopy modality as a process analytical technology (PAT) to monitor a critical quality attribute (drug content) during and after FDM 3D printing process. 3D printed caffeine tablets were used to manifest the feasibility of the NIR model as a quantitative analytical procedure and dose verification method. Caffeine tablets (0-40% w/w) were fabricated using polyvinyl alcohol and FDM 3D printing. The predictive performance of the NIR model was demonstrated in linearity (correlation coefficient, R²) and accuracy (root mean square error of prediction, RMSEP). The actual drug content values were determined using the reference high-performance liquid chromatography (HPLC) method. The model of full-completion caffeine tablets demonstrated linearity (R² = 0.985) and accuracy (RMSEP =1.4%), indicated to be an alternative dose quantitation method for 3D printed products. The ability of the models to assess caffeine contents during the 3D printing process could not be accurately achieved using the model built with complete tablets. Instead, by building a predictive model for each completion stage of 20%, 40%, 60% and 80%, the model of different completion caffeine tablets displayed linearity (R² of 0.991, 0.99, 0.987, and 0.983) and accuracy (RMSEP of 2.22%, 1.65%, 1.41%, 0.83%), respectively. Overall, this study demonstrated the feasibility of a low NIR model as a non-destructive, low-cost, compact, and rapid analysis dose verification method enabling the real-time release to facilitate 3D printing medicine production in the clinic.

Advancing non-destructive analysis of 3D printed medicines

Pharmaceutical 3D printing (3DP) has attracted significant interest over the past decade for its ability to produce personalised medicines on demand. However, current quality control (QC) requirements for traditional large-scale pharmaceutical manufacturing are irreconcilable with the production offered by 3DP. The US Food and Drug Administration (FDA) and the UK Medicines and Healthcare Products Regulatory Agency (MHRA) have recently published documents supporting the implementation of 3DP for point-of-care (PoC) manufacturing along with regulatory hurdles. The importance of process analytical technology (PAT) and non-destructive analytical tools in translating pharmaceutical 3DP has experienced a surge in recognition. This review seeks to highlight the most recent research on non-destructive pharmaceutical 3DP analysis, while also proposing plausible QC systems that complement the pharmaceutical 3DP workflow. In closing, outstanding challenges in integrating these analytical tools into pharmaceutical 3DP workflows are discussed.

In situ thermal image analysis of selective laser sintering for oral dosage form manufacturing

Additive Manufacturing (AM) is a fast-growing approach to produce personalized oral dosage forms. Even though some AM technologies are promising as alternative to conventional compounding with resulting dosage manipulation, they still suffer from a lack of quality control. Due to the high regulatory demands and standards applied to dosage forms in the case of dose accuracy and tablet properties such as friability, effective quality control is a key feature in promoting AM as a valid technology for patient-tailored medications. One of the AM techniques used is selective laser sintering, which allows for capturing the surface state layer-by-layer during the printing process. It provides the opportunity to apply non-destructive quality control based on image analysis extracting essential data at each layer of the sintering process. This work is devoted to establishing the value of data gathered via thermal image analysis for the subsequent quality control.

3D Printed Pharmaceutical Systems for Personalized Treatment in Metabolic Syndrome

The current healthcare system is widely based on the concept of “one size fit for all”, which emphasizes treating a disease by prescribing the same drug to all patients with equivalent doses and dosing frequency. This medical treatment scenario has shown varied responses with either no or weak pharmacological effects and exaggerated adverse reactions preceded by more patient complications. The hitches to the concept of “one size fits all” have devoted the attention of many researchers to unlocking the concept of personalized medicine (PM). PM delivers customized therapy with the highest safety margin for an individual patient’s needs. PM has the potential to revolutionize the current healthcare system and pave the way to alter drug choices and doses according to a patient’s clinical responses, providing physicians with the best treatment outcomes. The 3D printing techniques is a solid-form fabrication method whereby successive layers of materials based on computer-aided designs were deposited to form 3D structures. The 3D printed formulation achieves PM goals by delivering the desired dose according to patient needs and drug release profile to achieve a patient’s personal therapeutic and nutritional needs. This pre-designed drug release profile attains optimum absorption and distribution, exhibiting maximum efficacy and safety profiles. This review aims to focus on the role of the 3D printing technique as a promising tool to design PM in metabolic syndrome (MS).

