A mechanistic compartmental model for total antibody uptake in tumors

Prediction of the Optimal Dosing Regimen Using a Mathematical Model of Tumor Uptake for Immunocytokine-Based Cancer Immunotherapy

Purpose: Optimal dosing is critical for immunocytokine-based cancer immunotherapy to maximize efficacy and minimize toxicity. Cergutuzumab amunaleukin (CEA-IL2v) is a novel CEA-targeted immunocytokine. We set out to develop a mathematical model to predict intratumoral CEA-IL2v concentrations following various systemic dosing intensities.Experimental Design: Sequential measurements of CEA-IL2v plasma concentrations in 74 patients with solid tumors were applied in a series of differential equations to devise a model that also incorporates the peripheral concentrations of IL2 receptor-positive cell populations (i.e., CD8⁺, CD4⁺, NK, and B cells), which affect tumor bioavailability of CEA-IL2v. Imaging data from a subset of 14 patients were subsequently utilized to additionally predict antibody uptake in tumor tissues.Results: We created a pharmacokinetic/pharmacodynamic mathematical model that incorporates the expansion of IL2R-positive target cells at multiple dose levels and different schedules of CEA-IL2v. Model-based prediction of drug levels correlated with the concentration of IL2R-positive cells in the peripheral blood of patients. The pharmacokinetic model was further refined and extended by adding a model of antibody uptake, which is based on drug dose and the biological properties of the tumor. In silico predictions of our model correlated with imaging data and demonstrated that a dose-dense schedule comprising escalating doses and shortened intervals of drug administration can improve intratumoral drug uptake and overcome consumption of CEA-IL2v by the expanding population of IL2R-positive cells.Conclusions: The model presented here allows simulation of individualized treatment plans for optimal dosing and scheduling of immunocytokines for anticancer immunotherapy. Clin Cancer Res; 24(14); 3325-33. ©2018 AACRSee related commentary by Ruiz-Cerdá et al., p. 3236.

Model of Intraperitoneal Targeted α-Particle Therapy Shows That Posttherapy Cold-Antibody Boost Enhances Microtumor Radiation Dose and Treatable Tumor Sizes

Intraperitoneally administered radiolabeled monoclonal antibodies (mAbs) have been tested in several clinical trials, often with promising results, but have never proven curative. Methods: We have previously presented simulations of clinically relevant amounts of intraperitoneal ⁹⁰Y-mAbs for treatment of minimal disease and shown that such treatments are unlikely to eradicate microtumors. Our previous model simulated the kinetics of intraperitoneally infused radiolabeled mAbs in humans and showed the benefit of instead using α-emitters such as ²¹¹At. In the current work, we introduce penetration of mAbs into microtumors with radii of up to 400 μm. Calculations were performed using dynamic simulation software. To determine the radiation dose distribution in nonvascularized microtumors of various sizes after intraperitoneal ²¹¹At-radioimmunotherapy, we used an in-house-developed Monte Carlo program for microdosimetry. Our aim was to find methods that optimize the therapy for as wide a tumor size range as possible. Results: Our results show that high-specific-activity radiolabeled mAbs that are bound to a tumor surface will penetrate slowly compared with the half-lives of ²¹¹At and shorter-lived radionuclides. The inner-core cells of tumors with radii exceeding 100 μm may therefore not be sufficiently irradiated. For lower specific activities, the penetration rate and dose distribution will be more favorable for such tumors, but the dose to smaller microtumors and single cells will be low. Conclusion: Our calculations show that the addition of a boost with unlabeled mAb 1-5 h after therapy results in sufficient absorbed doses both to single cells and throughout microtumors up to approximately 300 μm in radius. This finding should also hold for other high-affinity mAbs and short-lived α-emitters.

A three-time-point method for assessing kinetic parameters of 64Cu-labeled Ramucirumab trapping in VEGFR-2 positive lung tumors

OBJECTIVE

To describe a three-time-point method for estimating kinetic parameters involved in 64Cu-labeled Ramucirumab (64Cu-NOTA-RamAb) trapping of VEGFR-2 positive lung tumors.

