Mathematical and computational modeling of nano-engineered drug delivery systems

Revolutionizing oral care: Reactive oxygen species (ROS)-Regulating biomaterials for combating infection and inflammation

The human oral cavity is home to a delicate symbiosis between its indigenous microbiota and the host, the balance of which is easily perturbed by local or systemic factors, leading to a spectrum of oral diseases such as dental caries, periodontitis, and pulp infections. Reactive oxygen species (ROS) play crucial roles in the host's innate immune defenses. However, in chronic inflammatory oral conditions, dysregulated immune responses can result in excessive ROS production, which in turn exacerbates inflammation and causes tissue damage. Conversely, the potent antimicrobial properties of ROS have inspired the development of various anti-infective therapies. Therefore, the strategic modulation of ROS by innovative biomaterials is emerging as a promising therapeutic approach for oral infection and inflammation. This review begins by highlighting the state-of-the-art of ROS-regulating biomaterials, which are designed to generate, scavenge, or modulate ROS in a bidirectional manner. We then delve into the latest innovations in these biomaterials and their applications in treating a range of oral diseases, including dental caries, endodontic and periapical conditions, periodontitis, peri-implantitis, and oral candidiasis. The review concludes with an overview of the current challenges and future potential of these biomaterials in clinical settings. This review provides novel insights for the ongoing development of ROS-based therapeutic strategies for oral diseases.

Computational Approaches for Antimicrobial Peptide Delivery

Peptides constitute alternative molecules for the treatment of infections caused by bacteria, viruses, fungi, and protozoa. However, their therapeutic effectiveness is often limited by enzymatic degradation, chemical and physical instability, and toxicity toward healthy human cells. To improve their pharmacokinetic (PK) and pharmacodynamic (PD) profiles, novel routes of administration are being explored. Among these, nanoparticles have shown promise as potential carriers for peptides, although the design of delivery vehicles remains a slow and painstaking process, heavily reliant on trial and error. Recently, computational approaches have been introduced to accelerate the development of effective drug delivery systems for peptides. Here we present an overview of some of these computational strategies and discuss their potential to optimize drug development and delivery.

Unveiling the biological side of PET-derived biomarkers: a simulation-based approach applied to PDAC assessment

PURPOSE

Radiomics has revolutionized clinical research by enabling objective measurements of imaging-derived biomarkers. However, the true potential of radiomics necessitates a comprehensive understanding of the biological basis of extracted features to serve as a clinical decision support. In this work, we propose an end-to-end framework for the in silico simulation of [18F]FLT PET imaging process in Pancreatic Ductal Adenocarcinoma, accounting for the biological characterization of tissues (including perfusion and fibrosis) on tracer delivery. We thus establish a direct association between radiomics features and the underlying biological properties of tissues.

METHODS

We considered 4 immunohistochemically stained Whole Slide Images of pancreatic tissue of one healthy control and three patients with PDAC and/or precursor lesions. From marker-specific images, tissue-depending diffusivity properties were estimated and computational domains were built to simulate the [18F]FLT spatial-temporal uptake exploiting Partial Differential Equations and Finite Elements Method. Consequently, we simulated the imaging process obtaining surrogated PET images for the considered patients, and we performed image-derived features extraction from PET images to be mapped with biological properties via correlation estimation.

RESULTS

The framework captured the phenotypic differences and generated Time Activity Curves reflecting the underlying tissue composition. Image-derived biomarkers were ranked in view of their association with biological characteristics of the tissue, unveiling their molecular correlative. Moreover, we showed that the proposed pipeline could serve as a digital phantom to optimize the image acquisition for lesion detection.

CONCLUSIONS

This innovative framework holds the potential to enhance interpretability and reliability of radiomics, fostering the adoption in personalized nuclear medicine and patient care.

Computer-aided nanodrug discovery: recent progress and future prospects

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

Comprehensive Computational Investigation of the Porphyrin-Based COF as a Nanocarrier for Delivering Anti-Cancer Drugs: A Combined MD Simulation and DFT Calculation

As nanomaterials have gained prominence in drug delivery technology, exploring their feasibility through computational methods is beneficial before practical tests. In this study, we aim to evaluate the capability of the porphyrin-based covalent organic framework COF-366 as a nanocarrier for two anticancer drugs, irinotecan (IRI) and doxorubicin (DOX). The optimal binding conformation of the drug molecules on the COF surface was predicted by using molecular docking. Subsequently, molecular dynamic simulation (MD) was performed to assess the adsorption mechanism of drug molecules on the COF in the aqueous environment. The free energy of adsorption for DOX and IRI was estimated to be -20.07 and -23.89 kcal/mol, respectively. The adsorption of both drugs on the COF surface is mainly influenced by the π-π interaction. Furthermore, density functional theory (DFT) calculation, natural bond orbital (NBO), and quantum theory of atoms in molecules (QTAIM) analyses were employed to investigate the structural stability of Drug@COF complexes and gain a detailed understanding of the interaction between them at the molecular level. Based on DFT results, it was found that in addition to π-π interaction, the bis-piperidine-phenylene interaction affects the adsorption of IRI on the COF surface. Moreover, the diffusion behavior of the drug molecule inside the COF pore was simulated using a ten-layer COF. Based on the mean square displacement analysis, the diffusion coefficients of DOX and IRI within the COF pore were calculated to be 108 and 97 um2/s, respectively. This computational study sheds light on how different types of interactions between the drug molecule and COF affect the adsorption and diffusion process. Our findings validated that the porphyrin-based COF-366 can serve as a nanobased platform for delivering DOX and IRI.

