In silico models for nanotoxicity evaluation and prediction at the blood-brain barrier level: A mini-review

Development of a bottom-up coarse-grained model for interactions of lipids with TiO 2 nanoparticles

Understanding interactions of inorganic nanoparticles with biomolecules is important in many biotechnology, nanomedicine, and toxicological research, however, the size of typical nanoparticles makes their direct modeling by atomistic simulations unfeasible. Here, we present a bottom-up coarse-graining approach for modeling titanium dioxide (TiO  2 ) nanomaterials in contact with phospholipids that uses the inverse Monte Carlo method to optimize the effective interactions from the structural data obtained in small-scale all-atom simulations of TiO  2 surfaces with lipids in aqueous solution. The resulting coarse-grained models are able to accurately reproduce the structural details of lipid adsorption on different titania surfaces without the use of an explicit solvent, enabling significant computational resource savings and favorable scaling. Our coarse-grained simulations show that small spherical TiO  2 nanoparticles ( r = 2 nm) can only be partially wrapped by a lipid bilayer with phosphoethanolamine headgroups, however, the lipid adsorption increases with the radius of the nanoparticle. The current approach can be used to study the effect of the size and shape of TiO  2 nanoparticles on their interactions with cell membrane lipids, which can be a determining factor in membrane wrapping as well as the recently discovered phenomenon of nanoquarantining, which involves the formation of layered nanomaterial-lipid structures.

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.

Chemical Analysis and Molecular Modelling of Cyclodextrin-Formulated Propofol and Its Sodium Salt to Improve Drug Solubility, Stability and Pharmacokinetics (Cytogenotoxicity)

Propofol is a widely used general anesthetic in clinical practice, but its use is limited by its water-insoluble nature and associated pharmacokinetic and pharmacodynamic limitations. Therefore, researchers have been searching for alternative formulations to lipid emulsion to address the remaining side effects. In this study, novel formulations for propofol and its sodium salt Na-propofolat were designed and tested using the amphiphilic cyclodextrin (CD) derivative hydroxypropyl-β-cyclodextrin (HPβCD). The study found that spectroscopic and calorimetric measurements suggested complex formation between propofol/Na-propofolate and HPβCD, which was confirmed by the absence of an evaporation peak and different glass transition temperatures. Moreover, the formulated compounds showed no cytotoxicity and genotoxicity compared to the reference. The molecular modeling simulations based on molecular docking predicted a higher affinity for propofol/HPβCD than for Na-propofolate/HPβCD, as the former complex was more stable. This finding was further confirmed by high-performance liquid chromatography. In conclusion, the CD-based formulations of propofol and its sodium salt may be a promising option and a plausible alternative to conventional lipid emulsions.

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.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Promising advances in nanobiotic-based formulations for drug specific targeting against multidrug-resistant microbes and biofilm-associated infections

Antimicrobial agents and alternative strategies to combat bacterial infections have become urgent due to the rapid development of multidrug-resistant bacteria caused by the misuse and overuse of antibiotics, as well as the ineffectiveness of antibiotics against difficult-to-treat infectious diseases. Nanobiotics is one of the strategies being explored to counter the increase in antibiotic-resistant bacteria. Nanobiotics are antibiotic molecules encapsulated in nanoparticles or artificially engineered pure antibiotics that are ≤ 100 nm in size in at least one dimension. Formulation scientists recognize nanobiotic delivery systems as an effective strategy to overcome the limitations associated with conventional antibiotic therapy. This review highlights the general mechanisms by which nanobiotics can be used to target resistant microbes and biofilm-associated infections. We focus on the design elements, properties, characterization, and toxicity assessment of organic nanoparticles, inorganic nanoparticle and molecularly imprinted polymer-based nano-formulations that can be designed to improve the efficacy of nanobiotic formulation.

