Microfluidic Synthesis of Indomethacin-Loaded PLGA Microparticles Optimized by Machine Learning

Investigation of the Impact of Manufacturing Methods on Protein-Based Long-Acting Injectable Formulations: A Comparative Assessment for Microfluidics vs. Conventional Methods

Background/Objectives: Microparticle-based drug delivery systems offer several advantages for protein-based drug formulations, enhancing patient compliance and therapeutic efficiency through the sustained delivery of the active pharmaceutical ingredient. Over the past few decades, the microfluidics method has emerged as a continuous manufacturing process for preparing drug-encapsulating microparticles, mainly for small molecule drugs. However, comparative assessments for the conventional batch method vs. the microfluidics method for protein-based drug formulations have been lacking. The main objective of this study was to generate immunomodulatory protein drug-loaded injectable formulations using both conventional batch and microfluidics methods.

METHODS

Therefore, rhCCL22-loaded poly(lactic-co-glycolic) acid (PLGA) microparticles were prepared by conventional homogenization and microfluidics methods.

RESULTS

The resulting microparticles were analyzed comparatively, focusing on critical quality attributes such as microparticle size, size distribution, morphology, drug encapsulation efficiency, release kinetics, and batch-to-batch variations in relation to the manufacturing method. Our results demonstrated that the conventional method resulted in microparticles with denser surface porosity and wider size distribution as opposed to microparticles prepared by the microfluidics method, which could contribute to a significant difference in the drug-release kinetics. Additionally, our findings indicated minimal variation within batches for the microparticles prepared by the microfluidics method.

CONCLUSION

Overall, this study highlights the comparative assessment of several critical quality attributes and batch variations associated with the manufacturing methods of protein-loaded microparticles which is crucial for ensuring consistency in efficacy, regulatory compliance, and quality control in the drug formulation manufacturing process.

Innovative Pharmaceutical Techniques for Paediatric Dosage Forms: A Systematic Review on 3D Printing, Prilling/Vibration and Microfluidic Platform

The production of paediatric pharmaceutical forms represents a unique challenge within the pharmaceutical industry. The primary goal of these formulations is to ensure therapeutic efficacy, safety, and tolerability in paediatric patients, who have specific physiological needs and characteristics. In recent years, there has been a significant increase in attention towards this area, driven by the need to improve drug administration to children and ensure optimal and specific treatments. Technological innovation has played a crucial role in meeting these requirements, opening new frontiers in the design and production of paediatric pharmaceutical forms. In particular, three emerging technologies have garnered considerable interest and attention within the scientific and industrial community: 3D printing, prilling/vibration, and microfluidics. These technologies offer advanced approaches for the design, production, and customization of paediatric pharmaceutical forms, allowing for more precise dosage modulation, improved solubility, and greater drug acceptability. In this review, we delve into these cutting-edge technologies and their impact on the production of paediatric pharmaceutical forms. We analyse their potential, associated challenges, and recent developments, providing a comprehensive overview of the opportunities that these innovative methodologies offer to the pharmaceutical sector. We examine different pharmaceutical forms generated using these techniques, evaluating their advantages and disadvantages.

Machine learning assisted exploration of the influential parameters on the PLGA nanoparticles

Poly (lactic-co-glycolic acid) (PLGA)-based nanoparticles (NPs) are widely investigated as drug delivery systems. However, despite the numerous reviews and research papers discussing various physicochemical and technical properties that affect NP size and drug loading characteristics, predicting the influential features remains difficult. In the present study, we employed four different machine learning (ML) techniques to create ML models using effective parameters related to NP size, encapsulation efficiency (E.E.%), and drug loading (D.L.%). These parameters were extracted from the different literature. Least Absolute Shrinkage and Selection Operator was used to investigate the input parameters and identify the most influential features (descriptors). Initially, ML models were trained and validated using tenfold validation methods, and subsequently, next their performances were evaluated and compared in terms of absolute error, mean absolute, error and R-square. After comparing the performance of different ML models, we decided to use support vector regression for predicting the size and E.E.% and random forest for predicting the D.L.% of PLGA-based NPs. Furthermore, we investigated the interactions between these target variables using ML methods and found that size and E.E.% are interrelated, while D.L.% shows no significant relationship with the other targets. Among these variables, E.E.% was identified as the most influential parameter affecting the NPs' size. Additionally, we found that certain physicochemical properties of PLGA, including molecular weight (Mw) and the lactide-to-glycolide (LA/GA) ratio, are the most determining features for E.E.% and D.L.% of the final NPs, respectively.

