Physiologically Based Pharmacokinetic Modeling of Nanoparticles

Hepatotoxicity of Silver Nanoparticles: Benchmark Concentration Modeling of an In Vitro Transcriptomics Study in Human iPSC-derived Hepatocytes

Despite two decades of research on silver nanoparticle (AgNP) toxicity, a safe threshold for exposure has not yet been established, albeit being critically needed for risk assessment and regulatory decision-making. Traditionally, a point-of-departure (PoD) value is derived from dose response of apical endpoints in animal studies using either the no-observed-adverse-effect level (NOAEL) approach, or benchmark dose (BMD) modeling. To develop new approach methodologies (NAMs) to inform human risk assessment of AgNPs, we conducted a concentration response modeling of the transcriptomic changes in hepatocytes derived from human induced pluripotent stem cells (iPSCs) after being exposed to a wide range concentration (0.01-25 μg/ml) of AgNPs for 24 h. A plausible transcriptomic PoD of 0.21 μg/ml was derived for a pathway related to the mode-of-action (MOA) of AgNPs, and a more conservative PoD of 0.10 μg/ml for a gene ontology (GO) term not apparently associated with the MOA of AgNPs. A reference dose (RfD) could be calculated from either of the PoDs as a safe threshold for AgNP exposure. The current study illustrates the usefulness of in vitro transcriptomic concentration response study using human cells as a NAM for toxicity study of chemicals that lack adequate toxicity data to inform human risk assessment.

Physiologically based pharmacokinetic model for predicting the biodistribution of albumin nanoparticles after induction and recovery from acute lung injury

The application of nanomedicine in the treatment of acute lung injury (ALI) has great potential for the development of new therapeutic strategies. To gain insight into the kinetics of nanocarrier distribution upon time-dependent changes in tissue permeability after ALI induction in mice, we developed a physiologically based pharmacokinetic model for albumin nanoparticles (ANP). The model was calibrated using data from mice treated with intraperitoneal LPS (6 mg/kg), followed by intravenous ANP (0.5 mg/mouse or about 20.8 mg/kg) at 0.5, 6, and 24 h. The simulation results reproduced the experimental observations and indicated that the accumulation of ANP in the lungs increased, reaching a peak 6 h after LPS injury, whereas it decreased in the liver, kidney, and spleen. The model predicted that LPS caused an immediate (within the first 30 min) dramatic increase in lung and kidney tissue permeability, whereas splenic tissue permeability gradually increased over 24 h after LPS injection. This information can be used to design new therapies targeting specific organs affected by bacterial infections and potentially by other inflammatory insults.

Delivering CRISPR to the HIV-1 reservoirs

Human immunodeficiency virus type 1 (HIV-1) infection is well known as one of the most complex and difficult viral infections to cure. The difficulty in developing curative strategies arises in large part from the development of latent viral reservoirs (LVRs) within anatomical and cellular compartments of a host. The clustered regularly interspaced short palindromic repeats/ CRISPR-associated protein 9 (CRISPR/Cas9) system shows remarkable potential for the inactivation and/or elimination of integrated proviral DNA within host cells, however, delivery of the CRISPR/Cas9 system to infected cells is still a challenge. In this review, the main factors impacting delivery, the challenges for delivery to each of the LVRs, and the current successes for delivery to each reservoir will be discussed.

Nano- and microplastic PBK modeling in the context of human exposure and risk assessment

Insufficient data on nano- and microplastics (NMP) hinder robust evaluation of their potential health risks. Methodological disparities and the absence of established toxicity thresholds impede the comparability and practical application of research findings. The diverse attributes of NMP, such as variations in sizes, shapes, and compositions, complicate human health risk assessment. Although probability density functions (PDFs) show promise in capturing this diversity, their integration into risk assessment frameworks is limited. Physiologically based kinetic (PBK) models offer a potential solution to bridge the gap between external exposure and internal dosimetry for risk evaluation. However, the heterogeneity of NMP poses challenges for accurate biodistribution modeling. A literature review, encompassing both experimental and modeling studies, was conducted to examine biodistribution studies of monodisperse micro- and nanoparticles. The literature search in PubMed and Scopus databases yielded 39 studies that met the inclusion criteria. Evaluation criteria were adapted from previous Quality Assurance and Quality Control (QA-QC) studies, best practice guidelines from WHO (2010), OECD guidance (2021), and additional criteria specific to NMP risk assessment. Subsequently, a conceptual framework for a comprehensive NMP-PBK model was developed, addressing the multidimensionality of NMP particles. Parameters for an NMP-PBK model are presented. QA-QC evaluations revealed that most experimental studies scored relatively well (>0) in particle characterizations and environmental settings but fell short in criteria application for biodistribution modeling. The evaluation of modeling studies revealed that information regarding the model type and allometric scaling requires improvement. Three potential applications of PDFs in PBK modeling of NMP are identified: capturing the multidimensionality of the NMP continuum, quantifying the probabilistic definition of external exposure, and calculating the bio-accessibility fraction of NMP in the human body. A framework for an NMP-PBK model is proposed, integrating PDFs to enhance the assessment of NMP's impact on human health.

Exploring the potential and challenges of developing physiologically-based toxicokinetic models to support human health risk assessment of microplastic and nanoplastic particles

Microplastics (MPs) and nanoplastics (NPs) pollution has emerged as a significant and widespread environmental issue. Humans are inevitably exposed to MPs and NPs via ingestion, inhalation, and dermal contacts from various sources. However, mechanistic knowledge of their distribution, interaction, and potency in the body is still lacking. To address this knowledge gap, we have undertaken the task of elucidating the toxicokinetic (TK) behaviors of MPs and NPs, aiming to provide mechanistic information for constructing a conceptual physiologically based toxicokinetic (PBTK) model to support in silico modeling approaches. Our effort involved a thorough examination of the existing literature and data collation on the presence of MPs in the human body and in vitro/ex vivo/in vivo biodistribution across various cells and tissues. By comprehending the absorption, distribution, metabolism, and excretion mechanisms of MPs and NPs in relation to their physicochemical attributes, we established a foundational understanding of the link between external exposure and internal tissue dosimetry. We observed that particle size and surface chemistry have been thoroughly explored in previous experimental studies. However, certain attributes, such as polymer type, shape, and biofilm/biocorona, warrant attention and further examination. We discussed the fundamental disparities in TK properties of MPs/NPs from those of engineered nanoparticles. We proposed a preliminary PBTK framework with several possible modeling approaches and discussed existing challenges for further investigation. Overall, this article provides a comprehensive compilation of existing TK data of MPs/NPs, a critical overview of TK processes and mechanisms, and proposes potential PBTK modeling approaches, particularly regarding their applicability to the human system, and outlines future perspectives for developing PBTK models and their integration into human health risk assessment of MPs and NPs.

