Understanding the role of cross-arm binding efficiency in the activity of monoclonal and multispecific therapeutic antibodies

Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors

The spread of cancer from organ to organ (metastasis) is responsible for the vast majority of cancer deaths; however, most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing disease spread. It was recently discovered that tumor cell-secreted interleukin-6 (IL-6) and interleukin-8 (IL-8) synergize to enhance cancer metastasis in a cell-density dependent manner, and blockade of the IL-6 and IL-8 receptors (IL-6R and IL-8R) with a novel bispecific antibody, BS1, significantly reduced metastatic burden in multiple preclinical mouse models of cancer. Bispecific antibodies (BsAbs), which combine two different antigen-binding sites into one molecule, are a promising modality for drug development due to their enhanced avidity and dual targeting effects. However, while BsAbs have tremendous therapeutic potential, elucidating the mechanisms underlying their binding and inhibition will be critical for maximizing the efficacy of new BsAb treatments. Here, we describe a quantitative, computational model of the BS1 BsAb, exhibiting how modeling multivalent binding provides key insights into antibody affinity and avidity effects and can guide therapeutic design. We present detailed simulations of the monovalent and bivalent binding interactions between different antibody constructs and the IL-6 and IL-8 receptors to establish how antibody properties and system conditions impact the formation of binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes. Model results demonstrate how the balance of these complex types drives receptor inhibition, providing important and generalizable predictions for effective therapeutic design.

Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC

Immune checkpoint inhibitors remained the standard-of-care treatment for advanced non-small cell lung cancer (NSCLC) for the past decade. In unselected patients, anti-PD-(L)1 monotherapy achieved an overall response rate of about 20%. In this analysis, we developed a pharmacokinetic and pharmacodynamic module for our previously calibrated quantitative systems pharmacology model (QSP) to simulate the effectiveness of macrophage-targeted therapies in combination with PD-L1 inhibition in advanced NSCLC. By conducting in silico clinical trials, the model confirmed that anti-CD47 treatment is not an optimal option of second- and later-line treatment for advanced NSCLC resistant to PD-(L)1 blockade. Furthermore, the model predicted that inhibition of macrophage recruitment, such as using CCR2 inhibitors, can potentially improve tumor size reduction when combined with anti-PD-(L)1 therapy, especially in patients who are likely to respond to anti-PD-(L)1 monotherapy and those with a high level of tumor-associated macrophages. Here, we demonstrate the application of the QSP platform on predicting the effectiveness of novel drug combinations involving immune checkpoint inhibitors based on preclinical or early-stage clinical trial data.

Bispecific antibody drug conjugates: Making 1+1>2

Bispecific antibody‒drug conjugates (BsADCs) represent an innovative therapeutic category amalgamating the merits of antibody‒drug conjugates (ADCs) and bispecific antibodies (BsAbs). Positioned as the next-generation ADC approach, BsADCs hold promise for ameliorating extant clinical challenges associated with ADCs, particularly pertaining to issues such as poor internalization, off-target toxicity, and drug resistance. Presently, ten BsADCs are undergoing clinical trials, and initial findings underscore the imperative for ongoing refinement. This review initially delves into specific design considerations for BsADCs, encompassing target selection, antibody formats, and the linker-payload complex. Subsequent sections delineate the extant progress and challenges encountered by BsADCs, illustrated through pertinent case studies. The amalgamation of BsAbs with ADCs offers a prospective solution to prevailing clinical limitations of ADCs. Nevertheless, the symbiotic interplay among BsAb, linker, and payload necessitates further optimizations and coordination beyond a simplistic "1 + 1" to effectively surmount the extant challenges facing the BsADC domain.

Biparatopic antibodies: therapeutic applications and prospects

Biparatopic antibodies (bpAbs) bind distinct, non-overlapping epitopes on an antigen. This unique binding mode enables new mechanisms of action beyond monospecific and bispecific antibodies (bsAbs) that can make bpAbs effective therapeutics for various indications, including oncology and infectious diseases. Biparatopic binding can lead to superior affinity and specificity, promote antagonism, lock target conformation, and result in higher-order target clustering. Such antibody-target complexes can elicit strong agonism, increase immune effector function, or result in rapid target downregulation and lysosomal trafficking. These are not only attractive properties for therapeutic antibodies but are increasingly being explored for other modalities such as antibody-drug conjugates, T-cell engagers and chimeric antigen receptors. Recent advances in bpAb engineering have enabled the construction of ever more sophisticated formats that are starting to show promise in the clinic.

Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors

The spread of cancer from organ to organ (metastasis) is responsible for the vast majority of cancer deaths; however, most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing disease spread. It was recently discovered that tumor cell-secreted interleukin-6 (IL-6) and interleukin-8 (IL-8) synergize to enhance cancer metastasis in a cell-density dependent manner, and blockade of the IL-6 and IL-8 receptors (IL-6R and IL-8R) with a novel bispecific antibody, BS1, significantly reduced metastatic burden in multiple preclinical mouse models of cancer. Bispecific antibodies (BsAbs), which combine two different antigen-binding sites into one molecule, are a promising modality for drug development due to their enhanced avidity and dual targeting effects. However, while BsAbs have tremendous therapeutic potential, elucidating the mechanisms underlying their binding and inhibition will be critical for maximizing the efficacy of new BsAb treatments. Here, we describe a quantitative, computational model of the BS1 BsAb, exhibiting how modeling multivalent binding provides key insights into antibody affinity and avidity effects and can guide therapeutic design. We present detailed simulations of the monovalent and bivalent binding interactions between different antibody constructs and the IL-6 and IL-8 receptors to establish how antibody properties and system conditions impact the formation of binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes. Model results demonstrate how the balance of these complex types drives receptor inhibition, providing important and generalizable predictions for effective therapeutic design.

The PD-1- and LAG-3-targeting bispecific molecule tebotelimab in solid tumors and hematologic cancers: a phase 1 trial

Tebotelimab, a bispecific PD-1×LAG-3 DART molecule that blocks both PD-1 and LAG-3, was investigated for clinical safety and activity in a phase 1 dose-escalation and cohort-expansion clinical trial in patients with solid tumors or hematologic malignancies and disease progression on previous treatment. Primary endpoints were safety and maximum tolerated dose of tebotelimab when administered as a single agent (n = 269) or in combination with the anti-HER2 antibody margetuximab (n = 84). Secondary endpoints included anti-tumor activity. In patients with advanced cancer treated with tebotelimab monotherapy, 68% (184/269) experienced treatment-related adverse events (TRAEs; 22% were grade ≥3). No maximum tolerated dose was defined; the recommended phase 2 dose (RP2D) was 600 mg once every 2 weeks. There were tumor decreases in 34% (59/172) of response-evaluable patients in the dose-escalation cohorts, with objective responses in multiple solid tumor types, including PD-1-refractory disease, and in LAG-3+ non-Hodgkin lymphomas, including CAR-T refractory disease. To enhance potential anti-tumor responses, we tested margetuximab plus tebotelimab. In patients with HER2+ tumors treated with tebotelimab plus margetuximab, 74% (62/84) had TRAEs (17% were grade ≥3). The RP2D was 600 mg once every 3 weeks. The confirmed objective response rate in these patients was 19% (14/72), including responses in patients typically not responsive to anti-HER2/anti-PD-1 combination therapy. ClinicalTrials.gov identifier: NCT03219268 .

Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding

Surface plasmon resonance (SPR) is an extensively used technique to characterize antigen-antibody interactions. Affinity measurements by SPR typically involve testing the binding of antigen in solution to monoclonal antibodies (mAbs) immobilized on a chip and fitting the kinetics data using 1:1 Langmuir binding model to derive rate constants. However, when it is necessary to immobilize antigens instead of the mAbs, a bivalent analyte (1:2) binding model is required for kinetics analysis. This model is lacking in data analysis packages associated with high throughput SPR instruments and the packages containing this model do not explore multiple local minima and parameter identifiability issues that are common in non-linear optimization. Therefore, we developed a method to use a system of ordinary differential equations for analyzing 1:2 binding kinetics data. Salient features of this method include a grid search on parameter initialization and a profile likelihood approach to determine parameter identifiability. Using this method we found a non-identifiable parameter in data set collected under the standard experimental design. A simulation-guided improved experimental design led to reliable estimation of all rate constants. The method and approach developed here for analyzing 1:2 binding kinetics data will be valuable for expeditious therapeutic antibody discovery research.

Engineering of a Bispecific Nanofitin with Immune Checkpoint Inhibitory Activity Conditioned by the Cross-Arm Binding to EGFR and PDL1

Re-education of the tumor microenvironment with immune checkpoint inhibitors (ICI) has provided the most significant advancement in cancer management, with impressive efficacy and durable response reported. However, low response rates and a high frequency of immune-related adverse events (irAEs) remain associated with ICI therapies. The latter can be linked to their high affinity and avidity for their target that fosters on-target/off-tumor binding and subsequent breaking of immune self-tolerance in normal tissues. Many multispecific protein formats have been proposed to increase the tumor cell’s selectivity of ICI therapies. In this study, we explored the engineering of a bispecific Nanofitin by the fusion of an anti-epidermal growth factor receptor (EGFR) and anti-programmed cell death ligand 1 (PDL1) Nanofitin modules. While lowering the affinity of the Nanofitin modules for their respective target, the fusion enables the simultaneous engagement of EGFR and PDL1, which translates into a selective binding to tumor cells co-expressing EGFR and PDL1 only. We demonstrated that affinity-attenuated bispecific Nanofitin could elicit PDL1 blockade exclusively in an EGFR-directed manner. Overall, the data collected highlight the potential of this approach to enhance the selectivity and safety of PDL1 checkpoint inhibition.

