Therapeutic potential of procathepsin L-inhibiting and progesterone-entrapping dimethyl-ß-cyclodextrin nanoparticles in treating experimental sepsis.

The pathogenic mechanisms of bacterial infections and resultant sepsis are partly attributed to dysregulated inflammatory responses sustained by some late-acting mediators including the procathepsin-L (pCTS-L). It was entirely unknown whether any compounds of the U.S. Drug Collection could suppress pCTS-L-induced inflammation, and pharmacologically be exploited into possible therapies. Here, we demonstrated that a macrophage cell-based screening of a U.S. Drug Collection of 1360 compounds resulted in the identification of progesterone (PRO) as an inhibitor of pCTS-L-mediated production of several chemokines [e.g., Epithelial Neutrophil-Activating Peptide (ENA-78), Monocyte Chemoattractant Protein-1 (MCP-1) or MCP-3] and cytokines [e.g., Interleukin-10 (IL-10) or Tumor Necrosis Factor (TNF)] in primary human peripheral blood mononuclear cells (PBMCs). In vivo, these PRO-entrapping 2,6-dimethal-ß-cyclodextrin (DM-ß-CD) nanoparticles (containing 1.35 mg/kg PRO and 14.65 mg/kg DM-ß-CD) significantly increased animal survival in both male (from 30% to 70%, n = 20, P = 0.041) and female (from 50% to 80%, n = 30, P = 0.026) mice even when they were initially administered at 24 h post the onset of sepsis. This protective effect was associated with a reduction of sepsis-triggered accumulation of three surrogate biomarkers [e.g., Granulocyte Colony Stimulating Factor (G-CSF) by 40%; Macrophage Inflammatory Protein-2 (MIP-2) by 45%; and Soluble Tumor Necrosis Factor Receptor I (sTNFRI) by 80%]. Surface Plasmon Resonance (SPR) analysis revealed a strong interaction between PRO and pCTS-L (KD = 78.2 ± 33.7 nM), which was paralleled with a positive correlation between serum PRO concentration and serum pCTS-L level (ρ = 0.56, P = 0.0009) or disease severity (Sequential Organ Failure Assessment, SOFA; ρ = 0.64, P = 0.0001) score in septic patients. Our observations support a promising opportunity to explore DM-ß-CD nanoparticles entrapping lipophilic drugs as possible therapies for clinical sepsis.

Development of bispecific T cell engagers: harnessing quantitative systems pharmacology.

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

Population dynamics of immunological synapse formation induced by bispecific T cell engagers predict clinical pharmacodynamics and treatment resistance.

Effector T cells need to form immunological synapses (IS) with recognized target cells to elicit cytolytic effects. Facilitating IS formation is the principal pharmacological action of most T cell-based cancer immunotherapies. However, the dynamics of IS formation at the cell population level, the primary driver of the pharmacodynamics of many cancer immunotherapies, remains poorly defined. Using classic immunotherapy CD3/CD19 bispecific T cell engager (BiTE) as our model system, we integrate experimental and theoretical approaches to investigate the population dynamics of IS formation and their relevance to clinical pharmacodynamics and treatment resistance. Our models produce experimentally consistent predictions when defining IS formation as a series of spatiotemporally coordinated events driven by molecular and cellular interactions. The models predict tumor-killing pharmacodynamics in patients and reveal trajectories of tumor evolution across anatomical sites under BiTE immunotherapy. Our models highlight the bone marrow as a potential sanctuary site permitting tumor evolution and antigen escape. The models also suggest that optimal dosing regimens are a function of tumor growth, CD19 expression, and patient T cell abundance, which confer adequate tumor control with reduced disease evolution. This work has implications for developing more effective T cell-based cancer immunotherapies.

Incorporating lesion-to-lesion heterogeneity into early oncology decision making.

