Predict Drug Release Research Articles

Nanosponges for Poorly Water-soluble Drugs: Recent Advancements, Patents, and Future Prospective

Nanosponges (NS) have emerged as a groundbreaking solution to a myriad of formulation-related challenges in drug delivery. This review aims to provide a thorough understanding for scientists engaged in nanotechnology by exploring the preparation, characterization, and diverse applications of NS. Additionally, we delve into patent information, shedding light on innovative approaches and intellectual property landscapes surrounding Nanosponges technology. The evolution of Nanosponges marks a significant advancement in overcoming formulation obstacles. Resembling small sponges at the scale of viruses, these carriers can be loaded with a wide array of pharmaceuticals. Navigating through the bloodstream, they precisely target specific areas within the body, adhering to surfaces and initiating controlled and predictable drug release. The localized delivery capability of Nanosponges enhances the effectiveness of medications, as they concentrate at designated sites, optimizing therapeutic outcomes. A pivotal characteristic of Nanosponges is their liquid solubility, enabling efficient utilization for drugs with low solubility. The fluidic nature of these systems contributes to their versatility and applicability in addressing challenges associated with poorly soluble medications. Through an exploration of patents, this review offers insights into the intellectual property landscape, revealing innovative strategies, applications, and future directions of Nanosponges in drug delivery. In summary, this comprehensive review amalgamates current scientific knowledge with patent-related information, providing a holistic perspective on Nanosponges. This review becomes a helpful resource for researchers in the dynamic field of nanotechnology and drug delivery by illuminating formulation methodologies, evaluating patent landscapes, and forecasting future developments.

Modelling the controlled drug release of push-pull osmotic pump tablets using DEM

The push-pull osmotic pump tablet is a promising drug delivery approach, offering advantages over traditional dosage forms in achieving consistent and predictable drug release rates. In the current study, the drug release process of push-pull osmotic pump tablets is modelled for the first time using the discrete element method (DEM) incorporated with a microscopic diffusion-induced swelling model. The effects of dosage and formulation design, such as delivery orifice size, drug-to-polymer ratio, tablet surface curvature, friction between particles and cohesion of polymer particles, on the drug release performance are systematically analysed. Numerical results reveal that an enlarged delivery orifice significantly increases both the total drug release and the drug release rate. Moreover, the larger the swellable particle component in the tablet, the higher the drug release rate. Furthermore, the tablet surface curvature is found to affect the drug release profile, i.e. the final drug release percentage increases with the increasing tablet surface curvature. It is also found that the drug release rate could be controlled by adjusting the inter-particle friction and the cohesion of polymer particles in the formulation. This DEM study offers valuable insights into the mechanisms governing drug release in push-pull osmotic pump tablets.

Nanosponges: A Targeted Drug Delivery System

The latest advances in nanotechnology have resulted in the development of a targeted drug delivery system. However, in order to efficiently target a molecule to a specific place with a drug delivery system, a specialized drug delivery system is required. The discovery of nanosponge has been a big step toward overcoming challenges such as drug toxicity, low bioavailability, and predictable drug release because they can accept both hydrophilic and hydrophobic drugs. Nanosponges, a newly developed colloidal system, have the potential to address concerns such as medicine toxicity, lower bioavailability, and drug release over a large region because they can be adjusted to operate with both hydrophilic and hydrophobic pharmaceuticals. Nanosponges are small structures with a three-dimensional network and porous hollow. Nanosponges are small structures with a three-dimensional network and porous hollow. They can be easily created by crosslinking cyclodextrins with various chemicals. Because of Cyclodextrin's excellent biocompatibility, stability, and safety, a number of Cyclodextrin-based drug delivery systems have been rapidly developed. The nanosponge drug delivery system has a wide range of applications, including cancer, autoimmune illnesses, theranostic uses, increased bioavailability, and stability. This review delves into the benefits and downsides, preparation procedures, factors influencing their preparation, characterisation techniques, applications, and the most recent advancements in nanosponges. Nanosponges can also act as an efficient carrier of enzymes, proteins, vaccines, and antibodies. The current review focuses on the method of preparation, characterisation, and possible application in drug delivery systems.