MATERIALS AND METHODS

Positron emission tomography (microPET) data of tumor-bearing mice for 64Cu-NOTA-RamAb trapping in VEGFR-2 positive HCC4006 tumor were used, involving tissue activity measurements acquired at 3, 24 and 48 h post-injection, without and with administration of RamAb blocking dose. A kinetic model provided an analytical formula describing the tissue time-activity-curve, involving 64Cu-NOTA-RamAb uptake (Ki), release rate constant (kR) and fraction of free tracer in blood and interstitial volume (F).

RESULTS

Fitting analytical formula outcomes on mean microPET data yielded values of the kinetic parameters: Ki = 0.0314/0.0123 gram of blood per hour per gram of tissue, kR = 0.0387/0.0313 h-1 and F = 0.2075/0.2007 gram of blood per gram of tissue, without/with RamAb blocking dose, respectively (R = 0.99999 for the graph displaying microPET versus theoretical data; P < .01).

CONCLUSIONS

Three independent kinetic parameters (Ki, kR and F) can be assessed from three data points acquired at early, mid and late imaging, i.e., at 3, 24 and 48 h post-injection, for further characterization of 64Cu-NOTA-RamAb trapping in VEGFR-2 positive lung tumors.

Pharmacokinetic Considerations for Antibody-Drug Conjugates against Cancer

Antibody-drug conjugates (ADCs) are ushering in the next era of targeted therapy against cancer. An ADC for cancer therapy consists of a potent cytotoxic payload that is attached to a tumour-targeted antibody by a chemical linker, usually with an average drug-to-antibody ratio (DAR) of 3.5-4. The theory is to deliver potent cytotoxic payloads directly to tumour cells while sparing healthy cells. However, practical application has proven to be more difficult. At present there are only two ADCs approved for clinical use. Nevertheless, in the last decade there has been an explosion of options for ADC engineering to optimize target selection, Fc receptor interactions, linker, payload and more. Evaluation of these strategies requires an understanding of the mechanistic underpinnings of ADC pharmacokinetics. Development of ADCs for use in cancer further requires an understanding of tumour properties and kinetics within the tumour environment, and how the presence of cancer as a disease will impact distribution and elimination. Key pharmacokinetic considerations for the successful design and clinical application of ADCs in oncology are explored in this review, with a focus on the mechanistic determinants of distribution and elimination.

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

Residualization Rates of Near-Infrared Dyes for the Rational Design of Molecular Imaging Agents

PURPOSE

Near-infrared (NIR) fluorescence imaging is widely used for tracking antibodies and biomolecules in vivo. Clinical and preclinical applications include intraoperative imaging, tracking therapeutics, and fluorescent labeling as a surrogate for subsequent radiolabeling. Despite their extensive use, one of the fundamental properties of NIR dyes, the residualization rate within cells following internalization, has not been systematically studied. This rate is required for the rational design of probes and proper interpretation of in vivo results.

PROCEDURES

In this brief report, we measure the cellular residualization rate of eight commonly used dyes encompassing three core structures (cyanine, boron-dipyrromethene (BODIPY), and oxazine/thiazine/carbopyronin).

RESULTS

We identify residualizing (half-life >24 h) and non-residualizing (half-life <24 h) dyes in both the far-red (~650-680 nm) and near-infrared (~740-800 nm) regions.

CONCLUSIONS

This data will allow researchers to independently and rationally select the wavelength and residualizing nature of dyes for molecular imaging agent design.

Multiscale Modeling of Antibody-Drug Conjugates: Connecting Tissue and Cellular Distribution to Whole Animal Pharmacokinetics and Potential Implications for Efficacy

Antibody-drug conjugates exhibit complex pharmacokinetics due to their combination of macromolecular and small molecule properties. These issues range from systemic concerns, such as deconjugation of the small molecule drug during the long antibody circulation time or rapid clearance from nonspecific interactions, to local tumor tissue heterogeneity, cell bystander effects, and endosomal escape. Mathematical models can be used to study the impact of these processes on overall distribution in an efficient manner, and several types of models have been used to analyze varying aspects of antibody distribution including physiologically based pharmacokinetic (PBPK) models and tissue-level simulations. However, these processes are quantitative in nature and cannot be handled qualitatively in isolation. For example, free antibody from deconjugation of the small molecule will impact the distribution of conjugated antibodies within the tumor. To incorporate these effects into a unified framework, we have coupled the systemic and organ-level distribution of a PBPK model with the tissue-level detail of a distributed parameter tumor model. We used this mathematical model to analyze new experimental results on the distribution of the clinical antibody-drug conjugate Kadcyla in HER2-positive mouse xenografts. This model is able to capture the impact of the drug-antibody ratio (DAR) on tumor penetration, the net result of drug deconjugation, and the effect of using unconjugated antibody to drive ADC penetration deeper into the tumor tissue. This modeling approach will provide quantitative and mechanistic support to experimental studies trying to parse the impact of multiple mechanisms of action for these complex drugs.