Computational Indicator Approach for Assessment of Nanotoxicity of Two-Dimensional Nanomaterials

The increasing growth in the development of various novel nanomaterials and their biomedical applications has drawn increasing attention to their biological safety and potential health impact. The most commonly used methods for nanomaterial toxicity assessment are based on laboratory experiments. In recent years, with the aid of computer modeling and data science, several in silico methods for the cytotoxicity prediction of nanomaterials have been developed. An affordable, cost-effective numerical modeling approach thus can reduce the need for in vitro and in vivo testing and predict the properties of designed or developed nanomaterials. We propose here a new in silico method for rapid cytotoxicity assessment of two-dimensional nanomaterials of arbitrary chemical composition by using free energy analysis and molecular dynamics simulations, which can be expressed by a computational indicator of nanotoxicity (CIN_2D). We applied this approach to five well-known two-dimensional nanomaterials promising for biomedical applications: graphene, graphene oxide, layered double hydroxide, aloohene, and hexagonal boron nitride nanosheets. The results corroborate the available laboratory biosafety data for these nanomaterials, supporting the applicability of the developed method for predictive nanotoxicity assessment of two-dimensional nanomaterials.

Studies on Aggregated Nanoparticles Steering during Deep Brain Membrane Crossing

Many central nervous system (CNS) diseases, such as Alzheimer's disease (AD), affect the deep brain region, which hinders their effective treatment. The hippocampus, a deep brain area critical for learning and memory, is especially vulnerable to damage during early stages of AD. Magnetic drug targeting has shown high potential in delivering drugs to a targeted disease site effectively by applying a strong electromagnetic force. This study illustrates a nanotechnology-based scheme for delivering magnetic nanoparticles (MNP) to the deep brain region. First, we developed a mathematical model and a molecular dynamic simulation to analyze membrane crossing, and to study the effects of particle size, aggregation, and crossing velocities. Then, using in vitro experiments, we studied effective parameters in aggregation. We have also studied the process and environmental parameters. We have demonstrated that aggregation size can be controlled when particles are subjected to external electromagnetic fields. Our simulations and experimental studies can be used for capturing MNPs in brain, the transport of particles across the intact BBB and deep region targeting. These results are in line with previous in vivo studies and establish an effective strategy for deep brain region targeting with drug loaded MNPs through the application of an external electromagnetic field.

Advances in Non-Animal Testing Approaches towards Accelerated Clinical Translation of Novel Nanotheranostic Therapeutics for Central Nervous System Disorders

Nanotheranostics constitute a novel drug delivery system approach to improving systemic, brain-targeted delivery of diagnostic imaging agents and pharmacological moieties in one rational carrier platform. While there have been notable successes in this field, currently, the clinical translation of such delivery systems for the treatment of neurological disorders has been limited by the inadequacy of correlating in vitro and in vivo data on blood-brain barrier (BBB) permeation and biocompatibility of nanomaterials. This review aims to identify the most contemporary non-invasive approaches for BBB crossing using nanotheranostics as a novel drug delivery strategy and current non-animal-based models for assessing the safety and efficiency of such formulations. This review will also address current and future directions of select in vitro models for reducing the cumbersome and laborious mandate for testing exclusively in animals. It is hoped these non-animal-based modelling approaches will facilitate researchers in optimising promising multifunctional nanocarriers with a view to accelerating clinical testing and authorisation applications. By rational design and appropriate selection of characterised and validated models, ranging from monolayer cell cultures to organ-on-chip microfluidics, promising nanotheranostic particles with modular and rational design can be screened in high-throughput models with robust predictive power. Thus, this article serves to highlight abbreviated research and development possibilities with clinical translational relevance for developing novel nanomaterial-based neuropharmaceuticals for therapy in CNS disorders. By generating predictive data for prospective nanomedicines using validated in vitro models for supporting clinical applications in lieu of requiring extensive use of in vivo animal models that have notable limitations, it is hoped that there will be a burgeoning in the nanotherapy of CNS disorders by virtue of accelerated lead identification through screening, optimisation through rational design for brain-targeted delivery across the BBB and clinical testing and approval using fewer animals. Additionally, by using models with tissue of human origin, reproducible therapeutically relevant nanomedicine delivery and individualised therapy can be realised.

