Mechanistic Projection of First-in-Human Dose for Bispecific Immunomodulatory P-Cadherin LP-DART: An Integrated PK/PD Modeling Approach

Dosing Strategies and Quantitative Clinical Pharmacology for Bispecific T-Cell Engagers Development in Oncology

Bispecific T-cell Engagers (TCEs) are promising anti-cancer treatments that bind to both the CD3 receptors on T cells and an antigen on the surface of tumor cells, creating an immune synapse, leading to killing of malignant tumor cells. These novel therapies have unique development challenges, with specific safety risks of cytokine release syndrome. These on-target adverse events fortunately can be mitigated and deconvoluted from efficacy via innovative dosing strategies, making clinical pharmacology key in the development of these therapies. This review assesses dose selection and the role of quantitative clinical pharmacology in the development of the first eight approved TCEs. Model informed drug development (MIDD) strategies can be used at every stage to guide TCE development. Mechanistic modeling approaches allow for (1) efficacious yet safe first-in-human dose selection as compared with in vitro minimum anticipated biological effect level (MABEL) approach; (2) rapid escalation and reducing number of patients with subtherapeutic doses through model-based adaptive design; (3) virtual testing of different step-up dosing regimens that may not be feasible to be evaluated in the clinic; and (4) selection and justification of the optimal clinical step-up and full treatment doses. As the knowledge base around TCEs continues to grow, the relevance and utilization of MIDD strategies for supporting the development and dose optimization of these molecules are expected to advance, optimizing the benefit-risk profile for cancer patients.

Development of bispecific T cell engagers: harnessing quantitative systems pharmacology

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

Mechanism-based pharmacokinetic and pharmacodynamic modeling for bispecific antibodies: challenges and opportunities

INTRODUCTION

Unlike conventional antibodies, bispecific antibodies (bsAbs) are engineered antibody- or antibody fragment-based molecules that can simultaneously recognize two different epitopes or antigens. Over the past decade, there has been an explosion of bsAbs being developed across therapeutic areas. Development of bsAbs presents unique challenges and mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling has served as a powerful tool to optimize their development and realize their clinical utility.

AREAS COVERED

In this review, the guiding principles and case examples of how fit-for-purpose, mechanism-based PK/PD models have been applied to answer questions commonly encountered in bsAb development are presented. Such models characterize the key pharmacological elements of bsAbs, and they can be utilized for model-informed drug development. We also include the discussion of challenges, knowledge gaps and future direction for such models.

EXPERT OPINION

Mechanistic PK/PD modeling is a powerful tool to support the development of bsAbs. These models can be extrapolated to predict treatment outcomes based on mechanisms of action (MoA) and clinical observations to form positive learn-and-confirm cycles during drug development, due to their abilities to differentiate system- and drug-specific parameters. Meanwhile, the models should keep being adapted according to novel drug design and MoA, providing continuous opportunities for model-informed drug development.

Strategies for clinical dose optimization of T cell-engaging therapies in oncology

Innovative approaches in the design of T cell-engaging (TCE) molecules are ushering in a new wave of promising immunotherapies for the treatment of cancer. Their mechanism of action, which generates an in trans interaction to create a synthetic immune synapse, leads to complex and interconnected relationships between the exposure, efficacy, and toxicity of these drugs. Challenges thus arise when designing optimal clinical dose regimens for TCEs with narrow therapeutic windows, with a variety of dosing strategies being evaluated to mitigate key side effects such as cytokine release syndrome, neurotoxicity, and on-target off-tumor toxicities. This review evaluates the current approaches to dose optimization throughout the preclinical and clinical development of TCEs, along with perspectives for improvement of these strategies. Quantitative approaches used to aid the understanding of dose-exposure-response relationships are highlighted, along with opportunities to guide the rational design of next-generation TCE molecules, and optimize their dose regimens in patients.

Mathematical modeling of cancer immunotherapy for personalized clinical translation

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.