Enhanced Maturation of 3D Bioprinted Skeletal Muscle Tissue Constructs Encapsulating Soluble Factor-Releasing Microparticles

Several microfabrication technologies have been used to engineer native-like skeletal muscle tissues. However, the successful development of muscle remains a significant challenge in the tissue engineering field. Muscle tissue engineering aims to combine muscle precursor cells aligned within a highly organized 3D structure and biological factors crucial to support cell differentiation and maturation into functional myotubes and myofibers. In this study, the use of 3D bioprinting is proposed for the fabrication of muscle tissues using gelatin methacryloyl (GelMA) incorporating sustained insulin-like growth factor-1 (IGF-1)-releasing microparticles and myoblast cells. This study hypothesizes that functional and mature myotubes will be obtained more efficiently using a bioink that can release IGF-1 sustainably for in vitro muscle engineering. Synthesized microfluidic-assisted polymeric microparticles demonstrate successful adsorption of IGF-1 and sustained release of IGF-1 at physiological pH for at least 21 days. Incorporating the IGF-1-releasing microparticles in the GelMA bioink assisted in promoting the alignment of myoblasts and differentiation into myotubes. Furthermore, the myotubes show spontaneous contraction in the muscle constructs bioprinted with IGF-1-releasing bioink. The proposed bioprinting strategy aims to improve the development of new therapies applied to the regeneration and maturation of muscle tissues.

Revolutionizing drug formulation development: The increasing impact of machine learning

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

Microfluidic Synthesis of Magnetite Nanoparticles for the Controlled Release of Antibiotics

Magnetite nanoparticles (MNPs) have been intensively studied for biomedical applications, especially as drug delivery systems for the treatment of infections. Additionally, they are characterized by intrinsic antimicrobial properties owing to their capacity to disrupt or penetrate the microbial cell wall and induce cell death. However, the current focus has shifted towards increasing the control of the synthesis reaction to ensure more uniform nanoparticle sizes and shapes. In this context, microfluidics has emerged as a potential candidate method for the controlled synthesis of nanoparticles. Thus, the aim of the present study was to obtain a series of antibiotic-loaded MNPs through a microfluidic device. The structural properties of the nanoparticles were investigated through X-ray diffraction (XRD) and, selected area electron diffraction (SAED), the morphology was evaluated through transmission electron microscopy (TEM) and high-resolution TEM (HR-TEM), the antibiotic loading was assessed through Fourier-transform infrared spectroscopy (FT-IR) and, and thermogravimetry and differential scanning calorimetry (TG-DSC) analyses, and. the release profiles of both antibiotics was determined through UV-Vis spectroscopy. The biocompatibility of the nanoparticles was assessed through the MTT assay on a BJ cell line, while the antimicrobial properties were investigated against the S. aureus, P. aeruginosa, and C. albicans strains. Results proved considerable uniformity of the antibiotic-containing nanoparticles, good biocompatibility, and promising antimicrobial activity. Therefore, this study represents a step forward towards the microfluidic development of highly effective nanostructured systems for antimicrobial therapies.

Applied machine learning as a driver for polymeric biomaterials design

Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.

