Mathematical model of chronic pancreatitis

Circulating immune signatures in chronic pancreatitis with and without preceding acute pancreatitis: A pilot study

OBJECTIVE

To investigate profiles of circulating immune signatures in healthy controls and chronic pancreatitis patients (CP) with and without a preceding history of acute pancreatitis (AP).

METHODS

We performed a phase 1, cross-sectional analysis of prospectively collected serum samples from the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translation StuDies (PROCEED) study. All samples were collected during a clinically quiescent phase. CP subjects were categorized into two subgroups based on preceding episode(s) of AP. Healthy controls were included for comparison. Blinded samples were analyzed using an 80-plex Luminex assay of cytokines, chemokines, and adhesion molecules. Group and pairwise comparisons of analytes were performed between the subgroups.

RESULTS

In total, 133 patients with CP (111 with AP and 22 without AP) and 50 healthy controls were included. Among the 80 analytes studied, CP patients with a history of AP had significantly higher serum levels of pro-inflammatory cytokines (interleukin (IL)-6, IL-8, IL-1 receptor antagonist, IL-15) and chemokines (Cutaneous T-Cell Attracting Chemokine (CTACK), Monokine induced Gamma Interferon (MIG), Macrophage-derived Chemokine (MDC), Monocyte Chemoattractant Protein-1 (MCP-1)) compared to CP without preceding AP and controls. In contrast, CP patients without AP had immune profiles characterized by low systemic inflammation and downregulation of anti-inflammatory mediators, including IL-10.

CONCLUSION

CP patients with a preceding history of AP have signs of systemic inflammatory activity even during a clinically quiescent phase. In contrast, CP patients without a history of AP have low systemic inflammatory activity. These findings suggest the presence of two immunologically diverse subtypes of CP.

Mathematical models of cystic fibrosis as a systemic disease

Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.

Mechanotransductive receptor Piezo1 as a promising target in the treatment of fibrosis diseases

Fibrosis could happen in every organ, leading to organic malfunction and even organ failure, which poses a serious threat to global health. Early treatment of fibrosis has been reported to be the turning point, therefore, exploring potential correlates in the pathogenesis of fibrosis and how to reverse fibrosis has become a pressing issue. As a mechanism-sensitive cationic calcium channel, Piezo1 turns on in response to changes in the lipid bilayer of the plasma membrane. Piezo1 exerts multiple biological roles, including inhibition of inflammation, cytoskeletal stabilization, epithelial-mesenchymal transition, stromal stiffness, and immune cell mechanotransduction, interestingly enough. These processes are closely associated with the development of fibrotic diseases. Recent studies have shown that deletion or knockdown of Piezo1 attenuates the onset of fibrosis. Therefore, in this paper we comprehensively describe the biology of this gene, focusing on its potential relevance in pulmonary fibrosis, renal fibrosis, pancreatic fibrosis, and cardiac fibrosis diseases, except for the role of drugs (agonists), increased intracellular calcium and mechanical stress using this gene in alleviating fibrosis.

Optimal controlling of anti-TGF-[Formula: see text] and anti-PDGF medicines for preventing pulmonary fibrosis