Coarse‐Grained Model‐Assisted Design of Polymer Prodrug Nanoparticles with Enhanced Cytotoxicity: A Combined Theoretical and Experimental Study

AbstractTo achieve drug release from polymer prodrug nanoparticles, the drug‐polymer linker must be accessible for cleavage to release the drug, which can occur under certain physiological conditions (e.g., presence of specific enzymes). Supramolecular organization of polymer prodrug nanoparticles is crucial as it greatly affects the location of the linker, its surface exposure/solvation and thus its cleavage to release the drug. Since experimental access to these data is not straightforward, new methodologies are critically needed to access this information and to accelerate the development of more effective polymer prodrug nanoparticles, and replace the time‐consuming and resource‐intensive traditional trial‐and‐error strategy. In this context, we reported here the use of a coarse‐grained model to assist the design of polymer prodrug nanoparticles with enhanced cytotoxicity. By choosing the solvent accessible surface area as the critical parameter for predicting drug release and hence cytotoxicity of polymer prodrug nanoparticles, we developed an optimized polymer‐drug linker with enhanced hydrophilicity and solvation. Our hypothesis was then experimentally validated by the synthesis of the corresponding polymer prodrugs based on two different drugs (gemcitabine and paclitaxel), which demonstrated greater performances in terms of drug release and cytotoxicity on two cancer cell lines. Interestingly, our methodology can be easily applied to other polymer prodrug structures, which would contribute to the development of more efficient drug delivery systems via in silico screening.

Coarse-Grained Model-Assisted Design of Polymer Prodrug Nanoparticles with Enhanced Cytotoxicity: A Combined Theoretical and Experimental Study.

To achieve drug release from polymer prodrug nanoparticles, the drug-polymer linker must be accessible for cleavage to release the drug, which can occur under certain physiological conditions (e.g., presence of specific enzymes). Supramolecular organization of polymer prodrug nanoparticles is crucial as it greatly affects the location of the linker, its surface exposure/solvation and thus its cleavage to release the drug. Since experimental access to these data is not straightforward, new methodologies are critically needed to access this information and to accelerate the development of more effective polymer prodrug nanoparticles, and replace the time-consuming and resource-intensive traditional trial-and-error strategy. In this context, we reported here the use of a coarse-grained model to assist the design of polymer prodrug nanoparticles with enhanced cytotoxicity. By choosing the solvent accessible surface area as the critical parameter for predicting drug release and hence cytotoxicity of polymer prodrug nanoparticles, we developed an optimized polymer-drug linker with enhanced hydrophilicity and solvation. Our hypothesis was then experimentally validated by the synthesis of the corresponding polymer prodrugs based on two different drugs (gemcitabine and paclitaxel), which demonstrated greater performances in terms of drug release and cytotoxicity on two cancer cell lines. Interestingly, our methodology can be easily applied to other polymer prodrug structures, which would contribute to the development of more efficient drug delivery systems via in silico screening.

Enhancing Bone Cement Efficacy with Hydrogel Beads Synthesized by Droplet Microfluidics

Effective filling materials, typically bone cements, are essential for providing mechanical support during bone fracture treatment. A current challenge with bone cement lies in achieving continuous drug release and forming porous structures that facilitate cell migration and enhance osteoconductivity. We report a droplet microfluidics-based method for synthesizing uniform-sized gelatin hydrogel beads. A high hydrogel concentration and increased crosslinking levels were found to enhance drug loading as well as release performance. Consequently, the droplet microfluidic device was optimized in its design and fabrication to enable the stable generation of uniform-sized droplets from high-viscosity gelatin solutions. The size of the generated beads can be selectively controlled from 50 to 300 μm, featuring a high antibiotic loading capacity of up to 43% dry weight. They achieve continuous drug release lasting more than 300 h, ensuring sustained microbial inhibition with minimal cytotoxicity. Furthermore, the hydrogel beads are well suited for integration with calcium phosphate cement, maintaining structural integrity to form porous matrices and improve continuous drug release performance. The uniform size distribution of the beads, achieved through droplet microfluidic synthesis, ensures predictable drug release dynamics and a measurable impact on the mechanical properties of bone cements, positioning this technology as a promising enhancement to bone cement materials.

Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology.

Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices.

Novel Injectable, Self-Healing, Long-Effective Bacteriostatic, and Healed-Promoting Hydrogel Wound Dressing and Controlled Drug Delivery Mechanisms.

Multivalent ion cross-linking has been used to form hydrogels between sodium alginate (SA) and hyaluronic acid (HA) in previous studies. However, more stable and robust covalent cross-linking is rarely reported. Herein, we present a facile approach to fabricate a SA and HA hydrogel for wound dressings with injectable, good biocompatibility, and high ductility. HA was first reacted with ethylenediamine to graft an amino group. Then, it was cross-linked with oxidized SA with dialdehyde to form hydrogel networks. The dressing can effectively promote cell migration and wound healing. To increase the antibacterial property of the dressing, we successfully loaded tetracycline hydrochloride into the hydrogel as a model drug. The drug can be released slowly in the alkaline environment of chronic wounds, and the hydrogel releases drugs again in the more acidic environment with wound healing, achieving a long-term antibacterial effect. In addition, one-dimensional partial differential equations based on Fickian diffusion with time-varying diffusion coefficients and hydrogel thicknesses were used to model the entire complex drug release process and to predict drug release.

