A mechanistic pharmacokinetic model for intrathecal administration of antisense oligonucleotides.

Intrathecal administration is an important mode for delivering biological agents targeting central nervous system (CNS) diseases. However, current clinical practices lack a sound theorical basis for a quantitative understanding of the variables and conditions that govern the delivery efficiency and specific tissue targeting especially in the brain. This work presents a distributed mechanistic pharmacokinetic model (DMPK) for predictive analysis of intrathecal drug delivery to CNS. The proposed DMPK model captures the spatiotemporal dispersion of antisense oligonucleotides (ASO) along the neuraxis over clinically relevant time scales of days and weeks as a function of infusion, physiological and molecular properties. We demonstrate its prediction capability using biodistribution data of antisense oligonucleotide (ASO) administration in non-human primates. The results are in close agreement with the observed ASO pharmacokinetics in all key compartments of the central nervous system. The model enables determination of optimal injection parameters such as intrathecal infusion volume and duration for maximum ASO delivery to the brain. Our quantitative model-guided analysis is suitable for identifying optimal parameter settings to target specific brain regions with therapeutic drugs such as ASOs.

Dual targeting of IGF-1R and ErbB3 as a potential therapeutic regimen for ovarian cancer.

Therapeutically targeting receptor tyrosine kinases has proven to be paramount to overcoming chemotherapy resistance in several cancer indications, improving patient outcomes. Insulin-Like Growth Factor Receptor 1 (IGF-1R) and Epidermal Growth Factor Receptor 3 (ErbB3) have been implicated as two such drivers of resistance, however their simultaneous role in ovarian cancer chemotherapy resistance remains poorly elucidated. The aim of this work is to determine the effects of dual IGF-1R/ErbB3 inhibition on ovarian cancer cell signaling, growth, and in vivo efficacy. Assessment of in vitro chemotherapy response across a panel of ovarian cancer cell lines revealed that increased IGF-1R cell surface expression correlates with decreased sensitivity to chemotherapy, and that growth induced by IGF-1R and ErbB3 ligands is blocked by the tetravalent bispecific antibody targeting IGF-1R and ErbB3, istiratumab. In vitro chemotherapy treatment increased ovarian cancer cell line capacity to activate prosurvival PI3K signaling in response to ligand, which could be prevented with istiratumab treatment. Furthermore, in vivo efficacy of standard of care chemotherapies using a xenograft model of ovarian cancer was potentiated with istiratumab. Our results suggest a role for IGF-1R and ErbB3 in driving chemotherapy resistance of ovarian cancer.

Dual Inhibition of IGF-1R and ErbB3 Enhances the Activity of Gemcitabine and Nab-Paclitaxel in Preclinical Models of Pancreatic Cancer.

Purpose: Insulin-like growth factor receptor 1 (IGF-1R) is critically involved in pancreatic cancer pathophysiology, promoting cancer cell survival and therapeutic resistance. Assessment of IGF-1R inhibitors in combination with standard-of-care chemotherapy, however, failed to demonstrate significant clinical benefit. The aim of this work is to unravel mechanisms of resistance to IGF-1R inhibition in pancreatic cancer and develop novel strategies to improve the activity of standard-of-care therapies.Experimental Design: Growth factor screening in pancreatic cancer cell lines was performed to identify activators of prosurvival PI3K/AKT signaling. The prevalence of activating growth factors and their receptors was assessed in pancreatic cancer patient samples. Effects of a bispecific IGF-1R and ErbB3 targeting antibody on receptor expression, signaling, cancer cell viability and apoptosis, spheroid growth, and in vivo chemotherapy activity in pancreatic cancer xenograft models were determined.Results: Growth factor screening in pancreatic cancer cells revealed insulin-like growth factor 1 (IGF-1) and heregulin (HRG) as the most potent AKT activators. Both growth factors reduced pancreatic cancer cell sensitivity to gemcitabine or paclitaxel in spheroid growth assays. Istiratumab (MM-141), a novel bispecific antibody that blocks IGF-1R and ErbB3, restored the activity of paclitaxel and gemcitabine in the presence of IGF-1 and HRG in vitro Dual IGF-1R/ErbB3 blocking enhanced chemosensitivity through inhibition of AKT phosphorylation and promotion of IGF-1R and ErbB3 degradation. Addition of istiratumab to gemcitabine and nab-paclitaxel improved chemotherapy activity in vivoConclusions: Our findings suggest a critical role for the HRG/ErbB3 axis and support the clinical exploration of dual IGF-1R/ErbB3 blocking in pancreatic cancer. Clin Cancer Res; 24(12); 2873-85. ©2018 AACR.

Mapping network motif tunability and robustness in the design of synthetic signaling circuits.

