The field of metabolic modelling at the genome scale continues to expand, with increasing numbers of models being reconstructed, shared and curated. This expansion creates a need to adopt common principles regarding transparency and versioning, in addition to standardisation of file formats, annotations, and model testing. Here, we present a repository standard and template for Git-based genome-scale metabolic models (GEMs) supporting both newly reconstructed and actively curated models, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating research software best practices. Standard-GEM exposes GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models and its adoption reduces the effort required to share, validate and reuse GEMs.

Keywords: genome-scale metabolic model, open source, versioning, Git

As biomedical knowledge keeps growing, resources storing available information multiply and grow in size and complexity. Such resources can be in the format of molecular interaction maps, which represent cellular and molecular processes under normal or pathological conditions. However, these maps can be complex and hard to navigate, especially to novice users. Large Language Models (LLMs), particularly in the form of agentic frameworks, have emerged as a promising technology to support this exploration. In this article, we describe a user-driven process of prototyping, development, and user testing of Llemy, an LLM-based system for exploring these molecular interaction maps. By involving domain experts from the very first prototyping in the form of a hackathon and collecting both fine-grained and general feedback on more refined versions, we were able to evaluate the perceived utility and quality of the developed system, in particular for summarising maps and pathways, as well as prioritise the development of future features. We recommend continued user-driven development and benchmarking to keep the community engaged. This will also facilitate the transition towards open-weight LLMs to support the needs of the open research environment in an ever-changing technology landscape.

Keywords: SBGN, LLM

Computational models are essential for studying complex systems which, particularly in clinical settings, need to be quality-approved and transparent. To enhance the communication of a model’s features and capabilities, we propose an adaptation of the Findability, Accessibility, Interoperability and Reusability (FAIR) indicators published by the Research Data Alliance to assess models encoded in domain-specific standards, such as those established by COMBINE. The assessments guide FAIRification and add value to models.

Keywords: FAIR, computational models

Constraint-based modeling is a well-established modeling methodology used to study biological networks at both the medium-scale and genome-scale. Due to their large size and complexity, such steady-state flux models are typically ana- lyzed using constraint-based optimization techniques, such as Flux Balance Anal- ysis (FBA). The Flux Balance Constraints (FBC) Package extends SBML Level 3 to provide a standardized format for encoding, exchanging, and annotating constraint-based models. It includes support for modeling concepts such as objective functions, flux bounds, and annotation of model components that facilitate reaction balanc- ing. Version two extended the original release by adding support for encoding gene-protein associations. Version three builds upon and maintains backwards compatibility with Version two by introducing new elements and attributes that include: user-defined constraints, a new quadratic variable type and key-value pair annotation that can be used to store additional information relevant to constraint-based modeling. The only changes to existing attributes are that a Species charge is no longer restricted to an integer value and chemicalFormula can now include generic components. In addition to providing the elements necessary to uniquely encode constraint- based models, the FBC package provides an open platform that facilitates the continued, cross-community development of an interoperable, constraint-based model encoding format.

Keywords: SBML, FBC

Parameter estimation is a central task in data-driven computational modeling, and a clear, unambiguous, and commonly understood specification of parameter estimation problems is essential for reproducibility and interoperability. PEtab provides a standardized framework for defining such problems in systems biology and systems medicine, particularly for ordinary differential equation models. This document describes the second version of the PEtab format, PEtab 2.0. PEtab 2.0 simplifies and clarifies how estimation problems are defined, while also extending the standard to support more complex and realistic experimental setups.

