Immune-infiltrated kidney organoid-on-chip model for assessing T cell bispecific antibodies.

T cell bispecific antibodies (TCBs) are the focus of intense development for cancer immunotherapy. Recently, peptide-MHC (major histocompatibility complex)-targeted TCBs have emerged as a new class of biotherapeutics with improved specificity. These TCBs simultaneously bind to target peptides presented by the polymorphic, species-specific MHC encoded by the human leukocyte antigen (HLA) allele present on target cells and to the CD3 coreceptor expressed by human T lymphocytes. Unfortunately, traditional models for assessing their effects on human tissues often lack predictive capability, particularly for "on-target, off-tumor" interactions. Here, we report an immune-infiltrated, kidney organoid-on-chip model in which peripheral blood mononuclear cells (PBMCs) along with nontargeting (control) or targeting TCB-based tool compounds are circulated under flow. The target consists of the RMF peptide derived from the intracellular tumor antigen Wilms' tumor 1 (WT1) presented on HLA-A2 via a bivalent T cell receptor-like binding domain. Using our model, we measured TCB-mediated CD8+ T cell activation and killing of RMF-HLA-A2-presenting cells in the presence of PBMCs and multiple tool compounds. DP47, a non-pMHC-targeting TCB that only binds to CD3 (negative control), does not promote T cell activation and killing. Conversely, the nonspecific ESK1-like TCB (positive control) promotes CD8+ T cell expansion accompanied by dose-dependent T cell-mediated killing of multiple cell types, while WT1-TCB* recognizing the RMF-HLA-A2 complex with high specificity, leads solely to selective killing of WT1-expressing cells within kidney organoids under flow. Our 3D kidney organoid model offers a platform for preclinical testing of cancer immunotherapies and investigating tissue-immune system interactions.

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.

Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling.

Targeting intracellular WT1 in AML with a novel RMF-peptide-MHC-specific T-cell bispecific antibody.

Antibody-based immunotherapy is a promising strategy for targeting chemoresistant leukemic cells. However, classical antibody-based approaches are restricted to targeting lineage-specific cell surface antigens. By targeting intracellular antigens, a large number of other leukemia-associated targets would become accessible. In this study, we evaluated a novel T-cell bispecific (TCB) antibody, generated by using CrossMAb and knob-into-holes technology, containing a bivalent T-cell receptor-like binding domain that recognizes the RMFPNAPYL peptide derived from the intracellular tumor antigen Wilms tumor protein (WT1) in the context of HLA-A*02. Binding to CD3ε recruits T cells irrespective of their T-cell receptor specificity. WT1-TCB elicited antibody-mediated T-cell cytotoxicity against AML cell lines in a WT1- and HLA-restricted manner. Specific lysis of primary acute myeloid leukemia (AML) cells was mediated in ex vivo long-term cocultures by using allogeneic (mean ± standard error of the mean [SEM] specific lysis, 67 ± 6% after 13-14 days; n = 18) or autologous, patient-derived T cells (mean ± SEM specific lysis, 54 ± 12% after 11-14 days; n = 8). WT1-TCB-treated T cells exhibited higher cytotoxicity against primary AML cells than an HLA-A*02 RMF-specific T-cell clone. Combining WT1-TCB with the immunomodulatory drug lenalidomide further enhanced antibody-mediated T-cell cytotoxicity against primary AML cells (mean ± SEM specific lysis on days 3-4, 45.4 ± 9.0% vs 70.8 ± 8.3%; P = .015; n = 9-10). In vivo, WT1-TCB-treated humanized mice bearing SKM-1 tumors exhibited a significant and dose-dependent reduction in tumor growth. In summary, we show that WT1-TCB facilitates potent in vitro, ex vivo, and in vivo killing of AML cell lines and primary AML cells; these results led to the initiation of a phase 1 trial in patients with relapsed/refractory AML (#NCT04580121).