A next generation mathematical model for the in vitro to clinical translation of T-cell engagers

T-cell engager (TCE) molecules activate the immune system and direct it to kill tumor cells. The key mechanism of action of TCEs is to crosslink CD3 on T cells and tumor associated antigens (TAAs) on tumor cells. The formation of this trimolecular complex (i.e. trimer) mimics the immune synapse, leading to therapeutic-dependent T-cell activation and killing of tumor cells. Computational models supporting TCE development must predict trimer formation accurately. Here, we present a next-generation two-step binding mathematical model for TCEs to describe trimer formation. Specifically, we propose to model the second binding step with trans-avidity and as a two-dimensional (2D) process where the reactants are modeled as the cell-surface density. Compared to the 3D binding model where the reactants are described in terms of concentration, the 2D model predicts less sensitivity of trimer formation to varying cell densities, which better matches changes in EC₅₀ from in vitro cytotoxicity assay data with varying E:T ratios. In addition, when translating in vitro cytotoxicity data to predict in vivo active clinical dose for blinatumomab, the choice of model leads to a notable difference in dose prediction. The dose predicted by the 2D model aligns better with the approved clinical dose and the prediction is robust under variations in the in vitro to in vivo translation assumptions. In conclusion, the 2D model with trans-avidity to describe trimer formation is an improved approach for TCEs and is likely to produce more accurate predictions to support TCE development.

Antibody variable region engineering for improving cancer immunotherapy

The efficacy and specificity of conventional monoclonal antibody (mAb) drugs in the clinic require further improvement. Currently, the development and application of novel antibody formats for improving cancer immunotherapy have attracted much attention. Variable region-retaining antibody fragments, such as antigen-binding fragment (Fab), single-chain variable fragment (scFv), bispecific antibody, and bi/trispecific cell engagers, are engineered with humanization, multivalent antibody construction, affinity optimization and antibody masking for targeting tumor cells and killer cells to improve antibody-based therapy potency, efficacy and specificity. In this review, we summarize the application of antibody variable region engineering and discuss the future direction of antibody engineering for improving cancer therapies.

Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer

Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation.

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A mechanistic model of quantitative systems pharmacology in immuno-oncology

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Dynamics of tumor-associated macrophages are integrated into our previous work

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Conducting in silico clinical trials to predict clinical response to cancer therapy

Avidity in antibody effector functions and biotherapeutic drug design

Antibodies are the cardinal effector molecules of the immune system and are being leveraged with enormous success as biotherapeutic drugs. A key part of the adaptive immune response is the production of an epitope-diverse, polyclonal antibody mixture that is capable of neutralizing invading pathogens or disease-causing molecules through binding interference and by mediating humoral and cellular effector functions. Avidity - the accumulated binding strength derived from the affinities of multiple individual non-covalent interactions - is fundamental to virtually all aspects of antibody biology, including antibody-antigen binding, clonal selection and effector functions. The manipulation of antibody avidity has since emerged as an important design principle for enhancing or engineering novel properties in antibody biotherapeutics. In this Review, we describe the multiple levels of avidity interactions that trigger the overall efficacy and control of functional responses in both natural antibody biology and their therapeutic applications. Within this framework, we comprehensively review therapeutic antibody mechanisms of action, with particular emphasis on engineered optimizations and platforms. Overall, we describe how affinity and avidity tuning of engineered antibody formats are enabling a new wave of differentiated antibody drugs with tailored properties and novel functions, promising improved treatment options for a wide variety of diseases.

Virtual Populations for Quantitative Systems Pharmacology Models

Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.

Physiological Considerations for Modeling in vivo Antibody-Target Interactions

The number of therapeutic antibodies in development pipelines is increasing rapidly. Despite superior success rates relative to small molecules, therapeutic antibodies still face many unique development challenges. There is often a translational gap from their high target affinity and specificity to the therapeutic effects. Tissue microenvironment and physiology critically influence antibody-target interactions contributing to apparent affinity alterations and dynamic target engagement. The full potential of therapeutic antibodies will be further realized by contextualizing antibody-target interactions under physiological conditions. Here we review how local physiology such as physical stress, biological fluid, and membrane characteristics could influence antibody-target association, dissociation, and apparent affinity. These physiological factors in the early development of therapeutic antibodies are valuable toward rational antibody engineering, preclinical candidate selection, and lead optimization.