In the repair of injury, some transforming growth factor-[Formula: see text]s (TGF-[Formula: see text]s) and platelet-derived growth factors (PDGFs) bind to fibroblast receptors as ligands and cause the differentiation of fibroblasts into myofibroblasts. When the injury repair is repeated, the myofibroblasts proliferate excessively, forming fibrotic tissue. We goal to control myofibroblasts proliferation and apoptosis with anti-transforming growth factor-[Formula: see text] (anti-TGF-[Formula: see text]) and anti-platelet-derived growth factor (anti-PDGF) medicines. The novel optimal regulator control problem with two controls (medicines) is proposed to simulate how to the preventing pulmonary fibrosis. Idiopathic pulmonary fibrosis (IPF) consists of restoring a system of cells, protein, and tissue networks with injury and scar. Myofibroblasts proliferation back to its equilibrium position after it has been disturbed by abnormal repair. Thus, the optimal regulator control problem with a parabolic partial differential equation as a constraint, zero flux boundary, and given specific initial conditions, is considered. The myofibroblast diffusion equation stands as a governing dynamic system while the objective function is the summation of myofibroblast, anti-TGF-[Formula: see text] and anti-PDGF medicines for the fixed final time. Here, myofibroblast is a nonlinear state of time while anti-TGF-[Formula: see text] and anti-PDGF are two unknown control functions. In order to solve the corresponding problem a weighted Galerkin method is used. Firstly, we convert the myofibroblast diffusion equation to a system of ordinary differential equations using the Lagrangian interpolation polynomials defined at Gauss-Lobatto integration points. Secondly, by the calculus of variations, the optimal control problem is solved successfully using canonical Hamiltonian and extended Riccati equations. Numerical results are given, and the plots are depicted. Moreover, solutions to the problem in which there is no control are compared. Numerical results show that, over time, the myofibroblast increases and then remains constant when there is no control. In contrast, the current solution decreases and vanishes after 300 days by prescribing controller medicines for anti-TGF-[Formula: see text] and anti-PDGF. The optimal strategy proposed in this paper helps practitioners to reduce myofibroblasts by controlling both anti-TGF-[Formula: see text] and anti-PDGF medicines.

Optimal control of TGF-β to prevent formation of pulmonary fibrosis

In this paper, three optimal control problems are proposed to prevent forming lung fibrosis while control is transforming growth factor-β (TGF-β) in the myofibroblast diffusion process. Two diffusion equations for fibroblast and myofibroblast are mathematically formulated as the system's dynamic, while different optimal control model problems are proposed to find the optimal TGF-β. During solving the first optimal control problem with the regulator objection function, it is understood that the control function gets unexpected negative values. Thus, in the second optimal control problem, for the control function, the non-negative constraint is imposed. This problem is solved successfully using the extended canonical Hamiltonian equations with no flux boundary conditions. Pontryagin's minimum principle is used to solve the related optimal control problems successfully. In the third optimal control problem, the fibroblast equation is added to a dynamic system consisting of the partial differential equation. The two-dimensional diffusion equations for fibroblast and myofibroblast are transferred to a system of ordinary differential equations using the central finite differences explicit method. Three theorems and two propositions are proved using extended Pontryagin's minimum principle and the extended Hamiltonian equations. Numerical results are given. We believe that this optimal strategy can help practitioners apply some medication to reduce the TGF-β in preventing the formation of pulmonary fibrosis.

Immune cells and their inflammatory mediators modify β cells and cause checkpoint inhibitor-induced diabetes

Checkpoint inhibitors (CPIs) targeting programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) have revolutionized cancer treatment but can trigger autoimmune complications, including CPI-induced diabetes mellitus (CPI-DM), which occurs preferentially with PD-1 blockade. We found evidence of pancreatic inflammation in patients with CPI-DM with shrinkage of pancreases, increased pancreatic enzymes, and in a case from a patient who died with CPI-DM, peri-islet lymphocytic infiltration. In the NOD mouse model, anti-PD-L1 but not anti-CTLA-4 induced diabetes rapidly. RNA sequencing revealed that cytolytic IFN-γ+CD8+ T cells infiltrated islets with anti-PD-L1. Changes in β cells were predominantly driven by IFN-γ and TNF-α and included induction of a potentially novel β cell population with transcriptional changes suggesting dedifferentiation. IFN-γ increased checkpoint ligand expression and activated apoptosis pathways in human β cells in vitro. Treatment with anti-IFN-γ and anti-TNF-α prevented CPI-DM in anti-PD-L1-treated NOD mice. CPIs targeting the PD-1/PD-L1 pathway resulted in transcriptional changes in β cells and immune infiltrates that may lead to the development of diabetes. Inhibition of inflammatory cytokines can prevent CPI-DM, suggesting a strategy for clinical application to prevent this complication.

