Combinatorial chemotherapeutic efficacy in non-Hodgkin lymphoma can be predicted by a signaling model of CD20 pharmacodynamics

Network-Based Systems Analysis Explains Sequence-Dependent Synergism of Bortezomib and Vorinostat in Multiple Myeloma

Bortezomib and vorinostat exhibit synergistic effects in multiple myeloma (MM) cells when given in sequence, and the purpose of this study was to evaluate the molecular determinants of the interaction using a systems pharmacology approach. A Boolean network model consisting of 79 proteins and 225 connections was developed using literature information characterizing mechanisms of drug action and intracellular signal transduction. Network visualization and structural analysis were conducted, and model simulations were compared with experimental data. Critical biomarkers, such as pNFκB, p53, cellular stress, and p21, were identified using measures of network centrality and model reduction. U266 cells were then exposed to bortezomib (3 nM) and vorinostat (2 μM) as single agents or in simultaneous and sequential (bortezomib for first 24 h, followed by addition of vorinostat for another 24 h) combinations. Temporal changes for nine of the critical proteins in the reduced Boolean model were measured over 48 h, and cellular proliferation was measured over 96 h. A mechanism-based systems model was developed that captured the biological basis of a bortezomib and vorinostat sequence-dependent pharmacodynamic interaction. The model was further extended in vivo by linking in vitro parameter values and dynamics of p21, caspase-3, and pAKT biomarkers to tumor growth in xenograft mice reported in the literature. Network-based methodologies and pharmacodynamic principles were integrated successfully to evaluate bortezomib and vorinostat interactions in a mechanistic and quantitative manner. The model can be potentially applied to evaluate their combination regimens and explore in vivo dosing regimens.

Monoclonal Antibody Monitoring: Clinically Relevant Aspects, A Systematic Critical Review

Monoclonal antibody (mAb) therapy does not usually lead to a clinical response in all patients and resistance may increase over time after repeated mAb administration. This lack or loss of response to the treatment may originate from different and little-known epigenetic, biomolecular, or pathophysiological mechanisms, although an inadequate serum concentration is perhaps the most likely cause, even if not widely recognized and investigated yet. Patient factors that influence the pharmacokinetics (PK) of a mAb should be taken into account. Multiple analyses of patient-derived PK data have identified various factors influencing the clearance of mAbs. These factors include the presence of antidrug antibodies, low serum albumin, high serum levels of C-reactive protein, high body weight, and gender differences among others. The same clearance processes involved in systemic clearance after intravenous administration are also involved in local first-pass catabolism after subcutaneous administration of mAbs. Therapeutic drug monitoring has been proposed as a way to understand and respond to the variability in clinical response and remission. For both classes of mAbs with anti-inflammatory and antitumor effects, dose-guided optimization based on the measurement of serum concentrations in individual patients could be the next step for a personalized and targeted mAb therapy.

Development, Validation, and Comparison of Two Mass Spectrometry Methods (LC-MS/HRMS and LC-MS/MS) for the Quantification of Rituximab in Human Plasma

Rituximab is a chimeric immunoglobulin G1-kappa (IgG1κ) antibody targeting the CD20 antigen on B-lymphocytes. Its applications are various, such as for the treatment of chronic lymphoid leukemia or non-Hodgkin’s lymphoma in oncology, and it can also be used in the treatment of certain autoimmune diseases. Several studies support the interest in therapeutic drug monitoring to optimize dosing regimens of rituximab. Thus, two different laboratories have developed accurate and reproductive methods to quantify rituximab in human plasma: one using liquid chromatography quadripolar tandem mass spectrometer (LC-MS/MS) and the other, liquid chromatography orbitrap tandem mass spectrometer (LC-MS/HRMS). For both assays, quantification was based on albumin depletion or IgG-immunocapture, surrogate peptide analysis, and full-length stable isotope-labeled rituximab. With LC-MS/MS, the concentration range was from 5 to 500 µg/mL, the within- and between-run precisions were <8.5%, and the limit of quantitation was 5 µg/mL. With LC-MS/HRMS, the concentration range was from 10 to 200 µg/mL, the within- and between-run accuracy were <11.5%, and the limit of quantitation was 2 µg/mL. Rituximab plasma concentrations from 63 patients treated for vasculitis were compared. Bland–Altman analysis and Passing–Bablok regression showed the interchangeability between these two methods. Overall, these methods were robust and reliable and could be applied to routine clinical samples.