Deep Learning for the Accurate Prediction of Triggered Drug Delivery.

The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as carriers for chemotherapeutic agents, enabling precise drug delivery. The triggered release of these agents is vital for advancing this novel drug delivery system. Our research investigated this multifaceted delivery capability using liposomes and metal organic frameworks as nanocarriers and utilizing all three targeting techniques: passive, active, and triggered. Liposomes are modified using targeting ligands to render them more targeted toward certain cancers. Moieties are conjugated to the surfaces of these nanocarriers to allow for their binding to receptors overexpressed on cancer cells, thus increasing the accumulation of the agent at the tumor site. A novel class of nanocarriers, namely metal organic frameworks, has emerged, showing promise in cancer treatment. Triggering techniques (both intrinsic and extrinsic) can be used to release therapeutic agents from nanoparticles, thus enhancing the efficacy of drug delivery. In this study, we develop a predictive model combining experimental measurements with deep learning techniques. The model accurately predicts drug release from liposomes and MOFs under various conditions, including low- and high-frequency ultrasound (extrinsic triggering), microwave exposure (extrinsic triggering), ultraviolet light exposure (extrinsic triggering), and different pH levels (intrinsic triggering). The deep learning-based predictions significantly outperform linear predictions, proving the utility of advanced computational methods in drug delivery. Our findings demonstrate the potential of these nanocarriers and highlight the efficacy of deep learning models in predicting drug release behavior, paving the way for enhanced cancer treatment strategies.

Doxorubicin/poly(caprolactone)/polyethylene glycol nanoparticles prepared by electrospray

AbstractThe development of sustainable drug delivery systems is very important in the field of pharmaceutical research and has many advantages over the usual methods of drug administration, including improving effectiveness, reducing side effects, and increasing patient compliance. Among various techniques, electrospraying as a voltage‐driven process has emerged as a promising technology for the fabrication of drug‐loaded nanoparticles. In recent years, the application of electrospray approach for drug release has been widely investigated. However, optimization of electrospray process parameters and development of mathematical models to predict drug release kinetics from microparticles are still challenging. To address these issues, a combination of Central Composite Design (CCD) and Response Surface Methodology (RSM) has been used. This paper focuses on the application of CCD‐RSM for modeling and optimization of doxorubicin (DOX)‐poly(caprolactone) (PCL)‐polyethylene glycol (PEG) microparticles prepared using electrospray method. Doxorubicin, a widely used anticancer drug, exhibits potent cytotoxicity against a wide range of cancers. PCL‐PEG microparticles are promising as drug carriers due to their biocompatibility, controlled release properties, and ability to protect the encapsulated drug from degradation. The CCD‐RSM approach allows systematic investigation and optimization of several formulation and process variables, including polymer concentration, PCL‐PEG weight ratio, and applied voltage on drug release behavior. In addition, the effect of rheology and electrical conductivity of polymer fluid on the size of particles obtained from electrospraying was investigated. The effect of adding folic acid and doxorubicin in the optimized sample of nanoparticles and the effect of different concentrations of doxorubicin were investigated, and SEM, DLS, and FTIR techniques were used to characterize the microparticles, and the MMT test was performed to evaluate their cytotoxicity. The results showed that the optimized formulation is effective for sustained drug release over a long period for drug delivery applications.

Jackfruit peel cellulose nanocrystal – Alginate hydrogel for doripenem adsorption and release study

As a natural raw material to replace synthetic chemicals, cellulose and its derivatives are the most popular choices in the pharmaceutical industry. For drug delivery applications, cellulose is usually used as a cellulose nanocrystal (CNC). CNC-based hydrogels are widely utilized for drug delivery because drug molecules can be encapsulated in their pore-like structures. This study aims to develop CNC hydrogels for the delivery of doripenem antibiotics. CNC was obtained from jackfruit peel extraction, and alginate was used as a network polymer to produce hydrogels. Ionotropic gelation was used in the synthesis of CNC-alginate hydrogel composites. The maximum adsorption of doripenem by CNC was 65.7 mg/g, while the maximum adsorption by CNC-alginate was 98.4 mg/g. One of the most challenging aspects of drug delivery is predicting drug release from a solid matrix using simple and complex mathematical equations. The sigmoidal equation could represent the doripenem release from CNC, while the Ritger-Peppas equation could describe the doripenem release from CNC-Alginate. The biocompatibility testing of CNC and CNC-alginate against a 7F2 cell line indicates that both materials were non-toxic.