3D printing for personalised medicines: implications for policy and practice

The current healthcare dynamic has shifted from one-size-fits-all to patient-centred care, with our increased understanding of pharmacokinetics and pharmacogenomics demanding a switch to more individualised therapies. As the pharmaceutical industry remains yet to succumb to the push of a technological paradigm shift, pharmacists lack the means to provide completely personalised medicine (PM) to their patients in a safe, affordable, and widely accessible manner. As additive manufacturing technology has already established its strength in producing pharmaceutical formulations, it is necessary to next consider methods by which this technology can create PM accessible from pharmacies. In this article, we reviewed the limitations of current pharmaceutical manufacturing methods for PMs, three-dimensional (3D) printing techniques that are most beneficial for PMs, implications of bringing this technology into pharmacy practice, and implications for policy surrounding 3D printing techniques in the manufacturing of PMs.

Selective laser sintering additive manufacturing of dosage forms: Effect of powder formulation and process parameters on the physical properties of printed tablets

Large batches of placebo and drug-loaded solid dosage forms were successfully fabricated using selective laser sintering (SLS) 3D printing in this study. The tablet batches were prepared using either copovidone (N-vinyl-2-pyrrolidone and vinyl acetate, PVP/VA) or polyvinyl alcohol (PVA) and activated carbon (AC) as radiation absorbent, which was added to improve the sintering of the polymer. The physical properties of the dosage forms were evaluated at different pigment concentrations (i.e., 0.5 and 1.0 wt%) and at different laser energy inputs. The mass, hardness, and friability of the tablets were found to be tunable and structures with greater mass and mechanical strength were obtained with increasing carbon concentration and energy input. Amorphization of the active pharmaceutical ingredient in the drug-loaded batches, containing 10 wt% naproxen and 1 wt% AC, was achieved in-situ during printing. Thus, amorphous solid dispersions were prepared in a single-step process and produced tablets with mass losses below 1 wt%. These findings show how the properties of dosage forms can be tuned by careful selection of the process parameters and the powder formulation. SLS 3D printing can therefore be considered to be an interesting and promising technique for the fabrication of personalized medicines.

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 formulation development.

Investigating the Use of Magnetic Nanoparticles As Alternative Sintering Agents in Selective Laser Sintering (SLS) 3D Printing of Oral Tablets

Selective laser sintering (SLS) is a single-step, three-dimensional printing (3DP) process that is gaining momentum in the manufacturing of pharmaceutical dosage forms. It also offers opportunities for manufacturing various pharmaceutical dosage forms with a wide array of drug delivery systems. This research aimed to introduce carbonyl iron as a multifunctional magnetic and heat conductive ingredient for the fabrication of oral tablets containing isoniazid, a model antitubercular drug, via SLS 3DP process. Furthermore, the effects of magnetic iron particles on the drug release from the SLS printed tablets under a specially designed magnetic field was studied. Optimization of tablet quality was performed by adjusting SLS printing parameters. The independent factors studied were laser scanning speed, hatching space, and surface/chamber temperature. The responses measured were printed tablets' weight, hardness, disintegration time, and dissolution performance. It has been observed that, for the drug formulation with carbonyl iron, due to its inherent thermal conductivity, sintering tablets required relatively lower laser energy input to form the tablets of the same quality attributes as the other batches that contained no magnetic particles. Also, printed tablets with carbonyl iron released 25% more drugs under a magnetic field than those without it. It can be claimed that magnetic nanoparticles appear as an alternative conductive material to facilitate the sintering process during SLS 3DP of dosage forms.

Machine Learning-Enabled NIR Spectroscopy. Part 2: Workflow for Selecting a Subset of Samples from Publicly Accessible Data

An increasingly large dataset of pharmaceutics disciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al.. in AAPS PharmSciTech 23, 2022) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robust data on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset ( http://www.models.life.ku.dk/Tablets ) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. HIGHLIGHTS: • We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies • A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets • Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance • The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation • Model extension from lab-scale to pilot-scale successfully demonstrated.

