Application of artificial intelligence in modeling of the doxorubicin release behavior of pH and temperature responsive poly(NIPAAm-co-AAc)-PEG IPN hydrogel

DNA Hydrogels in Tissue Engineering: From Molecular Design to Next-Generation Biomedical Applications

DNA hydrogels have emerged as promising materials in tissue engineering due to their biocompatibility, programmability, and responsiveness to stimuli. Synthesized through physical and chemical crosslinking, these hydrogels can be categorized into functionalized types, such as those based on aptamers, and stimuli-responsive types that react to pH, temperature, and light. This review highlights their applications in tissue engineering, including drug delivery, cell culture, biosensing, and gene editing. DNA hydrogels can encapsulate therapeutic agents, support cell growth, and respond dynamically to environmental changes, making them ideal for tissue engineering. A comprehensive bibliometric analysis is included, identifying key research trends and emerging areas of interest in DNA hydrogel design, synthesis, and biomedical applications. The analysis provides a deeper understanding of the field's development and future research directions. Challenges such as mechanical strength, stability, and biosafety persist, but the integration of AI in hydrogel design shows promise for advancing their functionality in clinical applications.

Utilizing machine learning for predicting drug release from polymeric drug delivery systems

Polymeric drug delivery systems (PDDS) play a crucial role in controlled drug release, providing improved therapeutic outcomes. However, formulating PDDS and predicting their release profiles remain challenging due to their complex structures and the numerous variables that influence their behavior. Traditional mathematical and empirical prediction methods are limited in capturing these complexities. Recent studies have unveiled the potential of Machine Learning (ML) in revolutionizing drug delivery, particularly in formulating complex PDDS. This article provides an overview of the significant and fundamental principles of various ML strategies in estimating PDDS drug release behavior. Our focus extends to the accomplishments and pivotal discoveries in current research, spanning seven distinct sustained-release drug delivery systems: matrix tablets, microspheres, implants, hydrogels, films, 3D-printed dosage forms, and other innovations. Furthermore, it addresses the challenges associated with ML-based drug release prediction and presents current solutions while delving into future perspectives. Our investigation underscores the significance of Artificial Neural Networks in ML-based PDDS release profile prediction, surpassing both traditional and alternative ML-based methods. These extensive datasets can be drawn from literature-based resources or enhanced through specific algorithms. Moreover, ensemble-based models have proven advantageous in scenarios involving intricate relationships, such as a high number of output parameters. ML-based drug release prediction notably exhibits substantial promise in 3D-printed dosage forms, presenting a frontier for personalized medicine and precise drug delivery.

3D-Printed Hydrogels from Natural Polymers for Biomedical Applications: Conventional Fabrication Methods, Current Developments, Advantages, and Challenges

Hydrogels are network polymers with high water-bearing capacity resembling the extracellular matrix. Recently, many studies have focused on synthesizing hydrogels from natural sources as they are biocompatible, biodegradable, and readily available. However, the structural complexities of biological tissues and organs limit the use of hydrogels fabricated with conventional methods. Since 3D printing can overcome this barrier, more interest has been drawn toward the 3D printing of hydrogels. This review discusses the structure of hydrogels and their potential biomedical applications with more emphasis on natural hydrogels. There is a discussion on various formulations of alginates, chitosan, gelatin, and hyaluronic acid. Furthermore, we discussed the 3D printing techniques available for hydrogels and their advantages and limitations.

Tumor microenvironment as a target for developing anticancer hydrogels

OBJECTIVE

It has been reported that cancer cells get protected by a complex and rich multicellular environment i.e. the tumor microenvironment (TME) consisting of varying immune cells, endothelial cells, dendritic cells, fibroblasts, etc. This manuscript is aimed at the characteristic features of TME considered as potential target(s) for developing smart anticancer hydrogels.

SIGNIFICANCE

The stimuli-specific drug delivery systems especially hydrogels that can respond to the characteristic features of TME are fabricated for treating cancer. For developing anticancer formulations, TME targeting can be considered an alternative way as it enhances the cytotoxic potential and reduces the unwanted effects. This manuscript shall be of quite interest to academicians, researchers, and clinicians engaged in oncology.