Leveraging a physiologically-based quantitative translational modeling platform for designing B cell maturation antigen-targeting bispecific T cell engagers for treatment of multiple myeloma

Bispecific T cell engagers (TCEs) are an emerging anti-cancer modality that redirects cytotoxic T cells to tumor cells expressing tumor-associated antigens (TAAs), thereby forming immune synapses to exert anti-tumor effects. Designing pharmacokinetically acceptable TCEs and optimizing their size presents a considerable protein engineering challenge, particularly given the complexity of intercellular bridging between T cells and tumor cells. Therefore, a physiologically-relevant and clinically-verified computational modeling framework is of crucial importance to understand the protein engineering trade-offs. In this study, we developed a quantitative, physiologically-based computational framework to predict immune synapse formation for a variety of molecular formats of TCEs in tumor tissues. Our model incorporates a molecular size-dependent biodistribution using the two-pore theory, extravasation of T cells and hematologic cancer cells, mechanistic bispecific intercellular binding of TCEs, and competitive inhibitory interactions by shed targets. The biodistribution of TCEs was verified by positron emission tomography imaging of [89Zr]AMG211 (a carcinoembryonic antigen-targeting TCE) in patients. Parameter sensitivity analyses indicated that immune synapse formation was highly sensitive to TAA expression, degree of target shedding, and binding selectivity to tumor cell surface TAAs over shed targets. Notably, the model suggested a “sweet spot” for TCEs’ CD3 binding affinity, which balanced the trapping of TCEs in T-cell-rich organs. The final model simulations indicated that the number of immune synapses is similar (~55/tumor cell) between two distinct clinical stage B cell maturation antigen (BCMA)-targeting TCEs, PF-06863135 in an IgG format and AMG420 in a BiTE format, at their respective efficacious doses in multiple myeloma patients. This result demonstrates the applicability of the developed computational modeling framework to molecular design optimization and clinical benchmarking for TCEs, thus suggesting that this framework can be applied to other targets to provide a quantitative means to facilitate model-informed best-in-class TCE discovery and development.

Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation

Therapeutic monoclonal antibodies (mAbs) have been a promising therapeutic approach for several diseases and a wide variety of mAbs are being evaluated in clinical trials. To accelerate clinical development and improve the probability of success, pharmacokinetics and pharmacodynamics (PKPD) in humans must be predicted before clinical trials can begin. Traditionally, empirical-approach-based PKPD prediction has been applied for a long time. Recently, modeling and simulation (M&S) methods have also become valuable for quantitatively predicting PKPD in humans. Although several models (e.g., the compartment model, Michaelis–Menten model, target-mediated drug disposition model, and physiologically based pharmacokinetic model) have been established and used to predict the PKPD of mAbs in humans, more complex mechanistic models, such as the quantitative systemics pharmacology model, have been recently developed. This review summarizes the recent advances and future direction of M&S-based approaches to the quantitative prediction of human PKPD for mAbs.

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.

Generation of T-cell-redirecting bispecific antibodies with differentiated profiles of cytokine release and biodistribution by CD3 affinity tuning

T-cell-redirecting bispecific antibodies have emerged as a new class of therapeutic agents designed to simultaneously bind to T cells via CD3 and to tumor cells via tumor-cell-specific antigens (TSA), inducing T-cell-mediated killing of tumor cells. The promising preclinical and clinical efficacy of TSAxCD3 antibodies is often accompanied by toxicities such as cytokine release syndrome due to T-cell activation. How the efficacy and toxicity profile of the TSAxCD3 bispecific antibodies depends on the binding affinity to CD3 remains unclear. Here, we evaluate bispecific antibodies that were engineered to have a range of CD3 affinities, while retaining the same binding affinity for the selected tumor antigen. These agents were tested for their ability to kill tumor cells in vitro, and their biodistribution, serum half-life, and anti-tumor activity in vivo. Remarkably, by altering the binding affinity for CD3 alone, we can generate bispecific antibodies that maintain potent killing of TSA + tumor cells but display differential patterns of cytokine release, pharmacokinetics, and biodistribution. Therefore, tuning CD3 affinity is a promising method to improve the therapeutic index of T-cell-engaging bispecific antibodies.

