Accurate definition of control strategies using cross validated stepwise regression and Monte Carlo simulation.

Drug manufacturing processes must consistently deliver safe and effective product. A key part of achieving this is process validation utilizing Quality by Design (QbD) principles. To meet process validation requirements, process characterization (PC) studies are often performed to expand process understanding and establish an appropriate control strategy that enables the manufacturing process to consistently deliver a target product profile. Two key elements of the control strategy resulting from PC work are a list of critical process parameters (CPPs) and defined operating ranges (ORs). These are frequently derived based on mathematical models describing the relationship between process parameters and critical quality attributes (CQAs). Risk assessment and design of experiments (DOE) techniques are effectively deployed in the industry to identify parameters to study and build process understanding. However, traditional data analysis techniques do not fully utilize the data produced by these studies. In particular, stepwise regression algorithms based on p-values are prone to generate false positives and overfit data, potentially leading to unnecessarily complex control strategies. Many of the deficiencies of traditional stepwise regression can be alleviated by applying cross validation to stepwise regression algorithms, as well as Monte Carlo simulations to estimate model accuracy and predict CQA distributions. These methods can greatly enhance process understanding and assist in the selection of CPPs. A series of PC studies were performed in bioreactors to evaluate a process to produce a recombinant monoclonal antibody. The studies examined process parameters such as dissolved oxygen, pH, temperature, inoculation density, as well as cell density at two key process steps. The resulting data were analyzed using several Monte Carlo based methods. First, cross validation was used to determine model size and select parameters to be included in the model. Next, Monte Carlo cross validation was used to compare the accuracy of different models. Finally, simulated CQA profiles were generated to validate proposed ORs. This workflow provides greater process understanding based on a given PC data set and provides higher statistical confidence in both CPP selection and establishment of a control strategy.

Artificial Anti-Tumor Opsonizing Proteins with Fibronectin Scaffolds Engineered for Specificity to Each of the Murine FcγR Types.

We have engineered a panel of novel Fn3 scaffold-based proteins that bind with high specificity and affinity to each of the individual mouse Fcγ receptors (mFcγR). These binders were expressed as fusions to anti-tumor antigen single-chain antibodies and mouse serum albumin, creating opsonizing agents that invoke only a single mFcγR response rather than the broader activity of natural Fc isotypes, as well as all previously reported Fc mutants. This panel isolated the capability of each of the four mFcγRs to contribute to macrophage phagocytosis of opsonized tumor cells and in vivo tumor growth control with these monospecific opsonizing fusion proteins. All activating receptors (mFcγRI, mFcγRIII, and mFcγRIV) were capable of driving specific tumor cell phagocytosis to an equivalent extent, while mFcγRII, the inhibitory receptor, did not drive phagocytosis. Monospecific opsonizing fusion proteins that bound mFcγRI alone controlled tumor growth to an extent similar to the most active IgG2a murine isotype. As expected, binding to the inhibitory mFcγRII did not delay tumor growth, but unexpectedly, mFcγRIII also failed to control tumor growth. mFcγRIV exhibited detectable but lesser tumor-growth control leading to less overall survival compared to mFcγRI. Interestingly, in vivo macrophage depletion demonstrates their importance in tumor control with mFcγRIV engagement, but not with mFcγRI. This panel of monospecific mFcγR-binding proteins provides a toolkit for isolating the functional effects of each mFcγR in the context of an intact immune system.

Biopolymers codelivering engineered T cells and STING agonists can eliminate heterogeneous tumors.