Cellular networks are highly dynamic in their function, yet evolutionarily conserved in their core network motifs or topologies. Understanding functional tunability and robustness of network motifs to small perturbations in function and structure is vital to our ability to synthesize controllable circuits. In establishing core sets of network motifs, we selected topologies that are overrepresented in mammalian networks, including the linear, feedback, feed-forward, and bifan circuits. Static and dynamic tunability of network motifs were defined as the motif ability to respectively attain steady-state or transient outputs in response to pre-defined input stimuli. Detailed computational analysis suggested that static tunability is insensitive to the circuit topology, since all of the motifs displayed similar ability to attain predefined steady-state outputs in response to constant inputs. Dynamic tunability, in contrast, was tightly dependent on circuit topology, with some motifs performing superiorly in achieving observed time-course outputs. Finally, we mapped dynamic tunability onto motif topologies to determine robustness of motif structures to changes in topology and identify design principles for the rational assembly of robust synthetic networks.

Proteomic characterization of breast cancer xenografts identifies early and late bevacizumab-induced responses and predicts effective drug combinations.

PURPOSE: Neoangiogenesis is an important feature in tumor growth and progression, and combining chemotherapy and antiangiogenic drugs have shown clinical efficacy. However, as treatment-induced resistance often develops, our goal was to identify pathways indicating response and/or evolving resistance to treatment and inhibit these pathways to optimize the treatment strategies. EXPERIMENTAL DESIGN: To identify markers of response and/or resistance, reverse-phase protein array (RPPA) was used to characterize treatment-induced changes in a bevacizumab-responsive and a nonresponsive human breast cancer xenograft. Results were combined with bioinformatic modeling to predict druggable targets for optimization of the treatment. RESULTS: RPPA analysis showed that both tumor models responded to bevacizumab with an early (day 3) upregulation of growth factor receptors and downstream signaling pathways, with persistent mTOR signaling until the end of the in vivo experiment. Adding doxorubicin to bevacizumab showed significant and superior growth inhibition of basal-like tumors, whereas no additive effect was seen in the luminal-like model. The combination treatment corresponded to a continuous late attenuation of mTOR signaling in the basal-like model, whereas the inhibition was temporary in the luminal-like model. Integrating the bevacizumab-induced dynamic changes in protein levels with bioinformatic modeling predicted inhibition of phosphoinositide 3-kinase (PI3K) pathway to increase the efficacy of bevacizumab monotherapy. In vivo experiments combining bevacizumab and the PI3K/mTOR inhibitor BEZ235 confirmed their significant and additive growth-inhibitory effect in the basal-like model. CONCLUSIONS: Treatment with bevacizumab caused compensatory upregulation of several signaling pathways. Targeting such pathways increased the efficacy of antiangiogenic therapy.

MM-141, an IGF-IR- and ErbB3-directed bispecific antibody, overcomes network adaptations that limit activity of IGF-IR inhibitors.

Although inhibition of the insulin-like growth factor (IGF) signaling pathway was expected to eliminate a key resistance mechanism for EGF receptor (EGFR)-driven cancers, the effectiveness of IGF-I receptor (IGF-IR) inhibitors in clinical trials has been limited. A multiplicity of survival mechanisms are available to cancer cells. Both IGF-IR and the ErbB3 receptor activate the PI3K/AKT/mTOR axis, but ErbB3 has only recently been pursued as a therapeutic target. We show that coactivation of the ErbB3 pathway is prevalent in a majority of cell lines responsive to IGF ligands and antagonizes IGF-IR-mediated growth inhibition. Blockade of the redundant IGF-IR and ErbB3 survival pathways and downstream resistance mechanisms was achieved with MM-141, a tetravalent bispecific antibody antagonist of IGF-IR and ErbB3. MM-141 potency was superior to monospecific and combination antibody therapies and was insensitive to variation in the ratio of IGF-IR and ErbB3 receptors. MM-141 enhanced the biologic impact of receptor inhibition in vivo as a monotherapy and in combination with the mTOR inhibitor everolimus, gemcitabine, or docetaxel, through blockade of IGF-IR and ErbB3 signaling and prevention of PI3K/AKT/mTOR network adaptation.

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

Antibodies are essential components of the adaptive immune system that provide protection from extracellular pathogens 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 targets have lagged, despite the well-recognized importance of this cross-arm binding for optimal antigen engagement. 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 binding efficiency. We conclude that optimal design parameters for cross-arm binding strongly depend on the biological context of the disease, and that cross-arm binding efficiency needs to be considered for successful application of multispecific antibodies.

Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis.

