Index analysis: An approach to understand signal transduction with application to the EGFR signalling pathway

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. Beyond the applications in model interpretation, index analysis is envisioned to be a valuable tool in model reduction

Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models: An application to warfarin

Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications

Sample‐based robust model reduction for non‐linear systems biology models

Complex non-linear systems biology models comprise relevant knowledge on processes of pharmacological interest. They are, however, too complex to be used in inferential settings, for example, to allow for the estimation of patient-specific parameters for individual dose optimisation. Thus, there is a need for simple models with interpretable components to infer the drug effect in a clinical setting. In particular, it is essential to accurately quantify and simulate the interindividual variability in the drug response in order to account for covariates like body weight, age and genetic disposition. To this end, non-linear model order reduction and simplification methods can be used if they maintain model interpretability during reduction and consider an entire population rather than just a single reference individual. We present a sample-based approach for robust model order reduction and propose two improvements for efficiency. In particular, we introduce a new sampling method to generate the virtual population based on transformed latin hypercube sampling. Thereby, the sample is stratified in the relevant parameter-space directions, which are identified using empirical observability Gramians. We illustrate our approach in application to a blood coagulation pathway model, where we reduce the complexity from a 62-dimensional highly non-linear to a six-dimensional and a nine-dimensional system of ordinary differential equations for two scenarios, respectively

Design of FLAIR:a Phase 2b Study of the 5-Lipoxygenase Activating Protein Inhibitor AZD5718 in Patients With Proteinuric CKD

Introduction: Patients with chronic kidney disease (CKD) remain at risk for kidney and cardiovascular events resulting from residual albuminuria, despite available treatments. Leukotrienes are proinflammatory and vasoconstrictive lipid mediators implicated in the etiology of chronic inflammatory diseases. AZD5718 is a potent, selective, and reversible 5-lipoxygenase activating protein (FLAP) inhibitor that suppresses leukotriene production. Methods: FLAIR (FLAP Inhibition in Renal disease) is an ongoing phase 2b, randomized, double-blind, placebo-controlled, multicenter study to evaluate the efficacy and safety of AZD5718 in patients with proteinuric CKD with or without type 2 diabetes. Participants receive AZD5718 at 3 different doses or placebo once daily for 12 weeks, followed by an 8-week extension in which they also receive dapagliflozin (10 mg/d) as anticipated future standard of care. The planned sample size is 632 participants, providing 91% power to detect 30% reduction in urinary albumin-to-creatinine ratio (UACR) between the maximum dose of AZD5718 and placebo. The dose–response effect of AZD5718 on UACR after the dapagliflozin extension is the primary efficacy objective. Key secondary objectives are the dose–response effect of AZD5718 plus current standard of care on UACR and acute effects of treatment on the estimated glomerular filtration rate. Safety, tolerability, AZD5718 pharmacokinetics, and analyses of biomarkers that may predict or reflect response to AZD5718 are additional objectives. Conclusion: FLAIR will provide data on the effects of 5-lipoxygenase pathway inhibition in patients with proteinuric CKD with or without type 2 diabetes, and will form the basis for future clinical trials (ClinicalTrials.gov: NCT04492722)

Index analysis: An approach to understand signal transduction with application to the EGFR signalling pathway.

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. Beyond the applications in model interpretation, index analysis is envisioned to be a valuable tool in model reduction

A Markov model of fibrosis development in nonalcoholic fatty liver disease predicts fibrosis progression in clinical cohorts

