Multiscale pharmacokinetic modeling of systemic exposure of subcutaneously injected biotherapeutics

A Physiologically Based Pharmacokinetic Model Relates the Subcutaneous Bioavailability of Monoclonal Antibodies to the Saturation of FcRn-Mediated Recycling in Injection-Site-Draining Lymph Nodes

The bioavailability of a monoclonal antibody (mAb) or another therapeutic protein after subcutaneous (SC) dosing is challenging to predict from first principles, even if the impact of injection site physiology and drug properties on mAb bioavailability is generally understood. We used a physiologically based pharmacokinetic model to predict pre-systemic clearance after SC administration mechanistically by incorporating the FcRn salvage pathway in antigen-presenting cells (APCs) in peripheral lymph nodes, draining the injection site. Clinically observed data of the removal rate of IgG from the arm as well as its plasma concentration after SC dosing were mostly predicted within the 95% confidence interval. The bioavailability of IgG was predicted to be 70%, which mechanistically relates to macropinocytosis in the draining lymph nodes and transient local dose-dependent partial saturation of the FcRn receptor in the APCs, resulting in higher catabolism and consequently less drug reaching the systemic circulation. The predicted free FcRn concentration was reduced to 40-45%, reaching the minimum 1-2 days after the SC administration of IgG, and returned to baseline after 8-12 days, depending on the site of injection. The model predicted the uptake into APCs, the binding affinity to FcRn, and the dose to be important factors impacting the bioavailability of a mAb.

Lymphatic distribution considerations for subunit vaccine design and development

Subunit vaccines are an important platform for controlling current and emerging infectious diseases. The lymph nodes are the primary site generating the humoral response and delivery of antigens to these sites is critical to effective immunization. Indeed, the duration of antigen exposure within the lymph node is correlated with the antibody response. While current licensed vaccines are typically given through the intramuscular route, injecting vaccines subcutaneously allows for direct access to lymphatic vessels and therefore can enhance the transfer of antigen to the lymph nodes. However, protein subunit antigen uptake into the lymph nodes is inefficient, and subunit vaccines require adjuvants to stimulate the initial immune response. Therefore, formulation strategies have been developed to enhance the exposure of subunit proteins and adjuvants to the lymph nodes by increasing lymphatic uptake or prolonging the retention at the injection site. Given that lymph node exposure is a crucial consideration in vaccine design, in depth analyses of the pharmacokinetics of antigens and adjuvants should be the focus of future preclinical and clinical studies. This review will provide an overview of formulation strategies for targeting the lymphatics and prolonging antigen exposure and will discuss pharmacokinetic evaluations which can be applied toward vaccine development.

Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach

Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAbs). Compared with intravenous delivery, subcutaneous injections reduce administration costs, shorten the administration time, and are strongly preferred from a patient experience point of view. An understanding of the absorption process of a mAb from the injection site to the systemic circulation is critical to the process of subcutaneous mAb formulation development. In this study, we built a model to predict the absorption rate constant (ka), which denotes how fast a mAb is absorbed from the site of administration. Once trained, our model (enabled by the XGBoost algorithm in machine learning) can predict the ka of a mAb following a subcutaneous injection using in silico molecular properties alone (generated from the primary sequence). Our model does not need clinically observed plasma concentration-time data; this is a novel capability not previously achieved in predictive pharmacokinetic models. The model also showed improved performance when benchmarked against a recently reported mechanistic model that relied on clinical data to predict subcutaneous absorption of mAbs. We further interpreted the model to understand which molecular properties affect the absorption rate and showed that our findings are consistent with previous studies evaluating subcutaneous absorption through direct experimentation. Taken altogether, this study reports the development, validation, benchmarking, and interpretation of a model that can predict the clinical ka of a mAb using its primary sequence as the only input.