Ulgen K. Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials

Nanoparticles (NPs) have been widely used to improve the pharmacokinetic properties and tissue distribution of small molecules such as targeting to a specific tissue of interest, enhancing their systemic circulation, and enlarging their therapeutic properties. NPs have unique and complicated in vivo disposition properties compared to small molecule drugs due to their complex multifunctionality. Physiologically based pharmacokinetic (PBPK) modeling has been a powerful tool in the simulation of the absorption, distribution, metabolism, and elimination (ADME) characteristics of the materials, and it can be used in the characterization and prediction of the systemic disposition, toxicity, efficacy, and target exposure of various types of nanoparticles. In this review, recent advances in PBPK model applications related to the nanoparticles with unique properties, and dispositional features in the biological systems, ADME characteristics, the description of transport processes of nanoparticles in the PBPK model, and the challenges in PBPK model development of nanoparticles are delineated and juxtaposed with those encountered in small molecule models. Nanoparticle related, non-nanoparticle-related, and interspecies-scaling methods applied in PBPK modeling are reviewed. In vitro to in vivo extrapolation (IVIVE) methods being a promising computational tool to provide in vivo predictions from the results of in vitro and in silico studies are discussed. Finally, as a recent advancement ML/AI-based approaches and challenges in PBPK modeling in the estimation of ADME parameters and pharmacokinetic (PK) analysis results are introduced.

Biorobotic Drug Delivery for Biomedical Applications

Despite extensive efforts, current drug-delivery systems face biological barriers and difficulties in bench-to-clinical use. Biomedical robotic systems have emerged as a new strategy for drug delivery because of their innovative diminutive engines. These motors enable the biorobots to move independently rather than relying on body fluids. The main components of biorobots are engines controlled by external stimuli, chemical reactions, and biological responses. Many biorobot designs are inspired by blood cells or microorganisms that possess innate swimming abilities and can incorporate living materials into their structures. This review explores the mechanisms of biorobot locomotion, achievements in robotic drug delivery, obstacles, and the perspectives of translational research.

In Vitro Release from Polymeric Core/Shell Nanoparticles through the Lens of Multiscale Modeling

The large number of studies involving nanoparticles for cancer therapy is due to their peculiar features: they protect loaded active molecules while extending circulation time and can extravasate from the blood flow to the tumor to deliver drugs directly in the target area. Mathematical modeling can provide a preliminary in silico exploration of design space to optimize an experimental activity that often relies on a trial-and-error approach. However, because of the characteristic size of these vectors (10-1000 nm), numerous phenomena of interest occur at different time and length scales, making a single modeling technique insufficient to fully characterize the system of interest. In this work we employed a multiscale modeling approach, which bridges the phenomena of interest across different scales, to study the in vitro release from polymeric core/shell nanoparticles for cancer therapy loaded with an active compound assembled as a hydrophobic ion pair. The "computational microscope" provided by molecular dynamics simulations was used to track drug molecules through the release process at an atomic scale. The outcomes suggested that the drug is mainly partitioned in the polymer and released as hydrophobic ion pair rather than a free molecule, and that the hydrophobic ion pair is preferentially partitioned in Tween 20 micelles in the release media. A model at macroscale, aimed at describing the release rate and elucidating the release mechanism, was developed according to the results from molecular simulations and validated against experimental data. The outcomes provided insights that are challenging to be obtained experimentally and which supported the development and validation of a release model at macroscale. Overall, the adopted multiscale approach corroborated the experimental findings and provided significant insights into the mechanisms of release.

Development of an ultrasound-mediated nano-sized drug-delivery system for cancer treatment: from theory to experiment

Aim: To establish a methodology for understanding how ultrasound (US) induces drug release from nano-sized drug-delivery systems (NSDDSs) and enhances drug penetration and uptake in tumors. This aims to advance cancer treatment strategies.Materials & methods: We developed a multi-physics mathematical model to elucidate and understand the intricate mechanisms governing drug release, transport and delivery. Unique in vitro models (monolayer, multilayer, spheroid) and a tailored US exposure setup were introduced to evaluate drug penetration and uptake.Results: The results highlight the potential advantages of US-mediated NSDDSs over conventional NSDDSs and chemotherapy, notably in enhancing drug release and inducing cell death.Conclusion: Our sophisticated numerical and experimental methods aid in determining and quantifying drug penetration and uptake into solid tumors.

The interaction between particles and vascular endothelium in blood flow

Particle-based drug delivery systems have shown promising application potential to treat human diseases; however, an incomplete understanding of their interactions with vascular endothelium in blood flow prevents their inclusion into mainstream clinical applications. The flow performance of nano/micro-sized particles in the blood are disturbed by many external/internal factors, including blood constituents, particle properties, and endothelium bioactivities, affecting the fate of particles in vivo and therapeutic effects for diseases. This review highlights how the blood constituents, hemodynamic environment and particle properties influence the interactions and particle activities in vivo. Moreover, we briefly summarized the structure and functions of endothelium and simulated devices for studying particle performance under blood flow conditions. Finally, based on particle-endothelium interactions, we propose future opportunities for novel therapeutic strategies and provide solutions to challenges in particle delivery systems for accelerating their clinical translation. This review helps provoke an increasing in-depth understanding of particle-endothelium interactions and inspires more strategies that may benefit the development of particle medicine.