Nanoparticle-based optical interfaces for retinal neuromodulation: a review

Degeneration of photoreceptors in the retina is a leading cause of blindness, but commonly leaves the retinal ganglion cells (RGCs) and/or bipolar cells extant. Consequently, these cells are an attractive target for the invasive electrical implants colloquially known as "bionic eyes." However, after more than two decades of concerted effort, interfaces based on conventional electrical stimulation approaches have delivered limited efficacy, primarily due to the current spread in retinal tissue, which precludes high-acuity vision. The ideal prosthetic solution would be less invasive, provide single-cell resolution and an ability to differentiate between different cell types. Nanoparticle-mediated approaches can address some of these requirements, with particular attention being directed at light-sensitive nanoparticles that can be accessed via the intrinsic optics of the eye. Here we survey the available known nanoparticle-based optical transduction mechanisms that can be exploited for neuromodulation. We review the rapid progress in the field, together with outstanding challenges that must be addressed to translate these techniques to clinical practice. In particular, successful translation will likely require efficient delivery of nanoparticles to stable and precisely defined locations in the retinal tissues. Therefore, we also emphasize the current literature relating to the pharmacokinetics of nanoparticles in the eye. While considerable challenges remain to be overcome, progress to date shows great potential for nanoparticle-based interfaces to revolutionize the field of visual prostheses.

Exploring and Analyzing the Systemic Delivery Barriers for Nanoparticles

Most nanomedicines require efficient in vivo delivery to elicit diagnostic and therapeutic effects. However, en route to their intended tissues, systemically administered nanoparticles often encounter delivery barriers. To describe these barriers, we propose the term "nanoparticle blood removal pathways" (NBRP), which summarizes the interactions between nanoparticles and the body's various cell-dependent and cell-independent blood clearance mechanisms. We reviewed nanoparticle design and biological modulation strategies to mitigate nanoparticle-NBRP interactions. As these interactions affect nanoparticle delivery, we studied the preclinical literature from 2011-2021 and analyzed nanoparticle blood circulation and organ biodistribution data. Our findings revealed that nanoparticle surface chemistry affected the in vivo behavior more than other nanoparticle design parameters. Combinatory biological-PEG surface modification improved the blood area under the curve by ~418%, with a decrease in liver accumulation of up to 47%. A greater understanding of nanoparticle-NBRP interactions and associated delivery trends will provide new nanoparticle design and biological modulation strategies for safer, more effective, and more efficient nanomedicines.

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

Applications of Silver Nanoparticles in Patent Research

BACKGROUND

Silver nanoparticles (AgNPs) have been widely applied in research and industrial fields, finding applications in nanomedicine, drug delivery, biomedical devices, electronics, the energy sector, and environmental protection. Patents provide information on the industrial viability of product technologies, and the number of patent documents provides an estimate of the evolution of a specific technological field.

AIMS

The present work aims to describe the current trends in AgNPs patent applications. In addition, a retrospective study of published patents in Brazil is presented.

METHODS

Analyses of AgNPs-related patents were conducted using the free platform for patent search Lens® in 2010-2019 and articles published in same period using the Scholar® base. The patent applications and their evolution over time, major depositors and holders, and the main technological areas associated with AgNP applications have been described.

RESULTS

China and United States are the major patent applicants for nanotechnologies. The worldwide distribution of publications of journal articles shows that China, India, and the United States are the leading countries in the total number of articles published, in that order.

CONCLUSION

Our study of patent applications and published articles confirmed the growing global increase in new technologies involving NPs and AgNPs, particularly in the biotechnology area, in the fields of medicine and agriculture.

In Silico and In Vivo Pharmacokinetic Evaluation of 84-B10, a Novel Drug Candidate against Acute Kidney Injury and Chronic Kidney Disease

Acute kidney injury (AKI) and chronic kidney disease (CKD) have become public health problems due to high morbidity and mortality. Currently, drugs recommended for patients with AKI or CKD are extremely limited, and candidates based on a new mechanism need to be explored. 84-B10 is a novel 3-phenylglutaric acid derivative that can activate the mitochondrial protease, Lon protease 1 (LONP1), and may protect against cisplatin-induced AKI and unilateral ureteral obstruction- or 5/6 nephrectomy [5/6Nx]-induced CKD model. Preclinical studies have shown that 84-B10 has a good therapeutic effect, low toxicity, and is a good prospect for further development. In the present study, the UHPLC-MS/MS method was first validated then applied to the pharmacokinetic study and tissue distribution of 84-B10 in rats. Physicochemical properties of 84-B10 were then acquired in silico. Based on these physicochemical and integral physiological parameters, a physiological based pharmacokinetic (PBPK) model was developed using the PK-Sim platform. The fitting accuracy was estimated with the obtained experimental data. Subsequently, the validated model was employed to predict the pharmacokinetic profiles in healthy and chronic kidney injury patients to evaluate potential clinical outcomes. Cmax in CKD patients was about 3250 ng/mL after a single dose of 84-B10 (0.41 mg/kg), and Cmax,ss was 1360 ng/mL after multiple doses. This study may serve in clinical dosage setting in the future.

Safety of Innovative Nanotechnology Oral Formulations Loaded with Bioactive Menopause Molecules: Influence of Genotoxicity and Biochemical Parameters on a Menopausal Rat Model

In recent years, nanoparticles have gained significant importance due to their unique properties, such as pharmacological, electrical, optical, and magnetic abilities, contributing to the growth of the science and technology sector. Particular naturally derived biomolecules with beneficial effects on menopause disorder have been the subject of studies of pharmaceutical formulation to obtain alternative pharmaceutical forms with increased bioavailability and without side effects, as in nanostructured lipid carriers (NLCs) loaded with such active ingredients. In the present study, one stage of a broader project, we have performed pharmacotoxicology studies for six combinatory innovative nanocapsule pharmaceutical forms containing active natural biomolecules before considering them as oral formulas for (1) in vitro toxicity studies on culture cells and (2) in vivo preclinical studies on a surgically induced menopause model of Wistar female rats, and the influence of the NLCs on key biochemical parameters: lipid profile (TG, Chol, HDL), glycemic markers (Gli), bone markers (Pac, Palc, Ca, phosphorus), renal markers (Crea, urea, URAC), inflammation (TNF), oxidative stress (GSH, MDA), and estrogen-progesterone hormonal profile. The micronucleus test did not reveal the genotoxicity of the tested compounds; the menopause model showed no significant safety concerns for the six tested formulas evaluated using the blood biochemical parameters; and the results showed the potential hypoglycemic, hypolipidemic, hypouricemic, and antioxidant potential of one of the tested formulas containing nano diosgenin and glycyrrhizic acid.