Investigation on the use of fused deposition modeling for the production of IR dosage forms containing Timapiprant

The present work focused on evaluating the feasibility of fused deposition modeling (FDM) in the development of a dosage form containing Timapiprant (TMP), also known as CHF6532, which is a novel active molecule indicated in the potential treatment of eosinophilic asthma upon oral administration. The resulting product could be an alternative, with potential towards personalization, of immediate release (IR) tablets used in the clinical studies. Formulations based on different polymeric carriers were screened, leading to the identification of a polyvinyl alcohol-based one, which turned out acceptable for versatility in terms of active ingredient content, printability and dissolution performance (i.e. capability to meet the dissolution specification set, envisaging >80% of the drug dissolved within 30 min). Following an in-depth evaluation on the influence of TMP solid state and of the voids volume resulting from printing on dissolution, few prototypes with shapes especially devised for therapy customization were successfully printed and were compliant with the dissolution specification set.

Inkjet drug printing onto contact lenses: Deposition optimisation and non-invasive dose verification

Inkjet printing has the potential to advance the treatment of eye diseases by printing drugs on demand onto contact lenses for localised delivery and personalised dosing, while near-infrared (NIR) spectroscopy can further be used as a quality control method for quantifying the drug but has yet to be demonstrated with contact lenses. In this study, a glaucoma therapy drug, timolol maleate, was successfully printed onto contact lenses using a modified commercial inkjet printer. The drug-loaded ink prepared for the printer was designed to match the properties of commercial ink, whilst having maximal drug loading and avoiding ocular inflammation. This setup demonstrated personalised drug dosing by printing multiple passes. Light transmittance was found to be unaffected by drug loading on the contact lens. A novel dissolution model was built, and in vitro dissolution studies showed drug release over at least 3 h, significantly longer than eye drops. NIR was used as an external validation method to accurately quantify the drug dose. Overall, the combination of inkjet printing and NIR represent a novel method for point-of-care personalisation and quantification of drug-loaded contact lenses.

Releasing fast and slow: Non-destructive prediction of density and drug release from SLS 3D printed tablets using NIR spectroscopy

Selective laser sintering (SLS) 3D printing is a revolutionary 3D printing technology that has been found capable of creating drug products with varied release profiles by changing the laser scanning speed. Here, SLS 3D printed formulations (printlets) loaded with a narrow therapeutic index drug (theophylline) were produced using SLS 3D printing at varying laser scanning speeds (100-180 mm/s). The use of reflectance Fourier Transform - Near Infrared (FT-NIR) spectroscopy was evaluated as a non-destructive approach to predicting 3D printed tablet density and drug release at 2 h and 4 h. The printed drug products formulated with a higher laser speed exhibited an accelerated drug release and reduced density compared with the slower laser scanning speeds. Univariate calibration models were developed based on a baseline shift in the spectra in the third overtone region upon changing physical properties. For density prediction, the developed univariate model had high linearity (R² value = 0.9335) and accuracy (error < 0.029 mg/mm³). For drug release prediction at 2 h and 4 h, the developed univariate models demonstrated a linear correlation (R² values of 0.9383 and 0.9167, respectively) and accuracy (error < 4.4%). The predicted vs. actual dissolution profiles were found to be statistically similar (f₂ > 50) for all of the test printlets. Overall, this article demonstrates the feasibility of SLS 3D printing to produce drug products containing a narrow therapeutic index drug across a range of drug release profiles, as well as the potential for FT-NIR spectroscopy to predict the physical characteristics of SLS 3D printed drug products (drug release and density) as a non-destructive quality control method at the point-of-care.

