Minimal brain PBPK model to support the preclinical and clinical development of antibody therapeutics for CNS diseases.

There are several antibody therapeutics in preclinical and clinical development, industry-wide, for the treatment of central nervous system (CNS) disorders. Due to the limited permeability of antibodies across brain barriers, the quantitative understanding of antibody exposure in the CNS is important for the design of antibody drug characteristics and determining appropriate dosing regimens. We have developed a minimal physiologically-based pharmacokinetic (mPBPK) model of the brain for antibody therapeutics, which was reduced from an existing multi-species platform brain PBPK model. All non-brain compartments were combined into a single tissue compartment and cerebral spinal fluid (CSF) compartments were combined into a single CSF compartment. The mPBPK model contains 16 differential equations, compared to 100 in the original PBPK model, and improved simulation speed approximately 11-fold. Area under the curve ratios for minimal versus full PBPK models were close to 1 across species for both brain and plasma compartments, which indicates the reduced model simulations are similar to those of the original model. The minimal model retained detailed physiological processes of the brain while not significantly affecting model predictability, which supports the law of parsimony in the context of balancing model complexity with added predictive power. The minimal model has a variety of applications for supporting the preclinical development of antibody therapeutics and can be expanded to include target information for evaluating target engagement to inform clinical dose selection.

Multiscale, patient-specific computational fluid dynamics models predict formation of neointimal hyperplasia in saphenous vein grafts.

Stenosis due to neointimal hyperplasia (NIH) is among the major causes of peripheral graft failure. Its link to abnormal hemodynamics in the graft is complex, and isolated use of hemodynamic markers is insufficient to fully capture its progression. Here, a computational model of NIH growth is presented, establishing a link between computational fluid dynamics simulations of flow in the lumen and a biochemical model representing NIH growth mechanisms inside the vessel wall. For all three patients analyzed, NIH at proximal and distal anastomoses was simulated by the model, with values of stenosis comparable to the computed tomography scans.

An in silico study of the influence of vessel wall deformation on neointimal hyperplasia progression in peripheral bypass grafts.

Neointimal hyperplasia (NIH) is a major obstacle to graft patency in the peripheral arteries. A complex interaction of biomechanical factors contribute to NIH development and progression, and although haemodynamic markers such as wall shear stress have been linked to the disease, these have so far been insufficient to fully capture its behaviour. Using a computational model linking computational fluid dynamics (CFD) simulations of blood flow with a biochemical model representing NIH growth mechanisms, we analyse the effect of compliance mismatch, due to the presence of surgical stitches and/or to the change in distensibility between artery and vein graft, on the haemodynamics in the lumen and, subsequently, on NIH progression. The model enabled to simulate NIH at proximal and distal anastomoses of three patient-specific end-to-side saphenous vein grafts under two compliance-mismatch configurations, and a rigid wall case for comparison, obtaining values of stenosis similar to those observed in the computed tomography (CT) scans. The maximum difference in time-averaged wall shear stress between the rigid and compliant models was 3.4 Pa, and differences in estimation of NIH progression were only observed in one patient. The impact of compliance on the haemodynamic-driven development of NIH was small in the patient-specific cases considered.

Impaired LXRα Phosphorylation Attenuates Progression of Fatty Liver Disease.

Non-alcoholic fatty liver disease (NAFLD) is a very common indication for liver transplantation. How fat-rich diets promote progression from fatty liver to more damaging inflammatory and fibrotic stages is poorly understood. Here, we show that disrupting phosphorylation at Ser196 (S196A) in the liver X receptor alpha (LXRα, NR1H3) retards NAFLD progression in mice on a high-fat-high-cholesterol diet. Mechanistically, this is explained by key histone acetylation (H3K27) and transcriptional changes in pro-fibrotic and pro-inflammatory genes. Furthermore, S196A-LXRα expression reveals the regulation of novel diet-specific LXRα-responsive genes, including the induction of Ces1f, implicated in the breakdown of hepatic lipids. This involves induced H3K27 acetylation and altered LXR and TBLR1 cofactor occupancy at the Ces1f gene in S196A fatty livers. Overall, impaired Ser196-LXRα phosphorylation acts as a novel nutritional molecular sensor that profoundly alters the hepatic H3K27 acetylome and transcriptome during NAFLD progression placing LXRα phosphorylation as an alternative anti-inflammatory or anti-fibrotic therapeutic target.

