Phenotypic switching as a non-genetic mechanism of resistance predicts antibody therapy regimens.

Despite the specificity and effectiveness of antibody therapy, resistance to treatment remains a major barrier for their broad clinical applications. While genetic mutations are known to be critical, the impact of non-genetic mechanisms, such as epigenetic changes and phenotypic adaptations, on resistance to antibody-dependent cellular cytotoxicity (ADCC) is not fully understood. Our study investigated the non-genetic resistance mechanisms that colorectal cancer cells develop against cetuximab and the resulting ADCC pressure. Resistance clones exhibited decreased EGFR/HER2 expressions, enriched interferon-related pathways, and lower NK cell activation. Interestingly, these resistance clones regained sensitivity upon the withdrawal of therapeutic pressure, implying phenotypic plasticity and reversibility. To counter resistance, we developed a mathematical model recapitulating the phenotypic switching dynamics. The model predicted that intermittent dosing strategy outperforms continuous regimen in delaying treatment resistance. Our findings have implications for improving efficacy and circumventing resistance to targeted antibody therapies.

A Proximity-Dependent Biosensor System for Visualizing Cell-Cell Interactions Induced by Therapeutic Antibodies.

Despite the promise of therapeutic antibodies in engaging the immune system to eliminate malignant cells, many aspects of the complex interplay between immune cells and cancer cells induced by antibody therapy remain incompletely understood. This study aimed to develop a biosensor system that can evaluate direct cell-cell physical contact and interactions between immune effector and target cells induced by therapeutic antibodies in physiologically relevant environments. The system uses two structural complementary luciferase units (SmBit and LgBit) expressed on the respective membranes of effector and target cells. Upon cell-cell contact, the two subunits form active NanoLuc, generating a luminescent signal, allowing for real-time monitoring of cell-cell interactions and quantitatively assessing the pharmacological effects of therapeutic antibodies. We optimized the system to ensure selectivity by adjusting the spacer lengths between two luciferase units to minimize interference from nonspecific intercellular contact. The system was applied to quantitatively monitor cell-cell interactions between NK and target cells induced by rituximab and between T and target cells induced by blinatumomab in a 3D cell culture system. The biosensor system has the potential to characterize antibody pharmacology through a deeper understanding of antibody-mediated cell-cell interactions.

Realizing the Promise of Project Optimus: Challenges and Emerging Opportunities for Dose Optimization in Oncology Drug Development.

Project Optimus is a U.S. Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. While there is much promise in this initiative, there are several challenges that need to be addressed, including multi-dimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence (ToE) and with the mindset of model-informed drug development (MIDD), we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Passive Immunotherapies Targeting Amyloid-ß in Alzheimer's Disease: A Quantitative Systems Pharmacology Perspective.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by amyloid-ß (Aß) protein accumulation in the brain. Passive immunotherapies using monoclonal antibodies for targeting Aß have shown promise for AD treatment. Indeed, recent US Food and Drug Administration approval of aducanumab and lecanemab, alongside positive donanemab Phase III results demonstrated clinical efficacy after decades of failed clinical trials for AD. However, the pharmacological basis distinguishing clinically effective from ineffective therapies remains unclear, impeding development of potent therapeutics. This study aimed to provide a quantitative perspective for effectively targeting Aß with antibodies. We first reviewed the contradicting results associated with the amyloid hypothesis and the pharmacological basis of Aß immunotherapy. Subsequently, we developed a quantitative systems pharmacology (QSP) model that describes the non-linear progression of Aß pathology and the pharmacologic actions of the Aß-targeting antibodies. Using the QSP model, we analyzed various scenarios for effective passive immunotherapy for AD. The model revealed that binding exclusively to the Aß monomer has minimal effect on Aß aggregation and plaque reduction, making the antibody affinity toward Aß monomer unwanted, as it could become a distractive mechanism for plaque reduction. Neither early intervention, high brain penetration, nor increased dose could yield significant improvement of clinical efficacy for antibodies targeting solely monomers. Antibodies that bind all Aß species but lack effector function exhibited moderate effects in plaque reduction. Our model highlights the importance of binding aggregate Aß species and incorporating effector functions for efficient and early plaque reduction, guiding the development of more effective therapies for this devastating disease. SIGNIFICANCE STATEMENT: Despite previous unsuccessful attempts spanning several decades, passive immunotherapies utilizing monoclonal antibodies for targeting amyloid-beta (Aß) have demonstrated promise with two recent FDA approvals. However, the pharmacological basis that differentiates clinically effective therapies from ineffective ones remains elusive. Our study offers a quantitative systems pharmacology perspective, emphasizing the significance of selectively targeting specific Aß species and importance of antibody effector functions. This perspective sheds light on the development of more effective therapies for this devastating disease.