Novel Methods and Approaches for Safety Evaluation of Nanoparticle Formulations: A Focus Towards In Vitro Models and Adverse Outcome Pathways

Nanotoxicology is an emerging field employed in the assessment of unintentional hazardous effects produced by nanoparticles (NPs) impacting human health and the environment. The nanotoxicity affects the range between induction of cellular stress and cytotoxicity. The reasons so far reported for these toxicological effects are due to their variable sizes with high surface areas, shape, charge, and physicochemical properties, which upon interaction with the biological components may influence their functioning and result in adverse outcomes (AO). Thus, understanding the risk produced by these materials now is an important safety concern for the development of nanotechnology and nanomedicine. Since the time nanotoxicology has evolved, the methods employed have been majorly relied on in vitro cell-based evaluations, while these simple methods may not predict the complexity involved in preclinical and clinical conditions concerning pharmacokinetics, organ toxicity, and toxicities evidenced through multiple cellular levels. The safety profiles of nanoscale nanomaterials and nanoformulations in the delivery of drugs and therapeutic applications are of considerable concern. In addition, the safety assessment for new nanomedicine formulas lacks regulatory standards. Though the in vivo studies are greatly needed, the end parameters used for risk assessment are not predicting the possible toxic effects produced by various nanoformulations. On the other side, due to increased restrictions on animal usage and demand for the need for high-throughput assays, there is a need for developing and exploring novel methods to evaluate NPs safety concerns. The progress made in molecular biology and the availability of several modern techniques may offer novel and innovative methods to evaluate the toxicological behavior of different NPs by using single cells, cell population, and whole organisms. This review highlights the recent novel methods developed for the evaluation of the safety impacts of NPs and attempts to solve the problems that come with risk assessment. The relevance of investigating adverse outcome pathways (AOPs) in nanotoxicology has been stressed in particular.

Development of Polymeric Nanoparticles for Blood-Brain Barrier Transfer-Strategies and Challenges

Neurological disorders such as Alzheimer's disease, stroke, and brain cancers are difficult to treat with current drugs as their delivery efficacy to the brain is severely hampered by the presence of the blood-brain barrier (BBB). Drug delivery systems have been extensively explored in recent decades aiming to circumvent this barrier. In particular, polymeric nanoparticles have shown enormous potentials owing to their unique properties, such as high tunability, ease of synthesis, and control over drug release profile. However, careful analysis of their performance in effective drug transport across the BBB should be performed using clinically relevant testing models. In this review, polymeric nanoparticle systems for drug delivery to the central nervous system are discussed with an emphasis on the effects of particle size, shape, and surface modifications on BBB penetration. Moreover, the authors critically analyze the current in vitro and in vivo models used to evaluate BBB penetration efficacy, including the latest developments in the BBB-on-a-chip models. Finally, the challenges and future perspectives for the development of polymeric nanoparticles to combat neurological disorders are discussed.

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.

Effect of Calcium ion on synaptotagmin-like protein during pre-fusion of vesicle for exocytosis in blood-brain barrier

Background

Calcium signaling and membrane fusion play key roles in exocytosis of drug-containing vesicles through the blood-brain barrier (BBB). Identifying the role of synaptotagmin-like protein4-a (Slp4-a) in the presence of Ca²⁺ ions, at the pre-fusion stage of a vesicle with the basolateral membrane of endothelial cell, can reveal brain drug transportation across BBB.

Methods

We utilized molecular dynamics (MD) simulations with a coarse-grained PACE force field to investigate the behaviors of Slp4-a with vesicular and endothelial membranes at the pre-fusion stage of exocytosis since all-atom MD simulation or experiments are more time-consuming and expensive to capture these behaviors.

