Modeling and closed-loop control of particle size and initial burst of PLGA biodegradable nanoparticles for targeted drug delivery

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

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.

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.

Hypoxia and CD44 receptors dual-targeted nano-micelles with AGT-inhibitory activity for the targeting delivery of carmustine

Carmustine (BCNU) is a typical chemotherapy used for treatment of cerebroma and other solid tumors, which exerts antitumor effect by inducing DNA damage at O6 position of guanine. However, the clinical application of BCNU was extremely limited due to the drug resistance mainly mediated by O6-alkylguanine-DNA alkyltransferase (AGT) and absence of tumor-targeting ability. To overcome these limitations, we developed a hypoxia-responsive nanomicelle with AGT inhibitory activity, which was successfully loaded with BCNU. In this nano-system, hyaluronic acid (HA) acts as an active tumor-targeting ligand to bind the overexpressing CD44 receptors on the surface of tumor cells. An azo bond selectively breaks in hypoxic tumor microenvironment to release O6-benzylguanine (BG) as AGT inhibitor and BCNU as DNA alkylating agent. The obtained HA-AZO-BG NPs with shell core structure had an average particle size of 176.98 ± 11.19 nm and exhibited good stability. Meanwhile, HA-AZO-BG NPs possessed a hypoxia-responsive drug release profile. After immobilizing BCNU into HA-AZO-BG NPs, the obtained HA-AZO-BG/BCNU NPs exhibited obvious hypoxia-selectivity and superior cytotoxicity in T98G, A549, MCF-7 and SMMC-7721 cells with IC50 at 189.0, 183.2, 90.1 and 100.1 μm, respectively, under hypoxic condition. Near-infrared imaging in HeLa tumor xenograft models showed that HA-AZO-BG/DiR NPs could effectively accumulate in tumor site at 4 h of post-injection, suggesting its good tumor-targetability. In addition, in vivo anti-tumor efficacy and toxicity evaluation indicated that HA-AZO-BG/BCNU NPs was more effective and less harmful compared to the other groups. After treatment, the tumor weight of HA-AZO-BG/BCNU NPs group was 58.46 % and 63.33 % of the control group and BCNU group, respectively. Overall, HA-AZO-BG/BCNU NPs was expected to be a promising candidate for targeted delivery of BCNU and elimination of chemoresistance.

A bio-orthogonally functionalized chitosan scaffold with esterase-activatable release for nerve regeneration

Developing nerve conduits with biological cues is a promising approach for repairing peripheral nerve injuries. Although most biological cues incorporated into conduits generally exert their biological functions at the surface, they could not be released into the on-demand regeneration sites under physiological conditions. Herein, we firstly report a bio-orthogonally functionalized chitosan scaffold with esterase-activatable release for peripheral nerve regeneration. In this study, biological cues are not only selectively conjugated into nerve conduits by bio-orthogonal reaction, but also precisely released in on-demand regeneration sites via esterase-activatable cleavage for peripheral nerve repair. Moreover, this nerve scaffold with esterase-activatable release could promote Schwann cells proliferation. In a rat sciatic nerve defect model, the bio-orthogonally functionalized scaffold with esterase-activatable release significantly increased sciatic nerve function recovery and improved target muscles weight. This strategy of incorporating esterase-activatable bioactive cues into peripheral nerve conduits offers great potential in preclinical studies.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research

The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.

The Importance of Nanocarrier Design and Composition for an Efficient Nanoparticle-Mediated Transdermal Vaccination

The World Health Organization estimates that the pandemic caused by the SARS-CoV-2 virus claimed more than 3 million lives in 2020 alone. This situation has highlighted the importance of vaccination programs and the urgency of working on new technologies that allow an efficient, safe, and effective immunization. From this perspective, nanomedicine has provided novel tools for the design of the new generation of vaccines. Among the challenges of the new vaccine generations is the search for alternative routes of antigen delivery due to costs, risks, need for trained personnel, and low acceptance in the population associated with the parenteral route. Along these lines, transdermal immunization has been raised as a promising alternative for antigen delivery and vaccination based on a large absorption surface and an abundance of immune system cells. These features contribute to a high barrier capacity and high immunological efficiency for transdermal immunization. However, the stratum corneum barrier constitutes a significant challenge for generating new pharmaceutical forms for transdermal antigen delivery. This review addresses the biological bases for transdermal immunomodulation and the technological advances in the field of nanomedicine, from the passage of antigens facilitated by devices to cross the stratum corneum, to the design of nanosystems, with an emphasis on the importance of design and composition towards the new generation of needle-free nanometric transdermal systems.

Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems

The field of nanotechnology and personalised medicine is undergoing drastic changes in the approach and efficiency of experimentation. The COVID-19 pandemic has spiralled into mass stagnation of major laboratories around the globe and led to increased investment into remote systems for nanoparticle experiments. A significant number of laboratories now operate using automated systems; however, the extension to nanoparticle preparation and artificial intelligence-dependent databases holds great translational promise. The strive to combine automation with artificial intelligence (AI) grants the ability to optimise targeted therapeutic nanoparticles for unique cell types and patients. In this perspective, the current and future trends of automated approaches to nanomedicine synthesis are discussed and compared with traditional methods.