Zero-order Release of Metformin in Polyacrylamide Hydrogel

Water-soluble polymers with a three-dimensional crosslinked network structure, known as a hydrogel, has widespread applications in clinical and experimental medicine as drug carriers. Upon entering the targeted area for treatment, hydrogel loaded with drugs releases them through both diffusion and self-degradation mechanisms. In this study, the drug release process in polyacrylamide hydrogel uniformly loaded with metformin hydrochloride is investigated. To achieve a uniform drug release rate from the hydrogel, we establish a statistical model of drug release from polyacrylamide hydrogel using response surface methodology based on experimental design technique. The established statistical model can predict drug release behavior at a low cost within the design domain. The relationship between initial drug concentration and drug release rate in metformin-loaded polyacrylamide hydrogel has been investigated. A zero-order drug release effect in hydrogels is achieved through an optimized design of the initial distribution of drug concentration of a five-layered drug-containing hydrogel. This study has significant implications for the realization of drug molecule transport and sustained release in vivo.

Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside.

Artificial intelligence (AI) techniques have the potential to revolutionize drug release modeling, optimize therapy for personalized medicine, and minimize side effects. By applying AI algorithms, researchers can predict drug release profiles, incorporate patient-specific factors, and optimize dosage regimens to achieve tailored and effective therapies. This AI-based approach has the potential to improve treatment outcomes, enhance patient satisfaction, and advance the field of pharmaceutical sciences. International collaborations and professional organizations play vital roles in establishing guidelines and best practices for data collection and sharing. Open data initiatives can enhance transparency and scientific progress, facilitating algorithm validation.

Controlled Release of Flurbiprofen from 3D-Printed and Supercritical Carbon Dioxide Processed Methacrylate-Based Polymer.

The ability to engineer and predict drug release behavior during treatment is critical to the design and implementation of effective drug delivery systems. In this study, a drug delivery system consisting of a methacrylate-based polymer and flurbiprofen was studied, and its release profile in a controlled phosphate-buffered saline solution was characterized. The polymer, which was 3D printed and processed in supercritical carbon dioxide under different temperature and pressure settings, showed sustained drug release over a prolonged period. A computer algorithm was used to determine the drug release time duration before reaching steady state and the maximum drug release at steady state. Several empirical models were applied to fit the release kinetic data to gain information about the drug release mechanism. The diffusion coefficients for each system were also estimated using Fick's law. Based on the results, the influence of supercritical carbon dioxide processing conditions on the diffusion behavior is interpreted, providing insights into the effective and tunable design of drug delivery systems for targeted treatment specifications.

Nanosponges: A Novel Class of Versatile Drug Delivery System – Review

Nanotechnology advancements have resulted in the creation of tailored medicine delivery systems. However, properly targeting a molecule to a specific region with a drug delivery system necessitates using a specialized drug delivery system. The development of nanosponge has become a crucial step in solving some challenges such as drug toxicity, low bioavailability, and predictable drug release. Many drug delivery methods, such as nanoparticles, nanoemulsions, nanosuspensions, and nanosponges, have been developed via nanomedicine technology. Nanosponges are little sponges like the size of a virus that may be loaded with a vast range of medications. Nanosponges serve an essential function in regulated medication delivery. These mini sponges may flow throughout the human body until they reach the same target region. They adhere to the surface and begin to release the medicine in a regulated and predictable way. The outside surface is often porous, which allows for regulated medication release. The important feature of these sponges is their aqueous solubility, which makes them appropriate for medications with low solubility. Their molecular architecture is often composed of several polymer chains that can generate unique microdomains suited for co-encapsulating two medicines with different chemical structures. When used to release insoluble medications, nanosponges also shield the active components from physicochemical deterioration. Nanosponges can be made into a number of dosage forms, including parenteral, aerosol, tablets, topical, and capsules, thanks to their small size and spherical structure. This study focuses on the techniques of synthesis and applications of nanosponges in the realm of medication delivery.