Population-based meta-analysis of bortezomib exposure-response relationships in multiple myeloma patients

Bortezomib (Velcade®) is a reversible proteasome inhibitor that shows potent antineoplastic activity, by inhibiting the constitutively increased proteasome activity in myeloma cells, and is approved as a first-line therapy for multiple myeloma (MM). Although clinically successful, bortezomib exhibits a relatively narrow therapeutic index and can induce dose-limiting toxicities such as thrombocytopenia. This study aims to develop a quantitative and predictive pharmacodynamic model to investigate bortezomib dosing-regimens in a rational and efficient manner. Mean temporal profiles of bortezomib pharmacokinetics, proteasome activity, M-protein concentrations, and platelet counts following bortezomib monotherapy were extracted from published clinical studies. A population-based meta-analysis of bortezomib anti-myeloma activity and thrombocytopenia was conducted sequentially with a Stochastic Approximation Expectation Maximization algorithm in Monolix. The final pharmacodynamic model integrates drug-target interactions and cell signaling dynamics with temporal biomarkers of clinical efficacy and toxicity. Bortezomib pharmacokinetics, disease progression, and platelet dynamic profiles were well characterized in MM patients, and a local sensitivity analysis of the final model suggests that increased proteasome concentration could ultimately attenuate bortezomib antineoplastic activity in MM patients. In addition, model simulations confirm that a once-weekly dosing schedule represents an optimal therapeutic regimen with comparable antineoplastic activity but significantly reduced risk of thrombocytopenia. In conclusion, a pharmacodynamic model was successfully developed, which provides a quantitative, mechanism-based platform for probing bortezomib dosing-regimens. Further research is needed to determine whether this model could be used to individualize bortezomib regimens to maximize antineoplastic efficacy and minimize thrombocytopenia during MM treatment.

Systemic Exposure of Rituximab Increased by Ibrutinib: Pharmacokinetic Results and Modeling Based on the HELIOS Trial

INTRODUCTION

In the HELIOS trial, bendamustine/rituximab (BR) plus ibrutinib (BR-I) improved disease outcomes versus BR plus placebo in previously treated chronic lymphocytic leukemia/small lymphocytic lymphoma. Here, we describe the pharmacokinetic (PK) observations, along with modeling to further explore the interaction between ibrutinib and rituximab.

METHODS

578 subjects were randomized to ibrutinib or placebo with BR (6 cycles). Ibrutinib PK samples and tumor measurements were obtained from all subjects; a subset was evaluated for bendamustine and rituximab PK. Population rituximab PK was assessed using nonlinear mixed-effects modeling.

RESULTS

Dose-normalized plasma concentration-time bendamustine data were comparable between the arms. Systemic rituximab exposure was higher with BR-I versus BR; mean trough serum concentrations were 2- to 3-fold higher in the first three cycles and 1.2- to 1.7-fold higher subsequently. No relevant safety differences were observed. In the modeling, including treatment arm as a categorical covariate and tumor burden as a continuous time-varying covariate on overall rituximab clearance significantly improved fitting of the data.

CONCLUSIONS

BR-I led to higher dose-normalized systemic rituximab exposure versus BR and more rapid steady-state achievement. The modeling data suggest that rituximab disposition is, at least in part, target mediated. Determining the clinical significance of these findings requires further assessments.

TRIAL REGISTRATION

This study is registered at https://clinicaltrials.gov/ct2/show/NCT01611090 .

Current mathematical models for cancer drug discovery

INTRODUCTION

Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development. Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application. Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.