RECISTv1.1 (Response Evaluation Criteria In Solid Tumors) is the most commonly used response grading criteria in early oncology trials. In this perspective, we argue that RECISTv1.1 is ambiguous regarding lesion-to-lesion variation that can introduce bias in decision making. We show theoretical examples of how lesion-to-lesion variability causes bias in RECISTv1.1, leading to misclassification of patient response. Next, we review immune checkpoint inhibitor (ICI) clinical trial data and find that lesion-to-lesion heterogeneity is widespread in ICI-treated patients. We illustrate the implications of ignoring lesion-to-lesion heterogeneity in interpreting biomarker data, selecting treatments for patients with progressive disease, and go/no-go decisions in drug development. Further, we propose that Quantitative Systems Pharmacology (QSP) models can aid in developing better metrics of patient response and treatment efficacy by capturing patient responses robustly by considering lesion-to-lesion heterogeneity. Overall, we believe patient response evaluation with an appreciation of lesion-to-lesion heterogeneity can potentially improve decision-making at the early stage of oncology drug development and benefit patient care.

Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study.

BACKGROUND AND PURPOSE: Despite their use to treat cancers with specific genetic aberrations, targeted therapies elicit heterogeneous responses. Sources of variability are critical to targeted therapy drug development, yet there exists no method to discern their relative contribution to response heterogeneity. EXPERIMENTAL APPROACH: We use HER2-amplified breast cancer and two agents, neratinib and lapatinib, to develop a platform for dissecting sources of variability in patient response. The platform comprises four components: pharmacokinetics, tumor burden and growth kinetics, clonal composition, and sensitivity to treatment. Pharmacokinetics are simulated using population models to capture variable systemic exposure. Tumor burden and growth kinetics are derived from clinical data comprising over 800,000 women. The fraction of sensitive and resistant tumor cells is informed by HER2 immunohistochemistry. Growth rate-corrected drug potency is used to predict response. We integrate these factors and simulate clinical outcomes for virtual patients. The relative contributions of these factors to response heterogeneity arecompared. KEY RESULTS: The platform was verified with clinical data, including response rate and progression-free survival (PFS). For both neratinib and lapatinib, the growth rate of resistant clones influenced PFS to a higher degree than systemic drug exposure. Variability in exposure at labeled doses did not significantly influence response. Sensitivity to drug strongly influenced responses to neratinib. Variability in patient HER2 immunohistochemistry scores influenced responses to lapatinib. Exploratory twice daily dosing improved PFS for neratinib but not lapatinib. CONCLUSION AND IMPLICATIONS: The platform can dissect sources of variability in response to target therapy, which may facilitate decision-making during drug development.

A multispecies framework for modeling adaptive immunity and immunotherapy in cancer.

Predator-prey theory is commonly used to describe tumor growth in the presence of selective pressure from the adaptive immune system. These interactions are mediated by the tumor immunopeptidome (what the tumor "shows" the body) and the T-cell receptor (TCR) repertoire (how well the body "sees" cancer cells). The tumor immunopeptidome comprises neoantigens which can be gained and lost throughout tumorigenesis and treatment. Heterogeneity in the immunopeptidome is predictive of poor response to immunotherapy in some tumor types, suggesting that the TCR repertoire is unable to support a fully polyclonal response against every neoantigen. Importantly, while tumor and T-cell populations are known to compete with each other for intratumoral resources, whether between-lineage competition among peripheral T cells influences the TCR repertoire is unknown and difficult to interrogate experimentally. Computational models may offer a way to investigate these phenomena and deepen our understanding of the tumor-immune axis. Here, we construct a predator-prey-like model and calibrate it to preclinical and clinical data to describe tumor growth and immunopeptidome diversification. Simultaneously, we model the expansion of antigen-specific T-cell lineages and their consumption of both lineage-specific antigenic resources and lineage-agnostic, shared resources. This predator-prey-like framework accurately described clinically observed immunopeptidomes; recapitulated response-associated effects of immunotherapy, including immunoediting; and allowed exploration of treatment of tumors with varying growth and mutation rates.

Virtual clinical trials: A tool for predicting patients who may benefit from treatment beyond progression with pembrolizumab in non-small cell lung cancer.