PKPD modeling of acquired resistance to anti-cancer drug treatment.

Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials.

Translational PK/PD modeling to increase probability of success in drug discovery and early development.

In this review we present ways in which translational PK/PD modeling can address opportunities to enhance probability of success in drug discovery and early development. This is achieved by impacting efficacy and safety-driven attrition rates, through increased focus on the quantitative understanding and modeling of translational PK/PD. Application of the proposed principles early in the discovery and development phases is anticipated to bolster confidence of successfully evaluating proof of mechanism in humans and ultimately improve Phase II success. The present review is centered on the application of predictive modeling and simulation approaches during drug discovery and early development, and more specifically of mechanism-based PK/PD modeling. Case studies are presented, focused on the relevance of M&S contributions to real-world questions and the impact on decision making.

Model-based assessment of erlotinib effect in vitro measured by real-time cell analysis.

Real time cell analysis (RTCA) is an impedance-based technology which tracks various living cell characteristics over time, such as their number, morphology or adhesion to the extra cellular matrix. However, there is no consensus about how RTCA data should be used to quantitatively evaluate pharmacodynamic parameters which describe drug efficacy or toxicity. The purpose of this work was to determine how RTCA data can be analyzed with mathematical modeling to explore and quantify drug effect in vitro. The pharmacokinetic-pharmacodynamic erlotinib concentration profile predicted by the model and its effect on the human epidermoïd carcinoma cell line A431 in vitro was measured through RTCA output, designated as cell index. A population approach was used to estimate model parameter values, considering a plate well as the statistical unit. The model related the cell index to the number of cells by means of a proportionality factor. Cell growth was described by an exponential model. A delay between erlotinib pharmacokinetics and cell killing was described by a transit compartment model, and the effect potency, by an E max function of erlotinib concentration. The modeling analysis performed on RTCA data distinguished drug effects in vitro on cell number from other effects likely to modify the relationship between cell index and cell number. It also revealed a time-dependent decrease of erlotinib concentration over time, described by a mono-exponential pharmacokinetic model with nonspecific binding.

Leveraging modeling and simulation to optimize the therapeutic window for epigenetic modifier drugs.

Dysregulated epigenetic processes can lead to altered gene expression and give rise to malignant transformation and tumorigenesis. Epigenetic drugs aim to revert the phenotype of cancer cells to normally functioning cells, and are developed and applied to treat both hematological and solid cancers. Despite this promising therapeutic avenue, the successful development of epigenetic modulators has been challenging. We argue that besides identifying the right responder patient population, the selection of an optimized dosing regimen is equally important. For the majority of epigenetic modulators, hematological adverse effects such as thrombocytopenia, anemia or neutropenia are frequently observed and may limit their therapeutic potential. Therefore, one of the key challenges is to identify a dosing regimen that maximizes drug efficacy and minimizes toxicity. This requires a good understanding of the quantitative relationship between the administered dose, the drug exposure and the magnitude and duration of drug response related to safety and efficacy. With case examples, we highlight how modeling and simulation has been successfully applied to address those questions. As an outlook, we suggest the combination of efficacy and safety prediction models that capture the quantitative, mechanistic relationships governing the balance between their safety and efficacy dynamics. A stepwise approach for its implementation is presented. Utilizing in silico explorations, the impact of dosing regimen on the therapeutic window can be explored. This will serve as a basis to select the most promising dosing regimen that maximizes efficacy while minimizing adverse effects and to increase the probability of success for the given epigenetic drug.

Investigating brain uptake of a non-targeting monoclonal antibody after intravenous and intracerebroventricular administration.