METHODS

The manuscript was prepared by using the data available in the public domain in online resources such as Google Scholar, PubMed, Science Direct, Scopus, Web of Science, Research Gate, etc.

RESULTS

Smart hydrogels, sensitive to some specific features of TME such as low pH, high concentration of glutathione, specific enzymes, etc., are promising anticancer formulations as these improve the efficacy and lower the side effects of chemotherapy.

CONCLUSION

The stimuli-responsive hydrogels have been gaining more attention for delivering cytotoxic drugs to the TME in response to specific stimuli. The stimuli-responsive hydrogels, comprising of cytotoxic drug(s) and specific polymers have some special features such as similarity with biological matrix, ability to respond to various internal as well as external stimuli, improved permeability, porosity, biocompatibility, resemblance with soft living tissues, etc.; and are considered as the promising anticancer candidates.

New Perspectives of Hydrogels in Chronic Wound Management

Chronic wounds pose a substantial healthcare concern due to their prevalence and cost burden. This paper presents a detailed overview of chronic wounds and emphasizes the critical need for novel therapeutic solutions. The pathophysiology of wound healing is discussed, including the healing stages and the factors contributing to chronicity. The focus is on diverse types of chronic wounds, such as diabetic foot necrosis, pressure ulcers, and venous leg ulcers, highlighting their etiology, consequences, and the therapeutic issues they provide. Further, modern wound care solutions, particularly hydrogels, are highlighted for tackling the challenges of chronic wound management. Hydrogels are characterized as multipurpose materials that possess vital characteristics like the capacity to retain moisture, biocompatibility, and the incorporation of active drugs. Hydrogels' effectiveness in therapeutic applications is demonstrated by how they support healing, including preserving ideal moisture levels, promoting cellular migration, and possessing antibacterial properties. Thus, this paper presents hydrogel technology's latest developments, emphasizing drug-loaded and stimuli-responsive types and underscoring how these advanced formulations greatly improve therapy outcomes by enabling dynamic and focused reactions to the wound environment. Future directions for hydrogel research promote the development of customized hydrogel treatments and the incorporation of digital health tools to improve the treatment of chronic wounds.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Synthetic and biopolymers-based antimicrobial hybrid hydrogels: a focused review

The constantly accelerating occurrence of microbial infections and their antibiotic resistance has spurred advancement in the field of material sciences and has guided the development of novel materials with anti-bacterial properties. To address the clinical exigencies, the material of choice should be biodegradable, biocompatible, and able to offer prolonged antibacterial effects. As an attractive option, hydrogels have been explored globally as a potent biomaterial platform that can furnish essential antibacterial attributes owing to its three-dimensional (3D) hydrophilic polymeric network, adequate biocompatibility, and cellular adhesion. The current review focuses on the utilization of different antimicrobial hydrogels based on their sources (natural and synthetic). Further, the review also highlights the strategies for the generation of hydrogels with their advantages and disadvantages and their applications in different biomedical fields. Finally, the prospects in the development of hydrogels-based antimicrobial biomaterials are discussed along with some key challenges encountered during their development and clinical translation.

Theoretical trends in the dynamics simulations of molecular machines across multiple scales

Over the past few decades, molecular machines have been extensively studied, since they are composed of single molecules for functional materials capable of responding to external stimuli, enabling motion at scales ranging from the microscopic to the macroscopic level within molecular aggregates. This advancement holds the potential to efficiently transform external resources into mechanical movement, achieved through precise control of conformational changes in stimuli-responsive materials. However, the underlying mechanism that links microscopic and macroscopic motions remains unclear, demanding computational development associated with simulating the construction of molecular machines from single molecules. This bottleneck has impeded the design of more efficient functional materials. Advancements in theoretical simulations have successfully been developed in various computational models to unveil the operational mechanisms of stimulus-responsive molecular machines, which could help us reduce the costs in experimental trial-and-error procedures. It opens doors to the computer-aided design of innovative functional materials. In this perspective, we have reviewed theoretical approaches employed in simulating dynamic processes involving conformational changes in molecular machines, spanning different scales and environmental conditions. In addition, we have highlighted current challenges and anticipated future trends in the collective control of aggregates within molecular machines. Our goal is to provide a comprehensive overview of recent theoretical advancements in the field of molecular machines, offering valuable insights for the design of novel smart materials.