A T cell redirection platform for co-targeting dual antigens on solid tumors

In order to direct T cells to specific features of solid cancer cells, we engineered a bispecific antibody format, named Dual Antigen T cell Engager (DATE), by fusing a single-chain variable fragment targeting CD3 to a tumor-targeting antigen-binding fragment. In this format, multiple novel paratopes against different tumor antigens were able to recruit T-cell cytotoxicity to tumor cells in vitro and in an in vivo pancreatic ductal adenocarcinoma xenograft model. Since unique surface antigens in solid tumors are limited, in order to enhance selectivity, we further engineered “double-DATEs” targeting two tumor antigens simultaneously. The double-DATE contains an additional autonomous variable heavy-chain domain, which binds a second tumor antigen without itself eliciting a cytotoxic response. This novel modality provides a strategy to enhance the selectivity of immune redirection through binary targeting of native tumor antigens. The modularity and use of a common, stable human framework for all components enables a pipeline approach to rapidly develop a broad repertoire of tailored DATEs and double-DATEs with favorable biophysical properties and high potencies and selectivities.

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.

Design and Efficacy of a Monovalent Bispecific PD-1/CTLA4 Antibody That Enhances CTLA4 Blockade on PD-1+ Activated T Cells

The clinical benefit of PD-1 blockade can be improved by combination with CTLA4 inhibition but is commensurate with significant immune-related adverse events suboptimally limiting the doses of anti-CTLA4 mAb that can be used. MEDI5752 is a monovalent bispecific antibody designed to suppress the PD-1 pathway and provide modulated CTLA4 inhibition favoring enhanced blockade on PD-1⁺ activated T cells. We show that MEDI5752 preferentially saturates CTLA4 on PD-1⁺ T cells versus PD-1^- T cells, reducing the dose required to elicit IL2 secretion. Unlike conventional PD-1/CTLA4 mAbs, MEDI5752 leads to the rapid internalization and degradation of PD-1. Moreover, we show that MEDI5752 preferentially localizes and accumulates in tumors providing enhanced activity when compared with a combination of mAbs targeting PD-1 and CTLA4 in vivo. Following treatment with MEDI5752, robust partial responses were observed in two patients with advanced solid tumors. MEDI5752 represents a novel immunotherapy engineered to preferentially inhibit CTLA4 on PD-1⁺ T cells. SIGNIFICANCE: The unique characteristics of MEDI5752 represent a novel immunotherapy engineered to direct CTLA4 inhibition to PD-1⁺ T cells with the potential for differentiated activity when compared with current conventional mAb combination strategies targeting PD-1 and CTLA4. This molecule therefore represents a step forward in the rational design of cancer immunotherapy.See related commentary by Burton and Tawbi, p. 1008.This article is highlighted in the In This Issue feature, p. 995.

Molecular Mechanism of HER2 Rapid Internalization and Redirected Trafficking Induced by Anti-HER2 Biparatopic Antibody

Amplification and overexpression of HER2 (human epidermal growth factor receptor 2), an ErbB2 receptor tyrosine kinase, have been implicated in human cancer and metastasis. A bispecific tetravalent anti-HER2 antibody (anti-HER2-Bs), targeting two non-overlapping epitopes on HER2 in domain IV (trastuzumab) and domain II (39S), has been reported to induce rapid internalization and efficient degradation of HER2 receptors. In this study, we investigated the molecular mechanism of this antibody-induced rapid HER2 internalization and intracellular trafficking. Using quantitative fluorescent imaging, we compared the internalization kinetics of anti-HER2-Bs and its parental arm antibodies, alone or in combinations and under various internalization-promoting conditions. The results demonstrated that concurrent engagement of both epitopes was necessary for rapid anti-HER2-Bs internalization. Cellular uptake of anti-HER2-Bs and parental arm antibodies occurred via clathrin-dependent endocytosis; however, inside the cells antibodies directed different trafficking pathways. Trastuzumab dissociated from HER2 in 2 h, enabling the receptor to recycle, whereas anti-HER2-Bs stayed associated with the receptor throughout the entire endocytic pathway, promoting receptor ubiquitination, trafficking to the lysosomes, and efficient degradation. Consistent with routing HER2 to degradation, anti-HER2-Bs significantly reduced HER2 shedding and altered its exosomal export. Collectively, these results enable a better understanding of the mechanism of action of anti-Her2-Bs and can guide the rational design of anti-HER2 therapeutics as well as other bispecific molecules.

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.