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.

Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions

With more than 90 approved drugs by 2020, therapeutic antibodies have played a central role in shifting the treatment landscape of many diseases, including autoimmune disorders and cancers. While showing many therapeutic advantages such as long half-life and highly selective actions, therapeutic antibodies still face many outstanding issues associated with their pharmacokinetics (PK) and pharmacodynamics (PD), including high variabilities, low tissue distributions, poorly-defined PK/PD characteristics for novel antibody formats, and high rates of treatment resistance. We have witnessed many successful cases applying PK/PD modeling to answer critical questions in therapeutic antibodies’ development and regulations. These models have yielded substantial insights into antibody PK/PD properties. This review summarized the progress, challenges, and future directions in modeling antibody PK/PD and highlighted the potential of applying mechanistic models addressing the development questions.

One size does not fit all: navigating the multi-dimensional space to optimize T-cell engaging protein therapeutics

T-cell engaging biologics is a class of novel and promising immune-oncology compounds that leverage the immune system to eradicate cancer. Here, we compared and contrasted a bispecific diabody-Fc format, which displays a relatively short antigen-binding arm distance, with our bispecific IgG platform. By generating diverse panels of antigen-expressing cells where B cell maturation antigen is either tethered to the cell membrane or located to the juxtamembrane region and masked by elongated structural spacer units, we presented a systematic approach to investigate the role of antigen epitope location and molecular formats in immunological synapse formation and cytotoxicity. We demonstrated that diabody-Fc is more potent for antigen epitopes located in the membrane distal region, while bispecific IgG is more efficient for membrane-proximal epitopes. Additionally, we explored other parameters, including receptor density, antigen-binding affinity, and kinetics. Our results show that molecular format and antigen epitope location, which jointly determine the intermembrane distance between target cells and T cells, allow decoupling of cytotoxicity and cytokine release, while antigen-binding affinities appear to be positively correlated with both readouts. Our work offers new insight that could potentially lead to a wider therapeutic window for T-cell engaging biologics in general.

Allometric scaling of therapeutic monoclonal antibodies in preclinical and clinical settings

Constant technological advancement enabled the production of therapeutic monoclonal antibodies (mAbs) and will continue to contribute to their rapid expansion. Compared to small-molecule drugs, mAbs have favorable characteristics, but also more complex pharmacokinetics (PK), e.g., target-mediated nonlinear elimination and recycling by neonatal Fc-receptor. This review briefly discusses mAb biology, similarities and differences in PK processes across species and within human, and provides a detailed overview of allometric scaling approaches for translating mAb PK from preclinical species to human and extrapolating from adults to children. The approaches described here will remain vital in mAb drug development, although more data are needed, for example, from very young patients and mAbs with nonlinear PK, to allow for more confident conclusions and contribute to further growth of this field. Improving mAb PK predictions will facilitate better planning of (pediatric) clinical studies and enable progression toward the ultimate goal of expediting drug development.

Computational modelling of modern cancer immunotherapy

Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka-Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.

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.

Informing Development of Bispecific Antibodies Using Physiologically Based Pharmacokinetic-Pharmacodynamic Models: Current Capabilities and Future Opportunities