Exploring the Potential of Artificial Intelligence for Hydrogel Development-A Short Review

AI and ML have emerged as transformative tools in various scientific domains, including hydrogel design. This work explores the integration of AI and ML techniques in the realm of hydrogel development, highlighting their significance in enhancing the design, characterisation, and optimisation of hydrogels for diverse applications. We introduced the concept of AI train hydrogel design, underscoring its potential to decode intricate relationships between hydrogel compositions, structures, and properties from complex data sets. In this work, we outlined classical physical and chemical techniques in hydrogel design, setting the stage for AI/ML advancements. These methods provide a foundational understanding for the subsequent AI-driven innovations. Numerical and analytical methods empowered by AI/ML were also included. These computational tools enable predictive simulations of hydrogel behaviour under varying conditions, aiding in property customisation. We also emphasised AI's impact, elucidating its role in rapid material discovery, precise property predictions, and optimal design. ML techniques like neural networks and support vector machines that expedite pattern recognition and predictive modelling using vast datasets, advancing hydrogel formulation discovery are also presented. AI and ML's have a transformative influence on hydrogel design. AI and ML have revolutionised hydrogel design by expediting material discovery, optimising properties, reducing costs, and enabling precise customisation. These technologies have the potential to address pressing healthcare and biomedical challenges, offering innovative solutions for drug delivery, tissue engineering, wound healing, and more. By harmonising computational insights with classical techniques, researchers can unlock unprecedented hydrogel potentials, tailoring solutions for diverse applications.

A Comprehensive Review of Polysaccharide-Based Hydrogels as Promising Biomaterials

Polysaccharides have emerged as a promising material for hydrogel preparation due to their biocompatibility, biodegradability, and low cost. This review focuses on polysaccharide-based hydrogels' synthesis, characterization, and applications. The various synthetic methods used to prepare polysaccharide-based hydrogels are discussed. The characterization techniques are also highlighted to evaluate the physical and chemical properties of polysaccharide-based hydrogels. Finally, the applications of SAPs in various fields are discussed, along with their potential benefits and limitations. Due to environmental concerns, this review shows a growing interest in developing bio-sourced hydrogels made from natural materials such as polysaccharides. SAPs have many beneficial properties, including good mechanical and morphological properties, thermal stability, biocompatibility, biodegradability, non-toxicity, abundance, economic viability, and good swelling ability. However, some challenges remain to be overcome, such as limiting the formulation complexity of some SAPs and establishing a general protocol for calculating their water absorption and retention capacity. Furthermore, the development of SAPs requires a multidisciplinary approach and research should focus on improving their synthesis, modification, and characterization as well as exploring their potential applications. Biocompatibility, biodegradation, and the regulatory approval pathway of SAPs should be carefully evaluated to ensure their safety and efficacy.

Polymeric Membranes for Biomedical Applications

Polymeric membranes are selective materials used in a wide range of applications that require separation processes, from water filtration and purification to industrial separations. Because of these materials' remarkable properties, namely, selectivity, membranes are also used in a wide range of biomedical applications that require separations. Considering the fact that most organs (apart from the heart and brain) have separation processes associated with the physiological function (kidneys, lungs, intestines, stomach, etc.), technological solutions have been developed to replace the function of these organs with the help of polymer membranes. This review presents the main biomedical applications of polymer membranes, such as hemodialysis (for chronic kidney disease), membrane-based artificial oxygenators (for artificial lung), artificial liver, artificial pancreas, and membranes for osseointegration and drug delivery systems based on membranes.

