Current practices for QSP model assessment: an IQ consortium survey.

Quantitative Systems Pharmacology (QSP) modeling is increasingly applied in the pharmaceutical industry to influence decision making across a wide range of stages from early discovery to clinical development to post-marketing activities. Development of standards for how these models are constructed, assessed, and communicated is of active interest to the modeling community and regulators but is complicated by the wide variability in the structures and intended uses of the underlying models and the diverse expertise of QSP modelers. With this in mind, the IQ Consortium conducted a survey across the pharmaceutical/biotech industry to understand current practices for QSP modeling. This article presents the survey results and provides insights into current practices and methods used by QSP practitioners based on model type and the intended use at various stages of drug development. The survey also highlights key areas for future development including better integration with statistical methods, standardization of approaches towards virtual populations, and increased use of QSP models for late-stage clinical development and regulatory submissions.

Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis.

BACKGROUND: Mechanistic biosimulation can be used in drug development to form testable hypotheses, develop predictions of efficacy before clinical trial results are available, and elucidate clinical response to therapy. However, there is a lack of tools to simultaneously (1) calibrate the prevalence of mechanistically distinct, large sets of virtual patients so their simulated responses statistically match phenotypic variability reported in published clinical trial outcomes, and (2) explore alternate hypotheses of those prevalence weightings to reflect underlying uncertainty in population biology. Here, we report the development of an algorithm, MAPEL (Mechanistic Axes Population Ensemble Linkage), which utilizes a mechanistically-based weighting method to match clinical trial statistics. MAPEL is the first algorithm for developing weighted virtual populations based on biosimulation results that enables the rapid development of an ensemble of alternate virtual population hypotheses, each validated by a composite goodness-of-fit criterion. RESULTS: Virtual patient cohort mechanistic biosimulation results were successfully calibrated with an acceptable composite goodness-of-fit to clinical populations across multiple therapeutic interventions. The resulting virtual populations were employed to investigate the mechanistic underpinnings of variations in the response to rituximab. A comparison between virtual populations with a strong or weak American College of Rheumatology (ACR) score in response to rituximab suggested that interferon ß (IFNß) was an important mechanistic contributor to the disease state, a signature that has previously been identified though the underlying mechanisms remain unclear. Sensitivity analysis elucidated key anti-inflammatory properties of IFNß that modulated the pathophysiologic state, consistent with the observed prognostic correlation of baseline type I interferon measurements with clinical response. Specifically, the effects of IFNß on proliferation of fibroblast-like synoviocytes and interleukin-10 synthesis in macrophages each partially counteract reductions in synovial inflammation imparted by rituximab. A multianalyte biomarker panel predictive for virtual population therapeutic responses suggested population dependencies on B cell-dependent mediators as well as additional markers implicating fibroblast-like synoviocytes. CONCLUSIONS: The results illustrate how the MAPEL algorithm can leverage knowledge of cellular and molecular function through biosimulation to propose clear mechanistic hypotheses for differences in clinical populations. Furthermore, MAPEL facilitates the development of multianalyte biomarkers prognostic of patient responses in silico.

IL-23 stimulates epidermal hyperplasia via TNF and IL-20R2-dependent mechanisms with implications for psoriasis pathogenesis.

Aberrant cytokine expression has been proposed as an underlying cause of psoriasis, although it is unclear which cytokines play critical roles. Interleukin (IL)-23 is expressed in human psoriasis and may be a master regulator cytokine. Direct intradermal administration of IL-23 in mouse skin, but not IL-12, initiates a tumor necrosis factor-dependent, but IL-17A-independent, cascade of events resulting in erythema, mixed dermal infiltrate, and epidermal hyperplasia associated with parakeratosis. IL-23 induced IL-19 and IL-24 expression in mouse skin, and both genes were also elevated in human psoriasis. IL-23-dependent epidermal hyperplasia was observed in IL-19-/- and IL-24-/- mice, but was inhibited in IL-20R2-/- mice. These data implicate IL-23 in the pathogenesis of psoriasis and support IL-20R2 as a novel therapeutic target.

Target modulation and pharmacokinetics/pharmacodynamics translation of the BTK inhibitor poseltinib for model-informed phase II dose selection.