Sequential Exposure of Bortezomib and Vorinostat is Synergistic in Multiple Myeloma Cells

PURPOSE

To examine the combination of bortezomib and vorinostat in multiple myeloma cells (U266) and xenografts, and to assess the nature of their potential interactions with semi-mechanistic pharmacodynamic models and biomarkers.

METHODS

U266 proliferation was examined for a range of bortezomib and vorinostat exposure times and concentrations (alone and in combination). A non-competitive interaction model was used with interaction parameters that reflect the nature of drug interactions after simultaneous and sequential exposures. p21 and cleaved PARP were measured using immunoblotting to assess critical biomarker dynamics. For xenografts, data were extracted from literature and modeled with a PK/PD model with an interaction parameter.

RESULTS

Estimated model parameters for simultaneous in vitro and xenograft treatments suggested additive drug effects. The sequence of bortezomib preincubation for 24 hours, followed by vorinostat for 24 hours, resulted in an estimated interaction term significantly less than 1, suggesting synergistic effects. p21 and cleaved PARP were also up-regulated the greatest in this sequence.

CONCLUSIONS

Semi-mechanistic pharmacodynamic modeling suggests synergistic pharmacodynamic interactions for the sequential administration of bortezomib followed by vorinostat. Increased p21 and cleaved PARP expression can potentially explain mechanisms of their enhanced effects, which require further PK/PD systems analysis to suggest an optimal dosing regimen.

Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity

Pharmacokinetic (PK) and pharmacodynamic (PD) models seek to describe the temporal pattern of drug exposures and their associated pharmacological effects produced at micro- and macro-scales of organization. Antibody-based drugs have been developed for a large variety of diseases, with effects exhibited through a comprehensive range of mechanisms of action. Mechanism-based PK/PD and systems pharmacology models can play a major role in elucidating and integrating complex antibody pharmacological properties, such as nonlinear disposition and dynamical intracellular signaling pathways triggered by ligation to their cognate targets. Such complexities can be addressed through the use of robust computational modeling techniques that have proven powerful tools for pragmatic characterization of experimental data and for theoretical exploration of antibody efficacy and adverse effects. The primary objectives of such multi-scale mathematical models are to generate and test competing hypotheses and to predict clinical outcomes. In this review, relevant systems pharmacology and enhanced PD (ePD) models that are used as predictive tools for antibody-based drug action are reported. Their common conceptual features are highlighted, along with approaches used for modeling preclinical and clinically available data. Key examples illustrate how systems pharmacology and ePD models codify the interplay among complex biology, drug concentrations, and pharmacological effects. New hybrid modeling concepts that bridge cutting-edge systems pharmacology models with established PK/ePD models will be needed to anticipate antibody effects on disease in subpopulations and individual patients.

Feedback control indirect response models

A general framework is introduced for modeling pharmacodynamic processes that are subject to autoregulation, which combines the indirect response (IDR) model approach with methods from classical feedback control of engineered systems. The canonical IDR models are modified to incorporate linear combinations of feedback control terms related to the time course of the difference (the error signal) between the pharmacodynamic response and its basal value. Following the well-established approach of traditional engineering control theory, the proposed feedback control indirect response models incorporate terms proportional to the error signal itself, the integral of the error signal, the derivative of the error signal or combinations thereof. Simulations are presented to illustrate the types of responses produced by the proposed feedback control indirect response model framework, and to illustrate comparisons with other PK/PD modeling approaches incorporating feedback. In addition, four examples from literature are used to illustrate the implementation and applicability of the proposed feedback control framework. The examples reflect each of the four mechanisms of drug action as modeled by each of the four canonical IDR models and include: selective serotonin reuptake inhibitors and extracellular serotonin; histamine H2-receptor antagonists and gastric acid; growth hormone secretagogues and circulating growth hormone; β2-selective adrenergic agonists and potassium. The proposed feedback control indirect response approach may serve as an exploratory modeling tool and may provide a bridge for development of more mechanistic systems pharmacology models.

Mechanism-based mathematical modeling of combined gemcitabine and birinapant in pancreatic cancer cells

Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term Ψ was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, Ψ values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.

Biomarker- versus drug-driven tumor growth inhibition models: an equivalence analysis

The mathematical modeling of tumor xenograft experiments following the dosing of antitumor drugs has received much attention in the last decade. Biomarker data can further provide useful insights on the pathological processes and be used for translational purposes in the early clinical development. Therefore, it is of particular interest the development of integrated pharmacokinetic-pharmacodynamic (PK-PD) models encompassing drug, biomarker and tumor-size data. This paper investigates the reciprocal consistency of three types of models: drug-to-tumor, such as established drug-driven tumor growth inhibition (TGI) models, drug-to-biomarker, e.g. indirect response models, and biomarker-to-tumor, e.g. the more recent biomarker-driven TGI models. In particular, this paper derives a mathematical relationship that guarantees the steady-state equivalence of the cascade of drug-to-biomarker and biomarker-to-tumor models with a drug-to-tumor TGI model. Using the Simeoni TGI model as a reference, conditions for steady-state equivalence are worked out and used to derive a new biomarker-driven model. Simulated and real data are used to show that in realistic cases the steady-state equivalence extends also to transient responses. The possibility of predicting the drug-to-tumor potency of a new candidate drug based only on biomarker response is discussed.

Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells

Systems models of biological networks show promise for informing drug target selection/qualification, identifying lead compounds and factors regulating disease progression, rationalizing combinatorial regimens, and explaining sources of intersubject variability and adverse drug reactions. However, most models of biological systems are qualitative and are not easily coupled with dynamical models of drug exposure-response relationships. In this proof-of-concept study, logic-based modeling of signal transduction pathways in U266 multiple myeloma (MM) cells is used to guide the development of a simple dynamical model linking bortezomib exposure to cellular outcomes. Bortezomib is a commonly used first-line agent in MM treatment; however, knowledge of the signal transduction pathways regulating bortezomib-mediated cell cytotoxicity is incomplete. A Boolean network model of 66 nodes was constructed that includes major survival and apoptotic pathways and was updated using responses to several chemical probes. Simulated responses to bortezomib were in good agreement with experimental data, and a reduction algorithm was used to identify key signaling proteins. Bortezomib-mediated apoptosis was not associated with suppression of nuclear factor κB (NFκB) protein inhibition in this cell line, which contradicts a major hypothesis of bortezomib pharmacodynamics. A pharmacodynamic model was developed that included three critical proteins (phospho-NFκB, BclxL, and cleaved poly (ADP ribose) polymerase). Model-fitted protein dynamics and cell proliferation profiles agreed with experimental data, and the model-predicted IC50 (3.5 nM) is comparable to the experimental value (1.5 nM). The cell-based pharmacodynamic model successfully links bortezomib exposure to MM cellular proliferation via protein dynamics, and this model may show utility in exploring bortezomib-based combination regimens.

Model-Based Design of a Decision Tree for Treating HER2+ Cancers Based on Genetic and Protein Biomarkers

Human cancers are incredibly diverse with regard to molecular aberrations, dependence on oncogenic signaling pathways, and responses to pharmacological intervention. We wished to assess how cellular dependence on the canonical PI3K vs. MAPK pathways within HER2+ cancers affects responses to combinations of targeted therapies, and biomarkers predictive of their activity. Through an integrative analysis of mechanistic model simulations and in vitro cell line profiling, we designed a six-arm decision tree to stratify treatment of HER2+ cancers using combinations of targeted agents. Activating mutations in the PI3K and MAPK pathways (PIK3CA and KRAS), and expression of the HER3 ligand heregulin determined sensitivity to combinations of inhibitors against HER2 (lapatinib), HER3 (MM-111), AKT (MK-2206), and MEK (GSK-1120212; trametinib), in addition to the standard of care trastuzumab (Herceptin). The strategy used to identify effective combinations and predictive biomarkers in HER2-expressing tumors may be more broadly extendable to other human cancers.

