Brian D Harms

Merrimack Pharmaceuticals, Cambridge, Massachusetts, United States

Are you Brian D Harms?

Claim your profile

Publications (8)32.97 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Although inhibition of the IGF signaling pathway was expected to eliminate a key resistance mechanism for EGFR-driven cancers, the effectiveness of IGF-1R inhibitors in clinical trials has been limited. A multiplicity of survival mechanisms are available to cancer cells both IGF-1R and the ErbB3 receptor activate the PI3K/AKT/mTOR axis, but ErbB3 has only recently been pursued as a therapeutic target. We show that co-activation of the ErbB3 pathway is prevalent in a majority of cell lines responsive to IGF ligands and antagonizes IGF-1R mediated growth inhibition. Blockade of the redundant IGF-1R and ErbB3 survival pathways and downstream resistance mechanisms was achieved with MM-141, a tetravalent bispecific antibody antagonist of IGF-1R and ErbB3. MM-141 potency was superior to monospecific and combination antibody therapies and was insensitive to variation in the ratio of IGF-1R and ErbB3 receptors. MM-141 enhanced the biological impact of receptor inhibition in vivo as a monotherapy and in combination with the mTOR inhibitor everolimus, gemcitabine or docetaxel, through blockade of IGF-1R and ErbB3 signaling and prevention of PI3K/AKT/mTOR network adaptation.
    Molecular Cancer Therapeutics 11/2013; · 5.60 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Antibodies are essential components of the adaptive immune system that provide protection from extracellularpathogens and aberrant cells in the host.Immunoglobulins G, which have been adapted for therapeutic use due to their exquisite specificity of target recognition, are bivalent homodimers composed of two antigen binding Fab arms and an immune cell recruiting Fc module. In recent years significant progress has been made in optimizing properties of both Fab and Fc components to derive antibodies with improved affinity, stability, and effector function. However, systematic analyses of the efficiency with which antibodies crosslink their targetshave lagged, despite the well-recognized importance of this cross-arm binding for optimal antigenengagement.Such an understanding is particularly relevant given the variety of next-generation multispecific antibody scaffolds under development. In this manuscript we attempt to fill this gap by presenting a framework for analysis and optimization of antibody cross-arm engagement. We illustrate the power of this integrated approach by presenting case studies for rational multispecific antibody design based on quantitative assessment of the interplay between antibody valency, target expression, and cross-arm bindingefficiency. We conclude that optimal design parameters for cross-arm binding strongly depend on the biological contextof the disease,and thatcross-arm bindingefficiency needs to be considered for successful applicationof multispecific antibodies.
    Methods 07/2013; · 3.64 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Aberrant expression and activation of EGF receptor (EGFR) has been implicated in the development and progression of many human cancers. As such, targeted therapeutic inhibition of EGFR, for example by antibodies, is a promising anticancer strategy. The overall efficacy of antibody therapies results from the complex interplay between affinity, valence, tumor penetration and retention, and signaling inhibition. To gain better insight into this relationship, we studied a panel of EGFR single-chain Fv (scFv) antibodies that recognize an identical epitope on EGFR but bind with intrinsic monovalent affinities varying by 280-fold. The scFv were converted to Fab and IgG formats, and investigated for their ability to bind EGFR, compete with EGF binding, and inhibit EGF-mediated downstream signaling and proliferation. We observed that the apparent EGFR-binding affinity for bivalent IgG plateaus at intermediate values of intrinsic affinity of the cognate Fab, leading to a biphasic curve describing the ratio of IgG to Fab affinity. Mathematical modeling of antibody-receptor binding indicated that the biphasic effect results from nonequilibrium assay limitations. This was confirmed by further observation that the potency of EGF competition for antibody binding to EGFR improved with both intrinsic affinity and antibody valence. Similarly, both higher intrinsic affinity and bivalent binding improved the potency of antibodies in blocking cellular signaling and proliferation. Overall, our work indicates that higher intrinsic affinity combined with bivalent binding can achieve avidity that leads to greater in vitro potency of antibodies, which may translate into greater therapeutic efficacy.
    Molecular Cancer Therapeutics 05/2012; 11(7):1467-76. · 5.60 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The prevalence of ErbB2 amplification in breast cancer has resulted in the heavy pursuit of ErbB2 as a therapeutic target. Although both the ErbB2 monoclonal antibody trastuzumab and ErbB1/ErbB2 dual kinase inhibitor lapatinib have met with success in the clinic, many patients fail to benefit. In addition, the majority of patients who initially respond will unfortunately ultimately progress on these therapies. Activation of ErbB3, the preferred dimerization partner of ErbB2, plays a key role in driving ErbB2-amplified tumor growth, but we have found that current ErbB2-directed therapies are poor inhibitors of ligand-induced activation. By simulating ErbB3 inhibition in a computational model of ErbB2/ErbB3 receptor signaling, we predicted that a bispecific antibody that docks onto ErbB2 and subsequently binds to ErbB3 and blocks ligand-induced receptor activation would be highly effective in ErbB2-amplified tumors, with superior activity to a monospecific ErbB3 inhibitor. We have developed a bispecific antibody suitable for both large scale production and systemic therapy by generating a single polypeptide fusion protein of two human scFv antibodies linked to modified human serum albumin. The resulting molecule, MM-111, forms a trimeric complex with ErbB2 and ErbB3, effectively inhibiting ErbB3 signaling and showing antitumor activity in preclinical models that is dependent on ErbB2 overexpression. MM-111 can be rationally combined with trastuzumab or lapatinib for increased antitumor activity and may in the future complement existing ErbB2-directed therapies to treat resistant tumors or deter relapse.