Multiphysics-Informed Pharmacokinetic Modeling of Systemic Exposure of Intramuscularly Injected LNPs

Lipid nanoparticle (LNP) constructs have been widely developed for gene therapy delivery. Understanding local absorption and presystemic clearance kinetics of LNPs, however, remains limited. This subsequently restrains the prediction and assessment of the systemic exposure of locally injected LNPs. As such, a multiscale computational approach was developed by integrating multiphysics simulation of intramuscular absorption kinetics of LNPs with whole-body pharmacokinetics modeling, bridged by a presystemic lymphatic kinetic model. The overall framework was enabled by utilizing physiological parameters obtained from the literature and drug-related parameters derived from experiments. The multiscale modeling and simulation approach predicted the systemic exposure of LNPs administered intramuscularly, with a high degree of agreement between the predicted and the experimental data. Sensitivity analyses revealed that the local absorption rate, pinocytosis presystemic clearance rate, and lymph flow rate of the presystemic lymphatic compartment had the most significant impacts on Cmax. The study yielded refreshing perspectives on estimating systemic exposures of locally injected LNPs and their safety and effectiveness.

Multiphysics Simulation of Local Transport and Absorption Coupled with Pharmacokinetic Modeling of Systemic Exposure of Subcutaneously Injected Drug Solution

INTRODUCTION

Subcutaneous (SC) injectables have become more acceptable and feasible for administration of biologics and small molecules. However, efficient development of these products is limited to costly and time-consuming techniques, partially because absorption mechanisms and kinetics at the local site of injection remain poorly understood.

OBJECTIVE

To bridge formulation critical quality attributes (CQA) of injectables with local physiological conditions to predict systemic exposure of these products.

METHODOLOGY

We have previously developed a multiscale, multiphysics computational model to simulate lymphatic absorption and whole-body pharmacokinetics of monoclonal antibodies. The same simulation framework was applied in this study to compute the capillary absorption of solubilized small molecule drugs that are injected subcutaneously. Sensitivity analyses were conducted to probe the impact by key simulation parameters on the local and systemic exposures.

RESULTS

This framework was capable of determining which parameters had the biggest impact on small molecule absorption in the SC. Particularly, membrane permeability of a drug was found to have the biggest impact on drug absorption kinetics, followed by capillary density and drug diffusivity.

CONCLUSION

Our modelling framework proved feasible in predicting local transport and systemic absorption from the injection site of small molecules. Understanding the effect of these properties and how to model them may help to greatly expedite the development process.

MPET2: a multi-network poroelastic and transport theory for predicting absorption of monoclonal antibodies delivered by subcutaneous injection

Subcutaneous injection of monoclonal antibodies (mAbs) has attracted much attention in the pharmaceutical industry. During the injection, the drug is delivered into the tissue producing strong fluid flow and tissue deformation. While data indicate that the drug is initially uptaken by the lymphatic system due to the large size of mAbs, many of the critical absorption processes that occur at the injection site remain poorly understood. Here, we propose the MPET² approach, a multi-network poroelastic and transport model to predict the absorption of mAbs during and after subcutaneous injection. Our model is based on physical principles of tissue biomechanics and fluid dynamics. The subcutaneous tissue is modeled as a mixture of three compartments, i.e., interstitial tissue, blood vessels, and lymphatic vessels, with each compartment modeled as a porous medium. The proposed biomechanical model describes tissue deformation, fluid flow in each compartment, the fluid exchanges between compartments, the absorption of mAbs in blood vessels and lymphatic vessels, as well as the transport of mAbs in each compartment. We used our model to perform a high-fidelity simulation of an injection of mAbs in subcutaneous tissue and evaluated the long-term drug absorption. Our model results show good agreement with experimental data in depot clearance tests.

A biomimetic chip to assess subcutaneous bioavailability of monoclonal antibodies in humans

Subcutaneous (subQ) injection is a common route for delivering biotherapeutics, wherein pharmacokinetics is largely influenced by drug transport in a complex subQ tissue microenvironment. The selection of good drug candidates with beneficial pharmacokinetics for subQ injections is currently limited by a lack of reliable testing models. To address this limitation, we report here a Subcutaneous Co-Culture Tissue-on-a-chip for Injection Simulation (SubCuTIS). SubCuTIS possesses a 3D coculture tissue architecture, and it allows facile quantitative determination of relevant scale independent drug transport rate constants. SubCuTIS captures key in vivo physiological characteristics of the subQ tissues, and it differentiates the transport behavior of various chemically distinct molecules. We supplemented the transport measurements with theoretical modeling, which identified subtle differences in the local absorption rate constants of seven clinically available mAbs. Accounting for first-order proteolytic catabolism, we established a mathematical framework to assess clinical bioavailability using the local absorption rate constants obtained from SubCuTIS. Taken together, the technology described here broadens the applicability of organs-on-chips as a standardized and easy-to-use device for quantitative analysis of subQ drug transport.