Targeted therapeutics hold tremendous promise in inhibiting cancer cell proliferation. However, targeting proteins individually can be compensated for by bypass mechanisms and activation of regulatory loops. Designing optimal therapeutic combinations must therefore take into consideration the complex dynamic networks in the cell. In this study, we analyzed the insulin-like growth factor (IGF-1) signaling network in the MDA-MB231 breast cancer cell line. We used reverse-phase protein array to measure the transient changes in the phosphorylation of proteins after IGF-1 stimulation. We developed a computational procedure that integrated mass action modeling with particle swarm optimization to train the model against the experimental data and infer the unknown model parameters. The trained model was used to predict how targeting individual signaling proteins altered the rest of the network and identify drug combinations that minimally increased phosphorylation of other proteins elsewhere in the network. Experimental testing of the modeling predictions showed that optimal drug combinations inhibited cell signaling and proliferation, whereas nonoptimal combination of inhibitors increased phosphorylation of nontargeted proteins and rescued cells from cell death. The integrative approach described here is useful for generating experimental intervention strategies that could optimize drug combinations and discover novel pharmacologic targets for cancer therapy.

Environmentally-modulated changes in fluorescence distribution in cells with oscillatory genetic network dynamics.

We investigated the distribution of green fluorescent protein (GFP) expression levels in a population of E. coli cells expressing an artificial genetic regulatory network, known as the "repressilator". This network originally constructed by Elowitz and Leibler in 2000 consists of three cyclically-inhibiting promoter-repressor pairs. It is because of this architecture that the network has been known to oscillate at the single-cell level under certain conditions. A series of shake flask experiments were performed and analyzed using flow cytometry to test how cell populations carrying this system could be controlled extracellularly using the inducers anhydrotetracycline (aTc) and isopropyl-beta-d-thiogalactopyranoside (IPTG). With variation of [aTc], it exhibits a novel bi-threshold behavior, such that the entire culture reaches one of three steady states at a quasi-time-invariant "reference state." Also, there is significant hysteresis. Transiently, the middle state shows damping oscillations, while the low and high states show a stable steady state. The addition of IPTG serves to fine-tune the characteristics of the aTc-only expression, lowering the average and coefficient of variation (CV) of the distributions, and possibly perturbing the network to a different state. However, in modeling this system, the multiplicity and bi-threshold behavior are not theoretically possible according to the designed interactions. In order to explain this discrepancy, we hypothesize that one or more of the repressors have a significant nonspecific interaction with a promoter that does not contain its operator site. The new modeling results incorporating these extra interactions qualitatively match our experimental findings. After constructing plasmids to test these hypotheses, we discover that at least four of these interactions exist, which can create the low and high states and multiplicity seen experimentally. This genetic architecture has flexibility in its behavior that has not been demonstrated before, and the combination of experiment and modeling enlightened our understanding of the molecular interactions driving the network's behavior, leading us to discover the significance of nonspecific interactions.

Synthesis of PHBV block copolymers driven by an oscillatory genetic network.

Artificial genetic networks constitute a powerful tool to achieve various biotechnological objectives. In this work, we propose the modification of an oscillatory genetic network, known as the repressilator, to drive synthesis of poly(3hydroxybutyrate-co-3hydroxyvalerate) (PHBV) block copolymer chains in recombinant Escherichia coli cells. To study the feasibility of this idea, we developed a detailed mathematical model describing the dynamics of the genetic network, which drive the formation of monomer units that are subsequently incorporated into actively growing block copolymer chains. Extensive simulation studies have shown that appropriate choice of the molecular characteristics of the network and manipulation of extracelllular conditions lead to tight control of both the micro- and macro-structures of the resulting block copolymer chains. Thus, the model can guide network design aiming at producing block copolymer structures with desirable characteristics.

Genetic network driven control of PHBV copolymer composition.

We developed a detailed mathematical model describing the coupling between the molecular weight distribution dynamics of poly(3-hydroxybutyrate-co-3hydroxyvalerate) (PHBV) copolymer chains with those of hydroxybutyrate (HB) and hydroxyvalerate (HV) monomer formation. Sensitivity analysis of the model revealed that both the monomer composition and the molecular weight distribution of the copolymer chains are strongly affected by the ratio between the rates at which the two-monomer units are incorporated into the chains. This ratio depends on the relative HB and HV availability, which in turn is a function of the expression levels of genes encoding enzymes that catalyze monomer formation. Regulation of gene expression was accomplished through the aid of an artificial genetic network, the patterns of expression of which can be controlled by appropriately tuning the concentration of an extracellular inducer. Extensive simulations were used to study the effects of operating conditions and parameter uncertainties on the range of achievable copolymer compositions. Since the predicted conditions fell in the range of feasible bioprocessing manipulations, it is expected that such strategy could be successfully employed. Thus, the presented model constitutes a powerful tool for designing genetic networks that can drive the formation of PHBV copolymer structures with desirable characteristics.