Mathematical investigation into the role of macrophage heterogeneity on the temporal and spatio-temporal dynamics of non-small cell lung cancers

Non Small Cell Lung Cancer (NSCLC) is the most common type of lung cancer, and represents the leading cause of cancer-related deaths worldwide. Experimental studies have shown that these solid cancers are heavily infiltrated with macrophages: anti-tumour M1 macrophages, pro-tumour M2 macrophages, and macrophage subtypes sharing both M1 and M2 properties. In this study we aim to investigate qualitatively the role of macrophages with different functional phenotypes (especially those with mixed phenotypes) on cancer dynamics and the success of different immunotherapies for cancer. To this end, we start with two time-evolving mathematical models for cancer-immune interactions that consider: (i) the effect of the two extreme phenotypes, M1 and M2 cells; (ii) the effect of M1 and M2 cells, as well as a macrophage sub-population with a mixed phenotype (throughout this theoretical study we call these cells "M12 cells"). We compare the dynamics of the two models using computational approaches, paying particular attention to the effect of different anti-cancer immunotherapies that focus on macrophages. Since data available for NSCLC and macrophage interactions are incomplete, we perform a global sensitivity analysis to see the influence of input parameters on model outcomes. Finally, we consider extensions of the previous two models to include also the spatial movement of cells, and investigate the role of macrophages with extreme phenotypes and with mixed phenotypes, on the invasion of cancer cells into the surrounding extracellular matrix (ECM). We use numerical simulations to investigate the macrophages phenotypes at the tumour center versus the invasive margin. Again, we examine the impact of immunotherapies for cancer on the spatial dynamics of cancers and immune cells, and observe a shift in the phenotype of macrophages distributed at the tumour center and invasive margin.

Activated Pancreatic Stellate Cells Enhance the Warburg Effect to Cause the Malignant Development in Chronic Pancreatitis

Chronic pancreatitis (CP) is a precancerous condition associated with pancreatic ductal adenocarcinoma (PDAC), but its evolutionary mechanism is unclear. Pancreatic stellate cells (PSCs) are closely related to the occurrence and development of CP and PDAC, but it is not clear whether PSCs play a key role in this “inflammation-cancer transition”. Our research found that co-culture with activated PSCs promoted the proliferation, migration and invasion of normal pancreatic duct epithelial cells and pancreatic cancer cells. At the same time, activated PSCs had a significant effect on the expression of the glycolysis markers (pyruvate kinase M2, lactate dehydrogenase A, glucose transporter 1, hexokinase-II and monocarboxylate transporter 4; PKM2, LDHA, GLUT1, HK2 and MCT4) in normal pancreatic duct epithelial cells and pancreatic cancer cells and increased lactic acid production and glucose consumption in these two cells. In vivo experiments showed that the expression of the glycolysis markers in pancreatic duct epithelial cells and the marker protein (α-SMA) of activated PSCs in the pancreatic duct peripancreatic interstitium were higher in pancreatic cancer tissues and chronic pancreatitis tissues than in normal pancreatic tissues in both animals and humans. In addition, analysis of human tissue specimens showed that there is a correlation between the expression of glycolysis markers and α-SMA. These findings indicate that activated PSCs play an important role in the development and progression of chronic pancreatitis into pancreatic cancer by regulating and promoting aerobic glycolysis. Our research provides a new theoretical basis for further understanding the mechanism of CP malignancy and the selection of targets for reversing CP malignancy.

Mathematical model of atherosclerotic aneurysm

Atherosclerosis is a major cause of abdominal aortic aneurysm (AAA) and up to 80% of AAA patients have atherosclerosis. Therefore it is critical to understand the relationship and interactions between atherosclerosis and AAA to treat atherosclerotic aneurysm patients more effectively. In this paper, we develop a mathematical model to mimic the progression of atherosclerotic aneurysms by including both the multi-layer structured arterial wall and the pathophysiology of atherosclerotic aneurysms. The model is given by a system of partial differential equations with free boundaries. Our results reveal a 2D biomarker, the cholesterol ratio and DDR1 level, assessing the risk of atherosclerotic aneurysms. The efficacy of different treatment plans is also explored via our model and suggests that the dosage of anti-cholesterol drugs is significant to slow down the progression of atherosclerotic aneurysms while the additional anti-DDR1 injection can further reduce the risk.