Mathematical Based Calculation of Drug Penetration Depth in Solid Tumors

Cancer is a class of diseases characterized by out-of-control cells' growth which affect cells and make them damaged. Many treatment options for cancer exist. Chemotherapy as an important treatment option is the use of drugs to treat cancer. The anticancer drug travels to the tumor and then diffuses in it through capillaries. The diffusion of drugs in the solid tumor is limited by penetration depth which is different in case of different drugs and cancers. The computation of this depth is important as it helps physicians to investigate about treatment of infected tissue. Although many efforts have been made on studying and measuring drug penetration depth, less works have been done on computing this length from a mathematical point of view. In this paper, first we propose phase lagging model for diffusion of drug in the tumor. Then, using this model on one side and considering the classic diffusion on the other side, we compute the drug penetration depth in the solid tumor. This computed value of drug penetration depth is corroborated by comparison with the values measured by experiments.

Preclinical to Clinical Translation of Antibody-Drug Conjugates Using PK/PD Modeling: a Retrospective Analysis of Inotuzumab Ozogamicin

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model was used for preclinical to clinical translation of inotuzumab ozogamicin, a CD22-targeting antibody-drug conjugate (ADC) for B cell malignancies including non-Hodgkin's lymphoma (NHL) and acute lymphocytic leukemia (ALL). Preclinical data was integrated in a PK/PD model which included (1) a plasma PK model characterizing disposition and clearance of inotuzumab ozogamicin and its released payload N-Ac-γ-calicheamicin DMH, (2) a tumor disposition model describing ADC diffusion into the tumor extracellular environment, (3) a cellular model describing inotuzumab ozogamicin binding to CD22, internalization, intracellular N-Ac-γ-calicheamicin DMH release, binding to DNA, or efflux from the tumor cell, and (4) tumor growth and inhibition in mouse xenograft models. The preclinical model was translated to the clinic by incorporating human PK for inotuzumab ozogamicin and clinically relevant tumor volumes, tumor growth rates, and values for CD22 expression in the relevant patient populations. The resulting stochastic models predicted progression-free survival (PFS) rates for inotuzumab ozogamicin in patients comparable to the observed clinical results. The model suggested that a fractionated dosing regimen is superior to a conventional dosing regimen for ALL but not for NHL. Simulations indicated that tumor growth is a highly sensitive parameter and predictive of successful outcome. Inotuzumab ozogamicin PK and N-Ac-γ-calicheamicin DMH efflux are also sensitive parameters and would be considered more useful predictors of outcome than CD22 receptor expression. In summary, a multiscale, mechanism-based model has been developed for inotuzumab ozogamicin, which can integrate preclinical biomeasures and PK/PD data to predict clinical response.

Determination of Cellular Processing Rates for a Trastuzumab-Maytansinoid Antibody-Drug Conjugate (ADC) Highlights Key Parameters for ADC Design

Antibody-drug conjugates (ADCs) are a promising class of cancer therapeutics that combine the specificity of antibodies with the cytotoxic effects of payload drugs. A quantitative understanding of how ADCs are processed intracellularly can illustrate which processing steps most influence payload delivery, thus aiding the design of more effective ADCs. In this work, we develop a kinetic model for ADC cellular processing as well as generalizable methods based on flow cytometry and fluorescence imaging to parameterize this model. A number of key processing steps are included in the model: ADC binding to its target antigen, internalization via receptor-mediated endocytosis, proteolytic degradation of the ADC, efflux of the payload out of the cell, and payload binding to its intracellular target. The model was developed with a trastuzumab-maytansinoid ADC (TM-ADC) similar to trastuzumab-emtansine (T-DM1), which is used in the clinical treatment of HER2+ breast cancer. In three high-HER2-expressing cell lines (BT-474, NCI-N87, and SK-BR-3), we report for TM-ADC half-lives for internalization of 6-14 h, degradation of 18-25 h, and efflux rate of 44-73 h. Sensitivity analysis indicates that the internalization rate and efflux rate are key parameters for determining how much payload is delivered to a cell with TM-ADC. In addition, this model describing the cellular processing of ADCs can be incorporated into larger pharmacokinetics/pharmacodynamics models, as demonstrated in the associated companion paper.