Keywords: Parameter estimation, PETab

Stochastic simulations underpin computational research in fields from systems biology and epidemiology to finance and physics-informed machine learning, yet their reproducibility is hard to quantify because each run yields different outcomes. Existing practices to improve computational reproducibility focus on sharing code, simulation seeds, data, and software environments but do not ensure independent reproduction of results, limiting scientific rigor. Here we introduce the Empirical Characteristic Function Equality Convergence Test (EFECT), a universal computational framework for quantifying the statistical reproducibility of stochastic simulations based on empirical characteristic functions. EFECT defines a normalized EFECT error that measures distributional differences between two sets of simulation outputs over a model- and scale-independent range, and an EFECT convergence point that specifies the minimum sample size required to achieve a desired reproducibility threshold at a chosen significance level. EFECT applies to any simulation whose results can be represented as bounded, real-valued data, regardless of modelling formalism or source of stochasticity. To facilitate widespread adoption and data exchange, we implemented EFECT in an open-source software library and a compact, machine-readable standardized report. We rigorously evaluated the framework across more than 40 test cases spanning stochastic differential equations, agent- based models, Boolean networks, and partial differential equations. Applying EFECT to published models in pandemic epidemiology, financial stochastic volatility, and physics-informed neural networks reveals that commonly used sample sizes are often underpowered: substantial parameter differences can go undetected unless thousands of replicates are simulated. By providing a domain-agnostic statistical reproducibility metric, reusable software libraries, and a standardized report format, EFECT offers a practical framework for embedding quantitative reproducibility and replicability checks into stochastic simulation workflows across data-intensive disciplines. Thus, EFECT delivers the capability to make scientific inferences and policy decisions on the basis of reliable, reproducible stochastic simulations.

Keywords: COMBINE, reproducibility

Background: Hydrochlorothiazide is a widely prescribed thiazide diuretic used for the treatment of hypertension. Cardiac, hepatic, and renal impairments are common comorbidities in hypertensive patients and may contribute to substantial interindividual variability in pharmacokinetic (PK) and pharmacodynamic (PD) responses. This variability complicates dose optimization, particularly in clinically heterogeneous patient populations. Methods: We developed a whole-body physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of hydrochlorothiazide integrating oral absorption, systemic distribution, renal elimination, renal sodium, chloride, and fluid handling, and blood-pressure regulation. The model was calibrated and evaluated using systematically curated PK and PD data from 25 clinical studies in healthy individuals and patients with hypertension, cardiac impairment, hepatic impairment, or renal impairment. Results: Model predictions were consistent with observed dose-proportional PK and PD and captured variability in hydrochlorothiazide exposure and response across simulated comorbid conditions. Renal impairment was identified as the main driver of altered hydrochlorothiazide exposure and reduced urinary excretion. By contrast, hepatic and cardiac impairment affected PK primarily through secondary effects on renal function, rather than through direct effects on drug metabolism or absorption. Conclusions: The developed PBPK/PD model provides a mechanistic framework for understanding hydrochlorothiazide pharmacology across heterogeneous clinical conditions. It supports model-informed assessment of variability in exposure and response and may contribute to individualized antihypertensive therapy in patients with comorbidities.

Keywords: digital twin, hydrochlorothiazide, hypertension, physiologically based pharmacokinetic/pharmacodynamic model (PBPK/PD), pharmacokinetics, pharmacodynamics, personalized medicine

Logical models are widely used to study regulatory and signaling systems, yet their reuse, annotation, and exchange across tools remain challenging. Although SBML Level 3 Qualitative Models (SBML-qual) provides a standard representation, its XML-based syntax is difficult to inspect and edit directly. Here we introduce TabularQual, a spreadsheet-based, community-driven representation for Boolean and multi-valued logical models, together with a bidirectional converter between spreadsheets and SBML-qual. The converter is accessible programmatically and via a web interface to support diverse user workflows. We further describe two integration strategies that enable existing modeling tools to operate with TabularQual, either through SBML-qual exchange or via direct support. Case studies using the Stress Knowledge Map and CaSQ demonstrate how this integration supports model construction, curation, and reuse. TabularQual provides a practical bridge between human-readable model representations and standardized executable formats, supporting reproducibility, interoperability, and community-driven model development.

Keywords: Logical models, qual, SBML, standardization, COMBINE

A computational model in the form of a whole-body physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of dapagliflozin was developed to systematically evaluate the influence of patient-specific factors on drug disposition. Based on curated data from 28 clinical studies, the model simulates the absorption, distribution, metabolism and excretion (ADME) of the drug as well as its pharmacodynamics. The model accounts for variability in renal and hepatic function, and effects of food intake. The model is implemented in the Systems Biology Markup Language (SBML) standard. Analysis were performed utilizing the libroadrunner simulation library. Here, we demonstrate the computational reproducibility of the key findings from the primary publication, thereby verifying the consistency of the model implementation with the published results.

