Injectable drug delivery systems of doxorubicin revisited: In vitro-in vivo relationships using human clinical data

Modeling the Drug delivery Lifecycle of PLG Nanoparticles Using Intravital Microscopy

Polylactide-co-glycolide (PLG) nanoparticles hold immense promise for cancer therapy due to their enhanced efficacy and biodegradable matrix structure. Understanding their interactions with blood cells and subsequent biodistribution kinetics is crucial for optimizing their therapeutic potential. In this study, three doxorubicin-loaded PLG nanoparticle systems are synthesized and characterized, analyzing their size, zeta potential, morphology, and in vitro release behavior. Employing intravital microscopy in 4T1-tumor-bearing mice, real-time blood and tumor distribution kinetics are investigated. A mechanistic pharmacokinetic model is used to analyze biodistribution kinetics. Additionally, flow cytometry is utilized to identify cells involved in nanoparticle hitchhiking. Following intravenous injection, PLG nanoparticles exhibit an initial burst release (<1 min) and rapidly adsorb to blood cells (<5 min), hindering extravasation. Agglomeration leads to the clearance of one carrier species within 3 min. In stable dispersions, drug release rather than extravasation remains the dominant pathway for drug elimination from circulation. This comprehensive investigation provides valuable insights into the interplay between competing kinetics that influence the lifecycle of PLG nanoparticles post-injection. The findings advance the understanding of nanoparticle behavior and lay the foundation for improved cancer therapy strategies using nanoparticle-based drug delivery systems.

Understanding the In Vitro-In Vivo Nexus: Advanced correlation models predict clinical performance of liposomal doxorubicin

In the century of precision medicine and predictive modeling, addressing quality-related issues in the medical supply chain is critical, with 62 % of the disruptions being attributable to quality challenges. This study centers on the development and safety of liposomal doxorubicin, where animal studies alone often do not adequately explain the complex interplay between critical quality attributes and in vivo performances. Anchored in our aim to elucidate this in vitro-in vivo nexus, we compared TLD-1, a novel liposomal doxorubicin delivery system, against the established formulations Doxil® and Lipodox®. Robust in vitro-in vivo correlations (IVIVCs) with excellent coefficients of determination (R2 > 0.98) were obtained in the presence of serum under dynamic high-shear conditions. They provided the foundation for an advanced characterization and benchmarking strategy. Despite the smaller vesicle size and reduced core crystallinity of TLD-1, its release behavior closely resembled that of Doxil®. Nevertheless, subtle differences between the dosage forms observed in the in vitro setting were reflected in the bioavailabilities observed in vivo. Data from a Phase-I clinical trial facilitated the development of patient-specific IVIVCs using the physiologically-based nanocarrier biopharmaceutics model, enabling a more accurate estimation of doxorubicin exposure. This advancement could impact clinical practice by allowing for more precise dose estimation and aiding in the assessment of the interchangeability of generic liposomal doxorubicin.

Blood-Nanoparticle Interactions Create a Brain Delivery Superhighway for Doxorubicin

Purpose

This study investigated the brain targeting mechanism of doxorubicin-loaded polybutyl cyanoacrylate (PBCA) nanoparticles, particularly their interactions with the blood-brain barrier (BBB). The BBB protects the brain from drugs in the bloodstream and represents a crucial obstacle in the treatment of brain cancer.

Methods

An advanced computer model analyzed the brain delivery of two distinct formulations, Doxil® and surfactant-coated PBCA nanoparticles. Computational learning was combined with in vitro release and cell interaction studies to comprehend the underlying brain delivery pathways.

Results

Our analysis yielded a surprising discovery regarding the brain delivery mechanism of PBCA nanoparticles. While Doxil® exhibited the expected behavior, accumulating in the brain through extravasation in tumor tissue, PBCA nanoparticles employed a unique and previously uncharacterized mechanism. They underwent cell hitchhiking, resulting in a remarkable more than 1000-fold increase in brain permeation rate compared to Doxil® (2.59 × 10-4 vs 0.32 h-1).

Conclusion

The nonspecific binding to blood cells facilitated and intensified interactions of surfactant-coated PBCA nanoparticles with the vascular endothelium, leading to enhanced transcytosis. Consequently, the significant increase in circulation time in the bloodstream, coupled with improved receptor interactions, contributes to this remarkable uptake of doxorubicin into the brain.

A Design-Conversed Strategy Establishes the Performance Safe Space for Doxorubicin Nanosimilars

Nanomedicines exhibit multifaceted performances, yet their biopharmaceutics remain poorly understood and present several challenges in the translation from preclinical to clinical research. To address this issue and promote the production of high-quality nanomedicines, a systematic screening of the design space and in vivo performance is necessary. Establishing formulation performance specifications early on enables an informed selection of candidates and promotes the development of nanosimilars. The deconvolution of the pharmacokinetics enables the identification of key characteristics that influence their performances and disposition. Using an in vitro-in vivo rank-order relationship for doxorubicin nanoformulations, we defined in vitro release specifications for Doxil/Caelyx-like follow-on products. Additionally, our model predictions were used to establish the bioequivalence of Lipodox, a nanosimilar of Doxil/Caelyx. Furthermore, a virtual safe space was established, providing crucial insights into expected disposition kinetics and informing formulation development. By addressing bottlenecks in biopharmaceutics and formulation screening, our research advances the translation of nanomedicine from bench to bedside.