Monoclonal antibodies play an important role in the treatment of various diseases. However, the development of these drugs against neurological disorders where the drug target is located in the brain is challenging and requires a good understanding of the local drug concentration in the brain. In this original research, we investigated the systemic and local pharmacokinetics in the brain of healthy rats after either intravenous (IV) or intracerebroventricular (ICV) administration of EGFRvIII-T-Cell bispecific (TCB), a bispecific monoclonal antibody. We established an experimental protocol that allows serial sampling in serum, cerebrospinal fluid (CSF) and interstitial fluid (ISF) of the prefrontal cortex in freely moving rats. For detection of drug concentration in ISF, a push-pull microdialysis technique with large pore membranes was applied. Brain uptake into CSF and ISF was characterized and quantified with a reduced brain physiologically-based pharmacokinetic model. The model allowed us to interpret the pharmacokinetic processes of brain uptake after different routes of administration. The proposed model capturing the pharmacokinetics in serum, CSF and ISF of the prefrontal cortex suggests a barrier function between the CSF and ISF that impedes free antibody transfer. This finding suggests that ICV administration may not be better suited to reach higher local drug exposure as compared to IV administration. The model enabled us to quantify the relative contribution of the blood-brain barrier (BBB) and Blood-CSF-Barrier to the uptake into the interstitial fluid of the brain. In addition, we compared the brain uptake of three monoclonal antibodies after IV dosing. In summary, the presented approach can be applied to profile compounds based on their relative uptake in the brain and provides quantitative insights into which pathways are contributing to the net exposure in the brain.

Preclinical InVivo Data Integrated in a Modeling Network Informs a Refined Clinical Strategy for a CD3 T-Cell Bispecific in Combination with Anti-PD-L1.

TYRP1-TCB is a CD3 T-cell bispecific (CD3-TCB) antibody for the treatment of advanced melanoma. A tumor growth inhibition (TGI) model was developed using mouse xenograft data with TYRP1-TCB monotherapy or TYRP1-TCB plus anti-PD-L1 combination. The model was translated to humans to inform a refined clinical strategy. From xenograft mouse data, we estimated an EC50 of 0.345 mg/L for TYRP1-TCB, close to what was observed in vitro using the same tumor cell line. The model showed that, though increasing the dose of TYRP1-TCB in monotherapy delays the time to tumor regrowth and promotes higher tumor cell killing, it also induces a faster rate of tumor regrowth. Combination with anti-PD-L1 extended the time to tumor regrowth by 25% while also decreasing the tumor regrowth rate by 69% compared to the same dose of TYRP1-TCB alone. The model translation to humans predicts that if patients' tumors were scanned every 6 weeks, only 46% of the monotherapy responders would be detected even at a TYRP1-TCB dose resulting in exposures above the EC90. However, combination of TYRP1-TCB and anti-PD-L1 in the clinic is predicted to more than double the overall response rate (ORR), duration of response (DoR) and progression-free survival (PFS) compared to TYRP1-TCB monotherapy. As a result, it is highly recommended to consider development of CD3-TCBs as part of a combination therapy from the outset, without the need to escalate the CD3-TCB up to the Maximum Tolerated Dose (MTD) in monotherapy and without gating the combination only on RECIST-derived efficacy metrics.

Patient-derived micro-organospheres enable clinical precision oncology.

Patient-derived xenografts (PDXs) and patient-derived organoids (PDOs) have been shown to model clinical response to cancer therapy. However, it remains challenging to use these models to guide timely clinical decisions for cancer patients. Here, we used droplet emulsion microfluidics with temperature control and dead-volume minimization to rapidly generate thousands of micro-organospheres (MOSs) from low-volume patient tissues, which serve as an ideal patient-derived model for clinical precision oncology. A clinical study of recently diagnosed metastatic colorectal cancer (CRC) patients using an MOS-based precision oncology pipeline reliably assessed tumor drug response within 14 days, a timeline suitable for guiding treatment decisions in the clinic. Furthermore, MOSs capture original stromal cells and allow T cell penetration, providing a clinical assay for testing immuno-oncology (IO) therapies such as PD-1 blockade, bispecific antibodies, and T cell therapies on patient tumors.