Effect of tumor heterogeneity on enhancing drug delivery to vascularized tumors using thermo-sensitive liposomes triggered by hyperthermia: A multi-scale and multi-physics computational model

In this study, a novel multi-scale and multi-physics image-based computational model is introduced to assess the delivery of doxorubicin (Dox) loaded temperature-sensitive liposomes (TSLs) in the presence of hyperthermia. Unlike previous methodologies, this approach incorporates capillary network geometry extracted from images, resulting in a more realistic physiological tumor model. This model holds significant promise in advancing personalized medicine by integrating patient-specific tumor properties. The finite element method is employed to solve the equations governing intravascular and interstitial fluid flows, as well as the transport of therapeutic agents within the tissue. Realistic biological conditions and intricate processes like intravascular pressure, drug binding to cells, and cellular uptake are also considered to enhance the model's accuracy. The results underscore the significant impact of vascular architecture on treatment outcomes. Variation in vascular network pattern yielded changes of up to 38 % in the fraction of killed cells (FKCs) parameter under identical conditions. Pressure control of the parent vessels can also improve FKCs by approximately 17 %. Tailoring the treatment plan based on tumor-specific parameters emerged as a critical factor influencing treatment efficacy. For instance, changing the time interval between the administration of Dox-loaded TSLs and hyperthermia can result in a 48 % improvement in treatment outcomes. Additionally, devising a customized heating schedule led to a 20 % increase in treatment efficacy. Our proposed model highlights the significant effect of tumor characteristics and vascular network structure on the final treatment outcomes of the presented combination treatment.

Ultrasound-mediated nano-sized drug delivery systems for cancer treatment: Multi-scale and multi-physics computational modeling

Computational modeling enables researchers to study and understand various complex biological phenomena in anticancer drug delivery systems (DDSs), especially nano-sized DDSs (NSDDSs). The combination of NSDDSs and therapeutic ultrasound (TUS), that is, focused ultrasound and low-intensity pulsed ultrasound, has made significant progress in recent years, opening many opportunities for cancer treatment. Multiple parameters require tuning and optimization to develop effective DDSs, such as NSDDSs, in which mathematical modeling can prove advantageous. In silico computational modeling of ultrasound-responsive DDS typically involves a complex framework of acoustic interactions, heat transfer, drug release from nanoparticles, fluid flow, mass transport, and pharmacodynamic governing equations. Owing to the rapid development of computational tools, modeling the different phenomena in multi-scale complex problems involved in drug delivery to tumors has become possible. In the present study, we present an in-depth review of recent advances in the mathematical modeling of TUS-mediated DDSs for cancer treatment. A detailed discussion is also provided on applying these computational models to improve the clinical translation for applications in cancer treatment. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Multi-Scale and Multi-Physics Models of the Transport of Therapeutic/Diagnostic Cancer Agents

The effectiveness of tumor treatment heavily relies on the successful delivery of anticancer drugs [...].

Recent Advances in Micro- and Nano-Drug Delivery Systems Based on Natural and Synthetic Biomaterials

Polymeric drug delivery technology, which allows for medicinal ingredients to enter a cell more easily, has advanced considerably in recent decades. Innovative medication delivery strategies use biodegradable and bio-reducible polymers, and progress in the field has been accelerated by future possible research applications. Natural polymers utilized in polymeric drug delivery systems include arginine, chitosan, dextrin, polysaccharides, poly(glycolic acid), poly(lactic acid), and hyaluronic acid. Additionally, poly(2-hydroxyethyl methacrylate), poly(N-isopropyl acrylamide), poly(ethylenimine), dendritic polymers, biodegradable polymers, and bioabsorbable polymers as well as biomimetic and bio-related polymeric systems and drug-free macromolecular therapies have been employed in polymeric drug delivery. Different synthetic and natural biomaterials are in the clinical phase to mitigate different diseases. Drug delivery methods using natural and synthetic polymers are becoming increasingly common in the pharmaceutical industry, with biocompatible and bio-related copolymers and dendrimers having helped cure cancer as drug delivery systems. This review discusses all the above components and how, by combining synthetic and biological approaches, micro- and nano-drug delivery systems can result in revolutionary polymeric drug and gene delivery devices.

Molecular targets, therapeutic agents and multitasking nanoparticles to deal with cancer stem cells: A narrative review

There is increasing evidence that malignant tumors are initiated and maintained by a sub-population of tumor cells that have similar biological properties to normal adult stem cells. This very small population of Cancer Stem Cells (CSC) comprises tumor initiating cells responsible for cancer recurrence, drug resistance and metastasis. Conventional treatments such as chemotherapy, radiotherapy and surgery, in addition to being potentially toxic and non-specific, may paradoxically increase the population, spread and survival of CSCs. Next-generation sequencing and omics technologies are increasing our understanding of the pathways and factors involved in the development of CSCs, and can help to discover new therapeutic targets against CSCs. In addition, recent advances in nanomedicine have provided hope for the development of optimal specific therapies to eradicate CSCs. Moreover, the use of artificial intelligence and nano-informatics can elucidate new drug targets, and help to design drugs and nanoparticles (NPs) to deal with CSCs. In this review, we first summarize the properties of CSCs and describe the signaling pathways and molecular characteristics responsible for the emergence and survival of CSCs. Also, the location of CSCs within the tumor and the effect of host factors on the creation and maintenance of CSCs are discussed. Newly discovered molecular targets involved in cancer stemness and some novel therapeutic compounds to combat CSCs are highlighted. The optimum properties of anti-CSC NPs, including blood circulation and stability, tumor accumulation and penetration, cellular internalization, drug release, endosomal escape, and aptamers designed for specific targeting of CSCs are covered. Finally, some recent smart NPs designed for therapeutic and theranostic purposes to overcome CSCs are discussed.