Developing Exposure/Response Models for Anticancer Drug Treatment: Special Considerations

Anticancer agents often have a narrow therapeutic index (TI), requiring precise dosing to ensure sufficient exposure for clinical activity while minimizing toxicity. These agents frequently have complex pharmacology, and combination therapy may cause schedule-specific effects and interactions. We review anticancer drug development, showing how integration of modeling and simulation throughout development can inform anticancer dose selection, potentially improving the late-phase success rate. This article has a companion article in Clinical Pharmacology & Therapeutics with practical examples.

FLT3 and CDK4/6 inhibitors: signaling mechanisms and tumor burden in subcutaneous and orthotopic mouse models of acute myeloid leukemia

FLT3(ITD) subtype acute myeloid leukemia (AML) has a poor prognosis with currently available therapies. A number of small molecule inhibitors of FLT3 and/or CDK4/6 are currently under development. A more complete and quantitative understanding of the mechanisms of action of FLT3 and CDK4/6 inhibitors may better inform the development of current and future compounds that act on one or both of the molecular targets, and thus may lead to improved treatments for AML. In this study, we investigated in both subcutaneous and orthotopic AML mouse models, the mechanisms of action of three FLT3 and/or CDK4/6 inhibitors: AMG925 (Amgen), sorafenib (Bayer and Onyx), and quizartinib (Ambit Biosciences). A composite model was developed to integrate the plasma pharmacokinetics of these three compounds on their respective molecular targets, the coupling between the target pathways, as well as the resulting effects on tumor burden reduction in the subcutaneous xenograft model. A sequential modeling approach was used, wherein model structures and estimated parameters from upstream processes (e.g. PK, cellular signaling) were fixed for modeling subsequent downstream processes (cellular signaling, tumor burden). Pooled data analysis was employed for the plasma PK and cellular signaling modeling, while population modeling was applied to the tumor burden modeling. The resulting model allows the decomposition of the relative contributions of FLT3(ITD) and CDK4/6 inhibition on downstream signaling and tumor burden. In addition, the action of AMG925 on cellular signaling and tumor burden was further studied in an orthotopic tumor mouse model more closely representing the physiologically relevant environment for AML.

Network-based approaches in drug discovery and early development

Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target-drug combination with high potential for yielding clinical success within the efficacy-toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification.

Using network biology to bridge pharmacokinetics and pharmacodynamics in oncology

If mathematical modeling is to be used effectively in cancer drug development, future models must take into account both the mechanistic details of cellular signal transduction networks and the pharmacokinetics (PK) of drugs used to inhibit their oncogenic activity. In this perspective, we present an approach to building multiscale models that capture systems-level architectural features of oncogenic signaling networks, and describe how these models can be used to design combination therapies and identify predictive biomarkers in silico.

Lessons for the clinic from rituximab pharmacokinetics and pharmacodynamics

The anti-CD20 antibody rituximab (RTX; Rituxan®, MabThera®) was the first anti-cancer antibody approved by the US Food and Drug Administration in 1997 and it is now the most-studied unconjugated therapeutic antibody. The knowledge gained over the past 15 y on the pharmacodynamics (PD) of this antibody has led to the development of a new generation of anti-CD20 antibodies with enhanced efficacy in vitro. Studies on the pharmacokinetics (PK) properties and the effect of factors such as tumor load and localization, antibody concentration in the circulation and gender on both PK and clinical response has allowed the design of optimized schedules and novel routes of RTX administration. Although clinical results using newer anti-CD20 antibodies, such as ofatumumab and obinutuzumab, and novel administration schedules for RTX are still being evaluated, the knowledge gained so far on RTX PK and PD should also be relevant for other unconjugated monoclonal antibody therapeutics, and will be critically reviewed here.

Modeling tumor response after combined administration of different immune-stimulatory agents

The aims of this work were as follows: 1) to develop a semimechanistic pharmacodynamic model describing tumor shrinkage after administration of a previously developed antitumor vaccine (CyaA-E7) in combination with CpG (a TLR9 ligand) and/or cyclophosphamide (CTX), and 2) to assess the translational capability of the model to describe tumor effects of different immune-based treatments. Population approach with NONMEM version 7.2 was used to analyze the previously published data. These data were generated by injecting 5 × 10(5) tumor cells expressing human papillomavirus (HPV)-E7 proteins into C57BL/6 mice. Large and established tumors were treated with CpG and/or CTX administered alone or in combination with CyaA-E7. Applications of the model were assessed by comparing model-based simulations with preclinical and clinical outcomes obtained from literature. CpG effects were modeled: 1) as an amplification of the immune signal triggered by the vaccine and 2) by shortening the delayed response of the vaccine. CTX effects were included through a direct decrease of the tumor-induced inhibition of vaccine efficacy over time, along with a delayed induction of tumor cell death. A pharmacodynamic model, built based on plausible biologic mechanisms known for the coadjuvants, successfully characterized tumor response in all experimental scenarios. The model developed was satisfactory applied to reproduce clinical outcomes when CpG or CTX was used in combination with different vaccines. The results found after simulation exercise indicated that the contribution of the coadjuvants to the tumor response elicited by vaccines can be predicted for other immune-based treatments.

Antibody therapy of pediatric B-cell lymphoma

B-cell lymphoma in children accounts for about 10% of all pediatric malignancies. Chemotherapy has been very successful leading to an over-all 5-year survival between 80 and 90% depending on lymphoma type and extent of disease. Therapeutic toxicity remains high calling for better targeted and thus less toxic therapies. Therapeutic antibodies have become a standard element of B-cell lymphoma therapy in adults. Clinical experience in pediatric lymphoma patients is still very limited. This review outlines the rationale for antibody treatment of B-cell lymphomas in children and describes potential target structures on B-cell lymphoma cells. It summarizes the clinical experience of antibody therapy of B-cell lymphoma in children and gives an outlook on new developments and challenges for antibody therapy of pediatric B-cell lymphoma.

Issues, challenges, and opportunities in model-based drug development for monoclonal antibodies

Over the last two decades, there has been a simultaneous explosion in the levels of activity and capability in both monoclonal antibody (mAb) drug development and in the use of quantitative pharmacologic models to facilitate drug development. Both of these topics are currently areas of great interest to academia, the pharmaceutical and biotechnology industries, and to regulatory authorities. In this article, we summarize convergence of these two areas and discuss some of the current and historical applications of the use of mathematical-model-based techniques to facilitate the discovery and development of mAb therapeutics. We also consider some of the current issues and limitations in model-based antibody discovery/development and highlight areas of further opportunity.

Intracellular-signaling tumor-regression modeling of the pro-apoptotic receptor agonists dulanermin and conatumumab

Dulanermin (rhApo2L/TRAIL) and conatumumab bind to transmembrane death receptors and trigger the extrinsic cellular apoptotic pathway through a caspase-signaling cascade resulting in cell death. Tumor size time series data from rodent tumor xenograft (COLO205) studies following administration of either of these two pro-apoptotic receptor agonists (PARAs) were combined to develop a intracellular-signaling tumor-regression model that includes two levels of signaling: upstream signals unique to each compound (representing initiator caspases), and a common downstream apoptosis signal (representing executioner caspases) shared by the two agents. Pharmacokinetic (PK) models for each drug were developed based on plasma concentration data following intravenous and/or intraperitoneal administration of the compounds and were used in the subsequent intracellular-signaling tumor-regression modeling. A model relating the PK of the two PARAs to their respective and common downstream signals, and to the resulting tumor burden was developed using mouse xenograft tumor size measurements from 448 experiments that included a wide range of dose sizes and dosing schedules. Incorporation of a pro-survival signal--consistent with the hypothesis that PARAs may also result in the upregulation of pro-survival factors that can lead to a reduction in effectiveness of PARAs with treatment--resulted in improved predictions of tumor volume data, especially for data from the long-term dosing experiments.