Therapy operating characteristic curves: tools for precision chemotherapy

The therapy operating characteristic (TOC) curve, developed in the context of radiation therapy, is a plot of the probability of tumor control versus the probability of normal-tissue complications as the overall radiation dose level is varied, e.g., by varying the beam current in external-beam radiotherapy or the total injected activity in radionuclide therapy. This paper shows how TOC can be applied to chemotherapy with the administered drug dosage as the variable. The area under a TOC curve (AUTOC) can be used as a figure of merit for therapeutic efficacy, analogous to the area under an ROC curve (AUROC), which is a figure of merit for diagnostic efficacy. In radiation therapy, AUTOC can be computed for a single patient by using image data along with radiobiological models for tumor response and adverse side effects. The mathematical analogy between response of observers to images and the response of tumors to distributions of a chemotherapy drug is exploited to obtain linear discriminant functions from which AUTOC can be calculated. Methods for using mathematical models of drug delivery and tumor response with imaging data to estimate patient-specific parameters that are needed for calculation of AUTOC are outlined. The implications of this viewpoint for clinical trials are discussed.

Combination Radioimmunotherapy Approaches and Quantification of Immuno-PET

Monoclonal antibodies (mAbs), which play a prominent role in cancer therapy, can interact with specific antigens on cancer cells, thereby enhancing the patient's immune response via various mechanisms, or mAbs can act against cell growth factors and, thereby, arrest the proliferation of tumor cells. Radionuclide-labeled mAbs, which are used in radioimmunotherapy (RIT), are effective for cancer treatment because tumor associated-mAbs linked to cytotoxic radionuclides can selectively bind to tumor antigens and release targeted cytotoxic radiation. Immunological positron emission tomography (immuno-PET), which is the combination of PET with mAb, is an attractive option for improving tumor detection and mAb quantification. However, RIT remains a challenge because of the limited delivery of mAb into tumors. The transport and uptake of mAb into tumors is slow and heterogeneous. The tumor microenvironment contributed to the limited delivery of the mAb. During the delivery process of mAb to tumor, mechanical drug resistance such as collagen distribution or physiological drug resistance such as high intestinal pressure or absence of lymphatic vessel would be the limited factor of mAb delivery to the tumor at a potentially lethal mAb concentration. When α-emitter-labeled mAbs were used, deeper penetration of α-emitter-labeled mAb inside tumors was more important because of the short range of the α emitter. Therefore, combination therapy strategies aimed at improving mAb tumor penetration and accumulation would be beneficial for maximizing their therapeutic efficacy against solid tumors.

Spatial Modeling of Drug Delivery Routes for Treatment of Disseminated Ovarian Cancer

In ovarian cancer, metastasis is typically confined to the peritoneum. Surgical removal of the primary tumor and macroscopic secondary tumors is a common practice, but more effective strategies are needed to target microscopic spheroids persisting in the peritoneal fluid after debulking surgery. To treat this residual disease, therapeutic agents can be administered by either intravenous or intraperitoneal infusion. Here, we describe the use of a cellular Potts model to compare tumor penetration of two classes of drugs (cisplatin and pertuzumab) when delivered by these two alternative routes. The model considers the primary route when the drug is administered either intravenously or intraperitoneally, as well as the subsequent exchange into the other delivery volume as a secondary route. By accounting for these dynamics, the model revealed that intraperitoneal infusion is the markedly superior route for delivery of both small-molecule and antibody therapies into microscopic, avascular tumors typical of patients with ascites. Small tumors attached to peritoneal organs, with vascularity ranging from 2% to 10%, also show enhanced drug delivery via the intraperitoneal route, even though tumor vessels can act as sinks during the dissemination of small molecules. Furthermore, we assessed the ability of the antibody to enter the tumor by in silico and in vivo methods and suggest that optimization of antibody delivery is an important criterion underlying the efficacy of these and other biologics. The use of both delivery routes may provide the best total coverage of tumors, depending on their size and vascularity.