To infinity and beyond: Strategies for fabricating medicines in outer space

Recent advancements in next generation spacecrafts have reignited public excitement over life beyond Earth. However, to safeguard the health and safety of humans in the hostile environment of space, innovation in pharmaceutical manufacturing and drug delivery deserves urgent attention. In this review/commentary, the current state of medicines provision in space is explored, accompanied by a forward look on the future of pharmaceutical manufacturing in outer space. The hazards associated with spaceflight, and their corresponding medical problems, are first briefly discussed. Subsequently, the infeasibility of present-day medicines provision systems for supporting deep space exploration is examined. The existing knowledge gaps on the altered clinical effects of medicines in space are evaluated, and suggestions are provided on how clinical trials in space might be conducted. An envisioned model of on-site production and delivery of medicines in space is proposed, referencing emerging technologies (e.g. Chemputing, synthetic biology, and 3D printing) being developed on Earth that may be adapted for extra-terrestrial use. This review concludes with a critical analysis on the regulatory considerations necessary to facilitate the adoption of these technologies and proposes a framework by which these may be enforced. In doing so, this commentary aims to instigate discussions on the pharmaceutical needs of deep space exploration, and strategies on how these may be met.

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Space is a hostile environment that threatens human health and drug stability.

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Data on the behaviour of medicines in space is critical but lacking.

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Novel drug manufacturing and delivery strategies are needed to safeguard crewmembers’ safety.

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Chemputing, synthetic biology, and 3D printing are examples of such emerging technologies.

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A regulatory framework for space medicines must be implemented to assure quality.

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts

NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data.

Integrating pressure sensor control into semi-solid extrusion 3D printing to optimize medicine manufacturing

Semi-solid extrusion (SSE) is a three-dimensional printing (3DP) process that involves the extrusion of a gel or paste-like material via a syringe-based printhead to create the desired object. In pharmaceuticals, SSE 3DP has already been used to manufacture formulations for human clinical studies. To further support its clinical adoption, the use of a pressure sensor may provide information on the printability of the feedstock material in situ and under the exact printing conditions for quality control purposes. This study aimed to integrate a pressure sensor in an SSE pharmaceutical 3D printer for both material characterization and as a process analytical technology (PAT) to monitor the printing process. In this study, three materials of different consistency were tested (soft vaseline, gel-like mass and paste-like mass) under 12 different conditions, by changing flow rate, temperature, or nozzle diameter. The use of a pressure sensor allowed, for the first time, the characterization of rheological properties of the inks, which exhibited temperature-dependent, plastic and viscoelastic behaviours. Controlling critical material attributes and 3D printing process parameters may allow a quality by design (QbD) approach to facilitate a high-fidelity 3D printing process critical for the future of personalized medicine.

Innovations in Chewable Formulations: The Novelty and Applications of 3D Printing in Drug Product Design

Since their introduction, chewable dosage forms have gained traction due to their ability to facilitate swallowing, especially in paediatric, geriatric and dysphagia patients. Their benefits stretch beyond human use to also include veterinary applications, improving administration and palatability in different animal species. Despite their advantages, current chewable formulations do not account for individualised dosing and palatability preferences. In light of this, three-dimensional (3D) printing, and in particular the semi-solid extrusion technology, has been suggested as a novel manufacturing method for producing customised chewable dosage forms. This advanced approach offers flexibility for selecting patient-specific doses, excipients, and organoleptic properties, which are critical for ensuring efficacy, safety and adherence to the treatment. This review provides an overview of the latest advancements in chewable dosage forms for human and veterinary use, highlighting the motivations behind their use and covering formulation considerations, as well as regulatory aspects.

Traction of 3D and 4D Printing in the Healthcare Industry: From Drug Delivery and Analysis to Regenerative Medicine

Three-dimensional (3D) printing and 3D bioprinting are promising technologies for a broad range of healthcare applications from frontier regenerative medicine and tissue engineering therapies to pharmaceutical advancements yet must overcome the challenges of biocompatibility and resolution. Through comparison of traditional biofabrication methods with 3D (bio)printing, this review highlights the promise of 3D printing for the production of on-demand, personalized, and complex products that enhance the accessibility, effectiveness, and safety of drug therapies and delivery systems. In addition, this review describes the capacity of 3D bioprinting to fabricate patient-specific tissues and living cell systems (e.g., vascular networks, organs, muscles, and skeletal systems) as well as its applications in the delivery of cells and genes, microfluidics, and organ-on-chip constructs. This review summarizes how tailoring selected parameters (i.e., accurately selecting the appropriate printing method, materials, and printing parameters based on the desired application and behavior) can better facilitate the development of optimized 3D-printed products and how dynamic 4D-printed strategies (printing materials designed to change with time or stimulus) may be deployed to overcome many of the inherent limitations of conventional 3D-printed technologies. Comprehensive insights into a critical perspective of the future of 4D bioprinting, crucial requirements for 4D printing including the programmability of a material, multimaterial printing methods, and precise designs for meticulous transformations or even clinical applications are also given.

