A multi-scale numerical study of monoclonal antibodies uptake by initial lymphatics after subcutaneous injection.

This paper studies the transport of monoclonal antibodies through skin tissue and initial lymphatics, which impacts the pharmacokinetics of monoclonal antibodies. Our model integrates a macroscale representation of the entire skin tissue with a mesoscale model that focuses on the papillary dermis layer. Our results indicate that it takes hours for the drugs to disperse from the injection site to the papillary dermis before entering the initial lymphatics. Additionally, we observe an inhomogeneous drug distribution in the interstitial space of the papillary dermis, with higher drug concentrations near initial lymphatics and lower concentrations near blood capillaries. To validate our model, we compared our numerical simulation results with experimental data, finding a good alignment. Our parametric studies on the drug molecule properties and injection parameters suggest that a higher diffusion coefficient increases the transport and uptake rate while binding slows down these processes. Furthermore, shallower injection depths lead to faster lymphatic uptake, whereas the size of the injection plume has a minor effect on the uptake rate. These findings advance our understanding of drug transport and lymphatic absorption after subcutaneous injection, offering valuable insights for optimizing drug delivery strategies and the design of biotherapeutics.

Ultrasensitive and multiplexed tracking of single cells using whole-body PET/CT.

In vivo molecular imaging tools are crucially important for elucidating how cells move through complex biological systems; however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upward of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 becquerel per cell and a streamlined workflow to reliably label single cells with over 50 becquerel per cell of 18F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of more than 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.

A compartment model for subcutaneous injection of monoclonal antibodies.

Despite the growing popularity of subcutaneous (SC) administration for monoclonal antibodies (mAbs), there remains a limited understanding of the significance of mAb transport rate constants within the interstitial space and the lymphatic system on their pharmacokinetics. To bridge this knowledge gap, we introduce a compartmental model for subcutaneously administered mAbs. Our model differentiates FcRn-expressing cells across various sites, and the model predictions agree with experimental data from both human and rat studies. Our findings indicate that the time to reach the maximum mAb concentration in the plasma, denoted by Tmax, displays a weak positive correlation with mAb half-life and a negligible correlation with bioavailability. In contrast, the half-life of mAbs exhibits a strong positive correlation with bioavailability. Moreover, the rate of mAb transport from lymph to plasma significantly affects the mAb half-life. Increasing the transport rates of mAbs from the injection site to the lymph or from lymph to plasma enhances bioavailability. These insights, combined with our compartmental model, contribute to a deeper understanding of the pharmacokinetics of subcutaneously administered mAbs.

Numerical studies of the lymphatic uptake rate.

Lymphatic uptake is essential for transporting nutrients, wastes, immune cells, and therapeutic proteins. Despite its importance, the literature lacks a quantitative analysis of the factors that affect lymphatic uptake, including interstitial pressure, downstream pressure, and tissue deformation. In this paper, we present a coupled model of a poroelastic tissue with initial lymphatics and quantify the impact of these factors on the rate of lymphatic uptake. Our results indicate that the lymphatic uptake increases with the amplitude of the oscillating downstream pressure when the amplitude exceeds a threshold. Additionally, the cross-sectional area of initial lymphatics increases with the volumetric strain of the tissue, while the interstitial pressure increases when the strain rate becomes negative. Therefore, the lymphatic uptake reaches its maximum when the tissue has positive volumetric strain while being compressed. We have also investigated the effect of intersection angles and positions of two initial lymphatics and concluded that they have minor impacts on lymphatic uptake. However, the lymphatic uptake per unit length of initial lymphatics decreases with their total length. These findings advance our understanding of lymphatic uptake and can guide the development of strategies to accelerate the transport of therapeutics.

Hydrodynamic considerations for spring-driven autoinjector design.

In recent years, significant progress has been made in the studies of the spring-driven autoinjector, leading to an improved understanding of this device and its interactions with tissue and therapeutic proteins. The development of simulation tools that have been validated against experiments has also enhanced the prediction of the performance of spring-driven autoinjectors. This paper aims to address critical hydrodynamic considerations that impact the design of spring-driven autoinjectors, with a specific emphasis on sloshing and cavitation. Additionally, we present a framework that integrates simulation tools to predict the performance of spring-driven autoinjectors and optimize their design. This work is valuable to the pharmaceutic industry, as it provides crucial insights into the development of spring-driven autoinjectors and therapeutic proteins. This work can also enhance the efficacy and safety of the delivery of therapeutic proteins, ultimately improving patient outcomes.