In-depth analysis of patterns in selection of different physiologically-based pharmacokinetic modeling tools: Part II - Assessment of model reusability and comparison between open and non-open source-code software

Whilst the reproducibility of models in the area of systems biology and quantitative systems pharmacology has been the focus of attention lately, the concept of 'reusability' is not addressed. With the advent of the 'Model Master File' dominating some regulatory discussions on pharmaceutical applications of physiologically-based pharmacokinetic (PBPK) models, reusability becomes a vital aspect of confidence in their use. Herein, we define 'reusability' specifically in the context of PBPK models and investigate the influence of open versus non-open source-code (NOSC) nature of the software on the extent of 'reusability'. Original articles (n = 145) that were associated with the development of novel PBPK models were identified as source models and citations to these reports, which involved further PBPK model development, were explored (n > 1800) for reuse cases of the source PBPK model whether in full or partial form. The nature of source-code was a major determinant of external reusability for PBPK models (>50% of the NOSC models as opposed <25% of open source-code [OSC]). Full reusability of the models was not common and mostly involved internal reuse of the OSC model (by the group who had previously developed the original model). The results were stratified by the software utilised (various), organisations involved (academia, industry, regulatory), and type of reusability (full vs. partial). The clear link between external reuse of models and NOSC PBPK software might stem from many elements related to quality and trust that require further investigation, and challenges the unfounded notion that OSC models are associated with higher uptake for reuse.

A Perspective on Model-Informed IVIVC for Development of Subcutaneous Injectables

Subcutaneously administered drugs are growing in popularity for both large and small molecule drugs. However, development of these systems - particularly generics - is slowed due to a lack of formal guidance regarding preclinical testing and in vitro - in vivo correlations (IVIVC). Many of these methods, while appropriate for oral drugs, may not be optimized for the complex injection site physiologies, and release rate and absorption mechanisms of subcutaneous drugs. Current limitations for formulation design and IVIVC can be supported by implementing mechanistic, computational methods. These methods can help to inform drug development by identifying key drug and formulation attributes, and their effects on drug release rates. This perspective, therefore, addresses current guidelines in place for oral IVIVC development, how they may differ for subcutaneously administered compounds, and how modeling and simulation can be implemented to inform design of these products. As such, integration of modeling and simulation with current IVIVC systems can help in driving the development of subcutaneous injectables.

Multiphysics modeling and simulation of local transport and absorption kinetics of intramuscularly injected lipids nanoparticles

Recent clinical applications of mRNA vaccines highlight the critical role of drug delivery, especially when using lipid nanoparticles (LNPs) as the carrier for genetic payloads. However, kinetic and transport mechanisms for locally injected LNPs, such as lymphatic or cellular uptake and drug release, remain poorly understood. Herein, we developed a bottom-up multiphysics computational model to simulate the injection and absorption processes of LNPs in muscular tissues. Our purpose was to seek underlying connections between formulation attributes and local exposure kinetics of LNPs and the delivered drug. We were also interested in modeling the absorption kinetics from the local injection site to the systemic circulation. In our model, the tissue was treated as the homogeneous, poroelastic medium in which vascular and lymphatic vessel densities are considered. Tissue deformation and interstitial fluid flow (modeled using Darcy's Law) were also implemented. Transport of LNPs was described based on diffusion and advection; local disintegration and cellular uptake were also integrated. Sensitivity analyses of LNP and drug properties and tissue attributes were conducted using the simulation model. It was found that intrinsic tissue porosity and lymphatic vessel density affect the local transport kinetics; diffusivity, lymphatic permeability, and intracellular update kinetics also play critical roles. Simulated results were commensurate with experimental observations. This study could shed light on the development of LNP formulations and enable further development of whole-body pharmacokinetic models.