A Quantitative Systems Pharmacology Model of T Cell Engager Applied to Solid Tumor

Cancer immunotherapy has recently drawn remarkable attention as promising results in the clinic have shown its ability to improve the overall survival, and T cells are considered to be one of the primary effectors for cancer immunotherapy. Enhanced and restored T cell tumoricidal activity has shown great potential for killing cancer cells. Bispecific T cell engagers (TCEs) are a growing class of molecules that are designed to bind two different antigens on the surface of T cells and cancer cells to bring them in close proximity and selectively activate effector T cells to kill target cancer cells. New T cell engagers are being investigated for the treatment of solid tumors. The activity of newly developed T cell engagers showed a strong correlation with tumor target antigen expression. However, the correlation between tumor-associated antigen expression and overall response of cancer patients is poorly understood. In this study, we used a well-calibrated quantitative systems pharmacology (QSP) model extended to bispecific T cell engagers to explore their efficacy and identify potential biomarkers. In principle, patient-specific response can be predicted through this model according to each patient's individual characteristics. This extended QSP model has been calibrated with available experimental data and provides predictions of patients' response to TCE treatment.

Fine-tuning bispecific therapeutics

Bispecific therapeutics target two distinct antigens simultaneously and provide novel functionalities that are not attainable with single monospecific molecules or combinations of them. The unique potential of bispecific therapeutics is driving extensive efforts to discover synergistic dual targets, design molecular formats to integrate bispecific elements, and accelerate successful clinical translation. In particular, the past decade has witnessed a boom in the design and development of bispecific antibody formats with more than 100 collections to date. Despite the remarkable progress that has been made to expand the number of formats, qualitative fine-tuning of bispecific formats is needed to achieve optimal dual-target engagement based on understanding of the spatiotemporal interdependence of the two physically linked binding specificities and the complex target biology associated with bispecific approaches. This review provides insights into the design parameters - including affinity, valency, and geometry - that need to be considered at an early stage of development in order to take the best advantage of bispecific therapeutics.

Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors

The survival rate of patients with breast cancer has been improved by immune checkpoint blockade therapies, and the efficacy of their combinations with epigenetic modulators has shown promising results in preclinical studies. In this prospective study, we propose an ordinary differential equation (ODE)-based quantitative systems pharmacology (QSP) model to conduct an in silico virtual clinical trial and analyze potential predictive biomarkers to improve the anti-tumor response in HER2-negative breast cancer. The model is comprised of four compartments: central, peripheral, tumor, and tumor-draining lymph node, and describes immune activation, suppression, T cell trafficking, and pharmacokinetics and pharmacodynamics (PK/PD) of the therapeutic agents. We implement theoretical mechanisms of action for checkpoint inhibitors and the epigenetic modulator based on preclinical studies to investigate their effects on anti-tumor response. According to model-based simulations, we confirm the synergistic effect of the epigenetic modulator and that pre-treatment tumor mutational burden, tumor-infiltrating effector T cell (Teff) density, and Teff to regulatory T cell (Treg) ratio are significantly higher in responders, which can be potential biomarkers to be considered in clinical trials. Overall, we present a readily reproducible modular model to conduct in silico virtual clinical trials on patient cohorts of interest, which is a step toward personalized medicine in cancer immunotherapy.

De novo discovery of antibody drugs - great promise demands scrutiny

We live in an era of rapidly advancing computing capacity and algorithmic sophistication. "Big data" and "artificial intelligence"find progressively wider use in all spheres of human activity, including healthcare. A diverse array of computational technologies is being applied with increasing frequency to antibody drug research and development (R&D). Their successful applications are met with great interest due to the potential for accelerating and streamlining the antibody R&D process. While this excitement is very likely justified in the long term, it is less likely that the transition from the first use to routine practice will escape challenges that other new technologies had experienced before they began to blossom. This transition typically requires many cycles of iterative learning that rely on the deconstruction of the technology to understand its pitfalls and define vectors for optimization. The study by Vasquez et al. identifies a key obstacle to such learning: the lack of transparency regarding methodology in computational antibody design reports, which has the potential to mislead the community efforts.