Safety and Tumor Specificity of Cetuximab-IRDye800 for Surgical Navigation in Head and Neck Cancer

PURPOSE

Positive margins dominate clinical outcomes after surgical resections in most solid cancer types, including head and neck squamous cell carcinoma. Unfortunately, surgeons remove cancer in the same manner they have for a century with complete dependence on subjective tissue changes to identify cancer in the operating room. To effect change, we hypothesize that EGFR can be targeted for safe and specific real-time localization of cancer.

EXPERIMENTAL DESIGN

A dose escalation study of cetuximab conjugated to IRDye800 was performed in patients (n = 12) undergoing surgical resection of squamous cell carcinoma arising in the head and neck. Safety and pharmacokinetic data were obtained out to 30 days after infusion. Multi-instrument fluorescence imaging was performed in the operating room and in surgical pathology.

RESULTS

There were no grade 2 or higher adverse events attributable to cetuximab-IRDye800. Fluorescence imaging with an intraoperative, wide-field device successfully differentiated tumor from normal tissue during resection with an average tumor-to-background ratio of 5.2 in the highest dose range. Optical imaging identified opportunity for more precise identification of tumor during the surgical procedure and during the pathologic analysis of tissues ex vivo. Fluorescence levels positively correlated with EGFR levels.

CONCLUSIONS

We demonstrate for the first time that commercially available antibodies can be fluorescently labeled and safely administered to humans to identify cancer with sub-millimeter resolution, which has the potential to improve outcomes in clinical oncology.

Quantification of the binding potential of cell-surface receptors in fresh excised specimens via dual-probe modeling of SERS nanoparticles

The complete removal of cancerous tissue is a central aim of surgical oncology, but is difficult to achieve in certain cases, especially when the removal of surrounding normal tissues must be minimized. Therefore, when post-operative pathology identifies residual tumor at the surgical margins, re-excision surgeries are often necessary. An intraoperative approach for tumor-margin assessment, insensitive to nonspecific sources of molecular probe accumulation and contrast, is presented employing kinetic-modeling analysis of dual-probe staining using surface-enhanced Raman scattering nanoparticles (SERS NPs). Human glioma (U251) and epidermoid (A431) tumors were implanted subcutaneously in six athymic mice. Fresh resected tissues were stained with an equimolar mixture of epidermal growth factor receptor (EGFR)-targeted and untargeted SERS NPs. The binding potential (BP; proportional to receptor concentration) of EGFR – a cell-surface receptor associated with cancer – was estimated from kinetic modeling of targeted and untargeted NP concentrations in response to serial rinsing. EGFR BPs in healthy, U251, and A431 tissues were 0.06 ± 0.14, 1.13 ± 0.40, and 2.23 ± 0.86, respectively, which agree with flow-cytometry measurements and published reports. The ability of this approach to quantify the BP of cell-surface biomarkers in fresh tissues opens up an accurate new approach to analyze tumor margins intraoperatively.

Quantifying and Reducing Uncertainties in Cancer Therapy

There are two basic sources of uncertainty in cancer chemotherapy: how much of the therapeutic agent reaches the cancer cells, and how effective it is in reducing or controlling the tumor when it gets there. There is also a concern about adverse effects of the therapy drug. Similarly in external-beam radiation therapy or radionuclide therapy, there are two sources of uncertainty: delivery and efficacy of the radiation absorbed dose, and again there is a concern about radiation damage to normal tissues. The therapy operating characteristic (TOC) curve, developed in the context of radiation therapy, is a plot of the probability of tumor control vs. the probability of normal-tissue complications as the overall radiation dose level is varied, e.g. by varying the beam current in external-beam radiotherapy or the total injected activity in radionuclide therapy. The TOC can be applied to chemotherapy with the administered drug dosage as the variable. The area under a TOC curve (AUTOC) can be used as a figure of merit for therapeutic efficacy, analogous to the area under an ROC curve (AUROC), which is a figure of merit for diagnostic efficacy. In radiation therapy AUTOC can be computed for a single patient by using image data along with radiobiological models for tumor response and adverse side effects. In this paper we discuss the potential of using mathematical models of drug delivery and tumor response with imaging data to estimate AUTOC for chemotherapy, again for a single patient. This approach provides a basis for truly personalized therapy and for rigorously assessing and optimizing the therapy regimen for the particular patient. A key role is played by Emission Computed Tomography (PET or SPECT) of radiolabeled chemotherapy drugs.