Nucleoside-Based Supramolecular Hydrogels: From Synthesis and Structural Properties to Biomedical and Tissue Engineering Applications

Supramolecular hydrogels are of great interest in tissue scaffolding, diagnostics, and drug delivery due to their biocompatibility and stimuli-responsive properties. In particular, nucleosides are promising candidates as building blocks due to their manifold noncovalent interactions and ease of chemical modification. Significant progress in the field has been made over recent years to allow the use of nucleoside-based supramolecular hydrogels in the biomedical field, namely drug delivery and 3D bioprinting. For example, their long-term stability, printability, functionality, and bioactivity have been greatly improved by employing more than one gelator, incorporating different cations, including silver for antibacterial activity, or using additives such as boric acid or even biomolecules. This now permits their use as bioinks for 3D printing to produce cell-laden scaffolds with specified geometries and pore sizes as well as a homogeneous distribution of living cells and bioactive molecules. We have summarized the latest advances in nucleoside-based supramolecular hydrogels. Additionally, we discuss their synthesis, structural properties, and potential applications in tissue engineering and provide an outlook and future perspective on ongoing developments in the field.

In Vitro and Biological Characterization of Dexamethasone Sodium Phosphate Laden pH-Sensitive and Mucoadhesive Hydroxy Propyl β-Cyclodextrin-g-poly(acrylic acid)/Gelatin Semi-Interpenetrating Networks

The current study reports the fabrication and biological evaluation of hydroxy propyl β-cyclodextrin-g-poly(acrylic acid)/gelatin (HP-β-CD-g-poly(AA)/gelatin) semi-interpenetrating networks (semi-IPN) for colonic delivery of dexamethasone sodium phosphate (DSP). The prepared hydrogels showed pH-dependent swelling and mucoadhesive properties. The mucoadhesive strength of hydrogels increased with an increasing concentration of gelatin. Based on the swelling and mucoadhesive properties, AG-1 was chosen as the optimized formulation (0.33% w/w of gelatin and 16.66% w/w of AA) for further analysis. FTIR revealed the successful development of a polymeric network without any interaction with DSP. SEM images revealed a slightly rough surface after drug loading. Drug distribution at the molecular level was confirmed by XRD. In vitro drug release assay showed pH-dependent release, i.e., a minute amount of DSP was released at a pH of 1.2 while 90.58% was released over 72 h at pH 7.4. The optimized formulation did not show any toxic effects on a rabbit's vital organs and was also hemocompatible, thus confirming the biocompatible nature of the hydrogel. Conclusively, the prepared semi-IPN hydrogel possessed the necessary features, which can be exploited for the colonic delivery of DSP.

Facile Preparation and Dye Adsorption Performance of Poly(N-isopropylacrylamide-co-acrylic acid)/Molybdenum Disulfide Composite Hydrogels

Using N-isopropylacrylamide (NIPAM) and acrylic acid (AAc) as monomers, N,N'-methylenebisacrylamide (MBA) as a cross-linking agent, and molybdenum disulfide (MoS2) as functional particles, a P(NIPAM-co-AAc)/MoS2 composite hydrogel was prepared by free radical polymerization initiated by ultraviolet light. The results of Fourier transform infrared spectroscopy, Raman spectroscopy, and scanning electron microscopy show that MoS2 has been successfully introduced into the P(NIPAM-co-AAc) system, and the obtained composite hydrogel has a porous network structure. Studies on the swelling property and dye adsorption performance show that the addition of MoS2 can increase the swelling ratio of P(NIPAM-co-AAc) hydrogels to a certain extent and can significantly improve the ability of the P(NIPAM-co-AAc) hydrogel to adsorb methylene blue (MB). The adsorption process of MB by the composite hydrogels conforms to the pseudo-second-order kinetics and the Langmuir isotherm adsorption models. The estimated equilibrium adsorption capacity (Q m) using the Langmuir isotherm model can reach 1258 mg/g, mainly due to the electrostatic interaction between the negatively charged groups -COO- and MoS2 particles on the network structure and the positively charged dye MB. The adsorption of MB by P(NIPAM-co-AAc)/MoS2 composite hydrogels depends on the temperature during adsorption. Compared with room temperature, a high temperature of 40 °C above the poly(N-isopropylacrylamide) (PNIPAM) phase transition temperature (∼32 °C) leads to a decreased adsorption capacity of the P(NIPAM-co-AAc)/MoS2 composite hydrogel for MB due to the enhanced hydrophobic properties of the network structure and the decrease of the swelling ratio. The prepared hydrogel material can be used as a good adsorbent for dyes, which is promising in wastewater treatment.

