Modeling of celiac disease immune response and the therapeutic effect of potential drugs

Microscopic Colitis and Celiac Disease: Sharing More than a Diagnostic Overlap

Microscopic colitis (MC) is an emergent group of chronic inflammatory diseases of the colon, and celiac disease (CD) is a chronic gluten-induced immune-mediated enteropathy affecting the small bowel. We performed a narrative review to provide an overview regarding the relationship between both disorders, analyzing the most recent studies published at the epidemiological, clinical and pathophysiological levels. In fact, MC and CD are concomitantly prevalent in approximately 6% of the cases, mainly in the subset of refractory patients. Thus, physicians should screen refractory patients with CD against MC and vice versa. Both disorders share more than a simple epidemiological association, being multifactorial diseases involving innate and adaptive immune responses to known or unknown luminal factors based on a rather common genetic ground. Moreover, autoimmunity is a shared characteristic between the patients with MC and those with CD, with autoimmunity in the latter being quite well-established. Furthermore, CD and MC share some common clinical symptoms and risk factors and overlap with other gastrointestinal diseases, but some differences exist between both disorders. More studies are therefore needed to better understand the complex mechanisms involving the common pathogenetic ground contributing to the CD and MC epidemiological association.

Mathematical modeling in autoimmune diseases: from theory to clinical application

The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.

CYTOCON: The manually curated database of human in vivo cell and molecule concentrations

Cell and cYTOkine CONcentrations DataBase (CYTOCON DB) is a project undertaken by the InSysBio team and aimed at the development of a database that allows collecting, processing, and visualizing publicly available in vivo human data on baseline concentrations of cells, cytokines, chemokines, and other molecules. Besides manual curation, an important feature of CYTOCON is that most values found in papers are converted from a huge variety of original units (i.e., mg/ml and number of cells per mm² of biopsy surface section) to unified units: "pM" for cytokine and "kcell/L" for cell concentration. These features of the database can help researchers facilitate the creation and calibration of quantitative systems pharmacology (QSP) models. Possible applications can be: estimation of the average value or variability of the cell, cytokine or chemokine concentrations for patient groups with different characteristics, such as age, gender, disease severity, disease subtype, etc.; and analysis of correlations among the cell, cytokine, or chemokine concentrations in patients with different characteristics, such as severity of the disease.

FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective

The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

Immunopathogenesis and therapeutic approaches in pediatric celiac disease

Celiac Disease is an autoimmune enteropathy with increasing incidence worldwide in both adults and children. It occurs as an inflammatory condition with destruction of the normal architecture of villi on consumption of gluten and related protein products found in wheat, barley and rye. However, the exact pathogenesis is not yet fully understood. A gluten-free diet remains the main modality of therapy to date. While some patients continue to have symptoms even on a gluten-free diet, adherence to this diet is also difficult, especially for the children. Hence, there is continued interest in novel methods of therapy and the current research focus is on the promising novel non-dietary modalities of treatment. Here, we critically reviewed the existing literature regarding the pathogenesis of celiac disease in children including the role of in-utero exposure leading to neonatal and infant sensitization and its application for the development of new therapeutic approaches for these patients.

The potential utility of tight junction regulation in celiac disease: focus on larazotide acetate

Celiac disease (CD) is a common chronic immune disease triggered by gluten. Gliadin peptides pass through the epithelial layers, either paracellularly or transcellularly, to launch a potent adaptive immune response in the lamina propria. This aberrant immune response leads to diverse gastrointestinal and extra-gastrointestinal symptoms. Currently, the only treatment for CD is a strict lifelong adherence to a gluten-free diet (GFD), which can be challenging. An early effect of gluten in CD is an increase in gut permeability. Larazotide acetate, also known as AT-1001, is a synthetic peptide developed as a permeability regulator primarily targeting CD. In vitro studies indicate that larazotide acetate is capable of inhibiting the actin rearrangement caused by gliadin and clinical studies have been conducted using this peptide as a therapy for CD.