Future Nanotechnology-Based Strategies for Improved Management of Helicobacter pylori Infection

Helicobacter pylori (H. pylori) is a recalcitrant pathogen, which can cause gastric disorders. During the past decades, polypharmacy-based regimens, such as triple and quadruple therapies have been widely used against H. pylori. However, polyantibiotic therapies can disturb the host gastric/gut microbiota and lead to antibiotic resistance. Thus, simpler but more effective approaches should be developed. Here, some recent advances in nanostructured drug delivery systems to treat H. pylori infection are summarized. Also, for the first time, a drug release paradigm is proposed to prevent H. pylori antibiotic resistance along with an IVIVC model in order to connect the drug release profile with a reduction in bacterial colony counts. Then, local delivery systems including mucoadhesive, mucopenetrating, and cytoadhesive nanobiomaterials are discussed in the battle against H. pylori infection. Afterward, engineered delivery platforms including polymer-coated nanoemulsions and polymer-coated nanoliposomes are poposed. These bioinspired platforms can contain an antimicrobial agent enclosed within smart multifunctional nanoformulations. These bioplatforms can prevent the development of antibiotic resistance, as well as specifically killing H. pylori with no or only slight negative effects on the host gastrointestinal microbiota. Finally, the essential checkpoints that should be passed to confirm the potential effectiveness of anti-H. pylori nanosystems are discussed.

Physiologically Based Pharmacokinetic Modeling of Extracellular Vesicles

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.

Revisiting the in-vitro and in-vivo considerations for in-silico modelling of complex injectable drug products

Complex injectable drug products (CIDPs) have often been developed to modulate the pharmacokinetics along with efficacy for therapeutic agents used for remediation of chronic disorders. The effective development of CIDPs has exhibited complex kinetics associated with multiphasic drug release from the prepared formulations. Consequently, predictability of pharmacokinetic modelling for such CIDPs has been difficult and there is need for advanced complex computational models for the establishment of accurate prediction models for in-vitro-in-vivo correlation (IVIVC). The computational modelling aims at supplementing the existing knowledge with mathematical equations to develop formulation strategies for generation of predictable and discriminatory IVIVC. Such an approach would help in reduction of the burden of effect of hidden factors on preclinical to clinical translations. Computational tools like physiologically based pharmacokinetics (PBPK) modelling have combined physicochemical and physiological properties along with IVIVC characteristics of clinically used formulations. Such techniques have helped in prediction and understanding of variability in pharmacodynamic parameters of potential generic products to clinically used formulations like Doxil®, Ambisome®, Abraxane® in healthy and diseased population using mathematical equations. The current review highlights the important formulation characteristics, in-vitro, preclinical in-vivo aspects which need to be considered while developing a stimulatory predictive PBPK model in establishment of an IVIVC and in-vitro-in-vivo relationship (IVIVR).

Putting square pegs in round holes: why traditional pharmacokinetic principles cannot universally be applied to iron-carbohydrate complexes

Intravenous iron-carbohydrate complexes are nanomedicines that are commonly used to treat iron deficiency and iron deficiency anemia of various etiologies. Many challenges remain regarding these complex drugs in the context of fully understanding their pharmacokinetic parameters. Firstly, the measurement of the intact iron nanoparticles versus endogenous iron concentration fundamentally limits the availability of data for computational modeling. Secondly, the models need to include several parameters to describe the iron metabolism which is not completely defined and those identified (e.g. ferritin) exhibit considerable interpatient variability. Additionally, modeling is further complicated by the lack of traditional receptor/enzyme interactions. The known parameters of bioavailability, distribution, metabolism, and excretion for iron-carbohydrate nanomedicines will be reviewed and future challenges that currently prevent the direct application of physiologically-based pharmacokinetic or other computational modeling techniques will be discussed.