    Molecular Cancer Therapeutics 03/2012; 11(3):582-93. · 5.60 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Monoclonal antibodies are valuable as anticancer therapeutics because of their ability to selectively bind tumor-associated target proteins like receptor tyrosine kinases. Kinetic computational models that capture protein-protein interactions using mass action kinetics are a valuable tool for understanding the binding properties of monoclonal antibodies to their targets. Insights from the models can be used to explore different formats, to set antibody design specifications such as affinity and valence, and to predict potency. Antibody binding to target is driven by both intrinsic monovalent affinity and bivalent avidity. In this chapter, we describe a combined experimental and computational method of assessing the relative importance of these effects on observed drug potency. The method, which we call virtual flow cytometry (VFC), merges experimental measurements of monovalent antibody binding kinetics and affinity curves of antibody-antigen binding into a kinetic computational model of antibody-antigen interaction. The VFC method introduces a parameter χ, the avidity factor, which characterizes the ability of an antibody to cross-link its target through bivalent binding. This simple parameterization of antibody cross-linking allows the model to successfully describe and predict antibody binding curves across a wide variety of experimental conditions, including variations in target expression level and incubation time of antibody with target. We further demonstrate how computational models of antibody binding to cells can be used to predict target inhibition potency. Importantly, we demonstrate computationally that antibodies with high ability to cross-link antigen have significant potency advantages. We also present data suggesting that the parameter χ is a physical, epitope-dependent property of an antibody, and as a result propose that determination of antibody cross-linking and avidity should be incorporated into the screening of antibody panels for therapeutic development. Overall, our results suggest that antibody cross-linking, in addition to monovalent binding affinity, is a key design parameter of antibody performance.
    Methods in enzymology 01/2012; 502:67-87. · 1.90 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The signaling network downstream of the ErbB family of receptors has been extensively targeted by cancer therapeutics; however, understanding the relative importance of the different components of the ErbB network is nontrivial. To explore the optimal way to therapeutically inhibit combinatorial, ligand-induced activation of the ErbB-phosphatidylinositol 3-kinase (PI3K) axis, we built a computational model of the ErbB signaling network that describes the most effective ErbB ligands, as well as known and previously unidentified ErbB inhibitors. Sensitivity analysis identified ErbB3 as the key node in response to ligands that can bind either ErbB3 or EGFR (epidermal growth factor receptor). We describe MM-121, a human monoclonal antibody that halts the growth of tumor xenografts in mice and, consistent with model-simulated inhibitor data, potently inhibits ErbB3 phosphorylation in a manner distinct from that of other ErbB-targeted therapies. MM-121, a previously unidentified anticancer therapeutic designed using a systems approach, promises to benefit patients with combinatorial, ligand-induced activation of the ErbB signaling network that are not effectively treated by current therapies targeting overexpressed or mutated oncogenes.
    Science Signaling 02/2009; 2(77):ra31. · 7.65 Impact Factor
  • Source
    BMC Systems Biology 01/2007; · 2.98 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Short Abstract — By developing an experimentally-validated, mechanistic model of the IGF signaling pathway (including the PI3K and MAPK cascades) we show that understanding subtle differences in network dynamics is crucial for predicting the unintended or counter-productive effects of targeted inhibitors. ntuition based on static protein interactions is limited when signaling networks contain multiple feedback and crosstalk loops (1-5), as in the Insulin-like growth factor-1 (IGF-1) receptor pathway. Mechanistic modeling enables for examination of the role and importance of dynamic protein interactions and provides a foundation for developing targeted therapeutics (6). The IGF-1 receptor pathway is known to play an important role in breast cancer. Stimulation of the IGF-1 receptor results in activation of multiple pathways that generate survival and proliferation cues, such as the ERK and AKT pathways. Given the known role of IGF-1 in cancer, we have taken a quantitative approach to understanding the receptor signaling pathways at the molecular level.