Prediction of Solid-State Form of SLS 3D Printed Medicines Using NIR and Raman Spectroscopy

Selective laser sintering (SLS) 3D printing is capable of revolutionising pharmaceutical manufacturing, by producing amorphous solid dispersions in a one-step manufacturing process. Here, 3D-printed formulations loaded with a model BCS class II drug (20% w/w itraconazole) and three grades of hydroxypropyl cellulose (HPC) polymer (-SSL, -SL and -L) were produced using SLS 3D printing. Interestingly, the polymers with higher molecular weights (HPC-L and -SL) were found to undergo a uniform sintering process, attributed to the better powder flow characteristics, compared with the lower molecular weight grade (HPC-SSL). XRPD analyses found that the SLS 3D printing process resulted in amorphous conversion of itraconazole for all three polymers, with HPC-SSL retaining a small amount of crystallinity on the drug product surface. The use of process analytical technologies (PAT), including near infrared (NIR) and Raman spectroscopy, was evaluated, to predict the amorphous content, qualitatively and quantitatively, within itraconazole-loaded formulations. Calibration models were developed using partial least squares (PLS) regression, which successfully predicted amorphous content across the range of 0–20% w/w. The models demonstrated excellent linearity (R² = 0.998 and 0.998) and accuracy (RMSEP = 1.04% and 0.63%) for NIR and Raman spectroscopy models, respectively. Overall, this article demonstrates the feasibility of SLS 3D printing to produce solid dispersions containing a BCS II drug, and the potential for NIR and Raman spectroscopy to quantify amorphous content as a non-destructive quality control measure at the point-of-care.

Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.

Metamorphosis of Twin Screw Extruder-Based Granulation Technology: Applications Focusing on Its Impact on Conventional Granulation Technology

In order to be at pace with the market requirements of solid dosage forms and regulatory standards, a transformation towards systematic processing using continuous manufacturing (CM) and automated model-based control is being thought through for its fundamental advantages over conventional batch manufacturing. CM eliminates the key gaps through the integration of various processes while preserving quality attributes via the use of process analytical technology (PAT). The twin screw extruder (TSE) is one such equipment adopted by the pharmaceutical industry as a substitute for the traditional batch granulation process. Various types of granulation techniques using twin screw extrusion technology have been explored in the article. Furthermore, individual components of a TSE and their conjugation with PAT tools and the advancements and applications in the field of nutraceuticals and nanotechnology have also been discussed. Thus, the future of granulation lies on the shoulders of continuous TSE, where it can be coupled with computational mathematical studies to mitigate its complications.

Connected healthcare: Improving patient care using digital health technologies

Now more than ever, traditional healthcare models are being overhauled with digital technologies of Healthcare 4.0 increasingly adopted. Worldwide, digital devices are improving every stage of the patient care pathway. For one, sensors are being used to monitor patient metrics 24/7, permitting swift diagnosis and interventions. At the treatment stage, 3D printers are under investigation for the concept of personalised medicine by allowing patients access to on-demand, customisable therapeutics. Robots are also being explored for treatment, by empowering precision surgery, rehabilitation, or targeted drug delivery. Within medical logistics, drones are being leveraged to deliver critical treatments to remote areas, collect samples, and even provide emergency aid. To enable seamless integration within healthcare, the Internet of Things technology is being exploited to form closed-loop systems that remotely communicate with one another. This review outlines the most promising healthcare technologies and devices, their strengths, drawbacks, and opportunities for clinical adoption.

