Chelator boosted tumor-retention and pharmacokinetic properties: development of 64Cu labeled radiopharmaceuticals targeting neurotensin receptor.

PURPOSE: Accumulating evidence suggests that neurotensin (NTS) and neurotensin receptors (NTSRs) play key roles in lung cancer progression by triggering multiple oncogenic signaling pathways. This study aims to develop Cu-labeled neurotensin receptor 1 (NTSR1)-targeting agents with the potential for both imaging and therapeutic applications. METHOD: A series of neurotensin receptor antagonists (NRAs) with variable propylamine (PA) linker length and different chelators were synthesized, including [64Cu]Cu-CB-TE2A-iPA-NRA ([64Cu]Cu-4a-c, i = 1, 2, 3), [64Cu]Cu-NOTA-2PA-NRA ([64Cu]Cu-4d), [64Cu]Cu-DOTA-2PA-NRA ([64Cu]Cu-4e, also known as [64Cu]Cu-3BP-227), and [64Cu]Cu-DOTA-VS-2PA-NRA ([64Cu]Cu-4f). The series of small animal PET/CT were conducted in H1299 lung cancer model. The expression profile of NTSR1 was also confirmed by IHC using patient tissue samples. RESULTS: For most of the compounds studied, PET/CT showed prominent tumor uptake and high tumor-to-background contrast, but the tumor retention was strongly influenced by the chelators used. For previously reported 4e, [64Cu]Cu-labeled derivative showed initial high tumor uptake accompanied by rapid tumor washout at 24 h. The newly developed [64Cu]Cu-4d and [64Cu]Cu-4f demonstrated good tumor uptake and tumor-to-background contrast at early time points, but were less promising in tumor retention. In contrast, our lead compound [64Cu]Cu-4b demonstrated 9.57 ± 1.35, 9.44 ± 2.38 and 9.72 ± 4.89%ID/g tumor uptake at 4, 24, and 48 h p.i., respectively. Moderate liver uptake (11.97 ± 3.85, 9.80 ± 3.63, and 7.72 ± 4.68%ID/g at 4, 24, and 48 h p.i.) was observed with low uptake in most other organs. The PA linker was found to have a significant effect on drug distribution. Compared to [64Cu]Cu-4b, [64Cu]Cu-4a had a lower background, including a greatly reduced liver uptake, while the tumor uptake was only moderately reduced. Meanwhile, [64Cu]Cu-4c showed increased uptake in both the tumor and the liver. The clinical relevance of NTSR1 was also demonstrated by the elevated tumor expression in patient tissue samples. CONCLUSIONS: Through the side-by-side comparison, [64Cu]Cu-4b was identified as the lead agent for further evaluation based on its high and sustained tumor uptake and moderate liver uptake. It can not only be used to efficiently detect NTSR1 expression in lung cancer (for diagnosis, patient screening, and treatment monitoring), but also has the great potential to treat NTSR-positive lesions once chelating to the beta emitter 67Cu.

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.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice.

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

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.

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.

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.

Downregulation of Interferon-γ Receptor Expression Endows Resistance to Anti-Programmed Death Protein 1 Therapy in Colorectal Cancer.