The selective Bruton tyrosine kinase (BTK) inhibitor poseltinib has been shown to inhibit the BCR signal transduction pathway and cytokine production in B cells (Park et al. Arthritis Res. Ther. 18, 91, https://doi.org/10.1186/s13075-016-0988-z , 2016). This study describes the translation of nonclinical research studies to a phase I clinical trial in healthy volunteers in which pharmacokinetics (PKs) and pharmacodynamics (PDs) were evaluated for dose determination. The BTK protein kinase inhibitory effects of poseltinib in human peripheral blood mononuclear cells (PBMCs) and in rats with collagen-induced arthritis (CIA) were evaluated. High-dimensional phosphorylation analysis was conducted on human immune cells such as B cells, CD8 + memory cells, CD4 + memory cells, NK cells, neutrophils, and monocytes, to map the impact of poseltinib on BTK/PLC and AKT signaling pathways. PK and PD profiles were evaluated in a first-in-human study in healthy donors, and a PK/PD model was established based on BTK occupancy. Poseltinib bound to the BTK protein and modulated BTK phosphorylation in human PBMCs. High-dimensional phosphorylation analysis of 94 nodes showed that poseltinib had the highest impact on anti-IgM + CD40L stimulated B cells, however, lower impacts on anti-CD3/CD-28 stimulated T cells, IL-2 stimulated CD4 + T cells and NK cells, M-CSF stimulated monocytes, or LPS-induced granulocytes. In anti-IgM + CD40L stimulated B cells, poseltinib inhibited the phosphorylation of BTK, AKT, and PLCγ2. Moreover, poseltinib dose dependently improved arthritis disease severity in CIA rat model. In a clinical phase I trial for healthy volunteers, poseltinib exhibited dose-dependent and persistent BTK occupancy in PBMCs of all poseltinib-administrated patients in the study. More than 80% of BTK occupancy at 40 mg dosing was maintained for up to 48 h after the first dose. A first-in-human healthy volunteer study of poseltinib established target engagement with circulating BTK protein. Desirable PK and PD properties were observed, and a modeling approach was used for rational dose selection for subsequent trials. Poseltinib was confirmed as a potential BTK inhibitor for the treatment of autoimmune diseases.Trial registration: This article includes the results of a clinical intervention on human participants [NCT01765478].

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities.

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Preclinical QSP Modeling in the Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape.

A cross-industry survey was conducted to assess the landscape of preclinical quantitative systems pharmacology (QSP) modeling within pharmaceutical companies. This article presents the survey results, which provide insights on the current state of preclinical QSP modeling in addition to future opportunities. Our results call attention to the need for an aligned definition and consistent terminology around QSP, yet highlight the broad applicability and benefits preclinical QSP modeling is currently delivering.

Detecting rapid and transient upregulation of leukocyte integrin affinity induced by chemokines and chemoattractants.

The majority of integrins expressed on circulating leukocytes are quiescent and do not mediate adhesion. In the process of emigrating from blood into tissues, leukocytes are exposed to a variety of stimuli, including chemokines, which rapidly activate integrin function through changes in affinity and/or avidity. High affinity is brought about by a change in integrin conformation and results in formation of a stable bond with ligand. This can be measured in soluble ligand-binding assays. Integrin conformation changes also result in exposure of previously masked epitopes that can be detected with monoclonal antibodies. In this report, methods used to detect high affinity integrins are reviewed. Four different approaches are discussed, including detection of masked epitopes with monoclonal antibodies, and binding of soluble ligands, ligand-coated beads, and ligand-mimetic peptides. Improved techniques to measure rapid changes in integrin affinity in real time may be valuable both to study mechanisms of inside-out integrin activation and as a tool to screen for effective inhibitors of integrin activation in inflammatory disease models.

Preexisting autoantibodies predict efficacy of oral insulin to cure autoimmune diabetes in combination with anti-CD3.

We have previously developed a combination therapy (CT) using anti-CD3 monoclonal antibodies together with islet-(auto)antigen immunizations that can more efficiently reverse type 1 diabetes (T1D) than either entity alone. However, clinical translation of antigen-specific therapies in general is hampered by the lack of biomarkers that could be used to optimize the modalities of antigen delivery and to predict responders from nonresponders. To support the rapid identification of candidate biomarkers, we systematically evaluated multiple variables in a mathematical disease model. The in silico predictions were validated by subsequent laboratory data in NOD mice with T1D that received anti-CD3/oral insulin CT. Our study shows that higher anti-insulin autoantibody levels at diagnosis can distinguish responders and nonresponders among recipients of CT exquisitely well. In addition, early posttreatment changes in proinflammatory cytokines were indicative of long-term remission. Coadministration of oral insulin improved and prolonged the therapeutic efficacy of anti-CD3 therapy, and long-term protection was achieved by maintaining elevated insulin-specific regulatory T cell numbers that efficiently lowered diabetogenic effector memory T cells. Our validation of preexisting autoantibodies as biomarkers to distinguish future responders from nonresponders among recipients of oral insulin provides a compelling and mechanistic rationale to more rapidly translate anti-CD3/oral insulin CT for human T1D.

Virtual optimization of nasal insulin therapy predicts immunization frequency to be crucial for diabetes protection.

OBJECTIVE: Development of antigen-specific strategies to treat or prevent type 1 diabetes has been slow and difficult because of the lack of experimental tools and defined biomarkers that account for the underlying therapeutic mechanisms. RESEARCH DESIGN AND METHODS: The type 1 diabetes PhysioLab platform, a large-scale mathematical model of disease pathogenesis in the nonobese diabetic (NOD) mouse, was used to investigate the possible mechanisms underlying the efficacy of nasal insulin B:9-23 peptide therapy. The experimental aim was to evaluate the impact of dose, frequency of administration, and age at treatment on Treg induction and optimal therapeutic outcome. RESULTS: In virtual NOD mice, treatment efficacy was predicted to depend primarily on the immunization frequency and stage of the disease and to a lesser extent on the dose. Whereas low-frequency immunization protected from diabetes atrributed to Treg and interleukin (IL)-10 induction in the pancreas 1-2 weeks after treatment, high-frequency immunization failed. These predictions were confirmed with wet-lab approaches, where only low-frequency immunization started at an early disease stage in the NOD mouse resulted in significant protection from diabetes by inducing IL-10 and Treg. CONCLUSIONS: Here, the advantage of applying computer modeling in optimizing the therapeutic efficacy of nasal insulin immunotherapy was confirmed. In silico modeling was able to streamline the experimental design and to identify the particular time frame at which biomarkers associated with protection in live NODs were induced. These results support the development and application of humanized platforms for the design of clinical trials (i.e., for the ongoing nasal insulin prevention studies).