Quantitative Systems Pharmacology Model for Alzheimer Disease Indicates Targeting Sphingolipid Dysregulation as Potential Treatment Option.

Alzheimer disease (AD) is a devastating neurodegenerative disorder with high unmet medical need. Drug development is hampered by limited understanding of the disease and its driving factors. Quantitative Systems Pharmacology (QSP) modeling provides a comprehensive quantitative framework to evaluate the relevance of biological mechanisms in the context of disease and to predict the efficacy of novel treatments. Here, we report a QSP model for AD with a particular focus on investigating the relevance of dysregulation of cholesterol and sphingolipids. We show that our model captures the modulation of several biomarkers in subjects with AD, as well as the response to pharmacological interventions. We evaluate the impact of targeting the sphingosine-1-phosphate 5 receptor (S1PR5) as a potential novel treatment option for AD, and model predictions increase our confidence in this novel disease pathway. Future applications for the QSP model are in validation of further targets and identification of potential treatment response biomarkers.

Understanding the Effect of Statins and Patient Adherence in Atherosclerosis via a Quantitative Systems Pharmacology Model Using a Novel, Hybrid, and Multi-Scale Approach.

Background and Objective: Statins are one of the most prescribed drugs to treat atherosclerosis. They inhibit the hepatic HMG-CoA reductase, causing a reduction of circulating cholesterol and LDL levels. Statins have had undeniable success; however, the benefits of statin therapy crystallize only if patients adhere to the prescribed treatment, which is far away from reality since adherence decreases with time with around half of patients discontinue statin therapy within the first year. The objective of this work is to; firstly, demonstrate a formal in-silico methodology based on a hybrid, multiscale mathematical model used to study the effect of statin treatment on atherosclerosis under different patient scenarios, including cases where the influence of medication adherence is examined and secondly, to propose a flexible simulation framework that allows extensions or simplifications, allowing the possibility to design other complex simulation strategies, both interesting features for software development. Methods: Different mathematical modeling paradigms are used to present the relevant dynamic behavior observed in biological/physiological data and clinical trials. A combination of continuous and discrete event models are coupled to simulate the pharmacokinetics (PK) of statins, their pharmacodynamic (PD) effect on lipoproteins levels (e.g., LDL) and relevant inflammatory pathways whilst simultaneously studying the dynamic effect of flow-related variables on atherosclerosis progression. Results: Different scenarios were tested showing the impact of: (1) patient variability: a virtual population shows differences in plaque growth for different individuals could be as high as 100%; (2) statin effect on atherosclerosis: it is shown how a patient with a 1-year statin treatment will reduce his plaque growth by 2-3% in a 2-year period; (3) medical adherence: we show that a patient missing 10% of the total number of doses could increase the plaque growth by ~1% (after 2 years) compared to the same "regular" patient under a 1-year treatment with statins. Conclusions: The results in this paper describe the effect of pharmacological intervention combined with biological/physiological or behavioral factors in atherosclerosis progression and treatment in specific patients. It also provides an exemplar of basic research that can be practically developed into an application software.

Patient-Specific, Multi-Scale Modeling of Neointimal Hyperplasia in Vein Grafts.

Neointimal hyperplasia is amongst the major causes of failure of bypass grafts. The disease progression varies from patient to patient due to a range of different factors. In this paper, a mathematical model will be used to understand neointimal hyperplasia in individual patients, combining information from biological experiments and patient-specific data to analyze some aspects of the disease, particularly with regard to mechanical stimuli due to shear stresses on the vessel wall. By combining a biochemical model of cell growth and a patient-specific computational fluid dynamics analysis of blood flow in the lumen, remodeling of the blood vessel is studied by means of a novel computational framework. The framework was used to analyze two vein graft bypasses from one patient: a femoro-popliteal and a femoro-distal bypass. The remodeling of the vessel wall and analysis of the flow for each case was then compared to clinical data and discussed as a potential tool for a better understanding of the disease. Simulation results from this first computational approach showed an overall agreement on the locations of hyperplasia in these patients and demonstrated the potential of using new integrative modeling tools to understand disease progression.