Immune checkpoint inhibitors have emerged as a frontline treatment of a variety of malignancies. However, only a subset of patients respond to these therapies, and many initial responders eventually develop resistance, leading to tumor relapse. Programmed death protein 1 is one of the checkpoint inhibitors that is expressed on activated T cells and suppresses the antitumor immune response when binding to its ligand, programmed death ligand 1, on tumor cells. Previous studies indicated that loss-of-function mutations in the IFN-γ pathway could result in acquired resistance to immune checkpoint inhibitors in human patients with cancer. Here, we investigated the effects of the IFN-γ receptor downexpression on the response to an anti-PD-1 antibody (αPD1) in a murine colorectal cancer model and the underlying mechanisms of resistance. IFN-γ receptor (IFNGR) 1 was knocked down in MC38 cells, a murine colon adenocarcinoma cell line using IFNGR1 short hairpin RNA (shRNA) lentiviral particles. Then, MC38 IFNGR1 knockdown (KD) cells and negative control (SC) cells were used in this study. In the C57BL/6 xenograft model, the KD tumor demonstrated resistance to αPD1 in comparison with SC cells. The observed treatment resistance might be associated with reduced tumor-infiltrating immune cells (TILs). When mixed, the resistant (KD) and control cells (SC) grew in spatially separated tumor areas, and αPD1 did not impact this pattern of spatial distribution. Our findings have proved that downregulation of the IFNGR1 endowed resistance to αPD1 and provided the potential mechanisms involving the TILs. SIGNIFICANCE STATEMENT: Immunological checkpoint blockades have achieved substantial efficacy in a variety of tumors. However, only a subset of patients respond to these therapies, and innate and acquired resistance is widely present. Our study found that the downregulation of the IFN-γ receptor caused resistance to an anti-PD-1 antibody in a murine colorectal cancer model associated with the reduced tumor-infiltrating lymphocytes. Our findings have substantial implications for improving the efficacy of checkpoint blockades.

A cross-species comparison of antiretroviral penetration into lymph nodes using novel physiologically based pharmacokinetic models.

BACKGROUND: Investigating antiretroviral (ARV) penetration and pharmacology in lymph nodes is crucial to understanding mechanisms of HIV persistence in tissue, but sampling these tissues in humans is invasive and costly. Physiologically based pharmacokinetic (PBPK) modelling is a non-invasive solution for understanding lymph node penetration of ARVs across multiple species. OBJECTIVES: To develop customized PBPK models with a novel lymph node compartment, and use these models to describe the distribution of three ARVs-tenofovir, emtricitabine and efavirenz-into the plasma and lymph nodes of non-human primates (NHPs) and humans. MATERIALS AND METHODS: In this analysis, we utilized standard monkey and human PBPK models in PK-Sim, and added a novel lymph node compartment using MoBi. We used these models to describe the distribution of tenofovir, emtricitabine and efavirenz into NHP and human plasma and lymph nodes, and compared model-predicted versus observed AUC and Cmax. RESULTS: For all three ARVs, population simulations using the base and final models reasonably characterized observed plasma and tissue data in NHPs and humans, with predicted/observed AUC and Cmax ratios within 0.7-2.0. CONCLUSIONS: Overall, our novel PBPK model provides a framework for understanding lymph node penetration of ARVs or future HIV cure therapies.

Experimental Data and PBPK Modeling Quantify Antibody Interference in PEGylated Drug Carrier Delivery.

Physiologically-based pharmacokinetic (PBPK) modeling is a popular drug development tool that integrates physiology, drug physicochemical properties, preclinical data, and clinical information to predict drug systemic disposition. Since PBPK models seek to capture complex physiology, parameter uncertainty and variability is a prevailing challenge: there are often more compartments (e.g., organs, each with drug flux and retention mechanisms, and associated model parameters) than can be simultaneously measured. To improve the fidelity of PBPK modeling, one approach is to search and optimize within the high-dimensional model parameter space, based on experimental time-series measurements of drug distributions. Here, we employ Latin Hypercube Sampling (LHS) on a PBPK model of PEG-liposomes (PL) that tracks biodistribution in an 8-compartment mouse circulatory system, in the presence (APA+) or absence (naïve) of anti-PEG antibodies (APA). Near-continuous experimental measurements of PL concentration during the first hour post-injection from the liver, spleen, kidney, muscle, lung, and blood plasma, based on PET/CT imaging in live mice, are used as truth sets with LHS to infer optimal parameter ranges for the full PBPK model. The data and model quantify that PL retention in the liver is the primary differentiator of biodistribution patterns in naïve versus APA+ mice, and spleen the secondary differentiator. Retention of PEGylated nanomedicines is substantially amplified in APA+ mice, likely due to PL-bound APA engaging specific receptors in the liver and spleen that bind antibody Fc domains. Our work illustrates how applying LHS to PBPK models can further mechanistic understanding of the biodistribution and antibody-mediated clearance of specific drugs.

In Translation: FcRn across the Therapeutic Spectrum.