Biosafety of mesoporous silica nanoparticles; towards clinical translation

Mesoporous silica nanoparticles (MSNs) have attracted the attention of chemists, who have developed numerous systems for the encapsulation of a plethora of molecules, allowing the use of mesoporous silica nanoparticles for biomedical applications. MSNs have been extensively studied for their use in nanomedicine, in applications such as drug delivery, diagnosis, and bioimaging, demonstrating significant in vivo efficacy in different preclinical models. Nevertheless, for the transition of MSNs into clinical trials, it is imperative to understand the characteristics that make MSNs effective and safe. The biosafety properties of MSNs in vivo are greatly influenced by their physicochemical characteristics such as particle shape, size, surface modification, and silica framework. In this review, we compile the most relevant and recent progress in the literature up to the present by analyzing the contributions on biodistribution, biodegradability, and clearance of MSNs. Furthermore, the ongoing clinical trials and the potential challenges related to the administration of silica materials for advanced therapeutics are discussed. This approach aims to provide a solid overview of the state-of-the-art in this field and to encourage the translation of MSNs to the clinic.

Pharmacokinetics and tumor delivery of nanoparticles

Nanoparticles (NPs) have been widely used in different areas, including consumer products and medicine. In terms of biomedical applications, NPs or NP-based drug formulations have been extensively investigated for cancer diagnostics and therapy in preclinical studies, but the clinical translation rate is low. Therefore, a thorough and comprehensive understanding of the pharmacokinetics of NPs, especially in drug delivery efficiency to the target therapeutic tissue tumor, is important to design more effective nanomedicines and for proper assessment of the safety and risk of NPs. This review article focuses on the pharmacokinetics of both organic and inorganic NPs and their tumor delivery efficiencies, as well as the associated mechanisms involved. We discuss the absorption, distribution, metabolism, and excretion (ADME) processes following different routes of exposure and the mechanisms involved. Many physicochemical properties and experimental factors, including particle type, size, surface charge, zeta potential, surface coating, protein binding, dose, exposure route, species, cancer type, and tumor size can affect NP pharmacokinetics and tumor delivery efficiency. NPs can be absorbed with varying degrees following different exposure routes and mainly accumulate in liver and spleen, but also distribute to other tissues such as heart, lung, kidney and tumor tissues; and subsequently get metabolized and/or excreted mainly through hepatobiliary and renal elimination. Passive and active targeting strategies are the two major mechanisms of tumor delivery, while active targeting tends to have less toxicity and higher delivery efficiency through direct interaction between ligands and receptors. We also discuss challenges and perspectives remaining in the field of pharmacokinetics and tumor delivery efficiency of NPs.

Controlled Drug Release from Nanoengineered Polysaccharides

Polysaccharides are naturally occurring complex molecules with exceptional physicochemical properties and bioactivities. They originate from plant, animal, and microbial-based resources and processes and can be chemically modified. The biocompatibility and biodegradability of polysaccharides enable their increased use in nanoscale synthesis and engineering for drug encapsulation and release. This review focuses on sustained drug release studies from nanoscale polysaccharides in the fields of nanotechnology and biomedical sciences. Particular emphasis is placed on drug release kinetics and relevant mathematical models. An effective release model can be used to envision the behavior of specific nanoscale polysaccharide matrices and reduce impending experimental trial and error, saving time and resources. A robust model can also assist in translating from in vitro to in vivo experiments. The main aim of this review is to demonstrate that any study that establishes sustained release from nanoscale polysaccharide matrices should be accompanied by a detailed analysis of drug release kinetics by modeling since sustained release from polysaccharides not only involves diffusion and degradation but also surface erosion, complicated swelling dynamics, crosslinking, and drug-polymer interactions. As such, in the first part, we discuss the classification and role of polysaccharides in various applications and later elaborate on the specific pharmaceutical processes of polysaccharides in ionic gelling, stabilization, cross-linking, grafting, and encapsulation of drugs. We also document several drug release models applied to nanoscale hydrogels, nanofibers, and nanoparticles of polysaccharides and conclude that, at times, more than one model can accurately describe the sustained release profiles, indicating the existence of release mechanisms running in parallel. Finally, we conclude with the future opportunities and advanced applications of nanoengineered polysaccharides and their theranostic aptitudes for future clinical applications.

In-vivo processing of nanoassemblies: a neglected framework for recycling to bypass nanotoxicological therapeutics

The proof-of-concept of nanomaterials (NMs) in the fields of imaging, diagnosis, treatment, and theranostics shows the importance in biopharmaceuticals development due to structural orientation, on-targeting, and long-term stability. However, biotransformation of NMs and their modified form in human body via recyclable techniques are not explored owing to tiny structures and cytotoxic effects. Recycling of NMs offers advantages of dose reduction, re-utilization of the administered therapeutics providing secondary release, and decrease in nanotoxicity in human body. Therefore, approaches like in-vivo re-processing and bio-recycling are essential to overcome nanocargo system-associated toxicities such as hepatotoxicity, nephrotoxicity, neurotoxicity, and lung toxicity. After 3-5 stages of recycling process of some NMs of gold, lipid, iron oxide, polymer, silver, and graphene in spleen, kidney, and Kupffer's cells retain biological efficiency in the body. Thus, substantial attention towards recyclability and reusability of NMs for sustainable development necessitates further advancement in healthcare for effective therapy. This review article outlines biotransformation of engineered NMs as a valuable source of drug carriers and biocatalyst with critical strategies like pH modification, flocculation, or magnetization for recovery of NMs in the body. Furthermore, this article summarizes the challenges of recycled NMs and advances in integrated technologies such as artificial intelligence, machine learning, in-silico assay, etc. Therefore, potential contribution of NM's life-cycle in the recovery of nanosystems for futuristic developments require consideration in site-specific delivery, reduction of dose, remodeling in breast cancer therapy, wound healing action, antibacterial effect, and for bioremediation to develop ideal nanotherapeutics.

Influence of lung cancer model characteristics on tumor targeting behavior of nanodrugs

Evidence is mounting that there is a significant gap between the antitumor efficacy of nanodrugs in preclinical mouse tumor models and in clinical human tumors, and that differences in tumor models are likely to be responsible for this gap. Herein, we investigated the enhanced permeability and retention (EPR) effect in mouse lung cancer models with different tumor growth rates, volumes and locations, and analyzed the nanodrug tumor targeting behaviors limited by tumor vascular pathophysiological characteristics in various tumor models. The results showed that the fast-growing tumors were characterized by lower vascular tight junctions, leading to higher vascular paracellular transport activity and nanodrug tumor accumulation. The paracellular transport activity increased with the growth of tumor, but the vascular density and transcellular transport activity decreased, and as a result, the average tumor accumulation of passive targeting nanodrugs decreased. Orthotopic tumors were rich in blood vessels, but had low vascular transcellular and paracellular transport activities, making it difficult for nanodrug accumulation in orthotopic tumors via passive targeting strategies. The antitumor efficacy of passive targeting nanodrugs in various lung cancer-bearing mice validated the aforementioned nanodrug accumulation behavior, and nanodrugs based on the angiogenesis-tumor sequential targeting strategy achieved obviously improved efficacy in orthotopic lung cancer-bearing mice. These results suggest that the EPR effect varies in different tumor models and should not be used as a universal targeting strategy for antitumor nanodrugs. Besides, attention should be paid to the animal tumor models in the evaluation of nanodrugs so as to avoid exaggerating the antitumor efficacy.