A multiscale modelling approach to understand atherosclerosis formation: A patient-specific case study in the aortic bifurcation.

Atherogenesis, the formation of plaques in the wall of blood vessels, starts as a result of lipid accumulation (low-density lipoprotein cholesterol) in the vessel wall. Such accumulation is related to the site of endothelial mechanotransduction, the endothelial response to mechanical stimuli and haemodynamics, which determines biochemical processes regulating the vessel wall permeability. This interaction between biomechanical and biochemical phenomena is complex, spanning different biological scales and is patient-specific, requiring tools able to capture such mathematical and biological complexity in a unified framework. Mathematical models offer an elegant and efficient way of doing this, by taking into account multifactorial and multiscale processes and mechanisms, in order to capture the fundamentals of plaque formation in individual patients. In this study, a mathematical model to understand plaque and calcification locations is presented: this model provides a strong interpretability and physical meaning through a multiscale, complex index or metric (the penetration site of low-density lipoprotein cholesterol, expressed as volumetric flux). Computed tomography scans of the aortic bifurcation and iliac arteries are analysed and compared with the results of the multifactorial model. The results indicate that the model shows potential to predict the majority of the plaque locations, also not predicting regions where plaques are absent. The promising results from this case study provide a proof of concept that can be applied to a larger patient population.

From PK/PD to QSP: Understanding the Dynamic Effect of Cholesterol-Lowering Drugs on Atherosclerosis Progression and Stratified Medicine.

Current computational and mathematical tools are demonstrating the high value of using systems modeling approaches (e.g. Quantitative Systems Pharmacology) to understand the effect of a given compound on the biological and physiological mechanisms related to a specific disease. This review provides a short survey of the evolution of the mathematical approaches used to understand the effect of particular cholesterol-lowering drugs, from pharmaco-kinetic (PK) / pharmaco-dynamic (PD) models, through physiologically base pharmacokinetic models (PBPK) to QSP. These mathematical models introduce more mechanistic information related to the effect of these drugs on atherosclerosis progression and demonstrate how QSP could open new ways for stratified medicine in this field.

Development of a Patient-Specific Multi-Scale Model to Understand Atherosclerosis and Calcification Locations: Comparison with In vivo Data in an Aortic Dissection.

Vascular calcification results in stiffening of the aorta and is associated with hypertension and atherosclerosis. Atherogenesis is a complex, multifactorial, and systemic process; the result of a number of factors, each operating simultaneously at several spatial and temporal scales. The ability to predict sites of atherogenesis would be of great use to clinicians in order to improve diagnostic and treatment planning. In this paper, we present a mathematical model as a tool to understand why atherosclerotic plaque and calcifications occur in specific locations. This model is then used to analyze vascular calcification and atherosclerotic areas in an aortic dissection patient using a mechanistic, multi-scale modeling approach, coupling patient-specific, fluid-structure interaction simulations with a model of endothelial mechanotransduction. A number of hemodynamic factors based on state-of-the-art literature are used as inputs to the endothelial permeability model, in order to investigate plaque and calcification distributions, which are compared with clinical imaging data. A significantly improved correlation between elevated hydraulic conductivity or volume flux and the presence of calcification and plaques was achieved by using a shear index comprising both mean and oscillatory shear components (HOLMES) and a non-Newtonian viscosity model as inputs, as compared to widely used hemodynamic indicators. The proposed approach shows promise as a predictive tool. The improvements obtained using the combined biomechanical/biochemical modeling approach highlight the benefits of mechanistic modeling as a powerful tool to understand complex phenomena and provides insight into the relative importance of key hemodynamic parameters.

Towards personalised management of atherosclerosis via computational models in vascular clinics: technology based on patient-specific simulation approach.