Cytokine Release Syndrome By T-cell-Redirecting Therapies: Can We Predict and Modulate Patient Risk?

T-cell-redirecting therapies are promising new therapeutic options in the field of cancer immunotherapy, but the development of these modalities is challenging. A commonly observed adverse event in patients treated with T-cell-redirecting therapies is cytokine release syndrome (CRS). Its clinical manifestation is a burden on patients, and continues to be a big hurdle in the clinical development of this class of therapeutics. We review different T-cell-redirecting therapies, discuss key factors related to cytokine release and potentially leading to CRS, and present clinical mitigation strategies applied for those modalities. We propose to dissect those risk factors into drug-target-disease-related factors and individual patient risk factors. Aiming to optimize the therapeutic intervention of these modalities, we illustrate how the knowledge on drug-target-disease-related factors, such as target expression, binding affinity, and target accessibility, can be leveraged in a model-based framework and highlight with case examples how modeling and simulation is applied to guide drug discovery and development. We draw attention to the current gaps in predicting the individual patient's risk towards a high-grade CRS, which requires further considerations of risk factors related, but not limited to, the patient's demographics, genetics, underlying pathologies, treatment history, and environmental exposures. The drug-target-disease-related factors together with the individual patient's risk factors can be regarded as the patient's propensity for developing CRS in response to therapy. As an outlook, we suggest implementing a risk scoring system combined with mechanistic modeling to enable the prediction of an individual patient's risk of CRS for a given therapeutic intervention.

A Novel Approach for Quantifying the Pharmacological Activity of T-Cell Engagers Utilizing In Vitro Time Course Experiments and Streamlined Data Analysis.

CD3-bispecific antibodies are a new class of immunotherapeutic drugs against cancer. The pharmacological activity of CD3-bispecifics is typically assessed through in vitro assays of cancer cell lines co-cultured with human peripheral blood mononuclear cells (PBMCs). Assay results depend on experimental conditions such as incubation time and the effector-to-target cell ratio, which can hinder robust quantification of pharmacological activity. In order to overcome these limitations, we developed a new, holistic approach for quantification of the in vitro dose-response relationship. Our experimental design integrates a time-independent analysis of the dose-response across different time points as an alternative to the static, "snap-shot" analysis based on a single time point commonly used in dose-response assays. We show that the potency values derived from static in vitro experiments depend on the incubation time, which leads to inconsistent results across multiple assays and compounds. We compared the potency values from the time-independent analysis with a model-based approach. We find comparably accurate potency estimates from the model-based and time-independent analyses and that the time-independent analysis provides a robust quantification of pharmacological activity. This approach may allow for an improved head-to-head comparison of different compounds and test systems and may prove useful for supporting first-in-human dose selection.

Pharmacokinetics and Pharmacodynamics of T-Cell Bispecifics in the Tumour Interstitial Fluid.

The goal of this study is to investigate the pharmacokinetics in plasma and tumour interstitial fluid of two T-cell bispecifics (TCBs) with different binding affinities to the tumour target and to assess the subsequent cytokine release in a tumour-bearing humanised mouse model. Pharmacokinetics (PK) as well as cytokine data were collected in humanised mice after iv injection of cibisatamab and CEACAM5-TCB which are binding with different binding affinities to the tumour antigen carcinoembryonic antigen (CEA). The PK data were modelled and coupled to a previously published physiologically based PK model. Corresponding cytokine release profiles were compared to in vitro data. The PK model provided a good fit to the data and precise estimation of key PK parameters. High tumour interstitial concentrations were observed for both TCBs, influenced by their respective target binding affinities. In conclusion, we developed a tailored experimental method to measure PK and cytokine release in plasma and at the site of drug action, namely in the tumour. Integrating those data into a mathematical model enabled to investigate the impact of target affinity on tumour accumulation and can have implications for the PKPD assessment of the therapeutic antibodies.

Src/lck inhibitor dasatinib reversibly switches off cytokine release and T cell cytotoxicity following stimulation with T cell bispecific antibodies.