Disease progression in nonalcoholic steatohepatitis (NASH) is highly heterogenous and remains poorly understood. Fibrosis stage is currently the best predictor for development of end-stage liver disease and mortality. Better understanding and quantifying the impact of factors affecting NASH and fibrosis is essential to inform a clinical study design. We developed a population Markov model to describe the transition probability between fibrosis stages and mortality using a unique clinical nonalcoholic fatty liver disease cohort with serial biopsies over 3 decades. We evaluated covariate effects on all model parameters and performed clinical trial simulations to predict the fibrosis progression rate for external clinical cohorts. All parameters were estimated with good precision. Age and diagnosis of type 2 diabetes (T2D) were found to be significant predictors in the model. Increase in hepatic steatosis between visits was the most important predictor for progression of fibrosis. Fibrosis progression rate (FPR) was twofold higher for fibrosis stages 0 and 1 (F0-1) compared to fibrosis stage 2 and 3 (F2-3). A twofold increase in FPR was observed for T2D. A two-point steatosis worsening increased the FPR 11-fold. Predicted fibrosis progression was in good agreement with data from external clinical cohorts. Our fibrosis progression model shows that patient selection, particularly initial fibrosis stage distribution, can significantly impact fibrosis progression and as such the window for assessing drug efficacy in clinical trials. Our work highlights the increase in hepatic steatosis as the most important factor in increasing FPR, emphasizing the importance of well-defined lifestyle advise for reducing variability in NASH progression during clinical trials

A case‐study of model‐informed drug development of a novel PCSK9 anti sense oligonucleotide. Part 1: First time in man to phase II

Abstract Here, we show model‐informed drug development (MIDD) of a novel antisense oligonucleotide, targeting PCSK9 for treatment of hypocholesteremia. The case study exemplifies use of MIDD to analyze emerging data from an ongoing first‐in‐human study, utility of the US Food and Drug Administration MIDD pilot program to accelerate timelines, innovative use of competitor data to set biomarker targets, and use of MIDD to optimize sample size and dose selection, as well as to accelerate and de‐risk a phase IIb study. The focus of the case‐study is on the cross‐functional collaboration and other key MIDD enablers that are critical to maximize the value of MIDD, rather than the technical application of MIDD

Hepatic patatin-like phospholipase domain-containing 3 levels are increased in I148M risk allele carriers and correlate with NAFLD in humans

In nonalcoholic fatty liver disease (NAFLD) the patatin-like phospholipase domain-containing 3 (PNPLA3) rs738409 variant is a contributor. In mice, the Pnpla3 148M variant accumulates on lipid droplets and probably leads to sequestration of a lipase cofactor leading to impaired mobilization of triglycerides. To advance our understanding of the localization and abundance of PNPLA3 protein in humans, we used liver biopsies from patients with NAFLD to investigate the link to NAFLD and the PNPLA3 148M genotype. We experimentally qualified an antibody against human PNPLA3. Hepatic PNPLA3 protein fractional area and localization were determined by immunohistochemistry in biopsies from a well-characterized NAFLD cohort of 67 patients. Potential differences in hepatic PNPLA3 protein levels among patients related to degree of steatosis, lobular inflammation, ballooning, and fibrosis, and PNPLA3 I148M gene variants were assessed. Immunohistochemistry staining in biopsies from patients with NAFLD showed that hepatic PNPLA3 protein was predominantly localized to the membranes of small and large lipid droplets in hepatocytes. PNPLA3 protein levels correlated strongly with steatosis grade (p = 0.000027) and were also significantly higher in patients with lobular inflammation (p = 0.009), ballooning (p = 0.022), and significant fibrosis (stage 2-4, p = 0.014). In addition, PNPLA3 levels were higher in PNPLA3 rs738409 148M (CG, GG) risk allele carriers compared to 148I (CC) nonrisk allele carriers (p = 0.0029). Conclusion: PNPLA3 protein levels were associated with increased hepatic lipid content and disease severity in patients with NAFLD and were higher in PNPLA3 rs738409 (148M) risk allele carriers. Our hypothesis that increased hepatic levels of PNPLA3 may be part of the pathophysiological mechanism of NAFLD is supported.Funding Agencies|ALF Grants, Region Ostergotland; Astra Zeneca; Forskningsradet i Sydostra Sverige</p