Antibody therapeutics continue to represent a significant portion of the biotherapeutic pipeline, with growing promise for bispecific antibodies (BsAbs). BsAbs can target 2 different antigens at the same time, such as simultaneously binding tumor-cell receptors and recruiting cytotoxic immune cells. This simultaneous engagement of 2 targets can be potentially advantageous, as it may overcome disadvantages posed by a monotherapy approach, like the development of resistance to treatment. Combination therapy approaches that modulate 2 targets simultaneously offer similar advantages, but BsAbs are more efficient to develop. Unlike combination approaches, BsAbs can facilitate spatial proximity of targets that may be necessary to induce the desired effect. Successful development of BsAbs requires understanding antibody formatting and optimizing activity for both targets prior to clinical trials. To realize maximal efficacy, special attention is required to fully define pharmacokinetic (PK)/pharmacodynamic (PD) relationships enabling selection of dose and regimen. The application of physiologically based pharmacokinetics (PBPK) has been evolving to inform the development of novel treatment modalities such as bispecifics owing to the increase in our understanding of pharmacology, utility of multiscale models, and emerging clinical data. In this review, we discuss components of PBPK models to describe the PK characteristics of BsAbs and expand the discussion to integration of PBPK and PD models to inform development of BsAbs. A framework that can be adopted to build PBPK-PD models to inform the development of BsAbs is also proposed. We conclude with examples that highlight the application of PBPK-PD and share perspectives on future opportunities for this emerging quantitative tool.

Mechanistic prediction of first-in-human dose for bispecific CD3/EpCAM T-cell engager antibody M701, using an integrated PK/PD modeling method

AIM

M701 is a bispecific CD3/EpCAM T-cell engager antibody to treat malignant ascites. This study aimed to predict in vivo exposure-cytotoxicity relationship and human pharmacokinetics (PK) characteristics of M701, as well as to design optimal starting dose and effective dose for M701 first-in-human (FIH) study.

METHOD

Mechanistic in vitro PK/PD model was firstly developed based on in vitro data of M701's cytotoxicity and binding affinities with targeting receptors. The cell killing effect of M701 in vitro was driven by tri-molecular synapse, which formed by binding drug to both CD3 receptor on T cells and EpCAM receptor on tumor cells. Human exposure-response (E-R) curve in ascites was estimated using the same model structure with clinical systemic model parameters. Human PK was predicted by allometrically scaling monkey PK data, which was characterized using a two compartment model. Human PK model was integrated into in vivo synapse-based cell killing model to provide human PK/PD characteristics. Integrated human PK/PD model was applied in FIH dose design. Clinical starting dose and effective dose were suggested as the simulated drug concentration in human ascites that achieved the estimated in vivo minimally anticipated biological effect level (MABEL) and pharmacologically active level. Other approaches including PK-driven and receptor occupancy calculation were also employed in this study to verify the starting dose prediction.

RESULTS

In vitro M701 cytotoxicity curves under 24, 48, 72 h incubations were well captured by mechanistic synapse-based cell killing model. Human E-R curve in ascites was obtained based on in vitro model structure and clinical systematic parameters. We defined 10~20% and 80% of maximum cytotoxicity effect as in vivo MABEL and pharmacologically active level. Human E-R curve indicated in vivo EC₁₀, EC₂₀ and EC₈₀ were 0.56, 1.26 and 31.6 ng/mL. For human PK model, clearance (CL, CL_d), distribution volumes (V_c, V_p) and absorption rate were allometrically scaled using exponent of 0.9, 1 and -0.25. Predicted clearance and volume were 0.53- and 1.19-fold of observed data. Simulated average ascites M701 concentrations (calculated as C_{ave_ ascites} = AUC_τ/τ) were 0.81 and 32.5 ng/mL under dose of 5 and 200 μg within 2-hour i.p. infusion. By integrating human E-R curve and the simulated PK profile in ascites, we suggested 5 and 200 μg within 2-hour i.p. infusion as MABEL dose and pharmacologically active dose (PAD) for M701 FIH study. PK-driven approach predicted a starting dose of 5 μg, which was comparable to that predicted via PK/PD-driven approach.

CONCLUSIONS

This study predicted human ascites PK and E-R curve by integrating human PK model into in vivo synapse-based cell killing model. Optimal clinical MABEL dose and PAD of bispecific T cell engager antibody M701 were suggested based on current integrated PK/PD approach.