Results

The Slp4-a pulls lipid membranes (vesicular and endothelial) into close proximity and disorganizes lipid arrangement at contact points, which are predictors for initiation of fusion. Our MD results also indicate that Slp4-a needs Ca²⁺ to bind with weakly-charged POPE lipids (phosphatidylethanolamine).

Conclusions

Slp4-a is an important trigger for membrane fusion in BBB exocytosis. It binds to lipid membranes at multiple binding sites and triggers membrane disruption for fusion in calcium-dependent case.

General significance

Understanding the prefusion process of the vesicle will help to design better drug delivery mechanisms to the brain through formidable BBB.

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Role of Ca²⁺ on Slp4-a is studied for vesicle pre-fusion in EC to initiate exocytosis.

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Coarse-grained MD is used to study large scale conformation change of Slp-4a.

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Interaction between C2A domain and lipids is much stronger than that of C2B.

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Slp4-a can bind to bilayer membrane in Ca²⁺-bound case to close membrane gap.

Understanding Nanoparticle Toxicity to Direct a Safe-by-Design Approach in Cancer Nanomedicine

Nanomedicine is a rapidly growing field that uses nanomaterials for the diagnosis, treatment and prevention of various diseases, including cancer. Various biocompatible nanoplatforms with diversified capabilities for tumor targeting, imaging, and therapy have materialized to yield individualized therapy. However, due to their unique properties brought about by their small size, safety concerns have emerged as their physicochemical properties can lead to altered pharmacokinetics, with the potential to cross biological barriers. In addition, the intrinsic toxicity of some of the inorganic materials (i.e., heavy metals) and their ability to accumulate and persist in the human body has been a challenge to their translation. Successful clinical translation of these nanoparticles is heavily dependent on their stability, circulation time, access and bioavailability to disease sites, and their safety profile. This review covers preclinical and clinical inorganic-nanoparticle based nanomaterial utilized for cancer imaging and therapeutics. A special emphasis is put on the rational design to develop non-toxic/safe inorganic nanoparticle constructs to increase their viability as translatable nanomedicine for cancer therapies.

Nanoinformatics and biomolecular nanomodeling: a novel move en route for effective cancer treatment

Empowering role of nanoinformatics in design and elucidation of nanoparticles for effective cancer treatment has made this field a fascinating area for researchers, inspiring them to enhance up the quality and efficacy of existing anticancer medicines. Theoretical and computational modeling is being seen as a forefront solution for problems related to surface chemistry, optimized geometry, or other properties in nanoparticle designing and drug delivery. The current review aims to acquaint with the insight story of the incubation of in silico tools and techniques in nanotechnology to develop better anticancer nanomedicines. The review also recapitulates the assets and liabilities of this field and present an outline of existing inventiveness and endeavors of nanoinformatics. We propose how nanoinformatics could hasten up the advancements in anticancer nanomedicines through use of computational tools, nanoparticles repositories & various modeling and simulation methods.

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.

A Novel Magnetic Actuation Scheme to Disaggregate Nanoparticles and Enhance Passage across the Blood-Brain Barrier

The blood–brain barrier (BBB) hinders drug delivery to the brain. Despite various efforts to develop preprogramed actuation schemes for magnetic drug delivery, the unmodeled aggregation phenomenon limits drug delivery performance. This paper proposes a novel scheme with an aggregation model for a feed-forward magnetic actuation design. A simulation platform for aggregated particle delivery is developed and an actuation scheme is proposed to deliver aggregated magnetic nanoparticles (MNPs) using a discontinuous asymmetrical magnetic actuation. The experimental results with a Y-shaped channel indicated the success of the proposed scheme in steering and disaggregation. The delivery performance of the developed scheme was examined using a realistic, three-dimensional (3D) vessel simulation. Furthermore, the proposed scheme enhanced the transport and uptake of MNPs across the BBB in mice. The scheme presented here facilitates the passage of particles across the BBB to the brain using an electromagnetic actuation scheme.