Parenteral Lipid-Based Nanoparticles for CNS Disorders: Integrating Various Facets of Preclinical Evaluation towards More Effective Clinical Translation

Contemporary trends in combinatorial chemistry and the design of pharmaceuticals targeting brain disorders have favored the development of drug candidates with increased lipophilicity and poorer water solubility, with the expected improvement in delivery across the blood-brain barrier (BBB). The growing availability of innovative excipients/ligands allowing improved brain targeting and controlled drug release makes the lipid nanocarriers a reasonable choice to overcome the factors impeding drug delivery through the BBB. However, a wide variety of methods, study designs and experimental conditions utilized in the literature hinder their systematic comparison, and thus slows the advances in brain-targeting by lipid-based nanoparticles. This review provides an overview of the methods most commonly utilized during the preclinical testing of liposomes, nanoemulsions, solid lipid nanoparticles and nanostructured lipid carriers intended for the treatment of various CNS disorders via the parenteral route. In order to fully elucidate the structure, stability, safety profiles, biodistribution, metabolism, pharmacokinetics and immunological effects of such lipid-based nanoparticles, a transdisciplinary approach to preclinical characterization is mandatory, covering a comprehensive set of physical, chemical, in vitro and in vivo biological testing.

Interspecies evaluation of a physiologically based pharmacokinetic model to predict the biodistribution dynamics of dendritic nanoparticles

The exposure of a dendritic nanoparticle and its conjugated active pharmaceutical ingredient (API) was determined in mouse, rat and dog, with the aim of investigating interspecies differences facilitating clinical translation. Plasma area under the curves (AUCs) were found to be dose proportional across species, while dose normalized concentration time course profiles in plasma, liver and spleen were superimposable in mouse, rat and dog. A physiologically based pharmacokinetic (PBPK) model, previously developed for mouse, was evaluated as a suitable framework to prospectively capture concentration dynamics in rat and dog. The PBPK model, parameterized either by considering species-specific physiology or using alternate scaling methods such as allometry, was shown to capture exposure profiles across species. A sensitivity analysis highlighted API systemic clearance as a key parameter influencing released API levels. The PBPK model was utilized to simulate human exposure profiles, which overlaid dose-normalized data from mouse, rat and dog. The consistency in measured interspecies exposures as well as the capability of the PBPK model to simulate observed dynamics support its use as a powerful translational tool.

Modelling the biodistribution of inhaled gold nanoparticles in rats with interspecies extrapolation to humans

The increasing intentional and non-intentional exposure to nanoparticles (NPs) has raised the interest concerning their fate and biodistribution in the body of animals and humans after inhalation. In this context, Physiologically Based (pharmaco)Kinetic (PBK) modelling has emerged as an in silico approach that simulates the biodistribution kinetics of NPs in the body using mathematical equations. Due to restrictions in data availability, such models are first developed for rats or mice. In this work, we present the interspecies extrapolation of a PBK model initially developed for rats, in order to estimate the biodistribution of inhaled gold NPs (AuNPs) in humans. The extrapolation framework is validated by comparing the model results with experimental data from a clinical study performed on humans for inhaled AuNPs of two different sizes, namely 34 nm and 4 nm. The novelty of this work lies in the extrapolation of a PBK model for inhaled AuNPs to humans and comparison with clinical data. The extrapolated model is in good agreement with the experimental data, and provides insights for the mechanisms of inhaled AuNP translocation to the blood circulation, after inhalation. Finally, the biodistribution of the two sizes of AuNPs in the human body after 28 days post-exposure is estimated by the model and discussed.

Toxicokinetics, dose-response, and risk assessment of nanomaterials: Methodology, challenges, and future perspectives

The rapid growth of nanomaterial applications has raised safety concerns for human health. A number of studies have been conducted to assess the toxicokinetics, toxicology, dose-response, and risk assessment of different nanomaterials using in vitro and in vivo animal and human models. However, current studies cannot meet the demand for efficient assessment of toxicokinetics, dose-response relationships, or the toxicological risk arising from the rapidly increasing number of newly synthesized nanomaterials. In this article, we review the methods for conducting toxicokinetics, hazard identification, dose-response, exposure, and risk assessment studies of nanomaterials, identify the knowledge gaps, and discuss the challenges remaining. We provide the rationale behind the appropriate design of nanomaterial plasma toxicokinetic and tissue distribution studies, including caveats on the interpretation and correlation of in vitro and in vivo toxicology studies. The potential of using physiologically based pharmacokinetic (PBPK) models to extrapolate toxicokinetic and toxicity findings from in vitro to in vivo and from animals to humans is discussed, and the knowledge gaps of PBPK modeling for nanomaterials are identified. While challenges still exist, there has been progress in the toxicokinetics, hazard identification, and risk assessment of nanomaterials in the past two decades. Recent advancements in the field are highlighted with relevant examples. We also share latest guidelines as well as our perspectives on future studies needed to characterize the toxicokinetics, toxicity, and dose-response relationship in support of nanomaterial risk assessment. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.

Mycosynthesis of highly fluorescent selenium nanoparticles from Fusarium oxysporum, their antifungal activity against black fungus Aspergillus niger, and in-vivo biodistribution studies

In the past few years, photo-luminescent inorganic materials have been studied extensively as fluorescent sensors, and diagnostic and bioimaging tools. The assessment of photoluminescence (PL) properties of selenium nanoparticles (Se NPs), especially mycosynthesized Se NPs, is still in its infancy. Herein, we have biosynthesized highly dispersed fluorescent Se NPs (42 nm) using endophytic fungus Fusarium oxysporum, and fully characterized them using sophisticated instruments like TEM, XRD, UV–Vis spectrophotometer, FTIR, and PL spectrometer. To determine the therapeutic efficacy and side effect profiles, these crystalline Se NPs were radiolabeled with technetium-99m (^99mTc) and their biodistribution and renal clearance times were investigated in the normal Wister rat. The results showed that these Se NPs may be useful for targeting the lungs and liver dysfunction as significant accumulation of these NPs was observed in the liver (approx. 19.47 ± 4%) and lungs (at 6 ± 1%) after 10 min of post-injection. Quick circulation and the presence of Se NPs in kidney (3.8 ± 2%) also suggested the easy excretion of these NPs from the body through urinary tract. Furthermore, the antioxidant activity of Se NPs (IC₅₀, 159.5 μg/mL) has been investigated using DPPH free radical scavenging assay with scavenging efficacy of 80.4% where ascorbic acid (IC₅₀, 5.6 μg/mL) was used as a positive control. Additionally, the microscopic study of the inhibition zone encircled around Se NPs confirmed their strong antifungal and antisporulant activity against the black fungus Aspergillus niger.

