A physiologically-based nanocarrier biopharmaceutics model to reverse-engineer the in vivo drug release

Revisiting nanomedicine design strategies for follow-on products: A model-informed approach to optimize performance

The field of nanomedicine is undergoing a seismic transformations with the rise of nanosimilars, reshaping the pharmaceutical landscape and expanding beyond traditional innovators and generic manufacturers. Nanodrugs are increasingly replacing conventional therapies, offering improved efficacy and safety, while the demand for follow-on products drives market diversification. However, the transition from preclinical to clinical stages presents challenges due to the complex biopharmaceutical behavior of nanodrugs. This review highlights the integration of Quality-by-Design (QbD), in vitro-in vivo correlations (IVIVCs), machine learning, and Model-Informed Drug Development (MIDD) as key strategies to address these complexities. Additionally, it discusses the role of high-throughput processes in the optimization of the nanodrug development pipelines. Covering generations of delivery systems from liposomes to RNA-loaded nanoparticles, this review underscores the evolving market dynamics driven by recent advances in nanomedicine.

A bio-predictive release assay for liposomal prednisolone phosphate

Predictive performance assays are crucial for the development and approval of nanomedicines and their bioequivalent successors. At present, there are no established compendial methods that provide a reliable standard for comparing and selecting these formulation prototypes, and our understanding of the in vivo release remains still incomplete. Consequently, extensive animal studies, with enhanced analytical resolution for both, released and encapsulated drug, are necessary to assess bioequivalence. This significantly raises the cost and duration of nanomedicine development. This work presents the development of a discriminatory and biopredictive release test method for liposomal prednisolone phosphate. Using model-informed deconvolution, we identified an in vivo target release. The experimental design employed a discrete L-optimal configuration to refine the analytical method and determine the impact of in vitro parameters on the dosage form. A three-point specification evaluated the key phases of in vivo release: early (T-5%), intermediate (T-20%), and late release behavior (T-40%), compared to the in vivo release profile of the reference product, NanoCort®. Various levels of shear responses and the influence of clinically relevant release media compositions were tested. This enabled an assessment of the effect of shear on the release, an essential aspect of their in vivo deformation and release behavior. The type and concentration of proteins in the medium influence liposome release. Fetal bovine serum strongly impacted the discriminatory performance at intermediate shear conditions. The method provided deep insights into the release response of liposomes and offers an interesting workflow for in vitro bioequivalence evaluation.

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.

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.

Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery

Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.

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.

Physiologically based pharmacokinetic modeling of intravenously administered nanoformulated substances

The use of nanobiomaterials (NBMs) is becoming increasingly popular in the field of medicine. To improve the understanding on the biodistribution of NBMs, the present study aimed to implement and parametrize a physiologically based pharmacokinetic (PBPK) model. This model was used to describe the biodistribution of two NBMs after intravenous administration in rats, namely, poly(alkyl cyanoacrylate) (PACA) loaded with cabazitaxel (PACA-Cbz), and LipImage™ 815. A Bayesian parameter estimation approach was applied to parametrize the PBPK model using the biodistribution data. Parametrization was performed for two distinct dose groups of PACA-Cbz. Furthermore, parametrizations were performed three distinct dose groups of LipImage™ 815, resulting in a total of five different parametrizations. The results of this study indicate that the PBPK model can be adequately parametrized using biodistribution data. The PBPK parameters estimated for PACA-Cbz, specifically the vascular permeability, the partition coefficient, and the renal clearance rate, substantially differed from those of LipImage™ 815. This emphasizes the presence of kinetic differences between the different formulations and substances and the need of tailoring the parametrization of PBPK models to the NBMs of interest. The kinetic parameters estimated in this study may help to establish a foundation for a more comprehensive database on NBM-specific kinetic information, which is a first, necessary step towards predictive biodistribution modeling. This effort should be supported by the development of robust in vitro methods to quantify kinetic parameters.

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.

Need for Expansion of Pharmacy Education Globally for the Growing Field of Nanomedicine

The emerging landscape of nanomedicine includes a wide variety of active pharmaceutical ingredients and drug formulations. Their design provides nanomedicines with unique features leading to improved pharmacokinetics and pharmacodynamics. They are manufactured using conventional or biotechnological manufacturing processes. Their physical characteristics are vastly different from traditional small-molecule drugs. Pharmacists are important members of the multi-disciplinary team of scientists involved in their development and clinical application. Consequently, their training should lead to an understanding of the complexities associated with the production and evaluation of nanomedicines. Therefore, student pharmacists, post-doctoral researchers, and trainees should be given more exposure to this rapidly evolving class of therapeutics. This commentary will provide an overview of nanomedicine education within the selection of pharmacy programs globally, discuss the current regulatory challenges, and describe different approaches to incorporate nanomedicine science in pharmacy programs around the world.