Predicting Human Bioavailability of Subcutaneously Administered Monoclonal Antibodies Using Non-human Primate Linear Clearance and Antibody Isoelectric Point

The prediction of bioavailability is one of the major barriers in the clinical translation of subcutaneously (SC) administered therapeutic monoclonal antibodies (mAbs) due to the lack of reliable in vitro and preclinical in vivo predictive models. Recently, multiple linear regression (MLR) models were developed to predict human SC bioavailability of mAbs using human linear clearance (CL) and isoelectric point (pI) of the whole antibody or Fv regions as independent variables. Unfortunately, these models cannot be applied to mAbs at the preclinical development stage because human CLs of these mAbs are unknown. In this study, we predicted human SC bioavailability of mAbs using preclinical data only by two approaches. In the first approach, allometric scaling was used to predict human linear CL from non-human primate (NHP) linear CL. The predicted human CL and the pI of the whole antibody or Fv regions were then incorporated into two previously published MLR models to predict the human bioavailability of 61 mAbs. In the second approach, two MLR models were developed using NHP linear CL and the pI of whole antibody or Fv regions of 41 mAbs in a training set. The two models were validated using an independent test dataset containing 20 mAbs. The four MLR models generated 77-85% of predictions within 0.8- to 1.2-fold deviations from observed human bioavailability. Overall, this study demonstrated that human SC bioavailability of mAbs at the preclinical stage could be predicted using NHP CL and pI of mAbs.

Predicting Human Bioavailability of Subcutaneously Administered Fusion Proteins and Monoclonal Antibodies Using Human Intravenous Clearance or Antibody Isoelectric Point

There has been an increasing trend towards subcutaneous (SC) delivery of fusion proteins and monoclonal antibodies (mAbs) in recent years versus intravenous (IV) administration. The prediction of bioavailability is one of the major barriers in clinical translation of SC-administered therapeutic proteins due to a lack of reliable in vitro and preclinical in vivo predictive models. In this study, we explored the relationships between human SC bioavailability and physicochemical or pharmacokinetic properties of 19 Fc- or albumin-fusion proteins and 98 monoclonal antibodies. An inverse linear correlation was observed between human SC bioavailability and intravenous clearance (CL) or isoelectric point (pI). Multivariate regression models were developed using intravenous CL and pI of a training set (N = 59) as independent variables. The predictive models of mAbs were validated with an independent test set (N = 33). Two linear regression models resulted in 24 (73%) and 27 (82%) among 33 predictions within 0.8- to 1.2-fold deviations. Due to the small sample size of dataset, regression model validation was not conducted for fusion proteins. Overall, this study demonstrated that CL- and pI-based multivariate regression models could be used to predict human SC bioavailability of mAbs.

Application of physiologically-based pharmacokinetic models for therapeutic proteins and other novel modalities

The past two decades have seen diversification of drug development pipelines and approvals from traditional small molecule therapies to alternative modalities including monoclonal antibodies, engineered proteins, antibody drug conjugates (ADCs), oligonucleotides and gene therapies. At the same time, physiologically-based pharmacokinetic (PBPK) models for small molecules have seen increased industry and regulatory acceptance.This review focusses on the current status of the application of PBPK models to these newer modalities and give a perspective on the successes, challenges and future directions of this field.There is greatest experience in the development of PBPK models for therapeutic proteins, and PBPK models for ADCs benefit from prior experience for both therapeutic proteins and small molecules. For other modalities, the application of PBPK models is in its infancy.Challenges are discussed and a common theme is lack of availability of physiological and experimental data to characterise systems and drug parameters to enable a priori prediction of pharmacokinetics. Furthermore, sufficient clinical data are required to build confidence in developed models.The PBPK modelling approach provides a quantitative framework for integrating knowledge and data from multiple sources and can be built on as more data becomes available.