Advances of Cobalt Nanomaterials as Anti-Infection Agents, Drug Carriers, and Immunomodulators for Potential Infectious Disease Treatment

Infectious diseases remain the most serious public health issue, which requires the development of more effective strategies for infectious control. As a kind of ultra-trace element, cobalt is essential to the metabolism of different organisms. In recent decades, nanotechnology has attracted increasing attention worldwide due to its wide application in different areas, including medicine. Based on the important biological roles of cobalt, cobalt nanomaterials have recently been widely developed for their attractive biomedical applications. With advantages such as low costs in preparation, hypotoxicity, photothermal conversion abilities, and high drug loading ability, cobalt nanomaterials have been proven to show promising potential in anticancer and anti-infection treatment. In this review, we summarize the characters of cobalt nanomaterials, followed by the advances in their biological functions and mechanisms. More importantly, we emphatically discuss the potential of cobalt nanomaterials as anti-infectious agents, drug carriers, and immunomodulators for anti-infection treatments, which might be helpful to facilitate progress in future research of anti-infection therapy.

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

A proposed mathematical description of in vivo nanoparticle delivery

Nanoparticles are promising vehicles for the precise delivery of molecular therapies to diseased sites. Nanoparticles interact with a series of tissues and cells before they reach their target, which causes less than 1% of administered nanoparticles to be delivered to these target sites. Researchers have been studying the nano-bio interactions that mediate nanoparticle delivery to develop guidelines for designing nanoparticles with enhanced delivery properties. In this review article, we describe these nano-bio interactions with a series of mathematical equations that quantitatively define the nanoparticle delivery process. We employ a compartment model framework to describe delivery where nanoparticles are either (1) at the site of administration, (2) in the vicinity of target cells, (3) internalized by the target cells, or (4) sequestered away in off-target sites or eliminated from the body. This framework explains how different biological processes govern nanoparticle transport between these compartments, and the role of intercompartmental transport rates in determining the final nanoparticle delivery efficiency. Our framework provides guiding principles to engineer nanoparticles for improved targeted delivery.

Where Is Nano Today and Where Is It Headed? A Review of Nanomedicine and the Dilemma of Nanotoxicology

Worldwide nanotechnology development and application have fueled many scientific advances, but technophilic expectations and technophobic demands must be counterbalanced in parallel. Some of the burning issues today are the following: (1) Where is nano today? (2) How good are the communication and investment networks between academia/research and governments? (3) Is there any spotlight application for nanotechnology? Nanomedicine is a particular arm of nanotechnology within the healthcare landscape, focused on diagnosis, treatment, and monitoring of emerging (such as coronavirus disease 2019, COVID-19) and contemporary (including diabetes, cardiovascular diseases, neurodegenerative disorders, and cancer) diseases. However, it may only represent the bright side of the coin. In fact, in the recent past, the concept of nanotoxicology has emerged to address the dark shadows of nanomedicine. The nanomedicine field requires more nanotoxicological studies to identify undesirable effects and guarantee safety. Here, we provide an overall perspective on nanomedicine and nanotoxicology as central pieces of the giant puzzle of nanotechnology. First, the impact of nanotechnology on education and research is highlighted, followed by market trends and scientific output tendencies. In the next section, the nanomedicine and nanotoxicology dilemma is addressed through the interplay of in silico, in vitro, and in vivo models with the support of omics and microfluidic approaches. Lastly, a reflection on the regulatory issues and clinical trials is provided. Finally, some conclusions and future perspectives are proposed for a clearer and safer translation of nanomedicines from the bench to the bedside.

Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling

Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug’s efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.

Multiphysics pharmacokinetic model for targeted nanoparticles

Nanoparticles (NP) are being increasingly explored as vehicles for targeted drug delivery because they can overcome free therapeutic limitations by drug encapsulation, thereby increasing solubility and transport across cell membranes. However, a translational gap exists from animal to human studies resulting in only several NP having FDA approval. Because of this, researchers have begun to turn toward physiologically based pharmacokinetic (PBPK) models to guide in vivo NP experimentation. However, typical PBPK models use an empirically derived framework that cannot be universally applied to varying NP constructs and experimental settings. The purpose of this study was to develop a physics-based multiscale PBPK compartmental model for determining continuous NP biodistribution. We successfully developed two versions of a physics-based compartmental model, models A and B, and validated the models with experimental data. The more physiologically relevant model (model B) had an output that more closely resembled experimental data as determined by normalized root mean squared deviation (NRMSD) analysis. A branched model was developed to enable the model to account for varying NP sizes. With the help of the branched model, we were able to show that branching in vasculature causes enhanced uptake of NP in the organ tissue. The models were solved using two of the most popular computational platforms, MATLAB and Julia. Our experimentation with the two suggests the highly optimized ODE solver package DifferentialEquations.jl in Julia outperforms MATLAB when solving a stiff system of ordinary differential equations (ODEs). We experimented with solving our PBPK model with a neural network using Julia's Flux.jl package. We were able to demonstrate that a neural network can learn to solve a system of ODEs when the system can be made non-stiff via quasi-steady-state approximation (QSSA). Our model incorporates modules that account for varying NP surface chemistries, multiscale vascular hydrodynamic effects, and effects of the immune system to create a more comprehensive and modular model for predicting NP biodistribution in a variety of NP constructs.

Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats

Background

Physiologically based pharmacokinetic (PBPK) modeling is an important tool in predicting target organ dosimetry and risk assessment of nanoparticles (NPs). The methodology of building a multi-route PBPK model for NPs has not been established, nor systematically evaluated. In this study, we hypothesized that the traditional route-to-route extrapolation approach of PBPK modeling that is typically used for small molecules may not be appropriate for NPs. To test this hypothesis, the objective of this study was to develop a multi-route PBPK model for different sizes (1.4–200 nm) of gold nanoparticles (AuNPs) in adult rats following different routes of administration (i.e., intravenous (IV), oral gavage, intratracheal instillation, and endotracheal inhalation) using two approaches: a traditional route-to-route extrapolation approach for small molecules and a new approach that is based on route-specific data that we propose to be applied generally to NPs.

Results

We found that the PBPK model using this new approach had superior performance than the traditional approach. The final PBPK model was optimized rigorously using a Bayesian hierarchical approach with Markov chain Monte Carlo simulations, and then converted to a web-based interface using R Shiny. In addition, quantitative structure–activity relationships (QSAR) based multivariate linear regressions were established to predict the route-specific key biodistribution parameters (e.g., maximum uptake rate) based on the physicochemical properties of AuNPs (e.g., size, surface area, dose, Zeta potential, and NP numbers). These results showed the size and surface area of AuNPs were the main determinants for endocytic/phagocytic uptake rates regardless of the route of administration, while Zeta potential was an important parameter for the estimation of the exocytic release rates following IV administration.