Pharmaceutical liposomal delivery—specific considerations of innovation and challenges

Liposomal technology can enhance drug solubility and stability, achieving codelivery for combination therapy, and modulate the in vivo fate (e.g., site-specific distribution and controlled release), thereby improving treatment outcomes.

Nanomedicine at the crossroads - A quick guide for IVIVC

For many years, nanomedicine is pushing the boundaries of drug delivery. When applying these novel therapeutics, safety considerations are not only a key concern when entering clinical trials but also an important decision point in product development. Standing at the crossroads, nanomedicine may be able to escape the niche markets and achieve wider acceptance by the pharmaceutical industry. While there is a new generation of drug delivery systems, the extracellular vesicles, standing on the starting line, unresolved issues and new challenges emerge from their translation from bench to bedside. Some key features of injectable nanomedicines contribute to the predictability of the pharmacological and toxicological effects. So far, only a few of the physicochemical attributes of nanomedicines can be justified by a direct mathematical relationship between the in vitro and the in vivo responses. To further develop extracellular vesicles as drug carriers, we have to learn from more than 40 years of clinical experience in liposomal delivery and pass on this knowledge to the next generation. Our quick guide discusses relationships between physicochemical characteristics and the in vivo response, commonly referred to as in vitro-in vivo correlation. Further, we highlight the key role of computational methods, lay open current knowledge gaps, and question the established design strategies. Has the recent progress improved the predictability of targeted delivery or do we need another change in perspective?

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

A growing number of nanomedicines entered the clinical trials and improved our understanding of the in vivo responses expected in humans. The in vitro drug release represents an important critical quality attribute involved in pharmacokinetics. Establishing in vitro-in vivo relationships for nanomedicines requires a careful analysis of the clinical data with respect to the unique differences between drugs and nanomedicines. Also, the biorelevant assay must reflect the release mechanism of the carrier. Four drug delivery systems of doxorubicin were evaluated for their in vitro release behavior under biorelevant conditions using the dispersion releaser. The pharmacokinetics observed during the first-in-men clinical trials were analyzed using a custom-made physiologically-based nanocarrier biopharmaceutics model. The drug product Lipodox® and the clinical candidate NanoCore-7.4 were evaluated to validate the model. Afterward, the in vivo performances of the preclinical candidates NanoCore-6.4 and doxorubicin-loaded nano-cellular vesicle technology systems (an extracellular vesicle preparation) were predicted. In vitro and in vivo release were in good correlation as indicated by the coefficients of determination of 0.98648 (NanoCore-7.4) and 0.94107 (Lipodox®). The predictions required an estimation of the carrier half-life in blood circulation leading to considerable uncertainty. Still, the simulations narrow down the possible scenarios in the clinical evaluation of nanomedicines and provide a valuable addition to animal studies.

Exploring the Interplay between Drug Release and Targeting of Lipid-Like Polymer Nanoparticles Loaded with Doxorubicin

Targeted delivery of doxorubicin still poses a challenge with regards to the quantities reaching the target site as well as the specificity of the uptake. In the present approach, two colloidal nanocarrier systems, NanoCore-6.4 and NanoCore-7.4, loaded with doxorubicin and characterized by different drug release behaviors were evaluated in vitro and in vivo. The nanoparticles utilize a specific surface design to modulate the lipid corona by attracting blood-borne apolipoproteins involved in the endogenous transport of chylomicrons across the blood–brain barrier. When applying this strategy, the fine balance between drug release and carrier accumulation is responsible for targeted delivery. Drug release experiments in an aqueous medium resulted in a difference in drug release of approximately 20%, while a 10% difference was found in human serum. This difference affected the partitioning of doxorubicin in human blood and was reflected by the outcome of the pharmacokinetic study in rats. For the fast-releasing formulation NanoCore-6.4, the AUC_0→1h was significantly lower (2999.1 ng × h/mL) than the one of NanoCore-7.4 (3589.5 ng × h/mL). A compartmental analysis using the physiologically-based nanocarrier biopharmaceutics model indicated a significant difference in the release behavior and targeting capability. A fraction of approximately 7.310–7.615% of NanoCore-7.4 was available for drug targeting, while for NanoCore-6.4 only 5.740–6.057% of the injected doxorubicin was accumulated. Although the targeting capabilities indicate bioequivalent behavior, they provide evidence for the quality-by-design approach followed in formulation development.