Development of bispecific T cell engagers: harnessing quantitative systems pharmacology.

Bispecific T cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. Several bsTCEs have achieved marketing approval; dozens more are under clinical investigation. However, the clinical development of bsTCEs remains rife with challenges, including nuanced pharmacology, limited translatability of preclinical findings, frequent on-target toxicity, and convoluted dosing regimens. In this opinion article we present a distinct perspective on how quantitative systems pharmacology (QSP) can serve as a powerful tool for overcoming these obstacles. Recent advances in QSP modeling have empowered developers of bsTCEs to gain a deeper understanding of their context-dependent pharmacology, bridge gaps in experimental data, guide first-in-human (FIH) dose selection, design dosing regimens with expanded therapeutic windows, and improve long-term treatment outcomes. We use recent case studies to exemplify the potential of QSP techniques to support future bsTCE development.

Utilizing a Proximity Dependent Labeling Strategy to Study Cancer-Immune Intercellular Interactions In Vitro and In Vivo.

Immune cells play a critical role in surveilling and defending against cancer, emphasizing the importance of understanding how they interact and communicate with cancer cells to determine cancer status, treatment response, and the formation of the tumor microenvironment (TME). To this end, we conducted a study demonstrating the effectiveness of an enzyme-mediated intercellular proximity labeling (EXCELL) method, which utilizes a modified version of the sortase A enzyme known as mgSrtA, in detecting and characterizing immune-tumor cell interactions. The mgSrtA enzyme is expressed on the membrane of tumor cells, which is able to label immune cells that interact with tumor cells in a proximity-dependent manner. Our research indicates that the EXCELL technique can detect and characterize immune-tumor cell interactions in a time- and concentration-dependent manner, both in vitro and in vivo, without requiring pre-engineering of the immune cells. We also highlight its ability to detect various types of immune cell subpopulations in vivo that have migrated out of tumor into the spleen, providing insights into the role of peripheral T cell recruitment in tumor progression. Overall, our findings suggest that the EXCELL method has great potential for improving our understanding of immune cell dynamics within the TME, ultimately leading to more potent pharmacological effects and cancer immunotherapy strategies. Significance Statement The EXCELL method holds promise for detecting immune cell interactions with cancer cells, both in vitro and in vivo. It has important implications for studying immune tumor cell dynamics and potentially uncover novel subtypes of immune cells within the TME, both prior to and during immunotherapeutic interventions.

Decoding the Complexity of Immune-Cancer Cell Interactions: Empowering the Future of Cancer Immunotherapy.

The tumor and tumor microenvironment (TME) consist of a complex network of cells, including malignant, immune, fibroblast, and vascular cells, which communicate with each other. Disruptions in cell-cell communication within the TME, caused by a multitude of extrinsic and intrinsic factors, can contribute to tumorigenesis, hinder the host immune system, and enable tumor evasion. Understanding and addressing intercellular miscommunications in the TME are vital for combating these processes. The effectiveness of immunotherapy and the heterogeneous response observed among patients can be attributed to the intricate cellular communication between immune cells and cancer cells. To unravel these interactions, various experimental, statistical, and computational techniques have been developed. These include ligand-receptor analysis, intercellular proximity labeling approaches, and imaging-based methods, which provide insights into the distorted cell-cell interactions within the TME. By characterizing these interactions, we can enhance the design of cancer immunotherapy strategies. In this review, we present recent advancements in the field of mapping intercellular communication, with a particular focus on immune-tumor cellular interactions. By modeling these interactions, we can identify critical factors and develop strategies to improve immunotherapy response and overcome treatment resistance.