Conclusions

This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-022-00489-4.

Nano-fertilizers: A sustainable technology for improving crop nutrition and food security

Excessive use of synthetic fertilizers cause economic burdens, increasing soil, water and atmospheric pollution. Nano-fertilizers have shown great potential for their sustainable uses in soil fertility, crop production and with minimum or no environmental tradeoffs. Nano-fertilizers are of submicroscopic sizes, have a large surface area to volume ratio, can have nutrient encapsulation, and greater mobility hence they may increase plant nutrient access and crop yield. Due to these properties, nano-fertilizers are regarded as deliverable 'smart system of nutrients'. However, the problems in the agroecosystem are broader than existing developments. For example, nutrient delivery in different physicochemical properties of soils, moisture, and other agro-ecological conditions is still a challenge. In this context, the present review provides an overview of various uses of nanotechnology in agriculture, preference of nano-fertilizers over the conventional fertilizers, nano particles formation, mobility, and role in heterogeneous soils, with special emphasis on the development and use of chitosan-based nano-fertilizers.

Establishing the Pharmacokinetics of Genetic Vaccines is Essential for Maximising their Safety and Efficacy

In a typical course of drug development, thorough pharmacokinetic (PK) studies are essential for the determination of drug biodistribution, dosage and efficacy without toxicity. For vaccines, however, unless a new formulation component is used, most regulatory agencies rule out the need for studying the biodistribution of the vaccine antigenic material per se, and only dose-immunogenicity studies are performed. This is because traditional vaccines are meant to directly induce immunogenicity by locally recruiting immunocytes that will carry on with the pursuing immunogenic processes. Thus, the clinical outcome from traditional vaccines is determined mainly by an immunological response phase. Yet, the case is significantly different for the emergent genetic vaccines (vectorised DNA or mRNA vaccines), where the clinical outcome is dependent on a combination of two major response phases: a pharmacological phase that involves biodistribution, assimilation, gene translation and epitope(s) presentation, followed by an immunological phase, which is similar to that of traditional vaccines. From a mathematical perspective, processes involved in drug administration are typically subject to inter- and intra-patient statistical distributions like most physiological processes. Therefore, the clinical outcome after administering genetic vaccines obeys a statistical probability distribution combined of the sum of two major response probability distributions, pharmacological and immunological. This implies that the variance coefficient of the summed response probability distributions has a larger value than the variance of each underlying distribution. In other words, due to the multi-phased mode of action of genetic vaccines, their clinical outcome has more variability than that of traditional vaccines. This observation points toward the necessity for regulating genetic vaccines in a similar manner to bio-therapeutics to ensure better efficacy and safety. A structural PK model is provided to predict the sources of variability, biodistribution and dose optimisation.

Biopredictive tools for the development of injectable drug products

INTRODUCTION

Biopredictive release tests are commonly used in the evaluation of oral medicines. They support decision-making in formulation development and allow predictions of the expected in-vivo performances. So far, there is limited experience in the application of these methodologies to injectable drug products.

AREAS COVERED

Parenteral drug products cover a variety of dosage forms and administration sites including subcutaneous, intramuscular, and intravenous injections. In this area, developing biopredictive and biorelevant methodologies often confronts us with unique challenges and knowledge gaps. Here, we provide a formulation-centric approach and explain the key considerations and workflow when designing biopredictive assays. Also, we outline the key role of computational methods in achieving clinical relevance and put all considerations into context using liposomal nanomedicines as an example.

EXPERT OPINION

Biopredictive tools are the need of the hour to exploit the tremendous opportunities of injectable drug products. A growing number of biopharmaceuticals such as peptides, proteins, and nucleic acids require different strategies and a better understanding of the influences on drug absorption. Here, our design strategy must maintain the balance of robustness and complexity required for effective formulation development.

Impact of Particle Size on Toxicity, Tissue Distribution and Excretion Kinetics of Subchronic Intratracheal Instilled Silver Nanoparticles in Mice

The unique physicochemical properties of silver nanoparticles (AgNPs) make them useful in a wide range of sectors, increasing their propensity for human exposure, as well as the need for thorough toxicological assessment. The biodistribution of silver, hematological parameters and GSH/GSSG levels in the lung and liver were studied in mice that were intratracheally instilled with AgNP (5 and 50 nm) and AgNO₃ once a week for 5 weeks, followed by a recovery period of up to 28 days (dpi). Data was gathered to build a PBPK model after the entry of AgNPs into the lungs. AgNPs could be absorbed into the blood and might cross the physiological barriers and be distributed extensively in mice. Similar to AgNO₃, AgNP5 induced longer-lasting toxicity toward blood cells and increased GSH levels in the lung. The exposure to AgNP50 increased the GSH from 1 dpi onward in the liver and silver was distributed to the organs after exposure, but its concentration decreased over time. In AgNP5 treated mice, silver levels were highest in the spleen, kidney, liver and blood, persisting for at least 28 days, suggesting accumulation. The major route for excretion seemed to be through the urine, despite a high concentration of AgNP5 also being found in feces. The modeled silver concentration was in line with the in vivo data for the heart and liver.

A physiologically based pharmacokinetic model to predict pegylated liposomal doxorubicin disposition in rats and human

The use of nanoparticles (NPs) can support an enhancement of drug distribution, resulting in increased drug penetration into key tissues. Experimental in vitro data can be integrated into computational approaches to simulate NP absorption, distribution, metabolism and elimination (ADME) processes and provide quantitative pharmacokinetic predictions. The aim of this study is to develop a novel mechanistic and physiologically based pharmacokinetic (m-PBPK) model to predict the biodistribution of NPs focusing on Doxil. The main processes underpinning NPs ADME were represented considering molecular and cellular mechanisms such as stability in biological fluids, passive permeability and uptake activity by macrophages. A whole-body m-PBPK rat and human models were designed in Simbiology v. 9.6.0 (MATLAB R2019a). The m-PBPK models were successfully qualified across doxorubicin and Doxil^® in both rat and human since all PK parameters AUC_0-inf, C_max, t_1/2, Vd and Cl were within twofold, with an AUC_0-inf absolute average-fold error (AAFE) value of 1.23 and 1.16 and 1.76 and 1.05 for Doxorubicin and Doxil^® in rat and human, respectively. The time to maximum concentration in tissues for doxorubicin in both rat and human models was before 30 min of administration, while for Doxil^®, the t_max was after 24 h of administration. The organs that accumulate most NP are the spleen, liver and lungs, in both models. The m-PBPK represents a predictive platform for the integration of in vitro and formulation parameters in a physiological context to quantitatively predict the NP biodistribution. Schematic diagram of the whole-body m-PBPK models developed for Doxil^® in rat and human physiology.

The Promise of Nanotechnology in Personalized Medicine

Both personalized medicine and nanomedicine are new to medical practice. Nanomedicine is an application of the advances of nanotechnology in medicine and is being integrated into diagnostic and therapeutic tools to manage an array of medical conditions. On the other hand, personalized medicine, which is also referred to as precision medicine, is a novel concept that aims to individualize/customize therapeutic management based on the personal attributes of the patient to overcome blanket treatment that is only efficient in a subset of patients, leaving others with either ineffective treatment or treatment that results in significant toxicity. Novel nanomedicines have been employed in the treatment of several diseases, which can be adapted to each patient-specific case according to their genetic profiles. In this review, we discuss both areas and the intersection between the two emerging scientific domains. The review focuses on the current situation in personalized medicine, the advantages that can be offered by nanomedicine to personalized medicine, and the application of nanoconstructs in the diagnosis of genetic variability that can identify the right drug for the right patient. Finally, we touch upon the challenges in both fields towards the translation of nano-personalized medicine.

Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery

Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.

Emerging Nanotherapeutic Approaches to Overcome Drug Resistance in Cancers with Update on Clinical Trials

A key issue with modern cancer treatments is the emergence of resistance to conventional chemotherapy and molecularly targeted medicines. Cancer nanotherapeutics were created in order to overcome the inherent limitations of traditional chemotherapeutics. Over the last few decades, cancer nanotherapeutics provided unparalleled opportunities to understand and overcome drug resistance through clinical assessment of rationally designed nanoparticulate delivery systems. In this context, various design strategies such as passive targeting, active targeting, nano-drug, and multimodal nano-drug combination therapy provided effective cancer treatment. Even though cancer nanotherapy has made great technological progress, tumor biology complexity and heterogeneity and a lack of comprehensive knowledge of nano-bio interactions remain important roadblocks to future clinical translation and commercialization. The current developments and advancements in cancer nanotherapeutics employing a wide variety of nanomaterial-based platforms to overcome cancer treatment resistance are discussed in this article. There is also a review of various nanotherapeutics-based approaches to cancer therapy, including targeting strategies for the tumor microenvironment and its components, advanced delivery systems for specific targeting of cancer stem cells (CSC), as well as exosomes for delivery strategies, and an update on clinical trials. Finally, challenges and the future perspective of the cancer nanotherapeutics to reverse cancer drug resistance are discussed.

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

Background

Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined.

Methods

This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model.

Results

The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R2) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DEmax), delivery efficiency at 24 h (DE24), at 168 h (DE168), and at the last sampling time (DETlast). The corresponding R2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19-29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy.

Conclusion

This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.

Blood-brain barrier crossing using magnetic stimulated nanoparticles

Due to the low permeability and high selectivity of the blood-brain barrier (BBB), existing brain therapeutic technologies are limited by the inefficient BBB crossing of conventional drugs. Magnetic nanoparticles (MNPs) have shown great potential as nano-carriers for efficient BBB crossing under the external static magnetic field (SMF). To quantify the impact of SMF on MNPs' in vivo dynamics towards BBB crossing, we developed a physiologically based pharmacokinetic (PBPK) model for intraperitoneal (IP) injected superparamagnetic iron oxide nanoparticles coated by gold and conjugated with poly (ethylene glycol) (PEG) (SPIO-Au-PEG NPs) in mice. Unlike most reported PBPK models that ignore brain permeability, we first obtained the brain permeabilities with and without SMF by determining the concentration of SPIO-Au-PEG NPs in the cerebral blood and brain tissue. This concentration in the brain was simulated by the advection-diffusion equations and was numerically solved in COMSOL Multiphysics. The results from the PBPK model after incorporating the brain permeability showed a good agreement (regression coefficient R² = 0.848) with the in vivo results, verifying the capability of using the proposed PBPK model to predict the in vivo biodistribution of SPIO-Au-PEG NPs under the exposure to SMF. Furthermore, the in vivo results revealed that the distribution coefficient from blood to brain under the exposure to SMF (4.01%) is slightly better than the control group (3.68%). In addition, the modification of SPIO-Au-PEG NPs with insulin (SPIO-Au-PEG-insulin) showed an improvement of the brain bioavailability by 24.47% in comparison to the non-insulin group. With the SMF stimulation, the brain bioavailability of SPIO-Au-PEG-insulin was further improved by 3.91% compared to the group without SMF. The PBPK model and in vivo validation in this paper lay a solid foundation for future study on non-invasive targeted drug delivery to the brain.

Physiologically Based Pharmacokinetic Modelling and Simulation to Predict the Plasma Concentration Profile of Doxorubicin

Doxorubicin (DOX) is still an important anticancer agent despite its tricky pharmacokinetics (PK) and toxicity potential. The advent of systems pharmacology enables the construction of PK models able to predict the concentration profiles of drugs and shed light on the underlying mechanisms involved in PK and pharmacodynamics (PD). By utilizing existing published data and by analysing two clinical case studies we attempt to create physiologically based pharmacokinetic (PBPK) models for DOX using widely accepted methodologies. Based on two different approaches on three different key points we derived eight plausible models. The validation of the models provides evidence that is all performing as designed and opens the way for further exploitation by integrating metabolites and pharmacogenomic information.

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

Folate-Targeted Liposomal Formulations Improve Effects of Methotrexate in Murine Collagen-Induced Arthritis

Methotrexate (MTX) is first-line therapy for the treatment of rheumatoid arthritis (RA), however, its use may be limited by side effects notably post-injection malaise. When patients are intolerant or become unresponsive, second-line or antibody therapy may be indicated. A folate-targeted liposomal formulation of MTX (FL-MTX) is tropic to arthritic paws and prevents the onset of collagen-induced arthritis (CIA) in the mouse. We optimized the drug-to-lipid molar ratio to 0.15 and demonstrated the therapeutic efficacy of this form at 2 mg/kg MTX intraperitoneal (i.p.) twice a week. These improved liposomes were present in inflamed joints in proportion to the degree of swelling of the paw and bone remodeling activity. FL-MTX had lower hepatic and renal elimination of MTX than the free substance. FL-MTX provided equivalent results when given i.p. or subcutaneous (s.c.) and FL-MTX 2 mg/kg (drug/lipid 0.15), twice weekly, was similar to or more effective than 35 mg/kg MTX (same route and schedule) in reducing the incidence and swelling in the murine CIA model. These results suggest that FL-MTX is a more potent nanotherapeutic formulation than free MTX treatment. Its potential benefits for patients may include reduced frequency of treatment and lower overall doses for a given response.