Keywords: Dapagliflozin, PBPK/PD, SBML, Pharmacokinetics, Pharmacodynamics, Computational Model

Sirolimus is a potent mTOR inhibitor used primarily to prevent acute rejection in transplant recipients. Its clinical management is challenging because of its narrow therapeutic index, low and highly variable oral bioavailability, and pronounced inter-individual variability driven by CYP3A4/5-mediated metabolism and P-glycoprotein efflux. Extensive partitioning into erythrocytes further complicates its disposition and necessitates therapeutic drug monitoring. Here, we developed a mechanistic whole-body physiologically based pharmacokinetic (PBPK) digital twin of rapamycin that integrates complex absorption kinetics, nonlinear distribution, and first-pass metabolism. The SBML-encoded model was calibrated and evaluated against a comprehensive library of curated clinical pharmacokinetic data, comprising studies primarily in healthy volunteers and stable renal transplant recipients. The dataset covers diverse ethnic populations, cohorts with varying degrees of renal and hepatic impairment, and individuals with relevant genetic polymorphisms. The digital twin captured overall trends in rapamycin blood concentrations across a wide range of doses and dosing regimens. Simulations showed good agreement with observed data under hepatic and renal impairment, as well as under fasted and fed conditions. Furthermore, the model reproduced the magnitude of drug--drug interactions involving potent CYP3A4 inhibitors, CYP3A4 inducers, and concomitant immunosuppressive agents. This SBML-based digital twin provides a quantitative framework for characterizing sirolimus dose dependency and the multifactorial effects of intrinsic and extrinsic factors on systemic exposure. By providing the model in a standards-based, executable format together with simulation scripts and curated pharmacokinetic datasets, this work supports independent reproduction, transparent model evaluation, and systematic reuse in accordance with FAIR principles.

Keywords: rapamycin, sirolimus, PBPK modeling, digital twin, mTOR inhibitor, drug–drug interactions, organ impairment, personalized medicine

Background: Thrombotic events increase with age, necessitating anticoagulants with reliable pharmacokinetic (PK) and pharmacodynamic (PD) profiles. Apixaban has important therapeutic advantages, but individualised dosing remains challenging because exposure and response are influenced by renal and hepatic function, food intake, body weight, and other patient-specific factors. Existing physiologically based pharmacokinetics/pharmacodynamics (PBPK/PD) models are limited by data sources, transparency, and incomplete representation of metabolites and pharmacodynamics. Methods: A systematic literature review identified 35 apixaban PK/PD clinical studies, which were curated and used for model development, parameter optimisation, and evaluation. We developed an expanded whole-body PBPK/PD model of apixaban with explicit metabolite representation and enhanced pharmacodynamic components. The model follows a modular structure and is encoded in SBML to support interoperability and reproducibility. Results: The model reproduced observed clinical PK/PD data across all 35 studies, covering diverse doses, regimens, and populations. Simulations captured apixaban PK and PD under normal conditions and clinically relevant scenarios, including renal and hepatic impairment, fasted and fed states, and obesity. Conclusions: This open PBPK/PD digital twin provides quantitative insight into determinants of apixaban exposure and response. All model files, documentation, simulation scripts, and curated datasets are openly available under MIT and CC-BY licenses following FAIR principles.