MM-131, a bispecific anti-Met/EpCAM mAb, inhibits HGF-dependent and HGF-independent Met signaling through concurrent binding to EpCAM

Activation of the Met receptor tyrosine kinase, either by its ligand, hepatocyte growth factor (HGF), or via ligand-independent mechanisms, such as MET amplification or receptor overexpression, has been implicated in driving tumor proliferation, metastasis, and resistance to therapy. Clinical development of Met-targeted antibodies has been challenging, however, as bivalent antibodies exhibit agonistic properties, whereas monovalent antibodies lack potency and the capacity to down-regulate Met. Through computational modeling, we found that the potency of a monovalent antibody targeting Met could be dramatically improved by introducing a second binding site that recognizes an unrelated, highly expressed antigen on the tumor cell surface. Guided by this prediction, we engineered MM-131, a bispecific antibody that is monovalent for both Met and epithelial cell adhesion molecule (EpCAM). MM-131 is a purely antagonistic antibody that blocks ligand-dependent and ligand-independent Met signaling by inhibiting HGF binding to Met and inducing receptor down-regulation. Together, these mechanisms lead to inhibition of proliferation in Met-driven cancer cells, inhibition of HGF-mediated cancer cell migration, and inhibition of tumor growth in HGF-dependent and -independent mouse xenograft models. Consistent with its design, MM-131 is more potent in EpCAM-high cells than in EpCAM-low cells, and its potency decreases when EpCAM levels are reduced by RNAi. Evaluation of Met, EpCAM, and HGF levels in human tumor samples reveals that EpCAM is expressed at high levels in a wide range of Met-positive tumor types, suggesting a broad opportunity for clinical development of MM-131.

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.

Trispecific antibodies for CD16A-directed NK cell engagement and dual-targeting of tumor cells

Bispecific antibodies that redirect the lytic activity of cytotoxic immune effector cells, such as T- and NK cells, onto tumor cells have emerged as a highly attractive and clinically validated treatment modality for hematological malignancies. Advancement of this therapeutic concept into solid tumor indications, however, is hampered by the scarcity of targetable antigens that are surface-expressed on tumor cells but demonstrate only limited expression on healthy tissues. To overcome this limitation, the concept of dual-targeting, i.e. the simultaneous targeting of two tumor-expressed surface antigens with limited co-expression on non-malignant cells, with multispecific antibodies has been proposed to increase tumor selectivity of antibody-induced effector cell cytotoxicity. Here, a novel CD16A (FcγRIIIa)-directed trispecific, tetravalent antibody format, termed aTriFlex, is described, that is capable of redirecting NK cell cytotoxicity to two surface-expressed antigens. Using a BCMA/CD200-based in vitro model system, the potential use of aTriFlex antibodies for dual-targeting and selective induction of NK cell-mediated target cell lysis was investigated. Bivalent bispecific target cell binding was found to result in significant avidity gains and up to 17-fold increased in vitro potency. These data suggest trispecific aTriFlex antibodies may support dual-targeting strategies to redirect NK cell cytotoxicity with increased selectivity to enable targeting of solid tumor antigens.

Enhanced tumor-targeting selectivity by modulating bispecific antibody binding affinity and format valence

Bispecific antibodies are considered attractive bio-therapeutic agents owing to their ability to target two distinct disease mediators. Cross-arm avidity targeting of antigen double-positive cancer cells over single-positive normal tissue is believed to enhance the therapeutic efficacy, restrict major escape mechanisms and increase tumor-targeting selectivity, leading to reduced systemic toxicity and improved therapeutic index. However, the interplay of factors regulating target selectivity is not well understood and often overlooked when developing clinically relevant bispecific therapeutics. We show in vivo that dual targeting alone is not sufficient to endow selective tumor-targeting, and report the pivotal roles played by the affinity of the individual arms, overall avidity and format valence. Specifically, a series of monovalent and bivalent bispecific IgGs composed of the anti-HER2 trastuzumab moiety paired with affinity-modulated V_H and V_L regions of the anti-EGFR GA201 mAb were tested for selective targeting and eradication of double-positive human NCI-H358 non-small cell lung cancer target tumors over single-positive, non-target NCI-H358-HER2 CRISPR knock out tumors in nude mice bearing dual-flank tumor xenografts. Affinity-reduced monovalent bispecific variants, but not their bivalent bispecific counterparts, mediated a greater degree of tumor targeting selectivity, while the overall efficacy against the targeted tumor was not substantially affected.

Leverage nonclinical development of bispecifics by translational science

Bispecific antibody constructs (Bispecifics, bsAbs) may have greater functionality compared to established monoclonal antibodies because they bind to 2 different targets or, potentially, to 2 epitopes on the same target (dual targeting). This may result in enhanced binding avidity with preferential binding to cells that express both targets or binding to targets on different cells. However, development of these next-generation biologics, including new formats, creates unique challenges due to their increased complexity. Here we review aspects of bsAbs preclinical development programs that may increase the success rates of bsAbs in clinical development.