Comparison of Compartmental and Non-Compartmental Analysis to Detect Biopharmaceutical Similarity of Intravenous Nanomaterial-Based Rifabutin Formulations

Pharmacometric analysis is often used to quantify the differences and similarities between formulation prototypes. In the regulatory framework, it plays a significant role in the evaluation of bioequivalence. While non-compartmental analysis provides an unbiased data evaluation, mechanistic compartmental models such as the physiologically-based nanocarrier biopharmaceutics model promise improved sensitivity and resolution for the underlying causes of inequivalence. In the present investigation, both techniques were applied to two nanomaterial-based formulations for intravenous injection, namely, albumin-stabilized rifabutin nanoparticles and rifabutin-loaded PLGA nanoparticles. The antibiotic rifabutin holds great potential for the treatment of severe and acute infections of patients co-infected with human immunodeficiency virus and tuberculosis. The formulations differ significantly in their formulation and material attributes, resulting in an altered biodistribution pattern as confirmed in a biodistribution study in rats. The albumin-stabilized delivery system further undergoes a dose-dependent change in particle size which leads to a small yet significant change in the in vivo performance. A second analysis was conducted comparing the dose fraction-scaled pharmacokinetic profiles of three dose levels of albumin-stabilized rifabutin nanoparticles. The dose strength affects both the nanomaterial-related absorption and biodistribution of the carrier as well as the drug-related distribution and elimination parameters, increasing the background noise and difficulty of detecting inequivalence. Depending on the pharmacokinetic parameter (e.g., AUC, C_max, Cl_obs), the relative (percentage) difference from the average observed using non-compartmental modeling ranged from 85% to 5.2%. A change in the formulation type (PLGA nanoparticles vs. albumin-stabilized rifabutin nanoparticles) resulted in a similar level of inequivalence as compared to a change in the dose strength. A mechanistic compartmental analysis using the physiologically-based nanocarrier biopharmaceutics model led to an average difference of 152.46% between the two formulation prototypes. Albumin-stabilized rifabutin nanoparticles tested at different dose levels led to a 128.30% difference, potentially due to changes in particle size. A comparison of different dose strengths of PLGA nanoparticles, on average, led to a 3.87% difference. This study impressively illustrates the superior sensitivity of mechanistic compartmental analysis when dealing with nanomedicines.

In vitro-in vivo correlation in nanocarriers: From protein corona to therapeutic implications

Understanding and establishing a link between the physicochemical characteristics of nanoparticles (NPs) and their biological interactions poses to be a great challenge in the field of nanotherapeutics. Recent analytical advancements concerning bio-nanointerfaces have accelerated the quest to comprehend the fate of nanocarrier systems in vivo. Scientists have discovered that protein corona, an adsorbed layer of biomolecules on the surface of NPs takes a leading part in interacting with cells and in the cellular uptake process, thereby determining the in vivo behaviour of NPs. Another useful method to assess the in vivo fate of NPs is by performing dissolution testing. This forms the basis for in vitro in vivo correlation (IVIVC), relating in vitro dissolution of NPs and their in vivo properties. Scientists are continuously directing their efforts towards establishing IVIVC for different nanocarrier systems while concurrently gaining insights into protein corona. This review primarily summarizes the importance of protein corona and its interaction with nanoparticles. It also gives an insight into the factors affecting the interaction and various in vitro dissolution media used for varied nanocarrier systems. The article concludes with a discussion of the limitations of IVIVC modelling and its position from a regulatory perspective.

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.

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.

An Update to Dialysis-Based Drug Release Testing-Data Analysis and Validation Using the Pharma Test Dispersion Releaser

Currently, a wide variety of complex non-oral dosage forms are entering the global healthcare market. Although many assays have been described in recent research, harmonized procedures and standards for testing their in vitro performance remain widely unexplored. Among others, dialysis-based techniques such as the Pharma Test Dispersion Releaser are developed for testing the release of drugs from nanoparticles, liposomes, or extracellular vesicle preparations. Here, we provide advanced strategies and practical advice for the development and validation of dialysis-based techniques, including documentation, analysis, and interpretation of the raw data. For this purpose, key parameters of the release assay, including the hydrodynamics in the device at different stirring rates, the selectivity for particles and molecules, as well as the effect of excipients on drug permeation were investigated. At the highest stirring rate, a more than twofold increase in the membrane permeation rate (from 0.99 × 10⁻³ to 2.17 × 10⁻³ cm²/h) was observed. Additionally, we designed a novel computer model to identify important quality parameters of the dialysis experiment and to calculate error-corrected release profiles. Two hydrophilic creams of diclofenac, Voltaren^® Emulgel, and Olfen^® gel, were tested and provide first-hand evidence of the robustness of the assay in the presence of semisolid dosage forms.

Simulate SubQ: The Methods and the Media

For decades, there has been a growing interest in injectable subcutaneous formulations to improve the absorption of drugs into the systemic circulation and to prolong their release over a longer period. However, fluctuations in the blood plasma levels together with bioavailability issues often limit their clinical success. This warrants a closer look at the performance of long-acting depots, for example, and their dependence on the complex interplay between the dosage form and the physiological microenvironment. For this, biopredictive performance testing is used for a thorough understanding of the biophysical processes affecting the absorption of compounds from the injection site in vivo and their simulation in vitro. In the present work, we discuss in vitro methodologies including methods and media developed for the subcutaneous route of administration on the background of the most relevant absorption mechanisms. Also, we highlight some important knowledge gaps and shortcomings of the existing methodologies to provide the reader with a better understanding of the scientific evidence underlying these models.