Advancing pharmacy and healthcare with virtual digital technologies

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.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Cold Laser Sintering of Medicines: Toward Carbon Neutral Pharmaceutical Printing

Selective laser sintering (SLS) is an emerging three-dimensional (3D) printing technology that uses a laser to fuse powder particles together, which allows the fabrication of personalized solid dosage forms. It possesses great potential for commercial use. However, a major drawback of SLS is the need to heat the powder bed while printing; this leads to high energy consumption (and hence a large carbon footprint), which may hinder its translation to industry. In this study, the concept of cold laser sintering (CLS) is introduced. In CLS, the aim is to sinter particles without heating the powder bed, where the energy from the laser, alone, is sufficient to fuse adjacent particles. The study demonstrated that a laser power above 1.8 W was sufficient to sinter both KollicoatIR and Eudragit L100-55-based formulations at room temperature. The cold sintering printing process was found to reduce carbon emissions by 99% compared to a commercial SLS printer. The CLS printed formulations possessed characteristics comparable to those made with conventional SLS printing, including a porous microstructure, fast disintegration time, and molecular dispersion of the drug. It was also possible to achieve higher drug loadings than was possible with conventional SLS printing. Increasing the laser power from 1.8 to 3.0 W increased the flexural strength of the printed formulations from 0.6 to 1.6 MPa, concomitantly increasing the disintegration time from 5 to over 300 s. CLS appears to offer a new route to laser-sintered pharmaceuticals that minimizes impact on the environment and is fit for purpose in Industry 5.0.

A prospective, single-centre study of the feasibility of the use of augmented reality for improving the safety and traceability of injectable investigational cancer drug compounding

The compounding of injectable cancer drugs for clinical trials often requires specific procedures, with limited access to the starting materials and especially the active compound. These characteristics prevent the application of qualitative or quantitative analyses and quality control techniques. Hence, for some very complex compounding operations, double visual inspection is considered to be less reliable, more time-consuming and more human-resource-intensive than other methods. The compounding team at Lille University Hospital (Lille, France) has equipped one of its preparation areas with a new device: augmented reality (AR) eyewear connected to an oncology drug management system, as a support tool for compounding and quality control. The tool has been tested, adapted and improved within the unit and is now used for investigational drug compounding on a routine basis. In a prospective, single-centre study, we evaluated the feasibility of the implementation of this novel AR approach for the compounding of injectable investigational cancer drugs. During the 6-month study period, 564 clinical trial compounding operations were performed with the AR eyewear. The proportion of poor-quality photos taken with the AR eyewear fell over time, as users became more familiar with the tool. A user satisfaction survey highlighted a very high level of uptake and a wish to broaden the scope of the compounding performed with AR support. The AR eyewear constitutes an innovative, cost-effective tool that increased the level of safety without disrupting the unit's operating procedures. The tool's flexibility enabled its integration into a variety of working environments. The various improvements now being developed should help to further boost the added value of this novel device.

The effectiveness of checklists and error reporting systems in enhancing patient safety and reducing medical errors in hospital settings: A narrative review

Objectives

This narrative review aimed to explore the impact of checklists and error reporting systems on hospital patient safety and medical errors.

Methods

A systematic search of academic databases from 2013 to 2023 was conducted, and peer-reviewed studies meeting inclusion criteria were assessed for methodological rigor. The review highlights evidence supporting the efficacy of checklists in reducing medication errors, surgical complications, and other adverse events. Error reporting systems foster transparency, encouraging professionals to report incidents and identify systemic vulnerabilities.

Results

Checklists and error reporting systems are interconnected. Interprofessional collaboration is emphasized in checklist implementation. In this review, limitations arise due to the different methodologies used in the articles and potential publication bias. In addition, language restrictions may exclude valuable non-English research. While positive impacts are evident, success depends on organizational culture and resources.

Conclusions

This review contributes to patient safety knowledge by examining the relevant literature, emphasizing the importance of interventions, and calling for further research into their effectiveness across diverse healthcare and cultural settings. Understanding these dynamics is crucial for healthcare providers to optimize patient safety outcomes.