Therapies using T cells that are programmed to express chimeric antigen receptors (CAR T cells) consistently produce positive results in patients with hematologic malignancies. However, CAR T cell treatments are less effective in solid tumors for several reasons. First, lymphocytes do not efficiently target CAR T cells; second, solid tumors create an immunosuppressive microenvironment that inactivates T cell responses; and third, solid cancers are typified by phenotypic diversity and thus include cells that do not express proteins targeted by the engineered receptors, enabling the formation of escape variants that elude CAR T cell targeting. Here, we have tested implantable biopolymer devices that deliver CAR T cells directly to the surfaces of solid tumors, thereby exposing them to high concentrations of immune cells for a substantial time period. In immunocompetent orthotopic mouse models of pancreatic cancer and melanoma, we found that CAR T cells can migrate from biopolymer scaffolds and eradicate tumors more effectively than does systemic delivery of the same cells. We have also demonstrated that codelivery of stimulator of IFN genes (STING) agonists stimulates immune responses to eliminate tumor cells that are not recognized by the adoptively transferred lymphocytes. Thus, these devices may improve the effectiveness of CAR T cell therapy in solid tumors and help protect against the emergence of escape variants.

Temporally Programmed CD8α+ DC Activation Enhances Combination Cancer Immunotherapy.

Numerous synergistic cancer immunotherapy combinations have been identified, but the effects of relative dose timing are rarely considered. In established syngeneic mouse tumor models, we found that staggering interferon-α (IFNα) administration after, rather than before or simultaneously with, serum-persistent interleukin-2 (IL-2) and tumor-specific antibody significantly increased long-term survival. Successful combination therapy required IFNα-induced activation of cross-presenting CD8α+ dendritic cells (DCs) following the release of antigenic tumor debris by the IL-2- and antibody-mediated immune response. Due to decreased phagocytic ability post-maturation, DCs activated too early captured less antigen and could not effectively prime CD8+ T cells. Temporally programming DC activation to occur after tumoricidal activity enhanced tumor control by multiple distinct combination immunotherapies, highlighting dose schedule as an underappreciated factor that can profoundly affect the success of multi-component immunotherapies.

Eradication of large established tumors in mice by combination immunotherapy that engages innate and adaptive immune responses.

Checkpoint blockade with antibodies specific for cytotoxic T lymphocyte-associated protein (CTLA)-4 or programmed cell death 1 (PDCD1; also known as PD-1) elicits durable tumor regression in metastatic cancer, but these dramatic responses are confined to a minority of patients. This suboptimal outcome is probably due in part to the complex network of immunosuppressive pathways present in advanced tumors, which are unlikely to be overcome by intervention at a single signaling checkpoint. Here we describe a combination immunotherapy that recruits a variety of innate and adaptive immune cells to eliminate large tumor burdens in syngeneic tumor models and a genetically engineered mouse model of melanoma; to our knowledge tumors of this size have not previously been curable by treatments relying on endogenous immunity. Maximal antitumor efficacy required four components: a tumor-antigen-targeting antibody, a recombinant interleukin-2 with an extended half-life, anti-PD-1 and a powerful T cell vaccine. Depletion experiments revealed that CD8+ T cells, cross-presenting dendritic cells and several other innate immune cell subsets were required for tumor regression. Effective treatment induced infiltration of immune cells and production of inflammatory cytokines in the tumor, enhanced antibody-mediated tumor antigen uptake and promoted antigen spreading. These results demonstrate the capacity of an elicited endogenous immune response to destroy large, established tumors and elucidate essential characteristics of combination immunotherapies that are capable of curing a majority of tumors in experimental settings typically viewed as intractable.

Synergistic innate and adaptive immune response to combination immunotherapy with anti-tumor antigen antibodies and extended serum half-life IL-2.

Cancer immunotherapies under development have generally focused on either stimulating T cell immunity or driving antibody-directed effector functions of the innate immune system such as antibody-dependent cell-mediated cytotoxicity (ADCC). We find that a combination of an anti-tumor antigen antibody and an untargeted IL-2 fusion protein with delayed systemic clearance induces significant tumor control in aggressive isogenic tumor models via a concerted innate and adaptive response involving neutrophils, NK cells, macrophages, and CD8(+) T cells. This combination therapy induces an intratumoral "cytokine storm" and extensive lymphocyte infiltration. Adoptive transfer of anti-tumor T cells together with this combination therapy leads to robust cures of established tumors and development of immunological memory.

Antigen specificity can be irrelevant to immunocytokine efficacy and biodistribution.

