Towards patient stratification and treatment in the autoimmune disease lupus erythematosus using a systems pharmacology approach

The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases

Autoimmune diseases (AIDs) are a group of disorders in which the immune system attacks the body's own tissues, leading to chronic inflammation and organ damage. These diseases are difficult to treat due to variability in drug PK among individuals, patient responses to treatment, and the side effects of long-term immunosuppressive therapies. In recent years, pharmacometrics has emerged as a critical tool in drug discovery and development (DDD) and precision medicine. The aim of this review is to explore the diverse roles that pharmacometrics has played in addressing the challenges associated with DDD and personalized therapies in the treatment of AIDs. Methods: This review synthesizes research from the past two decades on pharmacometric methodologies, including Physiologically Based Pharmacokinetic (PBPK) modeling, Pharmacokinetic/Pharmacodynamic (PK/PD) modeling, disease progression (DisP) modeling, population modeling, model-based meta-analysis (MBMA), and Quantitative Systems Pharmacology (QSP). The incorporation of artificial intelligence (AI) and machine learning (ML) into pharmacometrics is also discussed. Results: Pharmacometrics has demonstrated significant potential in optimizing dosing regimens, improving drug safety, and predicting patient-specific responses in AIDs. PBPK and PK/PD models have been instrumental in personalizing treatments, while DisP and QSP models provide insights into disease evolution and pathophysiological mechanisms in AIDs. AI/ML implementation has further enhanced the precision of these models. Conclusions: Pharmacometrics plays a crucial role in bridging pre-clinical findings and clinical applications, driving more personalized and effective treatments for AIDs. Its integration into DDD and translational science, in combination with AI and ML algorithms, holds promise for advancing therapeutic strategies and improving autoimmune patients' outcomes.

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.

Disease trajectory of SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooled patient-level placebo (Standard-of-Care) data to enable model-informed drug development

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo-controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG-based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient-level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non-Asian patients, supporting Asia-inclusive global SLE drug development. These results describe the first population approach to support a model-informed drug development framework in SLE.

Unveiling the anti-senescence effects and senescence-associated secretory phenotype (SASP) inhibitory mechanisms of Scutellaria baicalensis Georgi in low glucose-induced astrocytes based on boolean network

BACKGROUND

Astrocytes senescence has been demonstrated in the aging brain and Alzheimer's disease (AD). Moreover, lower glucose metabolism has been confirmed in the early stage of AD. However, whether low glucose could induce astrocytes senescence remain ambiguous. Studies have shown that the ethanol extracts of Scutellaria baicalensis Georgi (SGE) exert neuroprotective and anti-aging effects, while whether SGE could delay astrocytes senescence was unclear.

PURPOSE

This study investigated the anti-senescence effect of SGE in low glucose-induced T98G cells and primary astrocytes, and explored the possible mechanisms based on boolean network.

METHODS

The neuroprotective effects of SGE in low glucose-induced T98G cells were evaluated by measurement of cell viability, LDH, ROS and ATP. The anti-senescence effects of SGE were investigated by detection of senescence-associated β-galactosidase (SA-β-Gal), senescence-associated secretory phenotype (SASP), cell cycle and senescence-related markers. The possible mechanisms of SGE in delaying astrocytes senescence were discovered through integrating transcriptomics with boolean network, and validation experiments were further performed.

RESULTS

Our results revealed that low glucose could induce astrocytes senescence, and SGE could delay astrocytes senescence by decreasing the staining rate of SA-β-gal, reducing secretions of SASP factors (IL-6, CXCL1, MMP-1), alleviating cell cycle arrest in G0/G1 phase, decreasing the formation of punctate DNA foci and down-regulating the expression of p16INK4A, p21 and γH2A.X. Transcriptomics and further verification results showed that SGE could markedly inhibit the mRNA expression levels of SASP factors (CXCL10, CXCL2, CCL2, IL-6, CXCR4, CCR7). Moreover, C-X-C motif chemokine 10 (CXCL10) was predicted to be the key SASP factor affecting the network stability by using boolean network. Further experiments validated that SGE could markedly reduce CXCL10 level, decrease the secretion of IL-6 and inhibit cell migration in CXCL10 induced primary astrocytes.

CONCLUSION

In summary, our research unmasks that the anti-senescence effects of SGE were highly correlated with the suppression of SASP secretions, and CXCL10 mediated the SASP inhibition effect of SGE in low glucose-induced astrocytes. Our study highlights that the delay of astrocytes senescence and the inhibition of SASP might be a new mechanism of SGE for alleviating neurodegenerative diseases such as AD.

From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building

Quantitative Systems Pharmacology (QSP) models capture the physiological underpinnings driving the response to a drug and express those in a semi-mechanistic way, often involving ordinary differential equations (ODEs). The process of developing a QSP model generally starts with the definition of a set of reasonable hypotheses that would support a mechanistic interpretation of the expected response which are used to form a network of interacting elements. This is a hypothesis-driven and knowledge-driven approach, relying on prior information about the structure of the network. However, with recent advances in our ability to generate large datasets rapidly, often in a hypothesis-neutral manner, the opportunity emerges to explore data-driven approaches to establish the network topologies and models in a robust, repeatable manner. In this paper, we explore the possibility of developing complex network representations of physiological responses to pharmaceuticals using a logic-based analysis of available data and then convert the logic relations to dynamic ODE-based models. We discuss an integrated pipeline for converting data to QSP models. This pipeline includes using k-means clustering to binarize continuous data, inferring likely network relationships using a Best-Fit Extension method to create a Boolean network, and finally converting the Boolean network to a continuous ODE model. We utilized an existing QSP model for the dual-affinity re-targeting antibody flotetuzumab to demonstrate the robustness of the process. Key output variables from the QSP model were used to generate a continuous data set for use in the pipeline. This dataset was used to reconstruct a possible model. This reconstruction had no false-positive relationships, and the output of each of the species was similar to that of the original QSP model. This demonstrates the ability to accurately infer relationships in a hypothesis-neutral manner without prior knowledge of a system using this pipeline.