Rational nanoparticle design: Optimization using insights from experiments and mathematical models

Polymeric nanoparticles are highly tunable drug delivery systems that show promise in targeting therapeutics to specific sites within the body. Rational nanoparticle design can make use of mathematical models to organize and extend experimental data, allowing for optimization of nanoparticles for particular drug delivery applications. While rational nanoparticle design is attractive from the standpoint of improving therapy and reducing unnecessary experiments, it has yet to be fully realized. The difficulty lies in the complexity of nanoparticle structure and behavior, which is added to the complexity of the physiological mechanisms involved in nanoparticle distribution throughout the body. In this review, we discuss the most important aspects of rational design of polymeric nanoparticles. Ultimately, we conclude that many experimental datasets are required to fully model polymeric nanoparticle behavior at multiple scales. Further, we suggest ways to consider the limitations and uncertainty of experimental data in creating nanoparticle design optimization schema, which we call quantitative nanoparticle design frameworks.

Ultrasound-mediated nano drug delivery for treating cancer: Fundamental physics to future directions

The application of biocompatible nanocarriers in medicine has provided several benefits over conventional treatment methods. However, achieving high treatment efficacy and deep penetration of nanocarriers in tumor tissue is still challenging. To address this, stimuli-responsive nano-sized drug delivery systems (DDSs) are an active area of investigation in delivering anticancer drugs. While ultrasound is mainly used for diagnostic purposes, it can also be applied to affect cellular function and the delivery/release of anticancer drugs. Therapeutic ultrasound (TUS) has shown potential as both a stand-alone anticancer treatment and a method to induce targeted drug release from nanocarrier systems. TUS approaches have been used to overcome various physiological obstacles, including endothelial barriers, the tumor microenvironment (TME), and immunological hurdles. Combining nanomedicine and ultrasound as a smart DDS can increase in situ drug delivery and improve access to impermeable tissues. Furthermore, smart DDSs can perform targeted drug release in response to distinctive TMEs, external triggers, or dual/multi-stimulus. This results in enhanced treatment efficacy and reduced damage to surrounding healthy tissue or organs at risk. Integrating DDSs and ultrasound is still in its early stages. More research and clinical trials are required to fully understand ultrasound's underlying physical mechanisms and interactions with various types of nanocarriers and different types of cells and tissues. In the present review, ultrasound-mediated nano-sized DDS, specifically focused on cancer treatment, is presented and discussed. Ultrasound interaction with nanoparticles (NPs), drug release mechanisms, and various types of ultrasound-sensitive NPs are examined. Additionally, in vitro, in vivo, and clinical applications of TUS are reviewed in light of the critical challenges that need to be considered to advance TUS toward an efficient, secure, straightforward, and accessible cancer treatment. This study also presents effective TUS parameters and safety considerations for this treatment modality and gives recommendations about system design and operation. Finally, future perspectives are considered, and different TUS approaches are examined and discussed in detail. This review investigates drug release and delivery through ultrasound-mediated nano-sized cancer treatment, both pre-clinically and clinically.

An Engineered Design of Self-Assembly Nanomedicine Guided by Molecular Dynamic Simulation for Photodynamic and Hypoxia-Directed Therapy

To overcome the hypoxia barrier in tumor therapy, a hypoxia-activated prodrug of docetaxel (DTX-PNB) was synthesized and self-assembled with indocyanine green (ICG), forming a combination nanomedicine ISDNN. With the guidance of molecular dynamic simulation, the ISDNN construction could be accurately controlled, achieving uniform size distribution and high drug loading up to 90%. Within the hypoxic tumor environment, ISDNN exerted ICG-mediated photodynamic therapy and aggravated hypoxia to boost DTX-PNB activation for chemotherapy, enabling enhanced antitumor efficacy.

Design and Optimization of a Nanoparticulate Pore Former as a Multifunctional Coating Excipient for pH Transition-Independent Controlled Release of Weakly Basic Drugs for Oral Drug Delivery

Bioavailability of weakly basic drugs may be disrupted by dramatic pH changes or unexpected pH alterations in the gastrointestinal tract. Conventional organic acids or enteric coating polymers cannot address this problem adequately because they leach out or dissolve prematurely, especially during controlled release applications. Thus, a non-leachable, multifunctional terpolymer nanoparticle (TPN) made of cross-linked poly(methacrylic acid) (PMAA)-polysorbate 80-grafted-starch (PMAA-PS 80-g-St) was proposed to provide pH transition-independent release of a weakly basic drug, verapamil HCl (VER), by a rationally designed bilayer-coated controlled release bead formulation. The pH-responsive PMAA and cross-linker content in the TPN was first optimized to achieve the largest possible increase in medium uptake alongside the smallest decrease in drug release rate at pH 6.8, relative to pH 1.2. Such TPNs maintained an acidic microenvironmental pH (pH_m) when loaded in ethylcellulose (EC) films, as measured using pH-indicating dyes. Further studies of formulations revealed that with the 1:2 VER:TPN ratio and 19% coating weight gain, bilayer-coated beads maintained a constant release rate over the pH transition and exhibited extended release up to 18 h. These results demonstrated that the multifunctional TPN as a pH_m modifier and pH-dependent pore former could overcome the severe pH-dependent solubility of weakly basic drugs.