The prevalence of osteoporotic fractures in the elderly in China: a systematic review and meta-analysis.

BACKGROUND: Prevalence information is the first step in developing preventive procedures or health services. This study was conducted to systematically evaluate the epidemiology of osteoporotic fractures in Chinese elderly aged ≥ 60 years and to provide evidence-based evidence for the prevention and treatment of osteoporotic fractures. METHODS: We identified relevant studies by searching the literature published in PubMed, Web of Science, Cochrane Library, Embase, CNKI, Wanfang Data, and VIP databases from the establishment of the database until August 2022. We used a random-effects model to obtain prevalence estimates and identified sources of heterogeneity and comparisons of prevalence among different groups through subgroup analysis and sensitivity analysis. RESULTS: A total of 29 articles were included in this study, and the prevalence of osteoporosis fractures in elderly Chinese was high (18.9%). The prevalence has increased significantly over the past decade (from 13.2% in 2000-2010 to 22.7% in 2012-2022). The prevalence of osteoporosis is higher in women than in men (18.5% vs 14.3%) and increases with age. The northern region was higher than the southern region (20.3% vs 18.9%), and the spine, hip, and distal forearm were the most common sites of fracture. CONCLUSION: The prevalence of osteoporotic fractures in the Chinese elderly is 18.9%, and timely prevention and treatment are necessary.

Population dynamics of immunological synapse formation induced by bispecific T cell engagers predict clinical pharmacodynamics and treatment resistance.

Effector T cells need to form immunological synapses (IS) with recognized target cells to elicit cytolytic effects. Facilitating IS formation is the principal pharmacological action of most T cell-based cancer immunotherapies. However, the dynamics of IS formation at the cell population level, the primary driver of the pharmacodynamics of many cancer immunotherapies, remains poorly defined. Using classic immunotherapy CD3/CD19 bispecific T cell engager (BiTE) as our model system, we integrate experimental and theoretical approaches to investigate the population dynamics of IS formation and their relevance to clinical pharmacodynamics and treatment resistance. Our models produce experimentally consistent predictions when defining IS formation as a series of spatiotemporally coordinated events driven by molecular and cellular interactions. The models predict tumor-killing pharmacodynamics in patients and reveal trajectories of tumor evolution across anatomical sites under BiTE immunotherapy. Our models highlight the bone marrow as a potential sanctuary site permitting tumor evolution and antigen escape. The models also suggest that optimal dosing regimens are a function of tumor growth, CD19 expression, and patient T cell abundance, which confer adequate tumor control with reduced disease evolution. This work has implications for developing more effective T cell-based cancer immunotherapies.

Incorporating lesion-to-lesion heterogeneity into early oncology decision making.

RECISTv1.1 (Response Evaluation Criteria In Solid Tumors) is the most commonly used response grading criteria in early oncology trials. In this perspective, we argue that RECISTv1.1 is ambiguous regarding lesion-to-lesion variation that can introduce bias in decision making. We show theoretical examples of how lesion-to-lesion variability causes bias in RECISTv1.1, leading to misclassification of patient response. Next, we review immune checkpoint inhibitor (ICI) clinical trial data and find that lesion-to-lesion heterogeneity is widespread in ICI-treated patients. We illustrate the implications of ignoring lesion-to-lesion heterogeneity in interpreting biomarker data, selecting treatments for patients with progressive disease, and go/no-go decisions in drug development. Further, we propose that Quantitative Systems Pharmacology (QSP) models can aid in developing better metrics of patient response and treatment efficacy by capturing patient responses robustly by considering lesion-to-lesion heterogeneity. Overall, we believe patient response evaluation with an appreciation of lesion-to-lesion heterogeneity can potentially improve decision-making at the early stage of oncology drug development and benefit patient care.

Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study.

BACKGROUND AND PURPOSE: Despite their use to treat cancers with specific genetic aberrations, targeted therapies elicit heterogeneous responses. Sources of variability are critical to targeted therapy drug development, yet there exists no method to discern their relative contribution to response heterogeneity. EXPERIMENTAL APPROACH: We use HER2-amplified breast cancer and two agents, neratinib and lapatinib, to develop a platform for dissecting sources of variability in patient response. The platform comprises four components: pharmacokinetics, tumor burden and growth kinetics, clonal composition, and sensitivity to treatment. Pharmacokinetics are simulated using population models to capture variable systemic exposure. Tumor burden and growth kinetics are derived from clinical data comprising over 800,000 women. The fraction of sensitive and resistant tumor cells is informed by HER2 immunohistochemistry. Growth rate-corrected drug potency is used to predict response. We integrate these factors and simulate clinical outcomes for virtual patients. The relative contributions of these factors to response heterogeneity arecompared. KEY RESULTS: The platform was verified with clinical data, including response rate and progression-free survival (PFS). For both neratinib and lapatinib, the growth rate of resistant clones influenced PFS to a higher degree than systemic drug exposure. Variability in exposure at labeled doses did not significantly influence response. Sensitivity to drug strongly influenced responses to neratinib. Variability in patient HER2 immunohistochemistry scores influenced responses to lapatinib. Exploratory twice daily dosing improved PFS for neratinib but not lapatinib. CONCLUSION AND IMPLICATIONS: The platform can dissect sources of variability in response to target therapy, which may facilitate decision-making during drug development.

A multispecies framework for modeling adaptive immunity and immunotherapy in cancer.

Predator-prey theory is commonly used to describe tumor growth in the presence of selective pressure from the adaptive immune system. These interactions are mediated by the tumor immunopeptidome (what the tumor "shows" the body) and the T-cell receptor (TCR) repertoire (how well the body "sees" cancer cells). The tumor immunopeptidome comprises neoantigens which can be gained and lost throughout tumorigenesis and treatment. Heterogeneity in the immunopeptidome is predictive of poor response to immunotherapy in some tumor types, suggesting that the TCR repertoire is unable to support a fully polyclonal response against every neoantigen. Importantly, while tumor and T-cell populations are known to compete with each other for intratumoral resources, whether between-lineage competition among peripheral T cells influences the TCR repertoire is unknown and difficult to interrogate experimentally. Computational models may offer a way to investigate these phenomena and deepen our understanding of the tumor-immune axis. Here, we construct a predator-prey-like model and calibrate it to preclinical and clinical data to describe tumor growth and immunopeptidome diversification. Simultaneously, we model the expansion of antigen-specific T-cell lineages and their consumption of both lineage-specific antigenic resources and lineage-agnostic, shared resources. This predator-prey-like framework accurately described clinically observed immunopeptidomes; recapitulated response-associated effects of immunotherapy, including immunoediting; and allowed exploration of treatment of tumors with varying growth and mutation rates.

Mapping lesion-specific response and progression dynamics and inter-organ variability in metastatic colorectal cancer.

Achieving systemic tumor control across metastases is vital for long-term patient survival but remains intractable in many patients. High lesion-level response heterogeneity persists, conferring many dissociated responses across metastatic lesions. Most studies of metastatic disease focus on tumor molecular and cellular features, which are crucial to elucidating the mechanisms underlying lesion-level variability. However, our understanding of lesion-specific heterogeneity on the macroscopic level, such as lesion dynamics in growth, response, and progression during treatment, remains rudimentary. This study investigates lesion-specific response heterogeneity through analyzing 116,542 observations of 40,612 lesions in 4,308 metastatic colorectal cancer (mCRC) patients. Despite significant differences in their response and progression dynamics, metastatic lesions converge on four phenotypes that vary with anatomical site. Importantly, we find that organ-level progression sequence is closely associated with patient long-term survival, and that patients with the first lesion progression in the liver often have worse survival. In conclusion, our study provides insights into lesion-specific response and progression heterogeneity in mCRC and creates impetus for metastasis-specific therapeutics.

Virtual clinical trials: A tool for predicting patients who may benefit from treatment beyond progression with pembrolizumab in non-small cell lung cancer.