Mechanisms of a Sustained Anti-inflammatory Drug Response in Alveolar Macrophages Unraveled with Mathematical Modeling

Both initiation and suppression of inflammation are hallmarks of the immune response. If not balanced, the inflammation may cause extensive tissue damage, which is associated with common diseases, e.g., asthma and atherosclerosis. Anti‐inflammatory drugs come with side effects that may be aggravated by high and fluctuating drug concentrations. To remedy this, an anti‐inflammatory drug should have an appropriate pharmacokinetic half‐life or better still, a sustained anti‐inflammatory drug response. However, we still lack a quantitative mechanistic understanding of such sustained effects. Here, we study the anti‐inflammatory response to a common glucocorticoid drug, dexamethasone. We find a sustained response 22 hours after drug removal. With hypothesis testing using mathematical modeling, we unravel the underlying mechanism—a slow release of dexamethasone from the receptor–drug complex. The developed model is in agreement with time‐resolved training and testing data and is used to simulate hypothetical treatment schemes. This work opens up for a more knowledge‐driven drug development to find sustained anti‐inflammatory responses and fewer side effects.

A mathematical model of the multiple sclerosis plaque

Multiple sclerosis is an autoimmune disease that affects white matter in the central nervous system. It is one of the primary causes of neurological disability among young people. Its characteristic pathological lesion is called a plaque, a zone of inflammatory activity and tissue destruction that expands radially outward by destroying the myelin and oligodendrocytes of white matter. The present paper develops a mathematical model of the multiple sclerosis plaques. Although these plaques do not provide reliable information of the clinical disability in MS, they are nevertheless useful as a primary outcome measure of Phase II trials. The model consists of a system of partial differential equations in a simplified geometry of the lesion, consisting of three domains: perivascular space, demyelinated plaque, and white matter. The model describes the activity of various pro- and anti-inflammatory cells and cytokines in the plaque, and quantifies their effect on plaque growth. We show that volume growth of plaques are in qualitative agreement with reported clinical studies of several currently used drugs. We then use the model to explore treatments with combinations of such drugs, and with experimental drugs. We finally consider the benefits of early vs. delayed treatment.

Chronic pancreatitis

Chronic pancreatitis is a multifactorial, fibroinflammatory syndrome in which repetitive episodes of pancreatic inflammation lead to extensive fibrotic tissue replacement, resulting in chronic pain, exocrine and endocrine pancreatic insufficiency, reduced quality of life, and a shorter life expectancy. The incidence and prevalence of chronic pancreatitis is rising and no curative treatment is available. Using novel diagnostic algorithms, definitive chronic pancreatitis can be diagnosed by imaging criteria alone, whereas probable chronic pancreatitis requires clinical features and imaging criteria. Criteria for the diagnosis of early chronic pancreatitis are still under discussion and need prospective validation in clinical trials. Cross-sectional imaging should be used first; endoscopic ultrasound is needed only when CT or MRI are inconclusive or to plan therapeutic interventions. Management of chronic pancreatitis requires an interdisciplinary approach including primary care practitioners, gastroenterologists, surgeons, radiologists, pain specialists, and nutritional therapists. Patients with chronic pancreatitis should be seen at least once a year and re-evaluated for causal risk factors, symptom control, and complications such as malnutrition, pancreatic exocrine insufficiency, and diabetes; refer to a specialised centre if symptoms are poorly controlled or there is risk of deterioration. Scoring systems to monitor disease progression have been developed and validated internationally. Interventional treatments for pain or cholestasis should be done by specialists only, and early discussion of treatment approaches should include all medical disciplines involved in care. Throughout this Seminar, we address research needs such as staging of pancreatitis, aspects of malnutrition and pain, and cancer surveillance, to help improve the care of patients.

Reduction of inflammation in chronic pancreatitis using a soy bread intervention: A feasibility study

INTRODUCTION

Chronic pancreatitis is a chronic inflammatory disease, which progresses to fibrosis. Currently there are no interventions to delay or stop the progression to irreversible organ damage. In this study, we assessed the tolerability and feasibility of administering soy bread to reduce circulating inflammatory mediators.