Enrolling patients in immunotherapy clinical trials is becoming increasingly competitive. Virtual clinical trials can help investigators answer key questions despite this. For example, pembrolizumab is the recommended first-line treatment for non-small cell lung cancer (NSCLC) with no driver alterations and a programmed death ligand 1 (PD-L1) Tumor Proportion Score ≥50%. Salvage therapies for relapsed/refractory patients are limited. Retrospective studies suggest that a subset of patients may benefit from pembrolizumab beyond progression; these results have not been validated in a prospective study. We constructed digital twins of patients and simulated clinical trials to predict the best salvage therapy after progressive disease (PD) on pembrolizumab. Response dynamics were evaluated at the lesion level to represent patients who experience systemic PD while individual lesions continue shrinking. With >25,000 radiographic lesion measurements from >500 patients, we simulated responses to pembrolizumab, chemotherapy, and PD on pembrolizumab followed by either pembrolizumab beyond progression or salvage chemotherapy. Switching all progressors to salvage chemotherapy was suboptimal. Virtual trials predicted progression-free survival (PFS) from pembrolizumab beyond progression to be comparable with salvage chemotherapy in patients whose PD was due to nontarget progression. A PFS-optimized regimen may improve disease control rates ≥15%. Pembrolizumab beyond progression may benefit a subset of patients with PD-L1-high, driver alteration-free NSCLC, but prospective studies are warranted.

Embracing Project Optimus: Can we Leverage Evolutionary Theory to Optimize Dosing in Oncology?

Project Optimus is a US Food and Drug Administration (FDA) initiative to reform dose selection in oncology drug development. Here, we focus on tumor evolution, a broadly observed phenomenon that invariably leads to therapeutic failure and disease relapse, and its effect on the exposure-response (E-R) relationships of oncology drugs. We propose a greater emphasis on tumor evolution during clinical development to facilitate the selection of optimal doses for molecularly targeted therapies and immunotherapies in oncology.

Cellular kinetics: A clinical and computational review of CAR-T cell pharmacology.

To the extent that pharmacokinetics influence the effectiveness of nonliving therapeutics, so too do cellular kinetics influence the efficacy of Chimeric Antigen Receptor (CAR) -T cell therapy. Like conventional therapeutics, CAR-T cell therapies undergo a distribution phase upon administration. Unlike other therapeutics, however, this distribution phase is followed by subsequent phases of expansion, contraction, and persistence. The magnitude and duration of these phases unequivocally influence clinical outcomes. Furthermore, the "pharmacodynamics" of CAR-T cells is truly dynamic, as cells can rapidly become exhausted and lose their therapeutic efficacy. Mathematical models are among the translational tools commonly applied to assess, characterize, and predict the complex cellular kinetics and dynamics of CAR-T cells. Here, we provide a focused review of the cellular kinetics of CAR-T cells, the mechanisms underpinning their complexity, and the mathematical modeling approaches used to interrogate them.

Speed and Location Both Matter: Antigen Stimulus Dynamics Controls CAR-T Cell Response.

Despite the success in B-cell malignancies, chimeric antigen receptor (CAR)-T cell therapies have not yet demonstrated consistent efficacy across all patients and tumor types, particularly against solid tumors. Higher rates of T cell exhaustion are associated with inferior clinical outcomes following CAR-T cell therapy, which is prevalent in solid tumors. T cell exhaustion may originate from persistent and chronic antigen stimulation by tumor cells that resist and/or evade T cell-mediated killing. We exploited CAR-T exhaustion with a classic negative feedback model (incoherent feedforward loop, IFFL) to investigate the balance between CAR-T cell activation and exhaustion under different antigen presentation dynamics. Built upon the experimental and clinical data, we hypothesize that the speed and anatomical location of antigenic stimulation are both crucial to CAR-T cell response. Chronic antigenic stimulation as well as the harsh tumor microenvironment present multiple barriers to CAR-T cell efficacy in solid tumors. Many therapeutic strategies are individually insufficient to improve of CAR-T responses against solid tumors, as they clear but one of the many barriers CAR-T cells face in solid tumors. A combination strategy targeting multiple barriers holds promise to improve CAR-T therapy in solid tumors.