The Hitchhiker's Guide to Human Therapeutic Nanoparticle Development

Nanomedicine plays an essential role in developing new therapies through novel drug delivery systems, diagnostic and imaging systems, vaccine development, antibacterial tools, and high-throughput screening. One of the most promising drug delivery systems are nanoparticles, which can be designed with various compositions, sizes, shapes, and surface modifications. These nanosystems have improved therapeutic profiles, increased bioavailability, and reduced the toxicity of the product they carry. However, the clinical translation of nanomedicines requires a thorough understanding of their properties to avoid problems with the most questioned aspect of nanosystems: safety. The particular physicochemical properties of nano-drugs lead to the need for additional safety, quality, and efficacy testing. Consequently, challenges arise during the physicochemical characterization, the production process, in vitro characterization, in vivo characterization, and the clinical stages of development of these biopharmaceuticals. The lack of a specific regulatory framework for nanoformulations has caused significant gaps in the requirements needed to be successful during their approval, especially with tests that demonstrate their safety and efficacy. Researchers face many difficulties in establishing evidence to extrapolate results from one level of development to another, for example, from an in vitro demonstration phase to an in vivo demonstration phase. Additional guidance is required to cover the particularities of this type of product, as some challenges in the regulatory framework do not allow for an accurate assessment of NPs with sufficient evidence of clinical success. This work aims to identify current regulatory issues during the implementation of nanoparticle assays and describe the major challenges that researchers have faced when exposing a new formulation. We further reflect on the current regulatory standards required for the approval of these biopharmaceuticals and the requirements demanded by the regulatory agencies. Our work will provide helpful information to improve the success of nanomedicines by compiling the challenges described in the literature that support the development of this novel encapsulation system. We propose a step-by-step approach through the different stages of the development of nanoformulations, from their design to the clinical stage, exemplifying the different challenges and the measures taken by the regulatory agencies to respond to these challenges.

Sub-structure-based category formation for the prioritisation of genotoxicity hazard assessment for pesticide residues: Sulphonyl ureas

In dietary risk assessment, residues of pesticidal ingredients or their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity and to identify structural alerts associated with genotoxic concern, a set of chemical sub-structures was derived for an example dataset of 74 sulphonyl urea agrochemicals for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This analysis resulted in a set of seven structural alerts that define the chemical space, in terms of the common parent and metabolic scaffolds, associated with the sulphonyl urea chemical class. An analysis of the available profiling schemes for DNA and protein reactivity shows the importance of investigating the predictivity of such schemes within a well-defined area of structural space. Structural space alerts, covalent chemistry profiling and physico-chemistry properties were combined to develop chemical categories suitable for chemical prioritisation. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for pesticide-class specific metabolism data as the basis for structural space alert development.

Experimental Data and PBPK Modeling Quantify Antibody Interference in PEGylated Drug Carrier Delivery

Physiologically-based pharmacokinetic (PBPK) modeling is a popular drug development tool that integrates physiology, drug physicochemical properties, preclinical data, and clinical information to predict drug systemic disposition. Since PBPK models seek to capture complex physiology, parameter uncertainty and variability is a prevailing challenge: there are often more compartments (e.g., organs, each with drug flux and retention mechanisms, and associated model parameters) than can be simultaneously measured. To improve the fidelity of PBPK modeling, one approach is to search and optimize within the high-dimensional model parameter space, based on experimental time-series measurements of drug distributions. Here, we employ Latin Hypercube Sampling (LHS) on a PBPK model of PEG-liposomes (PL) that tracks biodistribution in an 8-compartment mouse circulatory system, in the presence (APA+) or absence (naïve) of anti-PEG antibodies (APA). Near-continuous experimental measurements of PL concentration during the first hour post-injection from the liver, spleen, kidney, muscle, lung, and blood plasma, based on PET/CT imaging in live mice, are used as truth sets with LHS to infer optimal parameter ranges for the full PBPK model. The data and model quantify that PL retention in the liver is the primary differentiator of biodistribution patterns in naïve versus APA+ mice, and spleen the secondary differentiator. Retention of PEGylated nanomedicines is substantially amplified in APA+ mice, likely due to PL-bound APA engaging specific receptors in the liver and spleen that bind antibody Fc domains. Our work illustrates how applying LHS to PBPK models can further mechanistic understanding of the biodistribution and antibody-mediated clearance of specific drugs.

In vitro dissolution considerations associated with nano drug delivery systems

Nano drug delivery systems (NDDS) offer promising solution for the translation of future nanomedicines. As bioavailability and therapeutic outcomes can be improved by altering the drug release from these NDDS, it becomes essential to thoroughly understand their drug release kinetics. Moreover, U.S. Food and Drug Administration requires critical evaluation of potential safety, efficacy, and public health impacts of nanomaterials. Spiraling up market share of NDDS has also stimulated the pharmaceutical industry to develop their cost-effective generic versions after the expiry of patent and associated exclusivity. However, unlike the conventional dosage forms, the in vivo disposition of NDDS is highly intricate and different from their in vitro behavior. Significant challenges exist in the establishment of in vitro-in vivo correlation (IVIVC) due to incomplete understanding of nanoparticles' in vivo biofate and its impact on in vitro experimental protocols. A rational design of dissolution may serve as quality and quantity control tool and help develop a meaningful IVIVC for favorable economic implications. Clinically relevant drug product specifications (critical quality attributes) can be identified by establishing a link between in vitro performance and in vivo exposure. In vitro dissolution may also play a pivotal role to understand the dissolution-mediated clearance and safety of NDDS. Prevalent in vitro dissolution methods for NDDS and their limitations are discussed in this review, among which USP 4 is gaining more interest recently. Researchers are working diligently to develop biorelevant in vitro release assays to ensure optimal therapeutic performance of generic versions of these NDDS. This article focuses on these studies and presents important considerations for the future development of clinically relevant in vitro release methods. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.

Computational pharmaceutics - A new paradigm of drug delivery

In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.

Functionalized Graphene Platforms for Anticancer Drug Delivery

Two-dimensional nanomaterials are emerging as promising candidates for a wide range of biomedical applications including tissue engineering, biosensing, pathogen incapacitation, wound healing, and gene and drug delivery. Graphene, due to its high surface area, photothermal property, high loading capacity, and efficient cellular uptake, is at the forefront of these materials and plays a key role in this multidisciplinary research field. Poor water dispersibility and low functionality of graphene, however, hamper its hybridization into new nanostructures for future nanomedicine. Functionalization of graphene, either by covalent or non-covalent methods, is the most useful strategy to improve its dispersion in water and functionality as well as processability into new materials and devices. In this review, recent advances in functionalization of graphene derivatives by different (macro)molecules for future biomedical applications are reported and explained. In particular, hydrophilic functionalization of graphene and graphene oxide (GO) to improve their water dispersibility and physicochemical properties is discussed. We have focused on the anticancer drug delivery of polyfunctional graphene sheets.