A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery

Preparation, optimization, and evaluation of ligand-tethered atovaquone-proguanil-loaded nanoparticles for malaria treatment

This work focused on improving antimalarial therapy through the development and characterization of Atovaquone-Proguanil-loaded nanoparticles employing a 32 factorial design. The nanoparticles were prepared from combinations of Poly(lactic-co-glycolic acid) (PLGA) and Eudragit L100 polymers and different concentrations of PVA (polyvinyl alcohol). Based on the results obtained the formulations were characterized for the particle size, zeta potential, encapsulation efficiency, and percent drug release. Among the nine formulations, F5 proved to be the most favorable in the biophysical parameters with a particle size of 176.3 nm, a zeta potential of -33.5 mV, and an encapsulation efficiency of 86% was found in the present investigation. Experimental dissolution profile analysis indicated that F5 had a slow and controlled-release profile where approximately 92.5%. Besides, cytotoxicity studies employing MTT, LDH (lactate dehydrogenase), and Trypan blue reduction test also supported the biocompatibility of nanoparticles and F5 had the highest cell viability (96%) with the least LDH release of 4%. In stability studies conducted for six months, F5 was found to remain stable regarding physicochemical characteristics and drug release profile at different temperature conditions such as room temperature, 4 °C, and 45 °C. The use of folic acid-functionalized nanoparticles is more effective, according to parasitemia, survival rate, and weight loss in mice treated with the nanoparticles. This is because functionalized nanoparticles could be used to enhance anti-malarial therapies.

Revolutionizing prostate cancer therapy: Artificial intelligence - Based nanocarriers for precision diagnosis and treatment

Prostate cancer is one of the major health challenges in the world and needs novel therapeutic approaches to overcome the limitations of conventional treatment. This review delineates the transformative potential of artificial intelligence (AL) in enhancing nanocarrier-based drug delivery systems for prostate cancer therapy. With its ability to optimize nanocarrier design and predict drug delivery kinetics, AI has revolutionized personalized treatment planning in oncology. We discuss how AI can be integrated with nanotechnology to address challenges related to tumor heterogeneity, drug resistance, and systemic toxicity. Emphasis is placed on strong AI-driven advancements in the design of nanocarriers, structural optimization, targeting of ligands, and pharmacokinetics. We also give an overview of how AI can better predict toxicity, reduce costs, and enable personalized medicine. While challenges persist in the way of data accessibility, regulatory hurdles, and interactions with the immune system, future directions based on explainable AI (XAI) models, integration of multimodal data, and green nanocarrier designs promise to move the field forward. Convergence between AI and nanotechnology has been one key step toward safer, more effective, and patient-tailored cancer therapy.

Machine Learning in Polymer Research

Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid-state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer-biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine-human interface are shared.

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Biofunctionalized polymeric nanoparticles for the enhanced delivery of erlotinib in cancer therapy

Erlotinib, a potent epidermal growth factor receptor (EGFR) inhibitor, faces bioavailability challenges due to poor water solubility and stability. This study aims to optimize erlotinib-loaded PLGA nanoparticles using a 32 factorial design to enhance drug delivery and therapeutic efficacy. The effects of PLGA concentration (R1) and NaTPP concentration (R2) on nanoparticle characteristics, including particle size, zeta potential, and polydispersity index (PDI), were investigated. The optimal formulation (F5) was identified and characterized, showing a particle size of 169.1 nm, a zeta potential of 20.0 mV, and a PDI of 0.146, indicating uniform and stable nanoparticles. Transmission electron microscopy (TEM) confirmed spherical nanoparticles with minimal aggregation, while X-ray diffraction (XRD) indicated an amorphous state of erlotinib. Formulation F5 demonstrated an entrapment efficiency of 81.9% and a yield of 83.0%. In-vitro drug release studies revealed a sustained release pattern with 90.0% cumulative release at 48 h, following Zero Order kinetics. Cytotoxicity assays showed low cytotoxicity across various cell lines. Statistical analysis confirmed the significant impact of formulation variables on nanoparticle properties. The systematic optimization of erlotinib-loaded nanoparticles has successfully identified formulation F5 as an optimal candidate with favorable characteristics, including minimal particle size, high stability, controlled drug release, and a safe cytotoxicity profile. Notably, the optimized formulation (F5) enhances therapeutic efficacy through improved bioavailability and targeted delivery, addressing the limitations of conventional therapies. These findings suggest that the optimized erlotinib-loaded nanoparticles hold significant potential for enhanced drug delivery and therapeutic efficacy.