Computational systems biology approaches to anti-angiogenic cancer therapeutics

Angiogenesis is an exquisitely regulated process that is required for physiological processes and is also important in numerous diseases. Tumors utilize angiogenesis to generate the vascular network needed to supply the cancer cells with nutrients and oxygen, and many cancer drugs aim to inhibit tumor angiogenesis. Anti-angiogenic therapy involves inhibiting multiple cell types, molecular targets, and intracellular signaling pathways. Computational tools are useful in guiding treatment strategies, predicting the response to treatment, and identifying new targets of interest. Here, we describe progress that has been made in applying mathematical modeling and bioinformatics approaches to study anti-angiogenic therapeutics in cancer.

Multichannel imaging to quantify four classes of pharmacokinetic distribution in tumors

Low and heterogeneous delivery of drugs and imaging agents to tumors results in decreased efficacy and poor imaging results. Systemic delivery involves a complex interplay of drug properties and physiological factors, and heterogeneity in the tumor microenvironment makes predicting and overcoming these limitations exceptionally difficult. Theoretical models have indicated that there are four different classes of pharmacokinetic behavior in tissue, depending on the fundamental steps in distribution. In order to study these limiting behaviors, we used multichannel fluorescence microscopy and stitching of high-resolution images to examine the distribution of four agents in the same tumor microenvironment. A validated generic partial differential equation model with a graphical user interface was used to select fluorescent agents exhibiting these four classes of behavior, and the imaging results agreed with predictions. BODIPY-FL exhibited higher concentrations in tissue with high blood flow, cetuximab gave perivascular distribution limited by permeability, high plasma protein and target binding resulted in diffusion-limited distribution for Hoechst 33342, and Integrisense 680 was limited by the number of binding sites in the tissue. Together, the probes and simulations can be used to investigate distribution in other tumor models, predict tumor drug distribution profiles, and design and interpret in vivo experiments.

The safety and treatment response of combination therapy of radioimmunotherapy and radiofrequency ablation for solid tumor: a study in vivo

OBJECTION

To investigate the safety and treatment response of radioimmunotherapy (RIT) in combination with radiofrequency ablation (RFA) for the treatment of VX2 tumor on rabbit.

MATERIALS AND METHODS

A total of 36 rabbits bearing VX2 tumor on the thigh were randomly assigned into 3 groups (group I: 1-2 cm; group II: 2-3 cm; group III: 3-4 cm) and 4 subgroups (A: as control, just puncture the tumor using the RFA electrode without power output; B: RFA alone; C: 131I-chTNT intratumoral injection alone; D: RFA+131I-chTNT intratumoral injection 3 days later). The variation of blood assay, weight and survival among different groups and subgroups were used to assess the treatment safety. Ultrasound (US) was used to monitor and assess the tumor response after treatment.

RESULTS

According to the results of the weight and the blood assay among different groups, subgroups, and at two time points (one day before and the 16th day after treatment), no damages to the liver, kidney function and myelosuppression resulting from the treatment were found. No significant differences in survivals among the four subgroups (p = 0.087) were found. In addition, 131I-chTNT did not show significant inhibition effect on VX2 tumor progression according to US measurements.

CONCLUSION

131I-chTNT intratumoral injection alone or in combination with RFA is relatively safe for rabbit without significant toxicity and shows no significant effect on the survival. The treatment response is not as satisfactory as anticipated.

Comparison of Kinetic Models for Dual-Tracer Receptor Concentration Imaging in Tumors

Molecular differences between cancerous and healthy tissue have become key targets for novel therapeutics specific to tumor receptors. However, cancer cell receptor expression can vary within and amongst different tumors, making strategies that can quantify receptor concentration in vivo critical for the progression of targeted therapies. Recently a dual-tracer imaging approach capable of providing quantitative measures of receptor concentration in vivo was developed. It relies on the simultaneous injection and imaging of receptor-targeted tracer and an untargeted tracer (to account for non-specific uptake of the targeted tracer). Early implementations of this approach have been structured on existing "reference tissue" imaging methods that have not been optimized for or validated in dual-tracer imaging. Using simulations and mouse tumor model experimental data, the salient findings in this study were that all widely used reference tissue kinetic models can be used for dual-tracer imaging, with the linearized simplified reference tissue model offering a good balance of accuracy and computational efficiency. Moreover, an alternate version of the full two-compartment reference tissue model can be employed accurately by assuming that the K₁s of the targeted and untargeted tracers are similar to avoid assuming an instantaneous equilibrium between bound and free states (made by all other models).