METHODS

Subjects with chronic pancreatitis diagnosed using the American Pancreatic Association diagnostic guidelines were enrolled. During the dose escalation (DE) phase, subjects received one week of soy bread based using a 3 + 3 dose-escalation design, which was then followed by a maximally tolerated dose (MTD) phase with four weeks of intervention. Dose-limiting toxicities (DLTs) were monitored. Plasma cytokine levels were measured using a Meso Scale Discovery multiplex assay kit. Isoflavonoid excretion in 24-h urine collection was used to measure soy bread compliance.

RESULTS

Nine subjects completed the DE phase, and one subject completed the MTD phase without any DLTs at a maximum dosage of three slices (99 mg of isoflavones) per day. Reported compliance to soy bread intervention was 98%, and this was confirmed with urinary isoflavones and their metabolites detected in all subjects. There was a significant decline in the TNF-α level during the DE phase (2.667 vs 2.382 pg/mL, p = 0.039); other levels were similar.

CONCLUSIONS

In this feasibility study, there was excellent compliance with a short-term intervention using soy bread in chronic pancreatitis. Reduction was seen in at least one pro-inflammatory cytokine with short-term intervention. Larger cohorts and longer interventions with soy are warranted to assess the efficacy of reducing pro-inflammatory mediators of disease.

TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model

Drug resistance is a primary obstacle in cancer treatment. In many patients who at first respond well to treatment, relapse occurs later on. Various mechanisms have been explored to explain drug resistance in specific cancers and for specific drugs. In this paper, we consider resistance to anti-PD-1, a drug that enhances the activity of anti-cancer T cells. Based on results in experimental melanoma, it is shown, by a mathematical model, that resistances to anti-PD-1 can be significantly reduced by combining it with anti-TNF-α. The model is used to simulate the efficacy of the combined therapy with different range of doses, different initial tumor volume, and different schedules. In particular, it is shown that under a course of treatment with 3-week cycles where each drug is injected in the first day of either week 1 or week 2, injecting anti-TNF-α one week after anti-PD-1 is the most effective schedule in reducing tumor volume.

Overcoming Drug Resistance to BRAF Inhibitor

One of the most frequently found mutations in human melanomas is in the B-raf gene, making its protein BRAF a key target for therapy. However, in patients treated with BRAF inhibitor (BRAFi), although the response is very good at first, relapse occurs within 6 months, on the average. In order to overcome this drug resistance to BRAFi, various combinations of BRAFi with other drugs have been explored, and some are being applied clinically, such as a combination of BRAF and MEK inhibitors. Experimental data for melanoma in mice show that under continuous treatment with BRAFi, the pro-cancer MDSCs and chemokine CCL2 initially decrease but eventually increase to above their original level, while the anticancer T cells continuously decrease. In this paper, we develop a mathematical model that explains these experimental results. The model is used to explore the efficacy of combinations of BRAFi with anti-CCL2, anti-PD-1 and anti-CTLA-4, with the aim of eliminating or reducing drug resistance to BRAFi.

Chronic Pancreatitis: Managing a Difficult Disease

Chronic pancreatitis is characterized by progressive, irreversible morphologic and functional changes that are most commonly attributed to environmental insults, particularly when there is a genetic or anatomic predisposition. Heavy alcohol use and cigarette smoking are the most common environmental risk factors, but both may be absent. Antecedent episodes of acute pancreatitis occur in about half of patients. Abdominal pain is the most common symptom and requires a tailored approach depending on the anatomic changes in the pancreas. Other clinical manifestations include diabetes mellitus, exocrine pancreatic insufficiency, metabolic bone disease, pancreatic cancer, and anatomic complications. Current disease management is centered on risk factor reduction and screening for and treating disease complications. There are no current therapies to delay or retard disease progression, but there are ongoing efforts to more fully understand the natural history of chronic pancreatitis and underlying mechanisms of disease. These studies are expected to provide insights that will transform our approach to disease management and provide increased hope to patients.

A multiscale in silico model of endothelial to mesenchymal transformation in a tumor microenvironment

Endothelial to mesenchymal transformation (EndMT) is a process in which endothelial cells gain a mesenchymal-like phenotype in response to mechanobiological signals that results in the remodeling or repair of underlying tissue. While initially associated with embryonic development, this process has since been shown to occur in adult tissue remodeling including wound healing, fibrosis, and cancer. In an attempt to understand the role of EndMT in cancer progression and metastasis, we present a multiscale, three-dimensional, in silico model. The model couples tissue level phenomena such as extracellular matrix remodeling, cellular level phenomena such as migration and proliferation, and chemical transport in the tumor microenvironment to mimic in vitro tissue models of the cancer microenvironment. The model is used to study the presence of EndMT-derived activated fibroblasts (EDAFs) and varying substrate stiffness on tumor cell migration and proliferation. The simulations accurately model the behavior of tumor cells under given conditions. The presence of EDAFs and/or an increase in substrate stiffness resulted in an increase in tumor cell activity. This model lays the foundation of further studies of EDAFs in a tumor microenvironment on a cellular and subcellular physiological level.

A structural methodology for modeling immune-tumor interactions including pro- and anti-tumor factors for clinical applications

The immune system turns out to have both stimulatory and inhibitory factors influencing on tumor growth. In recent years, the pro-tumor role of immunity factors such as regulatory T cells and TGF-β cytokines has specially been considered in mathematical modeling of tumor-immune interactions. This paper presents a novel structural methodology for reviewing these models and classifies them into five subgroups on the basis of immune factors included. By using our experimental data due to immunotherapy experimentation in mice, these five modeling groups are evaluated and scored. The results show that a model with a small number of variables and coefficients performs efficiently in predicting the tumor-immune system interactions. Though immunology theorems suggest to employ a larger number of variables and coefficients, more complicated models are here shown to be inefficient due to redundant parallel pathways. So, these models are trapped in local minima and restricted in prediction capability. This paper investigates the mathematical models that were previously developed and proposes variables and pathways that are essential for modeling tumor-immune. Using these variables and pathways, a minimal structure for modeling tumor-immune interactions is proposed for future studies.

Genetically induced vs. classical animal models of chronic pancreatitis: a critical comparison

Chronic pancreatitis (CP) is an utmost complex disease that is pathogenetically linked to pancreas-intrinsic ( e.g., duct obstruction), environmental-toxic ( e.g., alcohol, smoking), and genetic factors. Studying such a complex disease naturally requires validated experimental models. In the past 2 decades, the various animal models of CP usually addressed either the pancreas-intrinsic ( e.g., the caerulein model), the environmental-toxic ( e.g., diet-induced models), or the genetic component of CP. As such, these models were far from mirroring CP in its full spectrum, and the correct choice of models was vital for valid scientific conclusions on CP. The quest for mechanistic, genetic models gave rise to models based on gene modification and transgene insertion, such as the PRSS1 and the IL-1β/IL-1β models. Recently, we witnessed the development of highly exciting models that rely on the importance of autophagy in CP, that is, the murine pancreas-specific Atg5 and LAMP2 knockout models. Today, critical comparison of these several models is more important than ever for guiding research on CP in an efficient direction. The present review outlines the characteristics of the new genetic models in comparison with the well-known classic models for CP, notes the caveats in the choice of models, and also indicates novel directions for model development.-Klauss, S., Schorn, S., Teller, S., Steenfadt, H., Friess, H., Ceyhan, G. O., Demir, I. K. Genetically induced vs. classical animal models of chronic pancreatitis: a critical comparison.

Chronic hepatitis B virus and liver fibrosis: A mathematical model

Hepatitis B virus (HBV) infection is a liver disorder that can result in cirrhosis, liver failure and hepatocellular carcinoma. HBV infection remains a major global health problem, as it affects more 350 million people chronically and kills roughly 600,000 people annually. Drugs currently used against HBV include IFN-α that decreases viremia, inflammation and the growth of liver fibrosis, and adefovir that decreases the viral load. Each of these drugs can have severe side-effects. In the present paper, we consider the treatment of chronic HBV by a combination of IFN-α and adefovir, and raise the following question: What should be the optimal ratio between IFN-α and adefovir in order to achieve the best 'efficacy' under constraints on the total amount of the drugs; here the efficacy is measured by the reduction of the levels of inflammation and of fibrosis? We develop a mathematical model of HBV pathogenesis by a system of partial differential equations (PDEs) and use the model to simulate a 'synergy map' which addresses the above question.