Keywords: apixaban; anticoagulation, pharmacokinetics/pharmacodynamics, PBPK/PD modelling, digital twin, personalised medicine, special populations, FAIR data, reproducible modelling

Automated analysis of hepatic vascular structures and lobules within whole-slide histological images is critical for ensuring accurate and timely morphometric evaluations and facilitating advancements in computational liver histology. Nonetheless, the intricate morphology of the tissue, variability in staining techniques, and the requirements for high-resolution imaging present substantial challenges to the precision of segmentation processes. We present a robust deep-learning pipeline using adaptive patch extraction and specialized U-Net architectures for segmenting vessels, bile ducts, and lobules in Glutamine Synthetase and Picro-Sirius-Red stained porcine liver sections. Our architecture incorporates a weight-boosted nnU-Net framework to effectively manage class imbalances and improve the detection of smaller vascular structures. Geometric data transformations enhanced the robustness and generalizability of the segmentation models. Evaluations conducted through five-fold cross-validation, as well as assessments utilizing independent test datasets, resulted in Dice similarity scores: 0.960 for lobules, 0.801 for central veins, 0.909 for hepatic arteries, 0.609 for portal veins, and 0.710 for bile ducts. The developed segmentation pipeline additionally supports comprehensive morphometric analyses of structural parameters, including number and size (diameter, area) of vascular structures, bile ducts, and lobules. For e.g., the diameter of hepatic arteries ranges between 30-90 µm. These findings underscore the practical relevance of adaptable segmentation frameworks in advancing computational histological analysis of liver tissue.

Keywords: Hepatic Vascular Segmentation, Lobule Detection, Histological Image, Analysis, Deep Learning, Lobule Segmentation

Bayesian methods offer a principled framework for parameter estimation and uncertainty quantification in physiologically-based pharmacokinetic (PBPK) modeling, yet the sensitivity of these approaches to key workflow hyperparameters remains insufficiently characterized. This thesis presents a reproducible methodology for evaluating Bayesian Markov Chain Monte Carlo (MCMC) parameter estimation across three pharmacokinetic models of increasing complexity: a simple chain model, a basic pharmacokinetic model, and a clinically validated PBPK model of indocyanine green (ICG). Synthetic pharmacokinetic datasets were generated from known parameter distributions and used to fit Bayesian MCMC samplers under systematically varied hyperparameters — number of timepoints, number of samples, coefficient of variation, and prior type. Posterior accuracy, point bias, computational cost, and sampling efficiency were evaluated across all conditions. Among the four hyperparameters investigated, prior specification emerged as the dominant determinant of posterior accuracy: well-specified priors yielded parameter estimates close to the true values across all models, while biased and incorrect priors introduced substantial and progressive deviations. The remaining hyperparameters had limited influence on estimation accuracy but the number of timepoints and samples directly increased computational cost. The entire workflow — built on SBML, libRoadRunner, PEtab, and pyPESTO — is fully open-source and FAIR-compliant, providing a transparent and reusable framework for validating Bayesian approaches in pharmacokinetic modeling.

Keywords: Baysian, parameter optimization, SBML, PEtab

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of 'data', we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.

Keywords: FAIR, CURE, reproducibility

Background/Objectives: Empagliflozin is an SGLT2 inhibitor prescribed for the management of type 2 diabetes mellitus, lowering blood glucose by increasing urinary glucose excretion (UGE) through inhibition of renal glucose reabsorption. PK/PD responses vary substantially across patient populations, complicating dose selection under altered organ function. Here, we developed a whole-body PBPK/PD digital twin integrating absorption, distribution, metabolism, and excretion with explicit modeling of renal glucose handling via the renal threshold for glucose. Methods: The model represents empagliflozin and its glucuronide metabolite, is implemented in SBML, and was calibrated and evaluated against curated PK/PD data from 27 clinical studies spanning healthy individuals, patients with type 2 diabetes, and cohorts with renal or hepatic impairment. Results: The model accurately captured observed clinical PK/PD data across all 27 studies, spanning a wide range of doses, dosing regimens, and patient populations. Good agreement between simulations and observations was obtained under normal and impaired renal and hepatic function, as well as under fasted and fed conditions, demonstrating the model's ability to reproduce empagliflozin disposition and pharmacodynamic response across clinically relevant scenarios. Conclusions: This SBML-based PBPK/PD digital twin provides quantitative insight into empagliflozin dose dependency and the impact of renal impairment, hepatic impairment, and food intake on PK/PD across clinically relevant populations. All model files, simulation scripts, and curated datasets are openly available in accordance with FAIR principles.