Targeting Siglecs with a sialic acid-decorated nanoparticle abrogates inflammation

Sepsis is the most frequent cause of death in hospitalized patients, and severe sepsis is a leading contributory factor to acute respiratory distress syndrome (ARDS). At present, there is no effective treatment for these conditions, and care is primarily supportive. Murine sialic acid-binding immunoglobulin-like lectin-E (Siglec-E) and its human orthologs Siglec-7 and Siglec-9 are immunomodulatory receptors found predominantly on hematopoietic cells. These receptors are important negative regulators of acute inflammatory responses and are potential targets for the treatment of sepsis and ARDS. We describe a Siglec-targeting platform consisting of poly(lactic-co-glycolic acid) nanoparticles decorated with a natural Siglec ligand, di(α2→8) N-acetylneuraminic acid (α2,8 NANA-NP). This nanoparticle induced enhanced oligomerization of the murine Siglec-E receptor on the surface of macrophages, unlike the free α2,8 NANA ligand. Furthermore, treatment of murine macrophages with these nanoparticles blocked the production of lipopolysaccharide-induced inflammatory cytokines in a Siglec-E-dependent manner. The nanoparticles were also therapeutically beneficial in vivo in both systemic and pulmonary murine models replicating inflammatory features of sepsis and ARDS. Moreover, we confirmed the anti-inflammatory effect of these nanoparticles on human monocytes and macrophages in vitro and in a human ex vivo lung perfusion (EVLP) model of lung injury. We also established that interleukin-10 (IL-10) induced Siglec-E expression and α2,8 NANA-NP further augmented the expression of IL-10. Indeed, the effectiveness of the nanoparticle depended on IL-10. Collectively, these results demonstrated a therapeutic effect of targeting Siglec receptors with a nanoparticle-based platform under inflammatory conditions.

Promises and pitfalls for recombinant oligoclonal antibodies-based therapeutics in cancer and infectious disease

Monoclonal antibodies (mAbs) have revolutionized the diagnosis and treatment of many human diseases and the application of combinations of mAbs has demonstrated improved therapeutic activity in both preclinical and clinical testing. Combinations of antibodies have several advantages such as the capacities to target multiple and mutating antigens in complex pathogens and to engage varied epitopes on multiple disease-related antigens (e.g. receptors) to overcome heterogeneity and plasticity. Oligoclonal antibodies are an emerging therapeutic format in which a novel antibody combination is developed as a single drug product. Here, we will provide historical context on the use of oligoclonal antibodies in oncology and infectious diseases and will highlight practical considerations related to their preclinical and clinical development programs.

A Modeling and Experimental Investigation of the Effects of Antigen Density, Binding Affinity, and Antigen Expression Ratio on Bispecific Antibody Binding to Cell Surface Targets

Despite the increasing number of multivalent antibodies, bispecific antibodies, fusion proteins, and targeted nanoparticles that have been generated and studied, the mechanism of multivalent binding to cell surface targets is not well understood. Here, we describe a conceptual and mathematical model of multivalent antibody binding to cell surface antigens. Our model predicts that properties beyond 1:1 antibody:antigen affinity to target antigens have a strong influence on multivalent binding. Predicted crucial properties include the structure and flexibility of the antibody construct, the target antigen(s) and binding epitope(s), and the density of antigens on the cell surface. For bispecific antibodies, the ratio of the expression levels of the two target antigens is predicted to be critical to target binding, particularly for the lower expressed of the antigens. Using bispecific antibodies of different valencies to cell surface antigens including MET and EGF receptor, we have experimentally validated our modeling approach and its predictions and observed several nonintuitive effects of avidity related to antigen density, target ratio, and antibody affinity. In some biological circumstances, the effect we have predicted and measured varied from the monovalent binding interaction by several orders of magnitude. Moreover, our mathematical framework affords us a mechanistic interpretation of our observations and suggests strategies to achieve the desired antibody-antigen binding goals. These mechanistic insights have implications in antibody engineering and structure/activity relationship determination in a variety of biological contexts.

The application of mathematical modelling to the design of bispecific monoclonal antibodies

Targeting multiple receptors with bispecific antibodies is a novel approach that may prevent the development of resistance to cancer treatments. Despite the initial promise, full clinical benefit of this technology has yet to be realized. We hypothesized that in order to optimally exploit bispecific antibody technology, thorough fundamental knowledge of their pharmacological properties compared to that of single agent combinations was needed. Therefore, we developed a mathematical model for the binding of bispecific antibodies to their targets that accounts for the spatial distribution of the binding receptors and the kinetics of binding, and is scalable for increasing valency. The model provided an adequate description of internal and literature-reported in vitro data on bispecific binding. Simulations of in vitro binding with the model indicated that bispecific antibodies are not always superior in their binding potency to combination of antibodies, and the affinity of bispecific arms must be optimized for maximum binding potency. Our results suggest that this tool can be used for the design and development of the next generation of anti-cancer bispecific compounds.