Modular design principle based on compartmental drug delivery systems

The current manufacturing solutions for oral solid dosage forms are fundamentally based on technologies from the 19th century. This approach is well suited for mass production of one-size-fits-all products; however, it does not allow for a straight-forward personalization and mass customization of the pharmaceutical end-product. In order to provide better therapies to the patients, a need for innovative manufacturing concepts and product design principles has been rising. Additive manufacturing opens up a possibility for compartmentalization of drug products, including design of spatially separated multidrug and functional excipient compartments. This compartmentalized solution can be further expanded to modular design thinking. Modular design is referring to combination of building blocks containing a given amount of drug compound(s) and related functional excipients into a larger final product. Implementation of modular design principles is paving the way for implementing the emerging personalization potential within health sciences by designing compartmental and reactive product structures that can be manufactured based on the individual needs of each patient. This review will introduce the existing compartmentalized product design principles and discuss the integration of these into edible electronics allowing for innovative control of drug release.

Hydrophilic Excipient-Independent Drug Release from SLA-Printed Pellets

Three-dimensional (3D) printing technology, specifically stereolithography (SLA) technology, has recently created exciting possibilities for the design and fabrication of sophisticated dosages for oral administration, paving a practical way to precisely manufacture customized pharmaceutical dosages with both personalized properties and sustained drug release behavior. However, the sustained drug release achieved in prior studies largely relies on the presence of hydrophilic excipients in the printing formulation, which unfortunately impedes the printability and formability of the corresponding printing formulations. The current study developed and prepared mini-sized oral pellets using the SLA technique and successfully accomplished a hydrophilic excipient-independent drug release behavior. With ibuprofen as the model drug, the customized photopolymerizable printing formulation included polyethylene glycol diacrylate (PEGDA) as a monomer and diphenyl (2,4,6-trimethylbenzoyl) phosphine oxide (TPO) as a photoinitiator. The produced mini-sized pellets were thoroughly investigated for various factors, including their printability, physical properties, microscopic features, drug content, and drug-release profiles. The drug release profiles from the printed pellets that were larger size (3 mm and 6 mm) followed the Ritger–Peppas model, demonstrating that the release was influenced by both the diffusion of the dissolved drug and by the erosion of the hydrophilic excipients (PEG400). The profiles from the smaller printed pellets (1 mm and 2 mm) followed first release kinetics, not only illustrating that the release was impacted only by drug diffusion, but also indicating that there is a size boundary between the dependent and independent hydrophilic excipients. These results could create practical benefits to the pharmaceutical industry in terms of the design and development personalized dosages using the SLA printing technique with controllable drug release by manipulating size alone.

Intervention of 3D printing in health care: transformation for sustainable development

INTRODUCTION

Three-dimensional (3D) technology is the practice of dropping material layer-by-layer in the construction of the desired object. The application of the 3D printing technique has been observed in miscellaneous domains. Personalized medicine becomes the most demanding trend in the health-care segment. Several advancements have been observed in the progress of 3D printing. However, the availability of finished products in the marketplace is very less. There is an utmost requirement to improve the knowledge and skills in the sustainable development of pharmaceutical and medical products by selecting suitable techniques and materials.

AREAS COVERED

This article covers the fundamental process of 3D printing, types, pharmaceutical-medical application, benefits, and challenges.

EXPERT OPINION

This technology is capable of designing the complex geometry of an organ. It is feasible to produce drug products by incorporating multiple drugs in various compartments in such a fashion that these drugs can release from the compartment at a predetermined rate. Additionally, this 3D process has the potential to revolutionize personalized therapy to different age-groups through design flexibility and accurate dosing. In the upcoming years, the potential application of this technology can be seen in a clinical setting where patients will get individualized medicine as per their needs.