Optimizing autoinjector devices using physics-based simulations and Gaussian processes.

Autoinjectors are becoming a primary drug delivery option to the subcutaneous space. These devices need to work robustly and autonomously to maximize drug bio-availability. However, current designs ignore the coupling between autoinjector dynamics and tissue biomechanics. Here we present a Bayesian framework for optimization of autoinjector devices that can account for the coupled autoinjector-tissue biomechanics and uncertainty in tissue mechanical behavior. The framework relies on replacing the high fidelity model of tissue insertion with a Gaussian process (GP). The GP model is accurate yet computationally affordable, enabling a thorough sensitivity analysis that identified tissue properties, which are not part of the autoinjector design space, as important variables for the injection process. Higher fracture toughness decreases the crack depth, while tissue shear modulus has the opposite effect. The sensitivity analysis also shows that drug viscosity and spring force, which are part of the design space, affect the location and timing of drug delivery. Low viscosity could lead to premature delivery, but can be prevented with smaller spring forces, while higher viscosity could prevent premature delivery while demanding larger spring forces and increasing the time of injection. Increasing the spring force guarantees penetration to the desired depth, but it can result in undesirably high accelerations. The Bayesian optimization framework tackles the challenge of designing devices with performance metrics coupled to uncertain tissue properties. This work is important for the design of other medical devices for which optimization in the presence of material behavior uncertainty is needed.

The role of liquid rheological properties on the injection process of a spring-driven autoinjector.

Accurate injection time prediction is essential in developing spring-driven autoinjector devices since the drug delivery is expected to finish within seconds to bring convenience, reduce the risk for early lift-off, and provide a consistent experience to users. The Carreau model captures the liquid's shear-dependent viscosity measured in our experiments. Thus, a quasi-steady model, which uses the Carreau model to describe the liquid's viscosity, is developed to predict the injection time of spring-driven autoinjectors. Analytical relations between the flow rate and the pressure drop in the needle are also obtained. The Carreau number in the spring-driven autoinjector is greater than one and smaller than a critical value; in this region, using the power-law model to describe the liquid viscosity accurately predicts the injection time, which agrees with the current literature findings. Additionally, a force threshold is identified for the friction force between the plunger and the syringe barrel, beyond which the injection time is infinite. Appreciation of this force threshold can help avoid device stalling and reduce the risk of underdosing. Moreover, the role of liquid's shear-thinning index on the injection time of spring-driven autoinjectors is quantified. Understanding the shear-thinning index allows formulators to experiment with excipients and pH to enhance confidence in drug/device combination product design and integration. Our experimental and theoretical results can help drug product and device developers with integrated product design and improve the patient experience.

A framework to optimize spring-driven autoinjectors.

The major challenges in the optimization of autoinjectors lie in developing an accurate model and meeting competing requirements. We have developed a computational model for spring-driven autoinjectors, which can accurately predict the kinematics of the syringe barrel, needle displacement (travel distance) at the start of drug delivery, and injection time. This paper focuses on proposing a framework to optimize the single-design of autoinjectors, which deliver multiple drugs with different viscosity. We replace the computational model for spring-driven autoinjectors with a surrogate model, i.e., a deep neural network, which improves computational efficiency 1,000 times. Using this surrogate, we perform Sobol sensitivity analysis to understand the effect of each model input on the quantities of interest. Additionally, we pose the design problem within a multi-objective optimization framework. We use our surrogate to discover the corresponding Pareto optimal designs via Pymoo, an open source library for multi-objective optimization. After these steps, we evaluate the robustness of these solutions and finally identify two promising candidates. This framework can be effectively used for device design optimization as the computation is not demanding, and decision-makers can easily incorporate their preferences into this framework.

An experimentally validated dynamic model for spring-driven autoinjectors.

This study focuses on developing a predictive dynamic model for spring-driven autoinjectors. The values of unknown physical parameters, such as the heat convection coefficient and the friction force between the plunger and the syringe barrel, are obtained by fitting the experimentally measured displacements of the plunger and the syringe barrel. The predicted kinematics of the components, such as the displacement and velocity of the syringe barrel, agree well with the experiments with a l2-norm error smaller than 10%. The predictions of the needle displacement at the start of drug delivery agree with the experimental measurements with a l2-norm error of 20%. The maximum air gap pressure and temperature decrease with the initial air gap height but increase with the elasticity and viscosity of the plunger and the mechanical stop. The proposed experimentally validated dynamic model can be effectively used for device design optimization as it is not computationally demanding.