A model to predict drug release from a single-layer osmotic controlled-release tablet

A single-layer osmotic controlled-release tablet, also referred to as an extrudable core system (ECS) tablet, has been modelled to predict the drug release rate as function of various parameters related to the excipients and the active pharmaceutical ingredient, the geometry of the tablet and the coating thickness. The analysis leads to a better understanding of the drug release process resulting in a mathematical tool for design of new formulations. The model accounts for all the main events occurring during the drug release process from a tablet coated with a semi-permeable membrane, namely, the solvent influx driven by the difference in osmotic pressure across the coating, dispersion of core components (drug, polymer and osmogen), swelling of the tablet due to solvent accumulation, build-up of hydrostatic pressure inside the tablet, tensile stress acting on the coating, the extrusion of the dispersed core components and dissolution of drug particles outside the tablet in the bulk. We also derive the condition for successful entrainment of drug particles based on the viscosity of the hydrated phase and other parameters of the system. The model was validated by comparing the predictions with drug release data for two drugs of varying solubility. The agreement between the predicted release and the measurements confirmed the suitability of the model in describing the drug release process in the osmotic controlled-release tablet.

A reliable QSPR model for predicting drug release rate from metal-organic frameworks: a simple and robust drug delivery approach.

During the drug release process, the drug is transferred from the starting point in the drug delivery system to the surface, and then to the release medium. Metal-organic frameworks (MOFs) potentially have unique features to be utilized as promising carriers for drug delivery, due to their suitable pore size, high surface area, and structural flexibility. The loading and release of various therapeutic drugs through the MOFs are effectively accomplished due to their tunable inorganic clusters and organic ligands. Since the drug release rate percentage (RES%) is a significant concern, a quantitative structure-property relationship (QSPR) method was applied to achieve an accurate model predicting the drug release rate from MOFs. Structure-based descriptors, including the number of nitrogen and oxygen atoms, along with two other adjusted descriptors, were applied for obtaining the best multilinear regression (BMLR) model. Drug release rates from 67 MOFs were applied to provide a precise model. The coefficients of determination (R2) for the training and test sets obtained were both 0.9999. The root mean square error for prediction (RMSEP) of the RES% values for the training and test sets were 0.006 and 0.005, respectively. To examine the precision of the model, external validation was performed through a set of new observations, which demonstrated that the model works to a satisfactory degree.

DRUG RELEASE FROM POROUS SPHERICAL PARTICLE: DIFFUSION MODEL WITH AN INTERMEDIATE COMPLEX FORMATION

This publication proposes a new model of drug release from a spherical particle that takes into account the transfer of the drug and the porous particle into the solution by forming an intermediate complex with the bio carrier, one of the components of the solution. The mathematical model is reduced to a dimension-less form that is convenient for qualitative analysis of the process. The problem is realized numerically. The initial stage of filling a porous particle with a bio carrier and stage of drug release into environment were analyzed. Two main types of kinetic curves corresponding to diffusive and convective modes were revealed. The kinetic curves with the initial stage of drug release delay were revealed. The model was in effects (unobvious at first glance) related to the interaction of opposite factors affecting the drug release. The different types of kinetic curves obtained on its basis correspond to different observational conditions, types of particles, and properties of bio fluids. This model can be improved to describe and predict drug release kinetics not only from single porous particles, but also from containers of more complex shape.

Modeling of the In Vitro Release Kinetics of Sonosensitive Targeted Liposomes.

Targeted liposomes triggered by ultrasound are a promising drug delivery system as they potentially improve the clinical outcomes of chemotherapy while reducing associated side effects. In this work, a comprehensive model fitting was performed for a large dataset of liposomal release profiles with seven targeting moieties (albumin, cRGD, estrone, hyaluronic acid, Herceptin, lactobionic acid, and transferrin) in addition to the control liposomes under ultrasound release protocols. Two levels of ultrasound frequencies were tested: low frequency (20 kHz) at 6.2, 9, and 10 mW/cm2 as well as high frequencies (1.07 MHz and 3 MHz) at 10.5 and 173 W/cm2. At a low frequency, Hixson-Crowell, Korsmeyer-Peppas, Gompertz, Weibull, and Lu-Hagen showed good fits to our release profiles at all three power densities. At high frequencies, the former three models reflected the best fit. These models will aid in predicting drug release profiles for future in vitro studies.

Validation of a compartmental model to predict drug release from porous structures produced by ScCO2 techniques

A global release model is proposed to study the drug release from porous materials for pharmaceutical applications. This model is defined by implementing a compartmental model where the release profile could be explained as the combination of mass transfer phenomena through three compartments as well as a desorption process or dissolution process from the support. This model was validated with five different systems produced with supercritical CO2 (aerogels, membranes, and fibers), showing different release processes. Numerical results indicate that this compartmental approach can be useful to determine adsorption and desorption constants as well as mass transfer resistances within the material. Likewise, this model can predict lag phases and imbibition phenomena. Therefore, the development of compartmental models can be an alternative to traditional models to successfully predict the drug profile of porous materials, achieving a complete understanding of the involved phenomena regardless of the material characteristics.