Analysis of Dynamics Targeting CNT-Based Drug Delivery through Lung Cancer Cells: Design, Simulation, and Computational Approach

Nowadays, carbon nano (CN) structures and specifically carbon nanotubes (CNTs), because of the nanotube’s nanoscale shape, are widely used in carrier and separation applications. The conjugation of CNTs with polysaccharide, proteins, drugs, and magnetic nanoparticles provides a chance for smart targeting and trajectory manipulation, which are used in the crucial field of life science applications, including for cancer disease diagnostics and treatments. Providing an optimal procedure for delivering a drug to a specific area based on mathematical criteria is key in systemic delivery design. Trajectory guidance and applied force control are the main parameters affected by systemic delivery. Moreover, a better understanding of the tissue parameters and cell membrane molecular behaviour are other factors that can be indirectly affected by the targeted delivery. Both sides are an essential part of successful targeting. The lung is one of the challenging organs for drug delivery inside the human body. It has a large surface area with a thin epithelium layer. A few severe diseases directly involve human lung cells, and optimal and successful drug delivery to the lung for the treatment procedure is vital. In this paper, we studied functionalized CNTs’ targeted delivery via crossing through the lung cell membrane. Molecular dynamics (MD) software simulated all the interaction forces. Mathematical modelling of the cell membrane and proposed delivery system based on the relation of velocity and force has been considered. Dynamics equations for CNTs were defined in the time and frequency domain using control theory methods. The proposed delivery system consists of two main parts: crossing through the cell membrane and targeting inside the cell. For both steps, a mathematical model and a proper magnetic field profile have been proposed. The designed system provides criteria for crossing through the cell membrane within 30 s to 5 min and a translocation profile of 1 to 100 Å.

Fluorinated PLGA Nanoparticles for Enhanced Drug Encapsulation and 19 F NMR Detection

In the continuous search for multimodal systems with combined diagnostic and therapeutic functions, several efforts have been made to develop multifunctional drug delivery systems. In this work, through a covalent approach, a new class of fluorinated poly(lactic-co-glycolic acid) co-polymers (F-PLGA) were designed that contain an increasing number of magnetically equivalent fluorine atoms. In particular, two novel compounds, F₃ -PLGA and F₉ -PLGA, were synthesized and their chemical structure and thermal stability were analyzed by solution NMR, DSC, and TGA. The obtained F-PLGA compounds were proven to form in aqueous solution colloidal stable nanoparticles (NPs) displaying a strong ¹⁹ F NMR signal. The fluorinated NPs also showed an enhanced ability to load hydrophobic drugs containing fluorine atoms compared to analogous pristine PLGA NPs. Preliminary in vitro studies showed high cell viability and the NP ability to intracellularly deliver and release a functioning drug.

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

Sustained delivery of olanzapine from sunflower oil-based polyol-urethane nanoparticles synthesised through a cyclic carbonate ring-opening reaction

The forefront horizon of biomedical investigations in recent decades is parcelling-up and delivery of drugs to achieve controlled/targeted release. In this regard, developing green-based delivery systems for a spatiotemporal controlling therapeutic agent have drawn a lot of attention. A facile route based on cyclic carbonate ring-opening reaction has been utilised to synthesise a bio-based polyol-containing urethane bond [polyol-urethane (POU)] as a nanoparticulate drug delivery system of olanzapine in order to enhance its bioavailability. After characterisation, the nanoparticles were also estimated for in vitro release, toxicity, and pharmacokinetic studies. As olanzapine has shown poor bioavailability and permeability in the brain, the sustained release of olanzapine from the designed carriers could enhance pharmacokinetic effectiveness. POU in the aqueous solution formed micelles with a hydrophobic core and embedded olanzapine under the influence of its hydrophobic nature. Drug release from the nanoparticles (90 ± 0.43 nm in diameter) indicated a specific pattern with initial burst release, and then a sustained release behaviour (82 ± 3% after 168 h), by the Higuchi-based release mechanism. Pharmacokinetics assessments of POU-olanzapine nanoparticles were carried in male Wistar rats through intravenous administration. The obtained results paved a way to introduce the POU as an efficient platform to enhance the bioavailability of olanzapine in therapeutic methods.

Intelligent Machine Learning: Tailor-Making Macromolecules

Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and successfully put into practice. The two tools are established through the amalgamation of the Kinetic Monte Carlo simulation approach and machine learning techniques. The former, an intelligent modeling tool, is able to model and visualize the intricate inter-relationships of polymerization recipes/conditions (as input variables) and microstructural features of the produced macromolecules (as responses). The latter is capable of precisely predicting optimal copolymerization conditions to simultaneously satisfy all predefined microstructural features. The effectiveness of the proposed intelligent modeling and optimization techniques for solving this extremely important 'inverse' engineering problem was successfully examined by investigating the possibility of tailor-making the microstructure of Olefin Block Copolymers via chain-shuttling coordination polymerization.