Telepharmacy in Indonesia: Navigating Knowledge, Perception, and Readiness Among 6,000 Pharmacists and Related Sociodemographic Determinants

Introduction: Telepharmacy can improve the delivery of pharmaceutical care services to patients. However, there are limited data regarding the knowledge, perceptions, and readiness (KPR) for telepharmacy in Indonesia. In this cross-sectional survey study, we assessed KPR and associated factors among Indonesian pharmacists, aiming to implement telepharmacy services in the future. Methods: Eligible participants were recruited from all provinces of Indonesia through a 24-item instrument. KPR scores were classified as low, moderate, and high. Sociodemographic characteristics and KPR of participants were summarized using descriptive statistics. Bivariate/multivariate ordinal logistic regression analyses were conducted to identify independent determinants of KPR. An adjusted odds ratio (AOR) with a 95% confidence interval (CI) was calculated for each determinant. Results: A total of 6,059 pharmacists provided responses. Overall, 58.28% had a high knowledge score, and 63.51% expressed moderate perceptions toward telepharmacy services. Moreover, 70.21% showed a moderate level of readiness. Gender (male; AOR: 1.21 [95% CI: 1.06-1.39]), stable internet access (AOR: 0.75 [95% CI: 0.64-0.86]), and central region (AOR: 1.13 [95% CI: 0.99-1.29]) were significantly associated with perception toward telepharmacy. Readiness was significantly associated with age (17-25 years; AOR: 0.73 [95% CI: (0.60-0.89]), gender (male; AOR: 0.83 [95% CI: 0.72-0.95]), stable internet access (AOR: 0.75 [95% CI: 0.64-0.89]), education level (master/doctoral; AOR: 1.33 [95% CI: 1.06-1.67]), and central region (AOR: 1.29 [95% CI: 1.12-1.49]). Interestingly, knowledge levels were not significantly correlated with specific factors. Conclusions: Participants demonstrated high knowledge, without significant influencing factors. However, they showed moderate perceptions and readiness levels, influenced by sociodemographic factors, including gender, age, education level, internet access, and regional disparities. Therefore, targeted interventions (e.g., telepharmacy training and regional outreach) are imperative to enhancing perceptions and readiness, fostering the effective integration of telepharmacy services, and advancing pharmaceutical care in Indonesia.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

The digital transformation in pharmacy: embracing online platforms and the cosmeceutical paradigm shift

In the face of rapid technological advancement, the pharmacy sector is undergoing a significant digital transformation. This review explores the transformative impact of digitalization in the global pharmacy sector. We illustrated how advancements in technologies like artificial intelligence, blockchain, and online platforms are reshaping pharmacy services and education. The paper provides a comprehensive overview of the growth of online pharmacy platforms and the pivotal role of telepharmacy and telehealth during the COVID-19 pandemic. Additionally, it discusses the burgeoning cosmeceutical market within online pharmacies, the regulatory challenges faced globally, and the private sector's influence on healthcare technology. Conclusively, the paper highlights future trends and technological innovations, underscoring the dynamic evolution of the pharmacy landscape in response to digital transformation.

The contemplation of amylose for the delivery of ulcerogenic nonsteroidal anti-inflammatory drugs

This manuscript proposes an innovative approach to mitigate the gastrointestinal adversities linked with nonsteroidal anti-inflammatory drugs (NSAIDs) by exploiting amylose as a novel drug delivery carrier. The intrinsic attributes of V-amylose, such as its structural uniqueness, biocompatibility and biodegradability, as well as its capacity to form inclusion complexes with diverse drug molecules, are meticulously explored. Through a comprehensive physicochemical analysis of V-amylose and ulcerogenic NSAIDs, the plausibility of amylose as a protective carrier for ulcerogenic NSAIDs to gastrointestinal regions is elucidated. This review further discusses the potential therapeutic advantages of amylose-based drug delivery systems in the management of gastric ulcers. By providing controlled release kinetics and enhanced bioavailability, these systems offer promising prospects for the development of more effective ulcer therapies.

Validation of remote child weight and height measurements within a weight management trial

OBJECTIVE

The aim of this substudy within the Treatment Efforts Addressing Child Weight Management by Unifying Patients, Parents, and Providers (TEAM UP) pragmatic clinical trial was to compare the validity of anthropometric measurements collected remotely versus in person (≤7 days apart) among youth with obesity who were 6 to 15 years of age.

METHODS

Child (n = 37) weight and height were measured in person by a trained data assessor. These were compared with measurements taken remotely by the child's parent with live videoconferencing observation by a study data assessor. In-person and remote measurements were compared using Bland-Altman plots, Pearson correlations, and two one-sided paired t tests. A priori bounds of acceptability were set at ±0.68 kg to allow for typical weight fluctuations within the 7-day comparison period.

RESULTS

Measurements were highly correlated (height: r = 0.991, p < 0.0001; weight: r = 0.999; p = 0.03). For height, two one-sided t tests for upper, t(36) = 3.95, and lower, t(36) = -2.63, bounds (-1, 1) revealed an overall p = 0.006; absolute error was 3.5 cm. For weight, two one-sided t tests for upper, t(36) = 1.93, and lower, t(36) = -7.91, bounds (-0.68, 0.68) revealed an overall p = 0.03; absolute error was 1.7 kg.

CONCLUSIONS

The present findings support the utility and interpretation of remotely assessed weight management outcomes for both research and clinical purposes. These procedures may offer greater accessibility to evidence-based measurement.

The role of artificial intelligence in generating original scientific research

Artificial intelligence (AI) is a revolutionary technology that is finding wide application across numerous sectors. Large language models (LLMs) are an emerging subset technology of AI and have been developed to communicate using human languages. At their core, LLMs are trained with vast amounts of information extracted from the internet, including text and images. Their ability to create human-like, expert text in almost any subject means they are increasingly being used as an aid to presentation, particularly in scientific writing. However, we wondered whether LLMs could go further, generating original scientific research and preparing the results for publication. We taskedGPT-4, an LLM, to write an original pharmaceutics manuscript, on a topic that is itself novel. It was able to conceive a research hypothesis, define an experimental protocol, produce photo-realistic images of 3D printed tablets, generate believable analytical data from a range of instruments and write a convincing publication-ready manuscript with evidence of critical interpretation. The model achieved all this is less than 1 h. Moreover, the generated data were multi-modal in nature, including thermal analyses, vibrational spectroscopy and dissolution testing, demonstrating multi-disciplinary expertise in the LLM. One area in which the model failed, however, was in referencing to the literature. Since the generated experimental results appeared believable though, we suggest that LLMs could certainly play a role in scientific research but with human input, interpretation and data validation. We discuss the potential benefits and current bottlenecks for realising this ambition here.

Inkjet Printing of Pharmaceuticals

Inkjet printing (IJP) is an additive manufacturing process that selectively deposits ink materials, layer-by-layer, to create 3D objects or 2D patterns with precise control over their structure and composition. This technology has emerged as an attractive and versatile approach to address the ever-evolving demands of personalized medicine in the healthcare industry. Although originally developed for nonhealthcare applications, IJP harnesses the potential of pharma-inks, which are meticulously formulated inks containing drugs and pharmaceutical excipients. Delving into the formulation and components of pharma-inks, the key to precise and adaptable material deposition enabled by IJP is unraveled. The review extends its focus to substrate materials, including paper, films, foams, lenses, and 3D-printed materials, showcasing their diverse advantages, while exploring a wide spectrum of therapeutic applications. Additionally, the potential benefits of hardware and software improvements, along with artificial intelligence integration, are discussed to enhance IJP's precision and efficiency. Embracing these advancements, IJP holds immense potential to reshape traditional medicine manufacturing processes, ushering in an era of medical precision. However, further exploration and optimization are needed to fully utilize IJP's healthcare capabilities. As researchers push the boundaries of IJP, the vision of patient-specific treatment is on the horizon of becoming a tangible reality.

Electroactive Polymers for On-Demand Drug Release

Conductive materials have played a significant role in advancing society into the digital era. Such materials are able to harness the power of electricity and are used to control many aspects of daily life. Conductive polymers (CPs) are an emerging group of polymers that possess metal-like conductivity yet retain desirable polymeric features, such as processability, mechanical properties, and biodegradability. Upon receiving an electrical stimulus, CPs can be tailored to achieve a number of responses, such as harvesting energy and stimulating tissue growth. The recent FDA approval of a CP-based material for a medical device has invigorated their research in healthcare. In drug delivery, CPs can act as electrical switches, drug release is achieved at a flick of a switch, thereby providing unprecedented control over drug release. In this review, recent developments in CP as electroactive polymers for voltage-stimuli responsive drug delivery systems are evaluated. The review demonstrates the distinct drug release profiles achieved by electroactive formulations, and both the precision and ease of stimuli response. This level of dynamism promises to yield "smart medicines" and warrants further research. The review concludes by providing an outlook on electroactive formulations in drug delivery and highlighting their integral roles in healthcare IoT.

Designing greener active pharmaceutical ingredients: Insights from pharmaceutical industry into drug discovery and development

Active pharmaceutical ingredients (APIs), their metabolites and transformation products (TPs) are found as pollutants in the environment. They can impact human and environmental health. To address this issue, an efficient, long-term prevention strategy could be the design of APIs that have less impact on the natural environment, i.e. the design of greener APIs, by the implementation of environmental parameters into the drug discovery and development process (also abbreviated R&D for 'research and development'). Our study aimed to evaluate the feasibility of the design of greener APIs based on insights from drug design experts working in large, research-based pharmaceutical companies. The feasibility evaluation also identified needs and incentives for process modification. For this purpose, 30 R&D and environmental experts from seven globally active pharmaceutical companies were interviewed along a structured questionnaire. Main findings are that the interviewed experts saw manifold opportunities to include properties rendering APIs greener in different stages along the R&D process. This implementation would be favoured by the fact that the pharmaceutical R&D process is very flexible and relies on balancing multiple parameters. Furthermore, some API properties that reduce environmental risks were considered compatible with common desirable properties for application. Environmental properties should be considered early during R&D, i.e. when molecules are screened and optimized. It has been found that availability of suitable in silico models and in vitro assays is crucial for this environmental consideration. Their attributes, e.g. throughput and costs, determine at which process stage they can be successfully applied. An intensified exchange between R&D and environmental experts within and outside companies would push the industrial application of the benign by design approach for APIs forward. Collaboration across pharmaceutical companies, authorities, and academia is seen as highly promising in this respect. Financial, social, and regulatory incentives would support future design of greener APIs.

3D Printing of Dietary Products for the Management of Inborn Errors of Intermediary Metabolism in Pediatric Populations

The incidence of Inborn Error of Intermediary Metabolism (IEiM) diseases may be low, yet collectively, they impact approximately 6-10% of the global population, primarily affecting children. Precise treatment doses and strict adherence to prescribed diet and pharmacological treatment regimens are imperative to avert metabolic disturbances in patients. However, the existing dietary and pharmacological products suffer from poor palatability, posing challenges to patient adherence. Furthermore, frequent dose adjustments contingent on age and drug blood levels further complicate treatment. Semi-solid extrusion (SSE) 3D printing technology is currently under assessment as a pioneering method for crafting customized chewable dosage forms, surmounting the primary limitations prevalent in present therapies. This method offers a spectrum of advantages, including the flexibility to tailor patient-specific doses, excipients, and organoleptic properties. These elements are pivotal in ensuring the treatment's efficacy, safety, and adherence. This comprehensive review presents the current landscape of available dietary products, diagnostic methods, therapeutic monitoring, and the latest advancements in SSE technology. It highlights the rationale underpinning their adoption while addressing regulatory aspects imperative for their seamless integration into clinical practice.

Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

Exploiting Pharma 4.0 Technologies in the Non-Biological Complex Drugs Manufacturing: Innovations and Implications

The pharmaceutical industry has entered an era of transformation with the emergence of Pharma 4.0, which leverages cutting-edge technologies in manufacturing processes. These hold tremendous potential for enhancing the overall efficiency, safety, and quality of non-biological complex drugs (NBCDs), a category of pharmaceutical products that pose unique challenges due to their intricate composition and complex manufacturing requirements. This review attempts to provide insight into the application of select Pharma 4.0 technologies, namely machine learning, in silico modeling, and 3D printing, in the manufacturing process of NBCDs. Specifically, it reviews the impact of these tools on NBCDs such as liposomes, polymeric micelles, glatiramer acetate, iron carbohydrate complexes, and nanocrystals. It also addresses regulatory challenges associated with the implementation of these technologies and presents potential future perspectives, highlighting the incorporation of digital twins in this field of research as it seems to be a very promising approach, namely for the optimization of NBCDs manufacturing processes.

Practical Application of 3D Printing for Pharmaceuticals in Hospitals and Pharmacies

Three-dimensional (3D) printing is an unrivaled technique that uses computer-aided design and programming to create 3D products by stacking materials on a substrate. Today, 3D printing technology is used in the whole drug development process, from preclinical research to clinical trials to frontline medical treatment. From 2009 to 2020, the number of research articles on 3D printing in healthcare applications surged from around 10 to 2000. Three-dimensional printing technology has been applied to several kinds of drug delivery systems, such as oral controlled release systems, micropills, microchips, implants, microneedles, rapid dissolving tablets, and multiphase release dosage forms. Compared with conventional manufacturing methods of pharmaceutical products, 3D printing has many advantages, including high production rates due to the flexible operating systems and high drug loading with the desired precision and accuracy for potent drugs administered in small doses. The cost of production via 3D printing can be decreased by reducing material wastage, and the process can be adapted to multiple classes of pharmaceutically active ingredients, including those with poor solubility. Although several studies have addressed the benefits of 3D printing technology, hospitals and pharmacies have only implemented this process for a small number of practical applications. This article discusses recent 3D printing applications in hospitals and pharmacies for medicinal preparation. The article also covers the potential future applications of 3D printing in pharmaceuticals.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies

The emerging technologies of Industry 4.0 (I4.0) are crucial to incorporating agility, sustainability, smartness, and competitiveness in the business model, enabling long-term sustainability practices in the pharmaceutical supply chain (PSC). By leveraging the latest technologies of I4.0, pharmaceutical companies can gain real-time visibility into their supply chain (SC) operations, allowing them to make data-driven decisions that improve SC performance, efficiency, resilience, and sustainability. However, to date, no research has examined the critical success factors (CSFs) that enable the pharmaceutical industry to adopt I4.0 successfully to enhance overall SC sustainability. This study, therefore, analyzed the potential CSFs for adopting I4.0 to increase all facets of sustainability in the PSC, especially from the perspective of an emerging economy like Bangladesh. Initially, sixteen CSFs were identified through a comprehensive literature review and expert validation. Later, the finalized CSFs were clustered into three relevant groups and analyzed using a Bayesian best-worst method (BWM)-based multi-criteria decision-making (MCDM) framework. The study findings revealed that "sufficient investment for technological advancement", "digitalized product monitoring and traceability", and "dedicated and robust research and development (R&D) team" are the top three CSFs to adopt I4.0 in the PSC. The study's findings can aid industrial practitioners, managers, and policymakers in creating effective action plans for efficiently adopting I4.0 in PSC to avail of its competitive benefits and ensure a sustainable future for the pharmaceutical industry.

Harnessing personalized tailored medicines to digital-based data-enriched edible pharmaceuticals

Tailoring drug products to personalized medicines poses challenges for conventional dosage forms. The prominent reason is the restricted availability of flexible dosage strengths in the market. Inappropriate dosage strengths lead to adverse drug reactions or compromised therapeutic effects. The situation worsens when the drug has a narrow therapeutic window. To overcome these challenges, data-enriched edible pharmaceuticals (DEEP) are novel concepts for designing solid oral products. DEEP have individualized doses and information embedded in quick response (QR) code form. When data are presented in a QR code, the information is printed with edible ink that contains the drug in tailored doses required for the patients.

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.

Colonic drug delivery: Formulating the next generation of colon-targeted therapeutics

Colonic drug delivery can facilitate access to unique therapeutic targets and has the potential to enhance drug bioavailability whilst reducing off-target effects. Delivering drugs to the colon requires considered formulation development, as both oral and rectal dosage forms can encounter challenges if the colon's distinct physiological environment is not appreciated. As the therapeutic opportunities surrounding colonic drug delivery multiply, the success of novel pharmaceuticals lies in their design. This review provides a modern insight into the key parameters determining the effective design and development of colon-targeted medicines. Influential physiological features governing the release, dissolution, stability, and absorption of drugs in the colon are first discussed, followed by an overview of the most reliable colon-targeted formulation strategies. Finally, the most appropriate in vitro, in vivo, and in silico preclinical investigations are presented, with the goal of inspiring strategic development of new colon-targeted therapeutics.

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.

Automated Workflow for Usability Audits in the PHR Realm

Teleworking is an everyday reality in today’s world. Many companies are opting for this way of working remotely, as it improves employers’ satisfaction along with the work–life balance and it does not lead to any significant reduction in performance. In general, the comfortable use of software applications is a priority, and quality aspects such as usability are of vital importance. In this paper, a novel workflow is proposed. It consists of the following components: (1) a tool to manage usability evaluations based on expert reviews, called Usevalia; (2) a set of heuristics to be considered when performing the evaluations; (3) a catalogue of usability requirements to guide the evaluations; (4) a checklist related to the heuristics to verify that the evaluations have been performed correctly by all the auditors; and (5) a set of tasks to know in advance the functionality of the applications to be audited. Personal health records (PHRs) were taken as a case study. This methodology made it possible to involve a total of 31 working groups that evaluated the usability of four PHRs. Concerning the main results, the evaluators agreed that with Usevalia the usability evaluations could go a step further because they are coordinated and can work together remotely. In addition, analyses of usability scores provided insight into current proposals for improving usability of applications.