Caffeine-loaded nano/micro-carriers: Techniques, bioavailability, and applications

Caffeine, as one of the most consumed bioactive compounds globally, has gained considerable attention during the last years. Considering the bitter taste and adverse effects of high levels of caffeine consumption, it is crucial to apply a strategy for masking the caffeine's bitter taste and facilitating its programmable deliverance within a long time. Other operational parameters such as food processing parameters, exposure to sunlight and oxygen, and gastrointestinal digestion could also degrade the phenolic compounds in general and caffeine in special. To overcome these challenges, various nano/micro-platforms have been fabricated, including lipid-based (e.g., nanoliposomal vehicles; nanoemulsions, double emulsions, Pickering emulsions; microemulsions; niosomal vehicles; solid lipid nanoparticles and nanostructured lipid carriers), as well as biopolymeric (e.g., nanoparticles; hydrogels, organogels, oleogels; nanofibers and nanotubes; protein-polysaccharide nanocomplexes, conjugates; cyclodextrin inclusion complexes) and inorganic (e.g., gold and silica nanoparticles) nano/micro-structures. In this review, the findings on various caffeine-loaded nano/micro-carriers and their potential applications in functional food products/supplements will be discussed. Also, the controlled release and bioavailability of encapsulated caffeine will be given, and finally, the toxicity and safety of encapsulated caffeine will be presented.

Physiologically Based Pharmacokinetic (PBPK) Model of Gold Nanoparticle-Based Drug Delivery System for Stavudine Biodistribution

Computational modelling has gained attention for evaluating nanoparticle-based drug delivery systems. Physiologically based pharmacokinetic (PBPK) modelling provides a mechanistic approach for evaluating drug biodistribution. The aim of this work is to develop a specific PBPK model to simulate stavudine biodistribution after the administration of a 40 nm gold nanoparticle-based drug delivery system in rats. The model parameters used have been obtained from literature, in vitro and in vivo studies, and computer optimization. Based on these, the PBPK model was built, and the compartments included were considered as permeability rate-limited tissues. In comparison with stavudine solution, a higher biodistribution of stavudine into HIV reservoirs and the modification of pharmacokinetic parameters such as the mean residence time (MRT) have been observed. These changes are particularly noteworthy in the liver, which presents a higher partition coefficient (from 0.27 to 0.55) and higher MRT (from 1.28 to 5.67 h). Simulated stavudine concentrations successfully describe these changes in the in vivo study results. The average fold error of predicted concentrations after the administration of stavudine-gold nanoparticles was within the 0.5–2-fold error in all of the tissues. Thus, this PBPK model approach may help with the pre-clinical extrapolation to other administration routes or the species of stavudine gold nanoparticles.

Towards principled design of cancer nanomedicine to accelerate clinical translation

Nanotechnology in medical applications, especially in oncology as drug delivery systems, has recently shown promising results. However, although these advances have been promising in the pre-clinical stages, the clinical translation of this technology is challenging. To create drug delivery systems with increased treatment efficacy for clinical translation, the physicochemical characteristics of nanoparticles such as size, shape, elasticity (flexibility/rigidity), surface chemistry, and surface charge can be specified to optimize efficiency for a given application. Consequently, interdisciplinary researchers have focused on producing biocompatible materials, production technologies, or new formulations for efficient loading, and high stability. The effects of design parameters can be studied in vitro, in vivo, or using computational models, with the goal of understanding how they affect nanoparticle biophysics and their interactions with cells. The present review summarizes the advances and technologies in the production and design of cancer nanomedicines to achieve clinical translation and commercialization. We also highlight existing challenges and opportunities in the field.

Kinetics of Nanomedicine in Tumor Spheroid as an In Vitro Model System for Efficient Tumor-Targeted Drug Delivery With Insights From Mathematical Models

Numerous strategies have been developed to treat cancer conventionally. Most importantly, chemotherapy shows its huge promise as a better treatment modality over others. Nonetheless, the very complex behavior of the tumor microenvironment frequently impedes successful drug delivery to the tumor sites that further demands very urgent and effective distribution mechanisms of anticancer drugs specifically to the tumor sites. Hence, targeted drug delivery to tumor sites has become a major challenge to the scientific community for cancer therapy by assuring drug effects to selective tumor tissue and overcoming undesired toxic side effects to the normal tissues. The application of nanotechnology to the drug delivery system pays heed to the design of nanomedicine for specific cell distribution. Aiming to limit the use of traditional strategies, the adequacy of drug-loaded nanocarriers (i.e., nanomedicine) proves worthwhile. After systemic blood circulation, a typical nanomedicine follows three levels of disposition to tumor cells in order to exhibit efficient pharmacological effects induced by the drug candidates residing within it. As a result, nanomedicine propounds the assurance towards the improved bioavailability of anticancer drug candidates, increased dose responses, and enhanced targeted efficiency towards delivery and distribution of effective therapeutic concentration, limiting toxic concentration. These aspects emanate the proficiency of drug delivery mechanisms. Understanding the potential tumor targeting barriers and limiting conditions for nanomedicine extravasation, tumor penetration, and final accumulation of the anticancer drug to tumor mass, experiments with in vivo animal models for nanomedicine screening are a key step before it reaches clinical translation. Although the study with animals is undoubtedly valuable, it has many associated ethical issues. Moreover, individual experiments are very expensive and take a longer time to conclude. To overcome these issues, nowadays, multicellular tumor spheroids are considered a promising in vitro model system that proposes better replication of in vivo tumor properties for the future development of new therapeutics. In this review, we will discuss how tumor spheroids could be used as an in vitro model system to screen nanomedicine used in targeted drug delivery, aiming for better therapeutic benefits. In addition, the recent proliferation of mathematical modeling approaches gives profound insight into the underlying physical principles and produces quantitative predictions. The hierarchical tumor structure is already well decorous to be treated mathematically. To study targeted drug delivery, mathematical modeling of tumor architecture, its growth, and the concentration gradient of oxygen are the points of prime focus. Not only are the quantitative models circumscribed to the spheroid, but also the role of modeling for the nanoparticle is equally inevitable. Abundant mathematical models have been set in motion for more elaborative and meticulous designing of nanomedicine, addressing the question regarding the objective of nanoparticle delivery to increase the concentration and the augmentative exposure of the therapeutic drug molecule to the core. Thus, to diffuse the dichotomy among the chemistry involved, biological data, and the underlying physics, the mathematical models play an indispensable role in assisting the experimentalist with further evaluation by providing the admissible quantitative approach that can be validated. This review will provide an overview of the targeted drug delivery mechanism for spheroid, using nanomedicine as an advantageous tool.