BACKGROUND: T cell engagers are bispecific antibodies recognizing, with one moiety, the CD3ε chain of the T cell receptor and, with the other moiety, specific tumor surface antigens. Crosslinking of CD3 upon simultaneous binding to tumor antigens triggers T cell activation, proliferation and cytokine release, leading to tumor cell killing. Treatment with T cell engagers can be associated with safety liabilities due to on-target on-tumor, on-target off-tumor cytotoxic activity and cytokine release syndrome (CRS). Tyrosine kinases such as SRC, LCK or ZAP70 are involved in downstream signaling pathways after engagement of the T cell receptor and blocking these kinases might serve to abrogate T cell activation when required (online supplemental material 1). Dasatinib was previously identified as a potent kinase inhibitor that switches off CAR T cell functionality. METHODS: Using an in vitro model of target cell killing by human peripheral blood mononuclear cells, we assessed the effects of dasatinib combined with 2+1 T cell bispecific antibodies (TCBs) including CEA-TCB, CD19-TCB or HLA-A2 WT1-TCB on T cell activation, proliferation and target cell killing measured by flow cytometry and cytokine release measured by Luminex. To determine the effective dose of dasatinib, the Incucyte system was used to monitor the kinetics of TCB-mediated target cell killing in the presence of escalating concentrations of dasatinib. Last, the effects of dasatinib were evaluated in vivo in humanized NSG mice co-treated with CD19-TCB. The count of CD20+ blood B cells was used as a readout of efficacy of TCB-mediated killing and cytokine levels were measured in the serum. RESULTS: Dasatinib concentrations above 50 nM prevented cytokine release and switched off-target cell killing, which were subsequently restored on removal of dasatinib. In addition, dasatinib prevented CD19-TCB-mediated B cell depletion in humanized NSG mice. These data confirm that dasatinib can act as a rapid and reversible on/off switch for activated T cells at pharmacologically relevant doses as they are applied in patients according to the label. CONCLUSION: Taken together, we provide evidence for the use of dasatinib as a pharmacological on/off switch to mitigate off-tumor toxicities or CRS by T cell bispecific antibodies.

Predicting Tumor Killing and T-Cell Activation by T-Cell Bispecific Antibodies as a Function of Target Expression: Combining In Vitro Experiments with Systems Modeling.

Targeted T-cell redirection is a promising field in cancer immunotherapy. T-cell bispecific antibodies (TCB) are novel antibody constructs capable of binding simultaneously to T cells and tumor cells, allowing cross-linking and the formation of immunologic synapses. This in turn results in T-cell activation, expansion, and tumor killing. TCB activity depends on system-related properties such as tumor target antigen expression as well as antibody properties such as binding affinities to target and T cells. Here, we developed a systems model integrating in vitro data to elucidate further the mechanism of action and to quantify the cytotoxic effects as the relationship between targeted antigen expression and corresponding TCB activity. In the proposed model, we capture relevant processes, linking immune synapse formation to T-cell activation, expansion, and tumor killing for TCBs in vitro to differentiate the effect between tumor cells expressing high or low levels of the tumor antigen. We used cibisatamab, a TCB binding to carcinoembryonic antigen (CEA), to target different tumor cell lines with high and low CEA expression in vitro We developed a model to capture and predict our observations, as a learn-and-confirm cycle. Although full tumor killing and substantial T-cell activation was observed in high expressing tumor cells, the model correctly predicted partial tumor killing and minimal T-cell activation in low expressing tumor cells when exposed to cibisatamab. Furthermore, the model successfully predicted cytotoxicity across a wide range of tumor cell lines, spanning from very low to high CEA expression.

Predicting In Vivo Efficacy from In Vitro Data: Quantitative Systems Pharmacology Modeling for an Epigenetic Modifier Drug in Cancer.