Quantitative Clinical Pharmacology of T-Cell Engaging Bispecifics: Current Perspectives and Opportunities

T-cell directing/engaging bispecifics (TDBs) enable a powerful mode of action by activating T-cells through the creation of artificial immune synapses. Their pharmacological response involves the dynamic inter-relationships among T-cells, tumor cells, and TDBs. This results in complex and challenging issues in understanding pharmacokinetics, tissue distribution, target engagement, and exposure-response relationship. Dosing strategy plays a crucial role in determining the therapeutic window of TDBs because of the desire to maximize therapeutic efficacy in the context of known mechanism-related adverse events, such as cytokine release syndrome and neurological adverse events. Such adverse events are commonly reported as the most prominent events during the initial treatment cycles and dissipate over time. Therefore, the kinetic characterization of the inter-relationships between exposure/target engagement and safety/efficacy outcomes is crucial in designing the optimal dosing regimen to maximize the benefit/risk of TDB agents. In this review, we discuss the key clinical pharmacological considerations in drug discovery and development for TDBs and provide a summary of TDBs currently in clinical development. We also propose forward-looking perspectives and opportunities to derive insights through quantitative clinical pharmacology approaches.

Mitigating the risk of cytokine release syndrome in a Phase I trial of CD20/CD3 bispecific antibody mosunetuzumab in NHL: impact of translational system modeling

Mosunetuzumab, a T-cell dependent bispecific antibody that binds CD3 and CD20 to drive T-cell mediated B-cell killing, is currently being tested in non-Hodgkin lymphoma. However, potent immune stimulation with T-cell directed therapies poses the risk of cytokine release syndrome, potentially limiting dose and utility. To understand mechanisms behind safety and efficacy and explore safety mitigation strategies, we developed a novel mechanistic model of immune and antitumor responses to the T-cell bispecifics (mosunetuzumab and blinatumomab), including the dynamics of B- and T-lymphocytes in circulation, lymphoid tissues, and tumor. The model was developed and validated using mosunetuzumab nonclinical and blinatumomab clinical data. Simulations delineated mechanisms contributing to observed cell and cytokine (IL6) dynamics and predicted that initial step-fractionated dosing limits systemic T-cell activation and cytokine release without compromising tumor response. These results supported a change to a step-fractionated treatment schedule of mosunetuzumab in the ongoing Phase I clinical trial, enabling safer administration of higher doses.

Strategies and Recommendations for Using a Data-Driven and Risk-Based Approach in the Selection of First-in-Human Starting Dose: An International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) Assessment

Various approaches to first-in-human (FIH) starting dose selection for new molecular entities (NMEs) are designed to minimize risk to trial subjects. One approach uses the minimum anticipated biological effect level (MABEL), which is a conservative method intended to maximize subject safety and designed primarily for NMEs having high perceived safety risks. However, there is concern that the MABEL approach is being inappropriately used for lower risk molecules with negative impacts on drug development and time to patient access. In addition, ambiguity exists in how MABEL is defined and the methods used to determine it. The International Consortium for Innovation and Quality in Pharmaceutical Development convened a working group to understand current use of MABEL and its impact on FIH starting dose selection, and to make recommendations for FIH dose selection going forward. An industry-wide survey suggested the achieved or estimated maximum tolerated dose, efficacious dose, or recommended phase II dose was > 100-fold higher than the MABEL-based starting dose for approximately one third of NMEs, including trials in patients. A decision tree and key risk factor table were developed to provide a consistent, data driven-based, and risk-based approach for selecting FIH starting doses.

Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology

Bispecific antibodies (bsAbs) have become an integral component of the therapeutic research strategy to treat cancer. In addition to clinically validated immune cell re‐targeting, bsAbs are being designed for tumor targeting and as dual immune modulators. Explorative preclinical and emerging clinical data indicate potential for enhanced efficacy and reduced systemic toxicity. However, bsAbs are a complex modality with challenges to overcome in early clinical trials, including selection of relevant starting doses using a minimal anticipated biological effect level approach, and predicting efficacious dose despite nonintuitive dose response relationships. Multiple factors can contribute to variability in the clinic, including differences in functional affinity due to avidity, receptor expression, effector to target cell ratio, and presence of soluble target. Mechanistic modeling approaches are a powerful integrative tool to understand the complexities and aid in clinical translation, trial design, and prediction of regimens and strategies to reduce dose limiting toxicities of bsAbs. In this tutorial, the use of mechanistic modeling to impact decision making for bsAbs is presented and illustrated using case study examples.

Development of a minimal physiologically-based pharmacokinetic/pharmacodynamic model to characterize target cell depletion and cytokine release for T cell-redirecting bispecific agents in humans

T cell-redirecting bispecific antibodies (bsAbs) are highly potent tumor-killing molecules. Following bsAb mediated engagement with target cells, T cells get activated and kill target cells while inducing cytokine release, which at higher levels may lead to life-threatening cytokine release syndrome (CRS). Clinical evidence suggests that CRS can be mitigated by implementing a stepwise dosing strategy. Here, we developed a mechanism-based minimal physiologically-based pharmacokinetic/pharmacodynamic (mPBPK/PD) model using reported preclinical and clinical data from blinatumomab. The mPBPK/PD model reasonably captured blinatumomab PK and B cell depletion profiles in blood and in various tissue sites of action (i.e., red marrow perivascular niche, spleen, and lymph nodes) in patients with non-Hodgkin's lymphoma (NHL) and acute lymphoblastic leukemia (ALL). Using interleukin 6 (IL-6) as an example, our model quantitatively characterized the mitigation of cytokine release by a blinatumomab 5-15-60 µg/m²/day stepwise dosing regimen comparing to a 60 µg/m²/day flat dose in NHL patients. Furthermore, by only modifying the system parameters specific for ALL patients, the mPBPK/PD model successfully predicted the mitigation of IL-6 release by a blinatumomab 5-15 µg/m²/day stepwise dosing regimen comparing to a 15 µg/m²/day flat dose. Our work provided a case example to show how mPBPK/PD model can be used to support the discovery and clinical development of T cell-redirecting bsAbs.

A Modeling Framework to Characterize Cytokine Release upon T-Cell-Engaging Bispecific Antibody Treatment: Methodology and Opportunities

T‐cell–engaging bispecific antibodies (T‐BsAbs) are an important class of antibody therapeutics in immuno‐oncology. T‐BsAbs simultaneously bind to CD3 on T cells and a tumor‐associated antigen on tumor cells, activate T cells, and redirect T cells’ cytotoxicity against tumor cells. Cytokine release syndrome (CRS), a common dose‐limiting adverse event for T‐BsAbs, is associated with T‐cell activation. A “priming” dose strategy (i.e., a lower initial dose followed by a higher maintenance dose) has been implemented in the clinic to mitigate CRS and to achieve efficacious doses with T‐BsAbs. So far, the selection of the optimal priming dosing regimen is largely empirical. A “fit‐for‐purpose” semimechanistic pharmacokinetic/pharmacodynamic model was developed to characterize the cytokine release profiles upon T‐BsAb treatment, including the priming effect observed with repeated dosing. This model can be utilized to simulate cytokine profiles following various dosing regimens and may assist the design of clinical dosing strategies for T‐BsAbs programs.