As an essential modulator of IgG disposition, the neonatal Fc receptor (FcRn) governs the pharmacokinetics and functions many therapeutic modalities. In this review, we thoroughly reexamine the hitherto elucidated biological and thermodynamic properties of FcRn to provide context for our assessment of more recent advances, which covers antigen-binding fragment (Fab) determinants of FcRn affinity, transgenic preclinical models, and FcRn targeting as an immune-complex (IC)-clearing strategy. We further comment on therapeutic antibodies authorized for treating SARS-CoV-2 (bamlanivimab, casirivimab, and imdevimab) and evaluate their potential to saturate FcRn-mediated recycling. Finally, we discuss modeling and simulation studies that probe the quantitative relationship between in vivo IgG persistence and in vitro FcRn binding, emphasizing the importance of endosomal transit parameters.

Pharmacokinetics of gallic acid and protocatechuic acid in humans after dosing with Relinqing (RLQ) and the potential for RLQ-perpetrated drug-drug interactions on organic anion transporter (OAT) 1/3.

CONTEXT: Relinqing granules (RLQ) are being used alone or in combination with antibacterial drugs to treat urological disorders. OBJECTIVE: This study investigates the pharmacokinetics of RLQ in humans and the potential for RLQ-perpetrated interactions on transporters. MATERIALS AND METHODS: Twelve healthy subjects (six women and six men) participated to compare single- and multiple-dose pharmacokinetics of RLQ. In the single-dose study, all 12 subjects received 8 g of RLQ orally. After a 7-d washout period, the subjects received 8 g of RLQ for seven consecutive days (t.i.d.) and then a single dose. Gallic acid (GA) and protocatechuic acid (PCA) in plasma and urine samples were analysed using LC-MS/MS. The transfected cells were used to study the inhibitory effect of GA (50-5000 µg/L) and PCA (10-1000 µg/L) on transporters OAT1, OAT3, OCT2, OATP1B1, P-gp and BCRP. RESULTS: GA and PCA were absorbed into the blood within 1 h after administration and rapidly eliminated with a half-life of less than 2 h. The mean peak concentrations of GA (102 and 176 µg/L) and PCA (4.54 and 7.58 µg/L) were lower in males than females, respectively. The 24 h urine recovery rates of GA and PCA were about 10% and 5%, respectively. The steady-state was reached in 7 d without accumulation. GA was a potent inhibitor of OAT1 (IC50 = 3.73 µM) and OAT3 (IC50 = 29.41 µM), but not OCT2, OATP1B1, P-gp or BCRP. DISCUSSION AND CONCLUSIONS: GA and PCA are recommended as PK-markers in RLQ-related pharmacokinetic and drug interaction studies. We should pay more attention to the potential for RLQ-perpetrated interactions on transporters.

Correction: Dynamic metrics-based biomarkers to predict responders to anti-PD-1 immunotherapy.

Since the publication of this paper, the authors noticed that the funding information for Maureen A. Su was not included. Therefore the authors would like to add the following information to the Acknowledgements section.

Dynamic metrics-based biomarkers to predict responders to anti-PD-1 immunotherapy.

BACKGROUND: Anti-PD-1 immunotherapies have shown clinical benefit in multiple cancers, but response was only observed in a subset of patients. Predicting which patients will respond is an urgent clinical need, but current companion diagnosis based on PD-L1 IHC staining shows limited predictability. METHODS: A dynamic, metrics-based biomarker was developed to discriminate responders from non-responders for anti-PD-1 immunotherapy in B16F10 melanoma-bearing mice. RESULTS: Similar to patients, there was considerable heterogeneity in response to anti-PD-1 immunotherapy in mice. Compared with the control group, 45% of anti-PD-1 antibody-treated mice displayed improved survival (defined as responders) and the remainder only gained little, if any, survival benefit from PD-1 blockade (non-responders). Interestingly, the dynamics of IFN-γ secretion by peripheral lymphocytes was associated with faster secretion onset (shorter lag time), stronger exponential phase, shorter time to half magnitude, and higher magnitude of secretion in responders at day 10 after tumour inoculation. To sufficiently predict responders from non-responders, IFN-γ secretion descriptors as well as phenotypic markers were subjected to multivariate analysis using orthogonal partial least-squares discriminant analysis (OPLS-DA). CONCLUSIONS: By integrating phenotypic markers, IFN-γ secretion descriptors sufficiently predict response to anti-PD-1 immunotherapy. Such a dynamic, metrics-based biomarker holds high diagnostic potential for anti-PD-1 checkpoint immunotherapy.

Altered Hepatobiliary Disposition of Tolvaptan and Selected Tolvaptan Metabolites in a Rodent Model of Polycystic Kidney Disease.

Tolvaptan, a vasopressin V2-receptor antagonist, has demonstrated efficacy in slowing kidney function decline in patients with autosomal dominant polycystic kidney disease (ADPKD). In the pivotal clinical trial, the incidence of elevated liver enzymes was higher in patients receiving tolvaptan compared with placebo. Adjudication by a panel of expert hepatologists concluded a causal link of tolvaptan to liver injury in patients with ADPKD. An ex situ isolated perfused liver (IPL) study of tolvaptan disposition was undertaken in a rodent model of ADPKD, the polycystic kidney (PCK) rat (n = 5), and compared with wild-type (WT) Sprague-Dawley rats (n = 6). Livers were perfused with tolvaptan, followed by a tolvaptan-free washout phase. Total recovery (mean ± S.D. percentage of dose; PCK vs. WT) of tolvaptan and two metabolites, DM-4103 and DM-4107, quantified by liquid chromatography-tandem mass spectroscopy, was 58.14% ± 24.72% vs. 43.40% ± 18.11% in liver, 20.10% ± 9.15% vs. 21.17% ± 12.51% in outflow perfusate, and 0.08% ± 0.01% vs. 0.39% ± 0.32% in bile. DM-4103 recovery (mean ± S.D. percentage of dose) was decreased in PCK vs. WT bile (<0.01% ± <0.01% vs. 0.02% ± 0.01%; P = 0.0037), and DM-4107 recovery was increased in PCK vs. WT outflow perfusate (1.60% ± 0.57% vs. 0.43% ± 0.29%; P = 0.0017). A pharmacokinetic compartmental model assuming first-order processes was developed to describe the rate vs. time profiles of tolvaptan and DM-4103 + DM-4107 in rat IPLs. The model-derived estimate of tolvaptan's biliary clearance was significantly decreased in PCK compared with WT IPLs. The model predicted greater hepatocellular concentrations of tolvaptan and DM-4103 + DM-4107 in PCK compared with WT IPLs. Increased hepatocellular exposure to tolvaptan and metabolites may contribute to the hepatotoxicity in patients with ADPKD treated with tolvaptan.

Role of Interstitial Fluid Turnover on Target Suppression by Therapeutic Biologics Using a Minimal Physiologically Based Pharmacokinetic Model.

For therapeutic biologics against soluble ligands, the magnitude and duration of target suppression affect their therapeutic efficacy. Many factors have been evaluated in relation to target suppression but the interstitial fluid turnover rate in target tissues has not been considered. Inspired by the fact that etanercept exerts limited efficacy in Crohn's disease despite its high efficacy in rheumatoid arthritis, we developed a minimal physiologically based pharmacokinetic model to investigate the role of the tissue fluid turnover rate on soluble target suppression and assessed the interrelationships between binding constants and tissue fluid turnover. Interstitial fluid turnover rates in target tissues were found to strongly influence target binding kinetics. For tissues with low fluid turnover, stable binders (low koff) exhibit greater target suppression, but efficacy is often restricted by accumulation of the drug-target complex. For tissues with high fluid turnover, fast binders (high kon) are generally favored, but a plateau effect is present for antibodies with low dissociation rates (koff). Etanercept is often regarded as a fast tumor necrosis factor-α (TNF-α) binder (high kon) despite comparable binding affinity (KD, koff/kon) with adalimumab and infliximab. Crohn's disease largely involves the colon, a tissue with relatively slower fluid turnover than arthritis-associated joint synovium; this may explain why etanercept exerts poor TNF-α suppressive effect in Crohn's disease. This study highlights the importance of tissue interstitial fluid turnover in evaluation of therapeutic antibodies bound to soluble antigens.

A Multiscale Physiologically-Based Pharmacokinetic Model for Doxorubicin to Explore its Mechanisms of Cytotoxicity and Cardiotoxicity in Human Physiological Contexts.

PURPOSE: The mechanisms underlying doxorubicin cytotoxicity and cardiotoxicity were broadly explored but remain incompletely understood. A multiscale physiologically-based pharmacokinetic (PBPK) model was developed to assess doxorubicin dispositions at levels of system, tissue interstitial, cell, and cellular organelles. This model was adopted to explore the mechanisms-of-action/toxicity of doxorubicin in humans. METHODS: The PBPK model was developed by analyzing data from mice and the model was verified by scaling up to predict doxorubicin multiscale dispositions in rats and humans. The multiscale dispositions of doxorubicin in human heart and tumors were explicitly simulated to elucidate the potential mechanisms of its cytotoxicity and cardiotoxicity. RESULTS: The developed PBPK model was able to adequately describe doxorubicin dispositions in mice, rats and humans. In humans, prolonged infusion, a dosing regimen with less cardiotoxicity, was predicted with substantially reduced free doxorubicin concentrations at human heart interstitium, which were lower than the concentrations associated with oxidative stress. However, prolonged infusion did not reduce doxorubicin-DNA adduct at tumor nucleus, consistent with clinical observations that prolonged infusion did not compromise anti-tumor effect, indicating that one primary anti-tumor mechanism was DNA torsion. CONCLUSIONS: A multiscale PBPK model for doxorubicin was developed and further applied to explore its cytotoxic and cardiotoxic mechanisms.

Chemotherapeutic dosing implicated by pharmacodynamic modeling of in vitro cytotoxic data: a case study of paclitaxel.

Conventional maximum tolerated doses (MTD) in chemotherapy are recently challenged by an alternative dosing method with low doses and high dosing frequency (LDHF). Still, it remains unclear which chemotherapies would potentially benefit from LDHF. The pharmacokinetic (PK) differences between MTD and LDHF are drug exposure magnitude (concentration) and exposure duration (time), two fundamental PK elements that are associated with the pharmacodynamics (PD) of chemotherapies. Here we hypothesized that quantitatively analyzing the contribution of each PK element to the overall cytotoxic effects would provide insights to the selection of the preferred chemotherapeutic dosing. Based on in vitro cytotoxic data, we developed a cellular PD model, which assumed that tumor cells were generally comprised of two subpopulations that were susceptible to either concentration- or time-dependent cytotoxicity. The developed PD model exhibited high flexibility to describe diverse patterns of cell survival curves. Integrated with a PK model, the cellular PD model was further extended to predict and compare the anti-tumor effect of paclitaxel in two dosing regimens: multiple MTD bolus and continuous constant infusion (an extreme LDHF). Our simulations of paclitaxel in treatment of three types of cancers were consistent with clinical observations that LDHF yielded higher patient efficacy than MTD. Our further analysis suggested that the ratio between drug steady-state concentrations and its cytotoxic sensitivity (C ss /KC 50 ) was a critical factor that largely determines favored dosing regimen. LDHF would produce higher efficacy when the ratio C ss /KC 50 is greater than 1. Otherwise MTD was favored. The developed PD model presented an approach simply based on in vitro cytotoxic data to predict the potentially favored chemotherapeutic dosing between MTD and LDHF.

A unified strategy in selection of the best allometric scaling methods to predict human clearance based on drug disposition pathway.

1. It is critical to develop a unified strategy to select the best allometric scaling (AS) method for a given group of drugs. 2. A total of 446 drugs with known human CLiv, clear disposition pathway and animal (rat, dog, monkey) CLiv were analyzed. All drugs were stratified based on their disposition pathway, liver extraction ratio (ERH) and ratios of unbound clearance to renal glomerular filtration rate (RGFR). Up to 22 AS methods were applied and compared in prediction of human CLiv to each group of drugs. 3. AS methods that give the best prediction of human CLiv, were identified for drugs primarily eliminated through liver with a fraction of renal elimination (frenal) within 0.3-0.5 or ERH > 0.3, where human CLiv of more than 80% or 90% drugs could be accurately (within 2- or 3-fold error) predicted. For drugs with ERH < 0.3, acceptable accuracy could be achieved by a two species method TSR,D resulting more than 60% or 75% drugs were predicted within 2- or 3-fold error. 4. By stratified analysis of drugs, according to their disposition pathway and organ extraction ratio, a unified strategy was developed to select the best AS method in prediction of human CLiv.

Across-Species Scaling of Monoclonal Antibody Pharmacokinetics Using a Minimal PBPK Model.

PURPOSE: To examine the across-species scalability of monoclonal antibody (mAb) pharmacokinetics (PK) and assess similarities in tissue distribution across species using a recently developed minimal PBPK (mPBPK) model. METHODS: Twelve sets of antibody PK data from various species were obtained from the literature, which were jointly and individually analyzed. In joint analysis, vascular reflection coefficients for tissues with either tight (σ 1 ) or leaky endothelium (σ 2 ) were assumed consistent across species with systemic clearance allometrically scaled (CL = a∙BW (b) ). Four parameters (σ 1 , σ 2 , a, and b) were estimated in the joint analysis. In addition, the PK from each species was individually analyzed to assess species similarities in tissue distribution. RESULTS: Twelve mAb PK profiles were well-captured by the mPBPK model in the joint analysis. The estimated σ 1 ranged 0.690 to 0.999 with an average of 0.908; and σ 2 ranged 0.258 to 0.841 with an average of 0.579. Clearance was reasonably scaled and b ranged 0.695 to 1.27 averaging 0.91. Predictions of plasma concentrations for erlizumab and canakinumab in humans using parameters obtained from fitting animal data were consistent with actual measurements. CONCLUSIONS: Therapeutic mAbs given IV usually exhibit biexponential kinetics with their distribution properties best captured using physiological concepts. The mPBPK modeling approach may facilitate efforts in translating antibody distribution and overall PK across species.

Survey of monoclonal antibody disposition in man utilizing a minimal physiologically-based pharmacokinetic model.

Minimal physiologically based pharmacokinetic (mPBPK) models provide a simple and sensible approach that incorporates physiological elements into pharmacokinetic (PK) analysis when only plasma data are available. With this modeling concept, a second-generation mPBPK model was further developed with specific accommodations for the unique PK properties of monoclonal antibodies (mAb). This study applied this model to extensively survey mAb PK in man in order to seek general perspectives on mAb distributional and elimination features. Profiles for 72 antibodies were successfully analyzed with this model. The model results provide assessment regarding: (1) predominant clearance site, in plasma or interstitial fluid (ISF); (2) mAb ISF concentrations in two groups of lumped tissues with continuous (V(tight)) or fenestrated (V(leaky)) vascular endothelium; (3) Transcapillary escape rate (TER), an indicator of systemic vascular permeability. For 93% of surveyed mAbs, the model assuming clearance from plasma (CL) produced better or at least equivalent model performance than the model with clearance from ISF and yielded most consistent values of vascular reflection coefficients (σ1 and σ2) among all antibodies. The average mAb ISF concentration in V(tight) and V(leaky) at equilibrium was predicted to be about 6.8 and 37.9% of that in plasma. A positive correlation was detected between plasma clearance and TER among most mAbs, which could be interpreted as both parameters having common determinants related to ISF tissue distribution in this model context. The mAbs with relative higher plasma clearance (>0.035 L/h/70 kg) did not reveal such positive correlation between clearance and TER, implying that the factors contributing to high clearance may not necessarily increase tissue distribution and penetration. In conclusion, this mPBPK model offers a more mechanistic approach for analyzing plasma mAb PK than compartment models and generates parameters providing useful intrinsic distributional and elimination insights for a large number of mAbs that were examined in man.