Synthesis of nanoparticles via microfluidic devices and integrated applications

In recent years, nanomaterials have attracted the research intervention of experts in the fields of catalysis, energy, biomedical testing, and biomedicine with their unrivaled optical, chemical, and biological properties. From basic metal and oxide nanoparticles to complex quantum dots and MOFs, the stable preparation of various nanomaterials has always been a struggle for researchers. Microfluidics, as a paradigm of microscale control, is a remarkable platform for online stable synthesis of nanomaterials with efficient mass and heat transfer in microreactors, flexible blending of reactants, and precise control of reaction conditions. We describe the process of microfluidic preparation of nanoparticles in the last 5 years in terms of microfluidic techniques and the methods of microfluidic manipulation of fluids. Then, the ability of microfluidics to prepare different nanomaterials, such as metals, oxides, quantum dots, and biopolymer nanoparticles, is presented. The effective synthesis of some nanomaterials with complex structures and the cases of nanomaterials prepared by microfluidics under extreme conditions (high temperature and pressure), the compatibility of microfluidics as a superior platform for the preparation of nanoparticles is demonstrated. Microfluidics has a potent integration capability to combine nanoparticle synthesis with real-time monitoring and online detection, which significantly improves the quality and production efficiency of nanoparticles, and also provides a high-quality ultra-clean platform for some bioassays.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Role of Polymers in Microfluidic Devices

Polymers are sustainable and renewable materials that are in high demand due to their excellent properties. Natural and synthetic polymers with high flexibility, good biocompatibility, good degradation rate, and stiffness are widely used for various applications, such as tissue engineering, drug delivery, and microfluidic chip fabrication. Indeed, recent advances in microfluidic technology allow the fabrication of polymeric matrix to construct microfluidic scaffolds for tissue engineering and to set up a well-controlled microenvironment for manipulating fluids and particles. In this review, polymers as materials for the fabrication of microfluidic chips have been highlighted. Successful models exploiting polymers in microfluidic devices to generate uniform particles as drug vehicles or artificial cells have been also discussed. Additionally, using polymers as bioink for 3D printing or as a matrix to functionalize the sensing surface in microfluidic devices has also been mentioned. The rapid progress made in the combination of polymers and microfluidics presents a low-cost, reproducible, and scalable approach for a promising future in the manufacturing of biomimetic scaffolds for tissue engineering.

Microfluidic Systems For Manufacturing of Microparticle-Based Drug-Delivery Systems: Design, Construction, and Operation

Particles synthesized from biodegradable polymers hold great potential as controlled drug delivery systems. Continuous flow platforms based on microfluidics offer attractive advantages over conventional batch-emulsification techniques for the scalable fabrication of drug-loaded particles with controlled physicochemical properties. However, widespread utilization of microfluidic technologies for the manufacturing of drug-loaded particles has been hindered largely by the lack of practical guidelines toward cost-effective development and reliable operation of microfluidic systems. Here, we present a framework for rational design and construction of microfluidic systems using commercially available components for high-throughput production of uniform biodegradable particles encapsulating drugs. We also demonstrate successful implementation of this framework to devise a robust microfluidic system that is capable of producing drug-carrying particles with desired characteristics. The guidelines provided in this study will likely help broaden the applicability of microfluidic technologies for the synthesis of high-quality, drug-loaded biodegradable particles.

Poly(Lactic Acid)-Based Microparticles for Drug Delivery Applications: An Overview of Recent Advances

The sustained release of pharmaceutical substances remains the most convenient way of drug delivery. Hence, a great variety of reports can be traced in the open literature associated with drug delivery systems (DDS). Specifically, the use of microparticle systems has received special attention during the past two decades. Polymeric microparticles (MPs) are acknowledged as very prevalent carriers toward an enhanced bio-distribution and bioavailability of both hydrophilic and lipophilic drug substances. Poly(lactic acid) (PLA), poly(lactic-co-glycolic acid) (PLGA), and their copolymers are among the most frequently used biodegradable polymers for encapsulated drugs. This review describes the current state-of-the-art research in the study of poly(lactic acid)/poly(lactic-co-glycolic acid) microparticles and PLA-copolymers with other aliphatic acids as drug delivery devices for increasing the efficiency of drug delivery, enhancing the release profile, and drug targeting of active pharmaceutical ingredients (API). Potential advances in generics and the constant discovery of therapeutic peptides will hopefully promote the success of microsphere technology.