In Translation: FcRn across the Therapeutic Spectrum.

As an essential modulator of IgG disposition, the neonatal Fc receptor (FcRn) governs the pharmacokinetics and functions many therapeutic modalities. In this review, we thoroughly reexamine the hitherto elucidated biological and thermodynamic properties of FcRn to provide context for our assessment of more recent advances, which covers antigen-binding fragment (Fab) determinants of FcRn affinity, transgenic preclinical models, and FcRn targeting as an immune-complex (IC)-clearing strategy. We further comment on therapeutic antibodies authorized for treating SARS-CoV-2 (bamlanivimab, casirivimab, and imdevimab) and evaluate their potential to saturate FcRn-mediated recycling. Finally, we discuss modeling and simulation studies that probe the quantitative relationship between in vivo IgG persistence and in vitro FcRn binding, emphasizing the importance of endosomal transit parameters.

Spastin mutations impair coordination between lipid droplet dispersion and reticulum.

Lipid droplets (LD) are affected in multiple human disorders. These highly dynamic organelles are involved in many cellular roles. While their intracellular dispersion is crucial to ensure their function and other organelles-contact, underlying mechanisms are still unclear. Here we show that Spastin, one of the major proteins involved in Hereditary Spastic Paraplegia (HSP), controls LD dispersion. Spastin depletion in zebrafish affects metabolic properties and organelle dynamics. These functions are ensured by a conserved complex set of splice variants. M1 isoforms determine LD dispersion in the cell by orchestrating endoplasmic reticulum (ER) shape along microtubules (MTs). To further impact LD fate, Spastin modulates transcripts levels and subcellular location of other HSP key players, notably Seipin and REEP1. In pathological conditions, mutations in human Spastin M1 disrupt this mechanism and impacts LD network. Spastin depletion influences not only other key proteins but also modulates specific neutral lipids and phospholipids, revealing an impact on membrane and organelle components. Altogether our results show that Spastin and its partners converge in a common machinery that coordinates LD dispersion and ER shape along MTs. Any alteration of this system results in HSP clinical features and impacts lipids profile, thus opening new avenues for novel biomarkers of HSP.

EdgeProps: A Computational Platform for Correlative Analysis of Cell Dynamics and Near-Edge Protein Activity.

Developing molecular tools to visualize and control Rho GTPase signaling in living cells has been instrumental in elucidating the mechanisms of cytoskeletal reorganization and causal relationships between activation events in cell function. An indispensable part of such studies is the quantitative characterization of the spatiotemporal GTPase activity. Here we present a computational pipeline, EdgeProps, designed for comparative/correlative analysis of cell dynamics (edge velocity) and near-edge protein activity (intensity of a fluorescent signal). The tool offers a user-friendly interface with three functional modules for processing, visualization, and statistical characterization of single-cell imaging data.

Ratiometric Imaging Using a Single Dye Enables Simultaneous Visualization of Rac1 and Cdc42 Activation.

Biosensors that report endogenous protein activity in vivo can be based on environment-sensing fluorescent dyes. The dyes can be attached to reagents that bind selectively to a specific conformation of the targeted protein, such that binding leads to a fluorescence change. Dyes that are sufficiently bright for use at low, nonperturbing intracellular concentrations typically undergo changes in intensity rather than the shifts in excitation or emission maxima that would enable precise quantitation through ratiometric imaging. We report here mero199, an environment-sensing dye that undergoes a 33 nm solvent-dependent shift in excitation. The dye was used to generate a ratiometric biosensor of Cdc42 (CRIB199) without the need for additional fluorophores. CRIB199 was used in the same cell with a FRET sensor of Rac1 activation to simultaneously observe Cdc42 and Rac1 activity in cellular protrusions, indicating that Rac1 but not Cdc42 activity was reduced during tail retraction, and specific protrusions had reduced Cdc42 activity. A novel program (EdgeProps) used to correlate localized activation with cell edge dynamics indicated that Rac1 was specifically reduced during retraction.