Cytokine therapy can activate potent, sustained antitumor responses, but collateral toxicity often limits dosages. Although antibody-cytokine fusions (immunocytokines) have been designed with the intent to localize cytokine activity, systemic dose-limiting side effects are not fully ameliorated by attempted tumor targeting. Using the s.c. B16F10 melanoma model, we found that a nontoxic dose of IL-2 immunocytokine synergized with tumor-specific antibody to significantly enhance therapeutic outcomes compared with immunocytokine monotherapy, concomitant with increased tumor saturation and intratumoral cytokine responses. Examination of cell subset biodistribution showed that the immunocytokine associated mainly with IL-2R-expressing innate immune cells, with more bound immunocytokine present in systemic organs than the tumor microenvironment. More surprisingly, immunocytokine antigen specificity and Fcγ receptor interactions did not seem necessary for therapeutic efficacy or biodistribution patterns because immunocytokines with irrelevant specificity and/or inactive mutant Fc domains behaved similarly to tumor-specific immunocytokine. IL-2-IL-2R interactions, rather than antibody-antigen targeting, dictated immunocytokine localization; however, the lack of tumor targeting did not preclude successful antibody combination therapy. Mathematical modeling revealed immunocytokine size as another driver of antigen targeting efficiency. This work presents a safe, straightforward strategy for augmenting immunocytokine efficacy by supplementary antibody dosing and explores underappreciated factors that can subvert efforts to purposefully alter cytokine biodistribution.

A graphene-based physiometer array for the analysis of single biological cells.

A significant advantage of a graphene biosensor is that it inherently represents a continuum of independent and aligned sensor-units. We demonstrate a nanoscale version of a micro-physiometer - a device that measures cellular metabolic activity from the local acidification rate. Graphene functions as a matrix of independent pH sensors enabling subcellular detection of proton excretion. Raman spectroscopy shows that aqueous protons p-dope graphene - in agreement with established doping trajectories, and that graphene displays two distinct pKa values (2.9 and 14.2), corresponding to dopants physi- and chemisorbing to graphene respectively. The graphene physiometer allows micron spatial resolution and can differentiate immunoglobulin (IgG)-producing human embryonic kidney (HEK) cells from non-IgG-producing control cells. Population-based analyses allow mapping of phenotypic diversity, variances in metabolic activity, and cellular adhesion. Finally we show this platform can be extended to the detection of other analytes, e.g. dopamine. This work motivates the application of graphene as a unique biosensor for (sub)cellular interrogation.

Quantitative modeling of viable cell density, cell size, intracellular conductivity, and membrane capacitance in batch and fed-batch CHO processes using dielectric spectroscopy.

Dielectric spectroscopy was used to analyze typical batch and fed-batch CHO cell culture processes. Three methods of analysis (linear modeling, Cole-Cole modeling, and partial least squares regression), were used to correlate the spectroscopic data with routine biomass measurements [viable packed cell volume, viable cell concentration (VCC), cell size, and oxygen uptake rate (OUR)]. All three models predicted offline biomass measurements accurately during the growth phase of the cultures. However, during the stationary and decline phases of the cultures, the models decreased in accuracy to varying degrees. Offline cell radius measurements were unsuccessfully used to correct for the deviations from the linear model, indicating that physiological changes affecting permittivity were occurring. The beta-dispersion was analyzed using the Cole-Cole distribution parameters Deltaepsilon (magnitude of the permittivity drop), f(c) (critical frequency), and alpha (Cole-Cole parameter). Furthermore, the dielectric parameters static internal conductivity (sigma(i)) and membrane capacitance per area (C(m)) were calculated for the cultures. Finally, the relationship between permittivity, OUR, and VCC was examined, demonstrating how the definition of viability is critical when analyzing biomass online. The results indicate that the common assumptions of constant size and dielectric properties used in dielectric analysis are not always valid during later phases of cell culture processes. The findings also demonstrate that dielectric spectroscopy, while not a substitute for VCC, is a complementary measurement of viable biomass, providing useful auxiliary information about the physiological state of a culture.