A Murine, Bispecific Monoclonal Antibody Simultaneously Recognizing β-Glucan and MP65 Determinants in Candida Species

There is a real medical need of new diagnostic tools for the early recognition of invasive Candida infections. We exploited a rather simple and rapid redox methodology to construct a bispecific monoclonal antibody (bsmAb) that combines a monoclonal antibody (mAb) directed against 1,3-β-D-glucan, a well-known, pan-fungal diagnostic biomarker, with a mAb recognizing MP65, a major immunogenic mannoprotein secreted by C.albicans and other Candida species. The bsmAb (MP65/bglu mAb) was successfully produced and purified at high yields and proved to bind and reveal simultaneously, with high sensitivity, the β-glucan and MP65 antigens in both purified and native forms. The MP65/bglu mAb is the first bispecific antibody generated against a fungal microorganism and may prove useful for the concurrent detection of different and clinically significant Candida biomarkers in patient sera.

Cross-arm binding efficiency of an EGFR x c-Met bispecific antibody

Multispecific proteins, such as bispecific antibodies (BsAbs), that bind to two different ligands are becoming increasingly important therapeutic agents. Such BsAbs can exhibit markedly increased target binding and target residence time when both pharmacophores bind simultaneously to their targets. The cross-arm binding efficiency (χ) describes an increase in apparent affinity when a BsAb binds to the second target or receptor (R2) following its binding to the first target or receptor (R1) on the same cell. χ is an intrinsic characteristic of a BsAb mostly related to the binding epitopes on R1 and R2. χ can have significant impacts on the binding to R2 for BsAbs targeting two receptors on the same cell. JNJ-61186372, a BsAb that targets epidermal growth factor receptor (EGFR) and c-Met, was used as the model compound for establishing a method to characterize χ. The χ for JNJ-61186372 was successfully determined via fitting of in vitro cell binding data to a ligand binding model that incorporated χ. The model-derived χ value was used to predict the binding of JNJ-61186372 to individual EGFR and c-Met receptors on tumor cell lines, and the results agreed well with the observed IC50 for EGFR and c-Met phosphorylation inhibition by JNJ-61186372. Consistent with the model, JNJ-61186372 was shown to be more effective than the combination therapy of anti-EGFR and anti-c-Met monovalent antibodies at the same dose level in a mouse xenograft model. Our results showed that χ is an important characteristic of BsAbs, and should be considered for rationale design of BsAbs targeting two membrane bound targets on the same cell.

T cell-recruiting triplebody 19-3-19 mediates serial lysis of malignant B-lymphoid cells by a single T cell

Triplebody 19-3-19, an antibody-derived protein, carries three single chain fragment variable domains in tandem in a single polypeptide chain. 19-3-19 binds CD19-bearing lymphoid cells via its two distal domains and primary T cells via its CD3-targeting central domain in an antigen-specific manner. Here, malignant B-lymphoid cell lines and primary cells from patients with B cell malignancies were used as targets in cytotoxicity tests with pre-stimulated allogeneic T cells as effectors. 19-3-19 mediated up to 95 % specific lysis of CD19-positive tumor cells and, at picomolar EC₅₀ doses, had similar cytolytic potency as the clinically successful agent Blinatumomab. 19-3-19 activated resting T cells from healthy unrelated donors and mediated specific lysis of both autologous and allogeneic CD19-positive cells. 19-3-19 led to the elimination of 70 % of CD19-positive target cells even with resting T cells as effectors at an effector-to-target cell ratio of 1 : 10. The molecule is therefore capable of mediating serial lysis of target cells by a single T cell. These results highlight that central domains capable of engaging different immune effectors can be incorporated into the triplebody format to provide more individualized therapy tailored to a patient's specific immune status.

General, Label-Free Method for Determining K(d) and Ligand Concentration Simultaneously

Some of the most commonly used affinity reagents (e.g., antibodies) are often developed and used in conditions where their input concentrations ([L]0) and affinities (K(d)) are not known. Here, we have developed a general approach to determine both [L]0 and K(d) values simultaneously for affinity reagents (small molecules, proteins, and antibodies). To do this, we perform quantitative equilibrium exclusion immunoassays with two different concentrations of target and fit the data simultaneously to determine K(d) and [L]0. The results give accurate and reproducible measures of both values compared to established methods. By performing detailed error analysis, we demonstrate that our fitting gives unique solutions and indicates where K(d) and [L]0 measures are reliable. Furthermore, we found that a divalent model of antibody binding gives accurate K(d) and [L]0 values in both the forward (antibody immobilized) and the reverse (target immobilized) assays-addressing the long-term problem of obtaining quantitative data from reverse assays.

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.