A priori prediction of tumor payload concentrations: preclinical case study with an auristatin-based anti-5T4 antibody-drug conjugate

The objectives of this investigation were as follows: (a) to validate a mechanism-based pharmacokinetic (PK) model of ADC for its ability to a priori predict tumor concentrations of ADC and released payload, using anti-5T4 ADC A1mcMMAF, and (b) to analyze the PK model to find out main pathways and parameters model outputs are most sensitive to. Experiential data containing biomeasures, and plasma and tumor concentrations of ADC and payload, following A1mcMMAF administration in two different xenografts, were used to build and validate the model. The model performed reasonably well in terms of a priori predicting tumor exposure of total antibody, ADC, and released payload, and the exposure of released payload in plasma. Model predictions were within two fold of the observed exposures. Pathway analysis and local sensitivity analysis were conducted to investigate main pathways and set of parameters the model outputs are most sensitive to. It was discovered that payload dissociation from ADC and tumor size were important determinants of plasma and tumor payload exposure. It was also found that the sensitivity of the model output to certain parameters is dose-dependent, suggesting caution before generalizing the results from the sensitivity analysis. Model analysis also revealed the importance of understanding and quantifying the processes responsible for ADC and payload disposition within tumor cell, as tumor concentrations were sensitive to these parameters. Proposed ADC PK model provides a useful tool for a priori predicting tumor payload concentrations of novel ADCs preclinically, and possibly translating them to the clinic.

Current advances in mathematical modeling of anti-cancer drug penetration into tumor tissues

Delivery of anti-cancer drugs to tumor tissues, including their interstitial transport and cellular uptake, is a complex process involving various biochemical, mechanical, and biophysical factors. Mathematical modeling provides a means through which to understand this complexity better, as well as to examine interactions between contributing components in a systematic way via computational simulations and quantitative analyses. In this review, we present the current state of mathematical modeling approaches that address phenomena related to drug delivery. We describe how various types of models were used to predict spatio-temporal distributions of drugs within the tumor tissue, to simulate different ways to overcome barriers to drug transport, or to optimize treatment schedules. Finally, we discuss how integration of mathematical modeling with experimental or clinical data can provide better tools to understand the drug delivery process, in particular to examine the specific tissue- or compound-related factors that limit drug penetration through tumors. Such tools will be important in designing new chemotherapy targets and optimal treatment strategies, as well as in developing non-invasive diagnosis to monitor treatment response and detect tumor recurrence.

Tumor endothelial marker imaging in melanomas using dual-tracer fluorescence molecular imaging

PURPOSE

Cancer-specific endothelial markers available for intravascular binding are promising targets for new molecular therapies. In this study, a molecular imaging approach of quantifying endothelial marker concentrations (EMCI) is developed and tested in highly light-absorbing melanomas. The approach involves injection of targeted imaging tracer in conjunction with an untargeted tracer, which is used to account for nonspecific uptake and tissue optical property effects on measured targeted tracer concentrations.

PROCEDURES

Theoretical simulations and a mouse melanoma model experiment were used to test out the EMCI approach. The tracers used in the melanoma experiments were fluorescently labeled anti-Plvap/PV1 antibody (plasmalemma vesicle associated protein Plvap/PV1 is a transmembrane protein marker exposed on the luminal surface of endothelial cells in tumor vasculature) and a fluorescent isotype control antibody, the uptakes of which were measured on a planar fluorescence imaging system.

RESULTS

The EMCI model was found to be robust to experimental noise under reversible and irreversible binding conditions and was capable of predicting expected overexpression of PV1 in melanomas compared to healthy skin despite a 5-time higher measured fluorescence in healthy skin compared to melanoma: attributable to substantial light attenuation from melanin in the tumors.

CONCLUSIONS

This study demonstrates the potential of EMCI to quantify endothelial marker concentrations in vivo, an accomplishment that is currently unavailable through any other methods, either in vivo or ex vivo.