Systematic risk identification and assessment using a new risk map in pharmaceutical R&D

Delivering transformative therapies to patients while maintaining growth in the pharmaceutical industry requires an efficient use of research and development (R&D) resources and technologies to develop high-impact new molecular entities (NMEs). However, increasing global R&D competition in the pharmaceutical industry, growing impact of generics and biosimilars, more stringent regulatory requirements, as well as cost-constrained reimbursement frameworks challenge current business models of leading pharmaceutical companies. Big data-based analytics and artificial intelligence (AI) approaches have disrupted various industries and are having an increasing impact in the biopharmaceutical industry, with the promise to improve and accelerate biopharmaceutical R&D processes. Here, we systematically analyze, identify, assess, and categorize key risks across the drug discovery and development value chain using a new risk map approach, providing a comprehensive risk-reward analysis for pharmaceutical R&D.

Preparation of poly(acrylamide‐co‐2‐acrylamido‐2‐methylpropan sulfonic acid)‐g‐Carboxymethyl cellulose/Titanium dioxide hydrogels and modeling of their swelling capacity and mechanic strength behaviors by response surface method technique

It is very important that new generation, unique, high mechanical strength, and biocompatible hydrogel composites are developed due to their potential to be used as biomaterials in the biomedical field. Modeling of the swelling capacity and mechanical strength behavior of hydrogels is a domain of steadily increasing academic and industrial importance. These behaviors are difficult to model accurately due to hydrogels show very complex behavior depending on the content. In this study, a series of poly(acrylamide‐co‐2‐acrylamido‐2‐methylpropan sulfonic acid)‐g‐carboxymethyl cellulose/TiO2 (poly(AAm‐co‐AMPS)‐g‐CMC/TiO2) superabsorbent hydrogel composites were prepared by free‐radical graft copolymerization in aqueous solution. Structural and surface morphology characterizations were conducted by using Fourier‐transform infrared spectroscopy and scanning electron microscope analysis techniques. For modeling the equilibrium swelling capacity and fracture strength behaviors of hydrogels, the composition parameters (such as mole ratio of AMPS/AAm, wt% of CMC, and wt% of TiO2) was proposed by response surface method (RSM) Design Expert‐10 software. Statistical parameters showed that the RSM model has good performance in modeling the swelling capacity and mechanic fracture strength behaviors of poly(AAm‐co‐AMPS)‐g‐CMC/TiO2 hydrogel composites. According to the RSM model results, the maximum swelling capacity and fracture strength values were calculated as 270.39 g water/g polymer and 159.23 kPa, respectively.

Enhancing Clinical Translation of Cancer Using Nanoinformatics

Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients.

Polymeric Nanocarriers: A Transformation in Doxorubicin Therapies

Doxorubicin, a member of the anthracycline family, is a common anticancer agent often used as a first line treatment for the wide spectrum of cancers. Doxorubicin-based chemotherapy, although effective, is associated with serious side effects, such as irreversible cardiotoxicity or nephrotoxicity. Those often life-threatening adverse risks, responsible for the elongation of the patients' recuperation period and increasing medical expenses, have prompted the need for creating novel and safer drug delivery systems. Among many proposed concepts, polymeric nanocarriers are shown to be a promising approach, allowing for controlled and selective drug delivery, simultaneously enhancing its activity towards cancerous cells and reducing toxic effects on healthy tissues. This article is a chronological examination of the history of the work progress on polymeric nanostructures, designed as efficient doxorubicin nanocarriers, with the emphasis on the main achievements of 2010-2020. Numerous publications have been reviewed to provide an essential summation of the nanopolymer types and their essential properties, mechanisms towards efficient drug delivery, as well as active targeting stimuli-responsive strategies that are currently utilized in the doxorubicin transportation field.