A Translational Quantitative Systems Pharmacology Model for CD3 Bispecific Molecules: Application to Quantify T Cell-Mediated Tumor Cell Killing by P-Cadherin LP DART®

CD3 bispecific antibody constructs recruit cytolytic T cells to kill tumor cells, offering a potent approach to treat cancer. T cell activation is driven by the formation of a trimolecular complex (trimer) between drugs, T cells, and tumor cells, mimicking an immune synapse. A translational quantitative systems pharmacology (QSP) model is proposed for CD3 bispecific molecules capable of predicting trimer concentration and linking it to tumor cell killing. The model was used to quantify the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of a CD3 bispecific targeting P-cadherin (PF-06671008). It describes the disposition of PF-06671008 in the central compartment and tumor in mouse xenograft models, including binding to target and T cells in the tumor to form the trimer. The model incorporates T cell distribution to the tumor, proliferation, and contraction. PK/PD parameters were estimated for PF-06671008 and a tumor stasis concentration (TSC) was calculated as an estimate of minimum efficacious trimer concentration. TSC values ranged from 0.0092 to 0.064 pM across mouse tumor models. The model was translated to the clinic and used to predict the disposition of PF-06671008 in patients, including the impact of binding to soluble P-cadherin. The predicted terminal half-life of PF-06671008 in the clinic was approximately 1 day, and P-cadherin expression and number of T cells in the tumor were shown to be sensitive parameters impacting clinical efficacy. A translational QSP model is presented for CD3 bispecific molecules, which integrates in silico, in vitro and in vivo data in a mechanistic framework, to quantify and predict efficacy across species.

Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology

Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment-with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.

Bispecific Antibodies in the Treatment of Hematologic Malignancies

Monoclonal antibody therapies are an important approach for the treatment of hematologic malignancies, but typically show low single‐agent activity. Bispecific antibodies, however, redirect immune cells to the tumor for subsequent lysis, and preclinical and accruing clinical data support single‐agent efficacy of these agents in hematologic malignancies, presaging an exciting era in the development of novel bispecific formats. This review discusses recent developments in this area, highlighting the challenges in delivering effective immunotherapies for patients.

Development of a Target cell-Biologics-Effector cell (TBE) complex-based cell killing model to characterize target cell depletion by T cell redirecting bispecific agents

T-cell redirecting bispecific antibodies (bsAbs) or antibody-derived agents that combine tumor antigen recognition with CD3-mediated T cell recruitment are highly potent tumor-killing molecules. Despite the tremendous progress achieved in the last decade, development of such bsAbs still faces many challenges. This work aimed to develop a mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling framework that can be used to assist the development of T-cell redirecting bsAbs. A Target cell-Biologics-Effector cell (TBE) complex-based cell killing model was developed using in vitro and in vivo data, which incorporates information on binding affinities of bsAbs to CD3 and target receptors, expression levels of CD3 and target receptors, concentrations of effector and target cells, as well as respective physiological parameters. This TBE model can simultaneously evaluate the effect of multiple system-specific and drug-specific factors on the T-cell redirecting bsAb exposure-response relationship on a physiological basis; it reasonably captured multiple reported in vitro cytotoxicity data, and successfully predicted the effect of some key factors on in vitro cytotoxicity assays and the efficacious dose of blinatumomab in humans. The mechanistic nature of this model uniquely positions it as a knowledge-based platform that can be readily expanded to guide target selection, drug design, candidate selection and clinical dosing regimen projection, and thus support the overall discovery and development of T-cell redirecting bsAbs.

Quantitative translational modeling to facilitate preclinical to clinical efficacy & toxicity translation in oncology

Significant scientific advances in biomedical research have expanded our knowledge of the molecular basis of carcinogenesis, mechanisms of cancer growth, and the importance of the cancer immunity cycle. However, despite scientific advances in the understanding of cancer biology, the success rate of oncology drug development remains the lowest among all therapeutic areas. In this review, some of the key translational drug development objectives in oncology will be outlined. The literature evidence of how mathematical modeling could be used to build a unifying framework to answer these questions will be summarized with recommendations on the strategies for building such a mathematical framework to facilitate the prediction of clinical efficacy and toxicity of investigational antineoplastic agents. Together, the literature evidence suggests that a rigorous and unifying preclinical to clinical translational framework based on mathematical models is extremely valuable for making go/no-go decisions in preclinical development, and for planning early clinical studies.

Integrated Pharmacokinetic/Pharmacodynamic Model of a Bispecific CD3xCD123 DART Molecule in Nonhuman Primates: Evaluation of Activity and Impact of Immunogenicity

Purpose: Flotetuzumab (MGD006 or S80880) is a bispecific molecule that recognizes CD3 and CD123 membrane proteins, redirecting T cells to kill CD123-expressing cells for the treatment of acute myeloid leukemia. In this study, we developed a mathematical model to characterize MGD006 exposure-response relationships and to assess the impact of its immunogenicity in cynomolgus monkeys.Experimental Design: Thirty-two animals received multiple escalating doses (100-300-600-1,000 ng/kg/day) via intravenous infusion continuously 4 days a week. The model reflects sequential binding of MGD006 to CD3 and CD123 receptors. Formation of the MGD006/CD3 complex was connected to total T cells undergoing trafficking, whereas the formation of the trimolecular complex results in T-cell activation and clonal expansion. Activated T cells were used to drive the peripheral depletion of CD123-positive cells. Anti-drug antibody development was linked to MGD006 disposition as an elimination pathway. Model validation was tested by predicting the activity of MGD006 in eight monkeys receiving continuous 7-day infusions.Results: MGD006 disposition and total T-cell and CD123-positive cell profiles were well characterized. Anti-drug antibody development led to the suppression of T-cell trafficking but did not systematically abolish CD123-positive cell depletion. Target cell depletion could persist after drug elimination owing to the self-proliferation of activated T cells generated during the first cycles. The model was externally validated with the 7-day infusion dosing schedule.Conclusions: A translational model was developed for MGD006 that features T-cell activation and expansion as a key driver of pharmacologic activity and provides a mechanistic quantitative platform to inform dosing strategies in ongoing clinical studies. Clin Cancer Res; 24(11); 2631-41. ©2018 AACR.

Immunotherapy to Treat Cancer

This issue of Clinical Pharmacology & Therapeutics focuses on immunotherapy as an approach to treat cancer by generating or augmenting an immune response against it. The enthusiasm for immunotherapy has waxed and waned over the past century. Enthusiasm for immunotherapy has risen over the past decade due, in part, to data showing that cancer immunotherapy consistently improves overall survival in select patients with advanced-stage cancer. Antitumor immunotherapy has broad potential and could be used to treat many different types of advanced-stage cancer due to the durable and robust response that it elicits across a diverse spectrum of cancers. This issue covers various aspects of relevant therapeutic topics ranging from discovery of chimeric antigen receptor (CAR) T cells, development of novel immunotherapies using novel pharmacokinetic/dynamic modeling tools, to the utilization of immune checkpoint therapy. Regarding utilization, this issue addresses biomarker selection strategies for personalized treatment of non-small cell lung cancer (NSCLC) with immune checkpoint therapy and also the management of the unique immune response adverse events (irAEs).

abYsis: Integrated Antibody Sequence and Structure-Management, Analysis, and Prediction

abYsis is a web-based antibody research system that includes an integrated database of antibody sequence and structure data. The system can be interrogated in numerous ways-from simple text and sequence searches to sophisticated queries that apply 3D structural constraints. The publicly available version includes pre-analyzed sequence data from the European Molecular Biology Laboratory European Nucleotide Archive (EMBL-ENA) and Kabat as well as structure data from the Protein Data Bank. A researcher's own sequences can also be analyzed through the web interface. A defining characteristic of abYsis is that the sequences are automatically numbered with a series of popular schemes such as Kabat and Chothia and then annotated with key information such as complementarity-determining regions and potential post-translational modifications. A unique aspect of abYsis is a set of residue frequency tables for each position in an antibody, allowing "unusual residues" (those rarely seen at a particular position) to be highlighted and decisions to be made on which mutations may be acceptable. This is especially useful when comparing antibodies from different species. abYsis is useful for any researcher specializing in antibody engineering, especially those developing antibodies as drugs. abYsis is available at www.abysis.org.