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

Model-based computational precision medicine to develop combination therapies for autoimmune diseases

INTRODUCTION

The complex pathophysiology of autoimmune diseases (AIDs) is being progressively deciphered, providing evidence for a multiplicity of pro-inflammatory pathways underlying heterogeneous clinical phenotypes and disease evolution.

AREAS COVERED

Treatment strategies involving drug combinations are emerging as a preferred option to achieve remission in a vast majority of patients affected by systemic AIDs. The design of appropriate drug combinations can benefit from AID modeling following a comprehensive multi-omics molecular profiling of patients combined with Artificial Intelligence (AI)-powered computational analyses. Such disease models support patient stratification in homogeneous subgroups, shed light on dysregulated pro-inflammatory pathways and yield hypotheses regarding potential therapeutic targets and candidate biomarkers to stratify and monitor patients during treatment. AID models inform the rational design of combination therapies interfering with independent pro-inflammatory pathways related to either one of five prominent immune compartments contributing to the pathophysiology of AIDs, i.e. pro-inflammatory signals originating from tissues, innate immune mechanisms, T lymphocyte activation, autoantibodies and B cell activation, as well as soluble mediators involved in immune cross-talk.

EXPERT OPINION

The optimal management of AIDs in the future will rely upon rationally designed combination therapies, as a modality of a model-based Computational Precision Medicine taking into account the patients' biological and clinical specificities.

How scientific literature analysis yields innovative therapeutic hypothesis through integrative iterations

It is becoming generally accepted that the current diagnostic system often guarantees, rather than diminishes, disease heterogeneity. In effects, syndrome-dominated conceptual thinking has become a barrier to understanding the biological causes of complex, multifactorial diseases characterized by clinical and therapeutic heterogeneity. Furthermore, not only is the flood of currently available medical and biological information highly heterogeneous, it is also often conflicting. Together with the entire absence of functional models of pathogenesis and pathological evolution of complex diseases, this leads to a situation where illness activity cannot be coherently approached and where therapeutic developments become highly problematic. Acquisition of the necessary knowledge can be obtained, in parts, using in silico models produced through analytical approaches and processes collectively known as `Systems Biology'. However, without analytical approaches that specifically incorporate the facts that all that is called `information' is not necessarily useful nor utilisable and that all information should be considered as a priori suspect, modelling attempts will fail because of the much too numerous conflicting and, although correct in molecular terms, physiologically invalid reports. In the present essay, we suggest means whereby this body of problems could be functionally attacked and describe new analytical approaches that have demonstrated their efficacy in alleviating these difficulties.

Targeting Chaperone-Mediated Autophagy for Disease Therapy

PURPOSE OF THE REVIEW

To reason that targeting chaperone-mediated autophagy (CMA) represents a promising approach for disease therapy, we will summarize advances in researches on the relationship between CMA and diseases and discuss relevant strategies for disease therapy by targeting the CMA process.

RECENT FINDINGS

CMA is a unique kind of selective autophagy in lysosomes. Under physiological conditions, CMA participates in the maintenance of cellular homeostasis by protein quality control, bioenergetics, and timely regulated specific substrate-associated cellular processes. Under pathological conditions, CMA interplays with various disease conditions. CMA makes adaptive machinery to address stress, while disease-associated proteins alter CMA which is involved in pathogeneses of diseases. As more proteins are identified as CMA substrates and regulators, dysregulation of CMA has been implicated in an increasing number of diseases, while rectifying CMA alteration may be a benefit for these diseases.

SUMMARY

Alterations of CMA in diseases mainly including neurodegenerative diseases and many cancers raise the possibility of targeting CMA to recover cellular homeostasis as one potential strategy for therapy of relevant diseases.

Chaperone-mediated autophagy: Advances from bench to bedside

Protein homeostasis or proteostasis is critical for proper cellular function and survival. It relies on the balance between protein synthesis and degradation. Lysosomes play an important role in degrading and recycling intracellular components via autophagy. Among the three types of lysosome-based autophagy pathways, chaperone-mediated autophagy (CMA) selectively degrades cellular proteins with KFERQ-like motif by unique machinery. During the past several years, significant advances have been made in our understanding of how CMA itself is modulated and what physiological and pathological processes it may be involved in. One particularly exciting discovery is how other cellular stress organelles such as ER signal to CMA. As more proteins are identified as CMA substrates, CMA function has been associated with an increasing number of important cellular processes, organelles, and diseases, including neurodegenerative diseases. Here we will summarize the recent advances in CMA biology, highlight ER stress-induced CMA, and discuss the role of CMA in diseases.

A systems pharmacology model for inflammatory bowel disease

Motivation

The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets.

Results

In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.

Boolean network modeling in systems pharmacology

Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

Logic Modeling in Quantitative Systems Pharmacology

Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).

Advanced Boolean modeling of biological networks applied to systems pharmacology

Motivation

Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets.

Results

In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets.

Availability and Implementation

The source code is available at https://github.com/SPIDDOR/SPIDDOR .

Contact

itzirurzun@alumni.unav.es , itroconiz@unav.es.

Supplementary information

Supplementary data are available at Bioinformatics online.