Nanotechnology-Driven Delivery of Caffeine Using Ultradeformable Liposomes-Coated Hollow Mesoporous Silica Nanoparticles for Enhanced Follicular Delivery and Treatment of Androgenetic Alopecia

Androgenetic alopecia (AGA) is caused by the impact of dihydrotestosterone (DHT) on hair follicles, leading to progressive hair loss in men and women. In this study, we developed caffeine-loaded hollow mesoporous silica nanoparticles coated with ultradeformable liposomes (ULp-Caf@HMSNs) to enhance caffeine delivery to hair follicles. Caffeine, known to inhibit DHT formation, faces challenges in skin penetration due to its hydrophilic nature. We investigated caffeine encapsulated in liposomes, hollow mesoporous silica nanoparticles (HMSNs), and ultradeformable liposome-coated HMSNs to optimize drug delivery and release. For ultradeformable liposomes (ULs), the amount of polysorbate 20 and polysorbate 80 was varied. TEM images confirmed the mesoporous shell and hollow core structure of HMSNs, with a shell thickness of 25-35 nm and a hollow space of 80-100 nm. SEM and TEM analysis showed particle sizes ranging from 140-160 nm. Thermal stability tests showed that HMSNs coated with ULs exhibited a Td10 value of 325 °C and 70% residue ash, indicating good thermal stability. Caffeine release experiments indicated that the highest release occurred in caffeine-loaded HMSNs without a liposome coating. In contrast, systems incorporating ULp-Caf@HMSNs exhibited slower release rates, attributable to the dual encapsulation mechanism. Confocal laser scanning microscopy revealed that ULs-coated particles penetrated deeper into the skin than non-liposome particles. MTT assays confirmed the non-cytotoxicity of all HMSN concentrations to human follicle dermal papilla cells (HFDPCs). ULp-Caf@HMSNs promoted better cell viability than pure caffeine or caffeine-loaded HMSNs, highlighting enhanced biocompatibility without increased toxicity. Additionally, ULp-Caf@HMSNs effectively reduced ROS levels in DHT-damaged HFDPCs, suggesting they are promising alternatives to minoxidil for promoting hair follicle growth and reducing hair loss without increasing oxidative stress. This system shows promise for treating AGA.

Thermoresponsive lipids engineered magnetic nanoparticles for spatiotemporal delivery of hesperidin to inflammatory sites in animal model

Thermoresponsive nanoparticles are exploited as drug-delivery vehicles that release their payload upon increment in temperature. We prepared and characterized thermoresponsive lipid-anchored folic acid engineered magnetic nanoparticles (LP-HP-FANPs) that combine receptor-based targeting and thermoresponsive sustained release of hesperidin (HP) in response to endogenous inflammation site temperature. The progressive surface engineering of NPs was validated by FTIR analysis. Our LP-HP-FANPs had a particle size of 100.5 ± 1.76 nm and a zeta potential of 14.6 ± 2.65 mV. The HP encapsulation effectiveness of LP-HP-FANPs is around 91 ± 0.78%. AFM scans indicated that our modified nanoparticles were spherical. LP-HP-FANPs exhibit increased drug release (85.8% at pH 4.0, 50.9% at pH 7.0) at 40 °C. Animal studies showed no toxicity from nanoparticles. Compared to conventional drugs and HP, LP-HP-FANPs effectively decreased paw edema, cytokine levels, and total cell recruitment in thioglycollate-induced peritonitis (p < 0.05). LP-HP-FANPs substantially decreased cytokines compared to HP, HP-FA-NPs, and the standard medication (p < 0.05, p < 0.01, and p < 0.001). These findings imply that the synthesized HP-loaded formulation (LP-HP-FANPs) may be a potential anti-inflammatory formulation for clinical development.

Design, optimization, and evaluation of methotrexate loaded and albumin coated polymeric nanoparticles

Methotrexate is a potent anticancer drug whose strong efflux is facilitated by the brain's efflux transporter. As an efflux transporter blocker, albumin increased the drug's concentration in the brain. Methotrexate-loaded nanoparticles were produced by evaporating the emulsification fluid. Improvements and analyses were made to the following aspects of the generated nanoparticles: size, polydispersity, zeta potential, entrapment efficiency, percentage yield, scanning electron microscopy, in vitro drug release studies, and sterilization. The particle size was determined to be in the nano range, and homogeneity of particle size was suggested by a low polydispersity index result. Particle diameters of 168 nm were observed in the F5 preparation, and zeta potential values of -1.5 mV suggested that the preparation produced adequate repulsive interactions between the nanoparticles. Albumin and dopamine HCl were employed to coat the methotrexate-loaded nanoparticles to guarantee that the brain received an adequate amount of them. The homogeneity of albumin coated nanoparticles was demonstrated by the low% PDI values of 0.129 and 0.122 for albumin coated nanoparticles (MNPs-Alb) and polymerized dopamine HCl and albumin coated nanoparticles (MNPs-PMD-Alb), respectively. After 48 h of incubation, the cell viability measured at the same drug concentration (5 mg) decreased for the F5, albumin coated nanoparticles, polymerized dopamine HCl coated nanoparticles, and polymerized dopamine HCl and albumin coated nanoparticles, respectively. Our primary findings demonstrate that the albumin nanoparticles containing methotrexate are designed to deliver the drug gradually. With minimal cytotoxicity, the intended preparation might give the brain an appropriate dosage of methotrexate.