Engineered strategies to enhance tumor penetration of drug-loaded nanoparticles

Nanocarriers have been widely employed in preclinical studies and clinical trials for the delivery of anticancer drugs. The most important causes of failure in clinical translation of nanocarriers is their inefficient accumulation and penetration which arises from special characteristics of tumor microenvironment such as insufficient blood supply, dense extracellular matrix, and elevated interstitial fluid pressure. Various strategies such as engineering extracellular matrix, optimizing the physicochemical properties of nanocarriers have been proposed to increase the depth of tumor penetration; however, these strategies have not been very successful so far. Novel strategies such as transformable nanocarriers, transcellular transport of peptide-modified nanocarriers, and bio-inspired carriers have recently been emerged as an advanced generation of drug carriers. In this study, the latest developments of nanocarrier-based drug delivery to solid tumor are presented with their possible limitations. Then, the prospects of advanced drug delivery systems are discussed in detail.

Formulation design and mechanism study of hydrogel based on computational pharmaceutics theories

Formulation design and mechanism study of the drug delivery system (DDS) is an important but difficult subject in pharmaceutical research. The study of formulation factors is the most time- and labor-consuming work of formulation design. In this paper, a multiscale computational pharmaceutics strategy was developed to guide the systematic study of formulation factors of a typical polymer-based DDS, hydrogel, and further to guide the formulation design. According to the strategy, the combination of solubility parameter (δ) and diffusion coefficient (D) calculated by the AA-MD simulation was suggested as the general evaluation method for the matrix screening of the hydrogels at the pre-formulation stage. At the formulation design stage, the CG-MD simulation method was suggested to predict the morphology and drug-releasing behavior of the hydrogels under different formulation factors. The influence mechanism can be explained by the combination of multiple parameters, such as the microstructure diagram, the radius of gyration (Rg), the radial distribution function (RDF), and the free diffusion volume (V_diffusion). The simulation results are in good agreement with the in vitro release experiment, indicating that the strategy has good applicability.

Synchronizing In Silico, In Vitro, and In Vivo Studies for the Successful Nose to Brain Delivery of an Anticancer Molecule

Sesamol is a sesame seed constituent with reported activity against many types of cancer. In this work, two types of nanocarriers, solid lipid nanoparticles (SLNs) and polymeric nanoparticles (PNs), were exploited to improve sesamol efficiency against the glioma cancer cell line. The ability of the proposed systems for efficient brain targeting intranasally was also inspected. By the aid of two docking programs, the virtual loading pattern inside these nanocarriers was matched to the real experimental results. Interactions involved in sesamol-carrier binding were also assessed, followed by a discussion of how different scoring functions account for these interactions. The study is an extension of the computer-assisted drug formulation design series, which represents a promising initiative for an upcoming industrial innovation. The results proved the power of combined in silico tools in predicting members with the highest sesamol payload suitable for delivering a sufficient dose to the brain. Among nine carriers, glyceryl monostearate (GMS) and polycaprolactone (PCL) scored the highest sesamol payload practically and computationally. The EE % was 66.09 ± 0.92 and 61.73 ± 0.47 corresponding to a ΔG (binding energy) of -8.85 ± 0.16 and -5.04 ± 0.11, respectively. Dynamic light scattering evidenced the formation of 215.1 ± 7.2 nm and 414.25 ± 1.6 nm nanoparticles, respectively. Both formulations demonstrated an efficient cytotoxic effect and brain-targeting ability compared to the sesamol solution. This was evidenced by low IC₅₀ (38.50 ± 10.37 μM and 27.81 ± 2.76 μM) and high drug targeting efficiency (7.64 ± 1.89-fold and 13.72 ± 4.1-fold) and direct transport percentages (86.12 ± 3.89 and 92.198 ± 2.09) for GMS-SLNs and PCL-PNs, respectively. The results also showed how different formulations, having different compositions and characteristics, could affect the cytotoxic and targeting ability.

Co-delivery systems: hope for clinical application?

Cancer is a multidimensional and challenging disease to handle. Current statistics reveal that we are far from satisfying cancer treatment. Taking advantage of different therapeutic agents that affect multiple pathways has been established as highly productive. Nevertheless, owing to several hindrances to conventional combination therapy, such as lack of tumor targeting, non-uniform pharmacokinetic of the combined drugs, and off-target side effects, it is well documented that this treatment approach is unlikely to address all the difficulties observed in monotherapy. Co-delivery systems could enhance the therapeutic efficacy of the combination therapy by targeting cancer cells and improving the pharmacokinetic and physicochemical properties of the therapeutic agents. Nevertheless, it seems that present knowledge in responding to the challenges in cancer treatment is still inadequate and far from optimal treatment, which highlights the urgent need for systematic studies direct to identify various aspects of co-delivery systems. Accordingly, to gather informative data, save time, and achieve superior results, the following steps are necessary: (1) implementing computational methods to predict drug-drug interactions (DDIs) in vitro and in vivo, (2) meticulous cancer studies at the cellular and molecular levels to obtain specific criteria for selecting preclinical and clinical models, (3) extensive physiological and pharmacokinetic study of nanocarriers behavior in preclinical models, and (4) finding the optimal formulation and analyzing its behavior in cellular and animal models facilitates bridging in vivo models to clinical trials. This review aims to deliver an overview of co-delivery systems, rationales, and suggestions for further studies in this field.

Cell membrane-derived vesicles for delivery of therapeutic agents

Cell membranes have recently emerged as a new source of materials for molecular delivery systems. Cell membranes have been extruded or sonicated to make nanoscale vesicles. Unlike synthetic lipid or polymeric nanoparticles, cell membrane-derived vesicles have a unique multicomponent feature, comprising lipids, proteins, and carbohydrates. Because cell membrane-derived vesicles contain the intrinsic functionalities and signaling networks of their parent cells, they can overcome various obstacles encountered in vivo. Moreover, the different natural combinations of membranes from various cell sources expand the range of cell membrane-derived vesicles, creating an entirely new category of drug-delivery systems. Cell membrane-derived vesicles can carry therapeutic agents within their interior or can coat the surfaces of drug-loaded core nanoparticles. Cell membranes typically come from single cell sources, including red blood cells, platelets, immune cells, stem cells, and cancer cells. However, recent studies have reported hybrid sources from two different types of cells. This review will summarize approaches for manufacturing cell membrane-derived vesicles and treatment applications of various types of cell membrane-derived drug-delivery systems, and discuss challenges and future directions.

Computational model of silica nanoparticle penetration into tumor spheroids: Effects of methoxy and carboxy PEG surface functionalization and hyperthermia

Drug delivery to tumors suffers from poor solubility, specificity, diffusion through the tumor micro-environment and nonoptimal interactions with components of the extracellular matrix and cell surface receptors. Nanoparticles and drug-polymer complexes address many of these problems. However, large size exasperates the problem of slow diffusion through the tumor. Three-dimensional tumor spheroids are good models to evaluate approaches to mitigate these difficulties and aid in design strategies to improve the delivery of drugs to treat cancer effectively. Diffusion of drug carriers is highly dependent on cell uptake rate parameters (association/dissociation) and temperature. Hyperthermia increases molecular transport and is known to act synergistically with chemotherapy to improve treatment. This study presents a new inverse estimation approach based on Bayesian probability for estimating nanoparticle cell uptake rates from experiments. The parameters were combined with a finite element computational model of nanoparticle transport under hyperthermia conditions to explore its effect on tumor porosity, diffusion and particle binding (association and dissociation) at cell surfaces. Carboxy-PEG-silane (cPEGSi) nanoparticles showed higher cell uptake compared to methoxy-PEG-silane (mPEGSi) nanoparticles. Simulations were consistent with experimental results from Skov-3 ovarian cancer spheroids. Amorphous silica (cPEGSi) nanoparticles (58 nm) concentrated at the periphery of the tumor spheroids at 37°C but mild hyperthermia (43°C) increased nanoparticle penetration. Thus, hyperthermia may enhance cancer treatment by improving blood delivery to tumors, enhancing extravasation and penetration into tumors, trigger release of drug from the carrier at the tumor site and possibly lead to synergistic anti-cancer activity with the drug.

Enhanced Drug Delivery to Solid Tumors via Drug-Loaded Nanocarriers: An Image-Based Computational Framework

Objective

Nano-sized drug delivery systems (NSDDSs) offer a promising therapeutic technology with sufficient biocompatibility, stability, and drug-loading rates towards efficient drug delivery to solid tumors. We aim to apply a multi-scale computational model for evaluating drug delivery to predict treatment efficacy.

Methodology

Three strategies for drug delivery, namely conventional chemotherapy (one-stage), as well as chemotherapy through two- and three-stage NSDDSs, were simulated and compared. A geometric model of the tumor and the capillary network was obtained by processing a real image. Subsequently, equations related to intravascular and interstitial flows as well as drug transport in tissue were solved by considering real conditions as well as details such as drug binding to cells and cellular uptake. Finally, the role of periodic treatments was investigated considering tumor recurrence between treatments. The impact of different parameters, nanoparticle (NP) size, binding affinity of drug, and the kinetics of release rate, were additionally investigated to determine their therapeutic efficacy.

Results

Using NPs considerably increases the fraction of killed cells (FKCs) inside the tumor compared to conventional chemotherapy. Tumoral FKCs for two-stage DDS with smaller NP size (20nm) is higher than that of larger NPs (100nm), in all investigate release rates. Slower and continuous release of the chemotherapeutic agents from NPs have better treatment outcomes in comparison with faster release rate. In three-stage DDS, for intermediate and higher binding affinities, it is desirable for the secondary particle to be released at a faster rate, and the drug with slower rate. In lower binding affinities, high release rates have better performance. Results also demonstrate that after 5 treatments with three-stage DDS, 99.6% of tumor cells (TCs) are killed, while two-stage DDS and conventional chemotherapy kill 95.6% and 88.5% of tumor cells in the same period, respectively.

Conclusion

The presented framework has the potential to enable decision making for new drugs via computational modeling of treatment responses and has the potential to aid oncologists with personalized treatment plans towards more optimal treatment outcomes.

Understanding nano-engineered particle-cell interactions: biological insights from mathematical models

Understanding the interactions between nano-engineered particles and cells is necessary for the rational design of particles for therapeutic, diagnostic and imaging purposes. In particular, the informed design of particles relies on the quantification of the relationship between the physicochemical properties of the particles and the rate at which cells interact with, and subsequently internalise, particles. Quantitative models, both mathematical and computational, provide a powerful tool for elucidating this relationship, as well as for understanding the mechanisms governing the intertwined processes of interaction and internalisation. Here we review the different types of mathematical and computational models that have been used to examine particle-cell interactions and particle internalisation. We detail the mathematical methodology for each type of model, the benefits and limitations associated with the different types of models, and highlight the advances in understanding gleaned from the application of these models to experimental observations of particle internalisation. We discuss the recent proposal and ongoing community adoption of standardised experimental reporting, and how this adoption is an important step toward unlocking the full potential of modelling approaches. Finally, we consider future directions in quantitative models of particle-cell interactions and highlight the need for hybrid experimental and theoretical investigations to address hitherto unanswered questions.

Flow topology and targeted drug delivery in cardiovascular disease

Targeted drug delivery is a promising technique to direct the drug to the specific diseased region. Nanoparticles have provided an attractive approach for this purpose. In practice, the major focus of targeted delivery has been on targeting cell receptors. However, the complex fluid mechanics in diseased biomedical flows questions if a sufficient number of nanoparticles can reach the desired region. In this paper, we propose that hidden topological structures in cardiovascular flows identified with Lagrangian coherent structures (LCS) control drug transport and provide valuable information for optimizing targeted drug delivery efficiency. We couple image-based computational fluid dynamics (CFD) with continuum transport models to study nanoparticle transport in coronary artery disease. We simulate nanoparticle transport as well as the recently proposed shear targeted drug delivery system that couples micro-carriers with nanoparticle drugs. The role of the LCS formed near the stenosed artery in controlling drug transport is discussed. Our results motivate the design of smart micro-needles guided by flow topology, which could achieve optimal drug delivery efficiency.

Mechanistic Understanding From Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery

In this review, we outline the growing role that molecular dynamics simulation is able to play as a design tool in drug delivery. We cover both the pharmaceutical and computational backgrounds, in a pedagogical fashion, as this review is designed to be equally accessible to pharmaceutical researchers interested in what this new computational tool is capable of and experts in molecular modeling who wish to pursue pharmaceutical applications as a context for their research. The field has become too broad for us to concisely describe all work that has been carried out; many comprehensive reviews on subtopics of this area are cited. We discuss the insight molecular dynamics modeling has provided in dissolution and solubility, however, the majority of the discussion is focused on nanomedicine: the development of nanoscale drug delivery vehicles. Here we focus on three areas where molecular dynamics modeling has had a particularly strong impact: (1) behavior in the bloodstream and protective polymer corona, (2) Drug loading and controlled release, and (3) Nanoparticle interaction with both model and biological membranes. We conclude with some thoughts on the role that molecular dynamics simulation can grow to play in the development of new drug delivery systems.

Controlled anti-cancer drug release through advanced nano-drug delivery systems: Static and dynamic targeting strategies

Advances in nanomedicine, including early cancer detection, targeted drug delivery, and personalized approaches to cancer treatment are on the rise. For example, targeted drug delivery systems can improve intracellular delivery because of their multifunctionality. Novel endogenous-based and exogenous-based stimulus-responsive drug delivery systems have been proposed to prevent the cancer progression with proper drug delivery. To control effective dose loading and sustained release, targeted permeability and individual variability can now be described in more-complex ways, such as by combining internal and external stimuli. Despite these advances in release control, certain challenges remain and are identified in this research, which emphasizes the control of drug release and applications of nanoparticle-based drug delivery systems. Using a multiscale and multidisciplinary approach, this study investigates and analyzes drug delivery and release strategies in the nanoparticle-based treatment of cancer, both mathematically and clinically.

In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

BACKGROUND

Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly.

METHODS

To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status.

RESULTS

The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound.

CONCLUSION

It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.

Liposomes for Antibiotic Encapsulation and Delivery

When antibiotics are administered, orally or intravenously, they pass through different organs and layers of tissue on their way to the site of infection; this can cause dilution and/or intoxication. To overcome these problems, drug delivery vehicles have been used to encapsulate and deliver antibiotics, improving their therapeutic index while minimizing their adverse effects. Liposomes are self-assembled lipid vesicles made from at least one bilayer of phospholipids with an inner aqueous compartment. Liposomes are attractive vehicles to deliver antibiotics because they can encapsulate both hydrophobic and hydrophilic antibiotics, they have low toxicity, and they can change the biodistribution of the drug. Furthermore, liposomes have been approved by regulatory agencies. However, most developmental and mechanistic research in the field has been focused on encapsulation and delivery of anticancer drugs, a class of molecules that differ significantly in chemistry from antibiotics. In this critical Review, we discuss the state of knowledge regarding the design of liposomes for encapsulation and delivery of antibiotics and offer insight into the challenges and promises of using liposomes for antibiotic delivery.

Chitosan Derivatives and Their Application in Biomedicine

Chitosan is a product of the deacetylation of chitin, which is widely found in nature. Chitosan is insoluble in water and most organic solvents, which seriously limits both its application scope and applicable fields. However, chitosan contains active functional groups that are liable to chemical reactions; thus, chitosan derivatives can be obtained through the chemical modification of chitosan. The modification of chitosan has been an important aspect of chitosan research, showing a better solubility, pH-sensitive targeting, an increased number of delivery systems, etc. This review summarizes the modification of chitosan by acylation, carboxylation, alkylation, and quaternization in order to improve the water solubility, pH sensitivity, and the targeting of chitosan derivatives. The applications of chitosan derivatives in the antibacterial, sustained slowly release, targeting, and delivery system fields are also described. Chitosan derivatives will have a large impact and show potential in biomedicine for the development of drugs in future.