3D-Printed Oral Dosage Forms: Mechanical Properties, Computational Approaches and Applications

The aim of this review is to present the factors influencing the mechanical properties of 3D-printed oral dosage forms. It also explores how it is possible to use specific excipients and printing parameters to maintain the structural integrity of printed drug products while meeting the needs of patients. Three-dimensional (3D) printing is an emerging manufacturing technology that is gaining acceptance in the pharmaceutical industry to overcome traditional mass production and move toward personalized pharmacotherapy. After continuous research over the last thirty years, 3D printing now offers numerous opportunities to personalize oral dosage forms in terms of size, shape, release profile, or dose modification. However, there is still a long way to go before 3D printing is integrated into clinical practice. 3D printing techniques follow a different process than traditional oral dosage from manufacturing methods. Currently, there are no specific guidelines for the hardness and friability of 3D printed solid oral dosage forms. Therefore, new regulatory frameworks for 3D-printed oral dosage forms should be established to ensure that they meet all appropriate quality standards. The evaluation of mechanical properties of solid dosage forms is an integral part of quality control, as tablets must withstand mechanical stresses during manufacturing processes, transportation, and drug distribution as well as rough handling by the end user. Until now, this has been achieved through extensive pre- and post-processing testing, which is often time-consuming. However, computational methods combined with 3D printing technology can open up a new avenue for the design and construction of 3D tablets, enabling the fabrication of structures with complex microstructures and desired mechanical properties. In this context, the emerging role of computational methods and artificial intelligence techniques is highlighted.

Selective Laser Sintering (SLS), a New Chapter in the Production of Solid Oral Forms (SOFs) by 3D Printing

3D printing is a new emerging technology in the pharmaceutical manufacturing landscape. Its potential advantages for personalized medicine have been widely explored and commented on in the literature over recent years. More recently, the selective laser sintering (SLS) technique has been investigated for oral drug-delivery applications. Thus, this article reviews the work that has been conducted on SLS 3D printing for the preparation of solid oral forms (SOFs) from 2017 to 2020 and discusses the opportunities and challenges for this state-of-the-art technology in precision medicine. Overall, the 14 research articles reviewed report the use of SLS printers equipped with a blue diode laser (445-450 nm). The review highlights that the printability of pharmaceutical materials, although an important aspect for understanding the sintering process has only been properly explored in one article. The modulation of the porosity of printed materials appears to be the most interesting outcome of this technology for pharmaceutical applications. Generally, SLS shows great potential to improve compliance within fragile populations. The inclusion of "Quality by Design" tools in studies could facilitate the deployment of SLS in clinical practice, particularly where Good Manufacturing Practices (GMPs) for 3D-printing processes do not currently exist. Nevertheless, drug stability and powder recycling remain particularly challenging in SLS. These hurdles could be overcome by collaboration between pharmaceutical industries and compounding pharmacies.

Harnessing artificial intelligence for the next generation of 3D printed medicines

Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a 'one size fits all' paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP 'Internet of Things', moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0.

Machine learning predicts 3D printing performance of over 900 drug delivery systems

Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy trial-and-error approach in optimization. Artificial intelligence (AI) is a technology that could revolutionize pharmaceutical 3DP through analyzing large datasets. Herein, literature-mined data for developing AI machine learning (ML) models was used to predict key aspects of the 3DP formulation pipeline and in vitro dissolution properties. A total of 968 formulations were mined and assessed from 114 articles. The ML techniques explored were able to learn and provide accuracies as high as 93% for values in the filament hot melt extrusion process. In addition, ML algorithms were able to use data from the composition of the formulations with additional input features to predict the drug release of 3D printed medicines. The best prediction was obtained by an artificial neural network that was able to predict drug release times of a formulation with a mean error of ±24.29 min. In addition, the most important variables were revealed, which could be leveraged in formulation development. Thus, it was concluded that ML proved to be a suitable approach to modelling the 3D printing workflow.

Disrupting 3D printing of medicines with machine learning

3D printing (3DP) is a progressive technology capable of transforming pharmaceutical development. However, despite its promising advantages, its transition into clinical settings remains slow. To make the vital leap to mainstream clinical practice and improve patient care, 3DP must harness modern technologies. Machine learning (ML), an influential branch of artificial intelligence, may be a key partner for 3DP. Together, 3DP and ML can utilise intelligence based on human learning to accelerate drug product development, ensure stringent quality control (QC), and inspire innovative dosage-form design. With ML's capabilities, streamlined 3DP drug delivery could mark the next era of personalised medicine. This review details how ML can be applied to elevate the 3DP of pharmaceuticals and importantly, how it can expedite 3DP's integration into mainstream healthcare.