Phospholipase C, calcium, and calmodulin are critical for alpha4beta1 integrin affinity up-regulation and monocyte arrest triggered by chemoattractants.

During inflammation, monocytes roll on activated endothelium and arrest after stimulation by proteoglycan-bound chemokines and other chemoattractants. We investigated signaling pathways downstream of G protein-coupled receptors (GPCRs) that are relevant to alpha4beta1 integrin affinity up-regulation using formyl peptide receptor-transfected U937 cells stimulated with fMLP or stromal-derived factor-1alpha and human peripheral blood monocytes stimulated with multiple chemokines or chemoattractants. The up-regulation of soluble LDV peptide or vascular cell adhesion molecule-1 (VCAM-1) binding by these stimuli was critically dependent on activation of phospholipase C (PLC), inositol 1,4,5-triphosphate receptors, increased intracellular calcium, influx of extracellular calcium, and calmodulin, suggesting that this signaling pathway is required for alpha4 integrins to assume a high-affinity conformation. In fact, a rise in intracellular calcium following treatment with thapsigargin or ionomycin was sufficient to induce binding of ligand. Blockade of p44/42 and p38 mitogen-activated protein (MAP) kinases, phosphoinositide 3-kinase, or protein kinase C (PKC) signaling did not inhibit chemoattractant-induced LDV or VCAM-1 binding. However, activation of PKC by phorbol ester up-regulated alpha4beta1 affinity with kinetics distinct from those of GPCR signaling. A critical role for PLC and calmodulin was also established for leukocyte arrest and adhesion strengthening.

Development, cytokine profile and function of human interleukin 17-producing helper T cells.

T(H)-17 cells are a distinct lineage of proinflammatory T helper cells that are essential for autoimmune disease. In mice, commitment to the T(H)-17 lineage is dependent on transforming growth factor-beta and interleukin 6 (IL-6). Here we demonstrate that IL-23 and IL-1beta induced the development of human T(H)-17 cells expressing IL-17A, IL-17F, IL-22, IL-26, interferon-gamma, the chemokine CCL20 and transcription factor RORgammat. In situ, T(H)-17 cells were identified by expression of the IL-23 receptor and the memory T cell marker CD45RO. Psoriatic skin lesions contained IL-23-producing dendritic cells and were enriched in the cytokines produced by human T(H)-17 cells that promote the production of antimicrobial peptides in human keratinocytes. Our data collectively indicate that human and mouse T(H)-17 cells require distinct factors during differentiation and that human T(H)-17 cells may regulate innate immunity in epithelial cells.

Modulation of Mac-1 (CD11b/CD18)-mediated adhesion by the leukocyte-specific protein 1 is key to its role in neutrophil polarization and chemotaxis.

Leukocyte-specific protein 1 (LSP1) is an intracellular filamentous-actin binding protein which modulates cell motility. The cellular process in which LSP1 functions to regulate motility is not yet identified. In this study, we show that LSP1 negatively regulates fMLP-induced polarization and chemotaxis of neutrophils through its function on adhesion via specific integrins. Using LSP1-deficient (Lsp1(-/-)) mice, we show increased neutrophil migration into mouse knee joints during zymosan-induced acute inflammation, an inflammatory model in which the number of resident synoviocytes are not affected by LSP1-deficiency. In vitro chemotaxis experiments performed by time-lapse videomicroscopy showed that purified Lsp1(-/-) bone-marrow neutrophils exhibit an increased migration rate toward a gradient of fMLP as compared with wild-type neutrophils. This difference was observed when cells migrated on fibrinogen, but not fibronectin, suggesting a role for LSP1 in modulating neutrophil adhesion by specific integrins. LSP1 is also a negative regulator of fMLP-induced adhesion to fibrinogen or ICAM-1, but not to ICAM-2, VCAM-1, or fibronectin. These results suggest that LSP1 regulates the function of Mac-1 (CD11b/CD18), which binds only to fibrinogen and ICAM-1 among the substrates we tested. fMLP-induced filamentous actin polarization is also increased in the absence of LSP1 when cells were layered on fibrinogen, but not on fibronectin. Our findings suggest that the increased neutrophil recruitment in Lsp1(-/-) mice during acute inflammation derives from the negative regulatory role of LSP1 on neutrophil adhesion, polarization, and migration via specific integrins, such as Mac-1, which mediate neutrophil responses to chemotactic stimuli.