Reliably predicting in vivo efficacy from in vitro data would facilitate drug development by reducing animal usage and guiding drug dosing in human clinical trials. However, such prediction remains challenging. Here, we built a quantitative pharmacokinetic/pharmacodynamic (PK/PD) mathematical model capable of predicting in vivo efficacy in animal xenograft models of tumor growth while trained almost exclusively on in vitro cell culture data sets. We studied a chemical inhibitor of LSD1 (ORY-1001), a lysine-specific histone demethylase enzyme with epigenetic function, and drug-induced regulation of target engagement, biomarker levels, and tumor cell growth across multiple doses administered in a pulsed and continuous fashion. A PK model of unbound plasma drug concentration was linked to the in vitro PD model, which enabled the prediction of in vivo tumor growth dynamics across a range of drug doses and regimens. Remarkably, only a change in a single parameter-the one controlling intrinsic cell/tumor growth in the absence of drug-was needed to scale the PD model from the in vitro to in vivo setting. These findings create a framework for using in vitro data to predict in vivo drug efficacy with clear benefits to reducing animal usage while enabling the collection of dense time course and dose response data in a highly controlled in vitro environment.

PK/PD Mediated Dose Optimization of Emactuzumab, a CSF1R Inhibitor, in Patients With Advanced Solid Tumors and Diffuse-Type Tenosynovial Giant Cell Tumor.

Targeted biological therapies may achieve maximal therapeutic efficacy at doses below the maximum tolerated dose (MTD); therefore, the search for the MTD in clinical studies may not be ideal for these agents. Emactuzumab is an investigational monoclonal antibody that binds to and inhibits the activation of the cell surface colony-stimulating factor-1 receptor. Here, we show how modeling target-mediated drug disposition coupled with pharmacodynamic end points was used to optimize the dose of emactuzumab without defining an MTD. The model could be used to recommend doses across different disease indications. The approach recommended an optimal biological dose of emactuzumab for dosing every 2 weeks (q2w) ≥ 900 mg, approximately three-fold lower than the highest dose tested clinically. The model predicted that emactuzumab doses ≥ 900 mg q2w would achieve target saturation in excess of 90% over the entire dosing cycle. Subsequently, a dose of 1,000 mg q2w was used in the extension phase of a phase I study of emactuzumab in patients with advanced solid tumors or diffuse-type tenosynovial giant cell tumor. Clinical data from this study were consistent with model predictions. The model was also used to predict the optimum dose of emactuzumab for use with dosing every 3 weeks, enabling dosing flexibility with respect to comedications. In summary, this work demonstrates the value of quantitative clinical pharmacology approaches to dose selection in oncology as opposed to traditional MTD methods.

Modeling of tumor growth and anticancer effects of combination therapy.

Combination therapies are widely used in the treatment of patients with cancer. Selecting synergistic combination strategies is a great challenge during early drug development. Here, we present a pharmacokinetic/pharmacodynamic (PK/PD) model with a smooth nonlinear growth function to characterize and quantify anticancer effect of combination therapies using time-dependent data. To describe the pharmacological effect of combination therapy, an interaction term was introduced into a semi-mechanistic anticancer PK/PD model. This approach enables testing of a pharmacological hypothesis with respect to an anticipated pharmacological synergy of drug combinations, such as an assumed pharmacological synergy of complementary inhibition of a particular signaling pathway. The model was applied to three real data sets derived from preclinical screening experiments using xenograft mice. The suggested model fitted well the observed data from mono- to combination-therapy and allowed physiologically meaningful interpretation of the experiments. The tested drug combinations were assessed for their ability to act as synergistic modulators of tumor growth inhibition by the interaction parameter psi. The presented approach has practical implications because it can be applied to standard xenograft experiments and may assist in the selection of both optimal drug combinations and administration schedules. The unique feature of the presented approach is the ability to characterize the nature of combined drug interaction as well as to quantify the intensity of such interactions by assessing the time course of combined drug effect.