Polyester nanoparticles delivering chemotherapeutics: Learning from the past and looking to the future to enhance their clinical impact in tumor therapy

Polymeric nanoparticles (NPs), specifically those comprised of biodegradable and biocompatible polyesters, have been heralded as a game-changing drug delivery platform. In fact, poly(α-hydroxy acids) such as polylactide (PLA), poly(lactide-co-glycolide) (PLGA), and poly(ε-caprolactone) (PCL) have been heavily researched in the past three decades as the material basis of polymeric NPs for drug delivery applications. As materials, these polymers have found success in resorbable sutures, biodegradable implants, and even monolithic, biodegradable platforms for sustained release of therapeutics (e.g., proteins and small molecules) and diagnostics. Few fields have gained more attention in drug delivery through polymeric NPs than cancer therapy. However, the clinical translational of polymeric nanomedicines for treating solid tumors has not been congruent with the fervor or funding in this particular field of research. Here, we attempt to provide a comprehensive snapshot of polyester NPs in the context of chemotherapeutic delivery. This includes a preliminary exploration of the polymeric nanomedicine in the cancer research space. We examine the various processes for producing polyester NPs, including methods for surface-functionalization, and related challenges. After a detailed overview of the multiple factors involved with the delivery of NPs to solid tumors, the crosstalk between particle design and interactions with biological systems is discussed. Finally, we report state-of-the-art approaches toward effective delivery of NPs to tumors, aiming at identifying new research areas and re-evaluating the reasons why some research avenues have underdelivered. We hope our effort will contribute to a better understanding of the gap to fill and delineate the future research work needed to bring polyester-based NPs closer to clinical application. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Emerging Technologies.

Point biserial correlation symbiotic organism search nanoengineering based drug delivery for tumor diagnosis

Nanoparticulate systems have the prospect of accounting for a new making of drug delivery systems. Nanotechnology is manifested to traverse the hurdle of both physical and biological sciences by implementing nanostructures indistinct fields of science, particularly in nano-based drug delivery. The low delivery efficiency of nanoparticles is a critical obstacle in the field of tumor diagnosis. Several nano-based drug delivery studies are focused on for tumor diagnosis. But, the nano-based drug delivery efficiency was not increased for tumor diagnosis. This work proposes a method called point biserial correlation symbiotic organism search nanoengineering-based drug delivery (PBC-SOSN). The objective and aim of the PBC-SOSN method is to achieve higher drug delivery efficiency and lesser drug delivery time for tumor diagnosis. The contribution of the PBC-SOSN is to optimized nanonengineering-based drug delivery with higher r drug delivery detection rate and smaller drug delivery error detection rate. Initially, raw data acquired from the nano-tumor dataset, and nano-drugs for glioblastoma dataset, overhead improved preprocessed samples are evolved using nano variational model decomposition-based preprocessing. After that, the preprocessed samples as input are subjected to variance analysis and point biserial correlation-based feature selection model. Finally, the preprocessed samples and features selected are subjected to symbiotic organism search nanoengineering (SOSN) to corroborate the objective. Based on these findings, point biserial correlation-based feature selection and a symbiotic organism search nanoengineering were tested for their modeling performance with a nano-tumor dataset and nano-drugs for glioblastoma dataset, finding the latter the better algorithm. Incorporated into the method is the potential to adjust the drug delivery detection rate and drug delivery error detection rate of the learned method based on selected features determined by nano variational model decomposition for efficient drug delivery.

A Comprehensive Study on Nanoparticle Drug Delivery to the Brain: Application of Machine Learning Techniques

The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to limited understanding of how nanoparticle (NP) properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by using various predictive models based on collection of large data sets of 403 data points incorporating both numerical and categorical features. Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain. Furthermore, the physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve. The analysis employed various linear models, with a particular emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional accuracy. The model was validated via the preparation and administration of two distinct NP formulations via the intranasal and intravenous routes. Among the various modeling approaches, LMEMs exhibited superior performance in capturing underlying patterns. Factors such as the release rate and molecular weight had a negative impact on brain targeting. The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate.