A quantitative systems pharmacology (QSP) platform for preclinical to clinical translation of in-vivo CRISPR-Cas therapy

Background: In-vivo CRISPR Cas genome editing is a complex therapy involving lipid nanoparticle (LNP), messenger RNA (mRNA), and single guide RNA (sgRNA). This novel modality requires prior modeling to predict dose-exposure-response relationships due to limited information on sgRNA and mRNA biodistribution. This work presents a QSP model to characterize, predict, and translate the Pharmacokinetics/Pharmacodynamics (PK/PD) of CRISPR therapies from preclinical species (mouse, non-human primate (NHP)) to humans using two case studies: transthyretin amyloidosis and LDL-cholesterol reduction. Methods: PK/PD data were sourced from literature. The QSP model incorporates mechanisms post-IV injection: 1) LNP binding to opsonins in liver vasculature; 2) Phagocytosis into the Mononuclear Phagocytotic System (MPS); 3) LNP internalization via endocytosis and LDL receptor-mediated endocytosis in the liver; 4) Cellular internalization and transgene product release; 5) mRNA and sgRNA disposition via exocytosis and clathrin-mediated endocytosis; 6) Renal elimination of LNP and sgRNA; 7) Exonuclease degradation of sgRNA and mRNA; 8) mRNA translation into Cas9 and RNP complex formation for gene editing. Monte-Carlo simulations were performed for 1000 subjects and showed a reduction in serum TTR. Results: The rate of internalization in interstitial layer was 0.039 1/h in NHP and 0.007 1/h in humans. The rate of exocytosis was 6.84 1/h in mouse, 2690 1/h in NHP, and 775 1/h in humans. Pharmacodynamics were modeled using an indirect response model, estimating first-order degradation rate (0.493 1/d) and TTR reduction parameters in NHP. Discussion: The QSP model effectively characterized biodistribution and dose-exposure relationships, aiding the development of these novel therapies. The utility of platform QSP model can be paramount in facilitating the discovery and development of these novel agents.

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.

PBPK model of pegylated liposomal doxorubicin to simultaneously predict the concentration-time profile of encapsulated and free doxorubicin in tissues

The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict the concentrations of encapsulated and free doxorubicin in plasma and tissues in mice after intravenous injection of PEGylated liposomes (Doxil®). The PBPK model used in this study contains liposomes and free doxorubicin disposition components. The free doxorubicin disposition component was used to simulate the disposition of free doxorubicin produced by mononuclear phagocyte system (MPS)-degrading liposomes. The liver, spleen, kidneys, and lungs contain an additional MPS subcompartment. These compartments are interconnected through blood and lymphatic circulation. The model was validated strictly by four doses of external observed plasma and tissue concentration-time profiles. The fold error (FE) values were almost all within threefold. The sensitivity analysis revealed that the MPS-related parameters greatly influenced the model. The predicted in vivo distribution characteristics of the doxorubicin liposomes and doxorubicin solution were consistent with the observed values. The PBPK model was established based on the physiological mechanism and parameters of practical significance that can be measured in vitro. Thus, it can be used to study the pharmacokinetic properties of liposomes. This study also provides a reference for the establishment of liposome PBPK model.

Integration of In Vitro and In Vivo Models to Predict Cellular and Tissue Dosimetry of Nanomaterials Using Physiologically Based Pharmacokinetic Modeling

Nanomaterials (NMs) have been increasingly used in a number of areas, including consumer products and nanomedicine. Target tissue dosimetry is important in the evaluation of safety, efficacy, and potential toxicity of NMs. Current evaluation of NM efficacy and safety involves the time-consuming collection of pharmacokinetic and toxicity data in animals and is usually completed one material at a time. This traditional approach no longer meets the demand of the explosive growth of NM-based products. There is an emerging need to develop methods that can help design safe and effective NMs in an efficient manner. In this review article, we critically evaluate existing studies on in vivo pharmacokinetic properties, in vitro cellular uptake and release and kinetic modeling, and whole-body physiologically based pharmacokinetic (PBPK) modeling studies of different NMs. Methods on how to simulate in vitro cellular uptake and release kinetics and how to extrapolate cellular and tissue dosimetry of NMs from in vitro to in vivo via PBPK modeling are discussed. We also share our perspectives on the current challenges and future directions of in vivo pharmacokinetic studies, in vitro cellular uptake and kinetic modeling, and whole-body PBPK modeling studies for NMs. Finally, we propose a nanomaterial in vitro to in vivo extrapolation via physiologically based pharmacokinetic modeling (Nano-IVIVE-PBPK) framework for high-throughput screening of target cellular and tissue dosimetry as well as potential toxicity of different NMs in order to meet the demand of efficient evaluation of the safety, efficacy, and potential toxicity of a rapidly increasing number of NM-based products.

A New Pharmacokinetic Model Describing the Biodistribution of Intravenously and Intratumorally Administered Superparamagnetic Iron Oxide Nanoparticles (SPIONs) in a GL261 Xenograft Glioblastoma Model

Background

Superparamagnetic iron oxide nanoparticles (SPIONs) have displayed multifunctional applications in cancer theranostics following systemic delivery. In an effort to increase the therapeutic potential of local therapies (including focal hyperthermia), nanoparticles can also be administered intratumorally. Therefore, the development of a reliable pharmacokinetic model for the prediction of nanoparticle distribution for both clinically relevant routes of delivery is of high importance.

Materials and Methods

The biodistribution of SPIONs (of two different sizes – 130 nm and 60 nm) radiolabeled with zirconium-89 or technetium-99m following intratumoral or intravenous injection was investigated in C57/Bl6 mice bearing subcutaneous GL261 glioblastomas. Based on PET/CT biodistribution data, a novel pharmacokinetic model was established for a better understanding of the pharmacokinetics of the SPIONs after both administration routes.

Results

The PET image analysis of the nanoparticles (confirmed by histology) demonstrated the presence of radiolabeled nanoparticles within the glioma site (with low amounts in the liver and spleen) at all investigated time points following intratumoral injection. The mathematical model confirmed the dynamic nanoparticle redistribution in the organism over a period of 72 h with an equilibrium reached after 100 h. Intravenous injection of nanoparticles demonstrated a different distribution pattern with a rapid particle retention in all organs (particularly in liver and spleen) and a subsequent slow release rate.

Conclusion

The mathematical model demonstrated good agreement with experimental data derived from tumor mouse models suggesting the value of this tool to predict the real-time pharmacokinetic features of SPIONs in vivo. In the future, it is planned to adapt our model to other nanoparticle formulations to more precisely describe their biodistribution in in vivo model systems.

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials

There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (P_t) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs.

Animal simulations facilitate smart drug design through prediction of nanomaterial transport to individual tissue cells

Smart drug design for antibody and nanomaterial-based therapies allows optimization of drug efficacy and more efficient early-stage preclinical trials. The ideal drug must display maximum efficacy at target tissue sites, with transport from tissue vasculature to the cellular environment being critical. Biological simulations, when coupled with in vitro approaches, can predict this exposure in a rapid and efficient manner. As a result, it becomes possible to predict drug biodistribution within single cells of live animal tissue without the need for animal studies. Here, we successfully utilized an in vitro assay and a computational fluid dynamic model to translate in vitro cell kinetics (accounting for cell-induced degradation) to whole-body simulations for multiple species as well as nanomaterial types to predict drug distribution into individual tissue cells. We expect this work to assist in refining, reducing, and replacing animal testing, while providing scientists with a new perspective during the drug development process.

An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions

In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans or pre-clinical species. For small molecules, less animal data is often needed because there are a multitude of in vitro screening tools and simulation-based approaches to quantify uptake and deliver data that makes extrapolation to in vivo studies feasible. Small molecule simulations work because these materials often diffuse quickly and partition after reaching equilibrium shortly after dosing, but this cannot be applied to NMs. NMs interact with cells through energy dependent pathways, often taking hours or days to become fully internalized within the cellular environment. In vitro screening tools must capture these phenomena so that cell simulations built on mechanism-based models can deliver relationships between exposure dose and mechanistic biology, that is biology representative of fundamental processes involved in NM transport by cells (e.g. membrane adsorption and subsequent internalization). Here, we developed, validated, and applied the FORECAST method, a combination of a calibrated fluorescence assay (CF) with an artificial intelligence-based cell simulation to quantify rates descriptive of the time-dependent mechanistic biological interactions between NMs and individual cells. This work is expected to provide a means of extrapolation to pre-clinical or human biodistribution with cellular level resolution for NMs starting only from in vitro data.