Enrolling patients in immunotherapy clinical trials is becoming increasingly competitive. Virtual clinical trials can help investigators answer key questions despite this. For example, pembrolizumab is the recommended first-line treatment for non-small cell lung cancer (NSCLC) with no driver alterations and a programmed death ligand 1 (PD-L1) Tumor Proportion Score ≥50%. Salvage therapies for relapsed/refractory patients are limited. Retrospective studies suggest that a subset of patients may benefit from pembrolizumab beyond progression; these results have not been validated in a prospective study. We constructed digital twins of patients and simulated clinical trials to predict the best salvage therapy after progressive disease (PD) on pembrolizumab. Response dynamics were evaluated at the lesion level to represent patients who experience systemic PD while individual lesions continue shrinking. With >25,000 radiographic lesion measurements from >500 patients, we simulated responses to pembrolizumab, chemotherapy, and PD on pembrolizumab followed by either pembrolizumab beyond progression or salvage chemotherapy. Switching all progressors to salvage chemotherapy was suboptimal. Virtual trials predicted progression-free survival (PFS) from pembrolizumab beyond progression to be comparable with salvage chemotherapy in patients whose PD was due to nontarget progression. A PFS-optimized regimen may improve disease control rates ≥15%. Pembrolizumab beyond progression may benefit a subset of patients with PD-L1-high, driver alteration-free NSCLC, but prospective studies are warranted.

Embracing Project Optimus: Can we Leverage Evolutionary Theory to Optimize Dosing in Oncology?

Project Optimus is a US Food and Drug Administration (FDA) initiative to reform dose selection in oncology drug development. Here, we focus on tumor evolution, a broadly observed phenomenon that invariably leads to therapeutic failure and disease relapse, and its effect on the exposure-response (E-R) relationships of oncology drugs. We propose a greater emphasis on tumor evolution during clinical development to facilitate the selection of optimal doses for molecularly targeted therapies and immunotherapies in oncology.

Cellular kinetics: A clinical and computational review of CAR-T cell pharmacology.

To the extent that pharmacokinetics influence the effectiveness of nonliving therapeutics, so too do cellular kinetics influence the efficacy of Chimeric Antigen Receptor (CAR) -T cell therapy. Like conventional therapeutics, CAR-T cell therapies undergo a distribution phase upon administration. Unlike other therapeutics, however, this distribution phase is followed by subsequent phases of expansion, contraction, and persistence. The magnitude and duration of these phases unequivocally influence clinical outcomes. Furthermore, the "pharmacodynamics" of CAR-T cells is truly dynamic, as cells can rapidly become exhausted and lose their therapeutic efficacy. Mathematical models are among the translational tools commonly applied to assess, characterize, and predict the complex cellular kinetics and dynamics of CAR-T cells. Here, we provide a focused review of the cellular kinetics of CAR-T cells, the mechanisms underpinning their complexity, and the mathematical modeling approaches used to interrogate them.

[Experimental study on effect of complement C3 on bone formation observed by cell culture in vitro].

OBJECTIVE: To explore effect of lentivirus-mediated complement C3 silencing vector on the osteogenic ability of human B lymphocyte Raji-osteoblast cell line MG63 co-culture system and its mechanism. METHODS: The lentiviral complement C3 silencing vector was constructed and transfected into human B lymphocyte Raji to establish in vitro Raji-osteoblast cell line MG63 co-culture system. The cells were divided into blank control group (without special treatment), complement C3 silencing group (lentiviral complement C3 silencing vector transfection co-culture system) and model group(lentiviral vector transfection co-culture system). The expression of complement C3 in each group was detected by reverse transcription-polymerase chain reaction(RT-PCR) and Western-Blot at 24 h after culture, proliferation of MG63 cells was detected by CCK-8 at 0, 3, 6, 12, 24, 48 and 72 h, apoptosis of MG63 cells in each group was detected by flow cytometry at 24 h after culture, and alkaline phosphatase(AKP) activity of MG63 cells in each group was detected by AKP detection kit, and osteoprotegerin (OPG ) protein expression of MG63 cells in each group was detected by Western-Blot method. RESULTS: RT-PCR results showed that the expression level of C3 in complement C3 silencing group was lower than that in blank control group and model group, Western-Blot results showed that expression of C3 in complement C3 silencing group was lower than that in blank control group and model group, CCK-8 results showed that there was no difference in proliferation ability of MG63 among complement C3 silencing group and blank control group and model group at 3 and 6 h after culture;at 12 h after culture, proliferation ability of MG63 cells with C3 silencing was higher than that of blank control group and model group;at 24, 48 and 72 h after culture, proliferation ability of MG63 cell line with complement C3 silencing group were higher than that of blank control group and model group. Flow cytometry resluts showed that apoptosis of proliferation ability of MG63 cell line with complement C3 silencing group was lower than that of blank control group and model group;AKP detection kit suggested that AKP activity in complement C3 silencing group was higher than that in blank control group and model group, Western-Blot results showed that expression level of OPG protein in complement C3 silencing groupwas higher than that in blank control groupand model group. CONCLUSION: Silencing of complement C3 could enhance bone formation ability of osteoblast MG63, and it takes time to accumulate this ability. Complement C3 may affect osteogenesis by altering OPG / RANKL / RANK axis.

A Physiologically Based Pharmacokinetic Framework for Quantifying Antibody Distribution Gradients from Tumors to Tumor-Draining Lymph Nodes.

Immune checkpoint blockades prescribed in the neoadjuvant setting are now under active investigation for many types of tumors, and many have shown early success. The primary tumor (PT) and tumor-draining lymph node (TDLN) immune factors, along with adequate therapeutic antibody distributions to the PT and TDLN, are critical for optimal immune activation and anti-tumor efficacy in neoadjuvant immunotherapy. However, it remains largely unknown how much of the antibody can be distributed into the PT-TDLN axis at different clinical scenarios. The goal of the current work is to build a physiologically based pharmacokinetic (PBPK) model framework capable of characterizing antibody distribution gradients in the PT-TDLN axis across various clinical and pathophysiological scenarios. The model was calibrated using clinical data from immuno-PET antibody-imaging studies quantifying antibody pharmacokinetics (PK) in the blood, PTs, and TDLNs. The effects of metastatic lesion location, tumor-induced compression, and inflammation, as well as surgery, on antibody concentration gradients in the PT-TDLN axis were characterized. The PBPK model serves as a valuable tool to predict antibody exposures in various types of tumors, metastases, and the associated lymph node, supporting effective immunotherapy.

Physiological Considerations for Modeling in vivo Antibody-Target Interactions.

The number of therapeutic antibodies in development pipelines is increasing rapidly. Despite superior success rates relative to small molecules, therapeutic antibodies still face many unique development challenges. There is often a translational gap from their high target affinity and specificity to the therapeutic effects. Tissue microenvironment and physiology critically influence antibody-target interactions contributing to apparent affinity alterations and dynamic target engagement. The full potential of therapeutic antibodies will be further realized by contextualizing antibody-target interactions under physiological conditions. Here we review how local physiology such as physical stress, biological fluid, and membrane characteristics could influence antibody-target association, dissociation, and apparent affinity. These physiological factors in the early development of therapeutic antibodies are valuable toward rational antibody engineering, preclinical candidate selection, and lead optimization.

A PBPK model recapitulates early kinetics of anti-PEG antibody-mediated clearance of PEG-liposomes.

PEGylation is routinely used to extend the systemic circulation of various protein therapeutics and nanomedicines. Nonetheless, mounting evidence is emerging that individuals exposed to select PEGylated therapeutics can develop antibodies specific to PEG, i.e., anti-PEG antibodies (APA). In turn, APA increase both the risk of hypersensitivity to the drug as well as potential loss of efficacy due to accelerated blood clearance of the drug. Despite the broad implications of APA, the timescales and systemic specificity by which APA can alter the pharmacokinetics and biodistribution of PEGylated drugs remain not well understood. Here, we developed a physiologically based pharmacokinetic (PBPK) model designed to resolve APA's impact on both early- and late-phase pharmacokinetics and biodistribution of intravenously administered PEGylated drugs. Our model accurately recapitulates PK and biodistribution data obtained from PET/CT imaging of radiolabeled PEG-liposomes and PEG-uricase in mice with and without APA, as well as serum levels of PEG-uricase in humans. Our work provides another illustration of the power of high-resolution PBPK models for understanding the pharmacokinetic impacts of anti-drug antibodies and the dynamics with which antibodies can mediate clearance of foreign species.