The development of a new technology based on patient-specific modelling for personalised healthcare in the case of atherosclerosis is presented. Atherosclerosis is the main cause of death in the world and it has become a burden on clinical services as it manifests itself in many diverse forms, such as coronary artery disease, cerebrovascular disease/stroke and peripheral arterial disease. It is also a multifactorial, chronic and systemic process that lasts for a lifetime, putting enormous financial and clinical pressure on national health systems. In this Letter, the postulate is that the development of new technologies for healthcare using computer simulations can, in the future, be developed as in-silico management and support systems. These new technologies will be based on predictive models (including the integration of observations, theories and predictions across a range of temporal and spatial scales, scientific disciplines, key risk factors and anatomical sub-systems) combined with digital patient data and visualisation tools. Although the problem is extremely complex, a simulation workflow and an exemplar application of this type of technology for clinical use is presented, which is currently being developed by a multidisciplinary team following the requirements and constraints of the Vascular Service Unit at the University College Hospital, London.

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

BACKGROUND: Celiac disease (CD) is an autoimmune disorder that occurs in genetically predisposed people and is caused by a reaction to the gluten protein found in wheat, which leads to intestinal villous atrophy. Currently there is no drug for treatment of CD. The only known treatment is lifelong gluten-free diet. The main aim of this work is to develop a mathematical model of the immune response in CD patients and to predict the efficacy of a transglutaminase-2 (TG-2) inhibitor as a potential drug for treatment of CD. RESULTS: A thorough analysis of the developed model provided the following results:1. TG-2 inhibitor treatment leads to insignificant decrease in antibody levels, and hence remains higher than in healthy individuals.2. TG-2 inhibitor treatment does not lead to any significant increase in villous area.3. The model predicts that the most effective treatment of CD would be the use of gluten peptide analogs that antagonize the binding of immunogenic gluten peptides to APC. The model predicts that the treatment of CD by such gluten peptide analogs can lead to a decrease in antibody levels to those of normal healthy people, and to a significant increase in villous area. CONCLUSIONS: The developed mathematical model of immune response in CD allows prediction of the efficacy of TG-2 inhibitors and other possible drugs for the treatment of CD: their influence on the intestinal villous area and on the antibody levels. The model also allows to understand what processes in the immune response have the strongest influence on the efficacy of different drugs. This model could be applied in the pharmaceutical R&D arena for the design of drugs against autoimmune small intestine disorders and on the design of their corresponding clinical trials.

A multiscale model of atherosclerotic plaque formation at its early stage.

A multiscale model of atherosclerotic plaque formation at its early stage has been developed in order to integrate the various phenomena leading to fatty streak formation. The different scales considered in this model are in both the spatial domain (from cellular to organism level) and the time domain (from seconds to months). The cellular level was considered by modeling the transport and chemical interactions of low-density lipoproteins (LDL) and other agents in a stenosed artery. This was linked to arterial thickening (organ level). Mean blood LDL level (organism level) was selected as one of the critical factors for atherosclerotic formation along with wall shear stress (WSS) exerted on the endothelium (the inner portion of the artery). It was observed that plaque location was dependent on WSS and that plaque size, number, and growth was dependent on mean blood LDL levels as well.

On the formalization of multi-scale and multi-science processes for integrative biology.

The aim of this work is to introduce the general concept of 'Bond Graph' (BG) techniques applied in the context of multi-physics and multi-scale processes. BG modelling has a natural place in these developments. BGs are inherently coherent as the relationships defined between the 'elements' of the graph are strictly defined by causality rules and power (energy) conservation. BGs clearly show how power flows between components of the systems they represent. The 'effort' and 'flow' variables enable bidirectional information flow in the BG model. When the power level of a system is low, BGs degenerate into signal flow graphs in which information is mainly one-dimensional and power is minimal, i.e. they find a natural limitation when dealing with populations of individuals or purely kinetic models, as the concept of energy conservation in these systems is no longer relevant. The aim of this work is twofold: on the one hand, we will introduce the general concept of BG techniques applied in the context of multi-science and multi-scale models and, on the other hand, we will highlight some of the most promising features in the BG methodology by comparing with examples developed using well-established modelling techniques/software that could suggest developments or refinements to the current state-of-the-art tools, by providing a consistent framework from a structural and energetic point of view.