Keywords: digital twin, empagliflozin, Type 2 diabetes mellitus, physiologically based pharmacokinetic/pharmacodynamic model (PBPK/PD), pharmacokinetics, pharmacodynamics, personalized medicine

Background/Objectives: Dapagliflozin is an SGLT2 inhibitor prescribed for the management of type 2 diabetes mellitus. The drug lowers blood glucose levels by increasing urinary glucose excretion (UGE). Despite established efficacy, dapagliflozin demonstrates significant inter-individual variability in pharmacokinetics (PK) and pharmacodynamics (PD), with potential impact on treatment outcomes. Methods: To evaluate the sources of variability and to support patient stratification and model-informed individualized therapy, we developed a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of dapagliflozin using curated data from 28 clinical studies. This framework integrates absorption, distribution, metabolism, excretion, and pharmacodynamics, and accounts for key determinants of variability including renal and hepatic function, and food effects. Results: The simulations reproduced dose-dependent pharmacokinetics with predicted Cmax and AUC values typically within 10–15% of observed data. Renal impairment reduced UGE by 40–60% despite modest changes in plasma exposure, while hepatic impairment produced only small shifts in PK and PD. The model also reproduced the fed-state reduction of peak concentrations, consistent with the 30–50% decrease reported clinically. Conclusions: All model files, code, and curated datasets are openly available in line with FAIR standards and Open Science practices, enabling transparent and reproducible analyses and providing a mechanistic basis for individualized therapy in type 2 diabetes.

Keywords: dapagliflozin, type 2 diabetes mellitus, physiologically based pharmacokinetic/pharmacodynamic model (PBPK/PD), pharmacokinetics, pharmacodynamics, personalized medicine

Background/Objectives: Losartan, an angiotensin II receptor blocker (ARB) used to treat hypertension and heart failure, shows significant variability in pharmacokinetics (PK) and pharmacodynamics (PD) among individuals. Methods: In this study, we developed a physiologically based pharmacokinetic/ pharmacodynamic (PBPK/PD) model of losartan and its active metabolite, E3174, using curated data from 25 clinical trials. The model mechanistically describes the processes of absorption, hepatic metabolism, renal and fecal excretion, and pharmacodynamic blood pressure regulation. Simulation studies examined the effects of dose, hepatic and renal impairment, and genetic polymorphisms in cytochrome p450 2C9 (CYP2C9) and P-glycoprotein 1, also known as multidrug resistance protein 1 (MDR1) or ATP-binding cassette sub-family B member 1 (ABCB1), on the model. Results: The model successfully reproduced key PK/PD observations, including dose-dependent receptor blockade, attenuated responses with hepatic impairment, modest enhancement with renal impairment, and substantial variability in E3174 formation dependent on CYP2C9; the effects of ABCB1 were minimal. Specifically, dose dependency simulations confirmed the saturable nature of CYP2C9 metabolism, predicting a decreasing E3174-to-losartan ratio and a stronger, sustained suppression of blood pressure and aldosterone at higher doses. Hepatic impairment was predicted to lead to elevated losartan plasma concentrations (increased AUC) and attenuated metabolite formation, confirming the clinical need for dose reduction. Renal impairment simulations predicted stable losartan AUC but showed an overestimation of E3174 accumulation compared to observed data, where E3174 exposure remained stable. Genetic variability (CYP2C9) was the major determinant of response, with simulations confirming that reduced-function alleles lead to a 1.6- to 3-fold increase in losartan AUC and diminished blood pressure reduction. ABCB1 variability resulted in only minor modulation of systemic exposure and blood pressure effects. Conclusions: This mechanistic digital twin framework provides a quantitative basis for understanding variability in losartan therapy and supports its application in individualized dosing strategies.

Keywords: losartan, angiotensin II receptor blocker, ARB, PBPK/PD modeling, pharmacokinetics, pharmacodynamics, SBML

A digital twin in the form of a whole-body physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of losartan was developed to systematically evaluate the influence of patient-specific factors on drug disposition and effects. Based on curated data from 25 clinical studies, the model simulates the absorption, distribution, metabolism and excretion (ADME) as well as pharmacological effects of the drug. The model accounts for variability caused by the differences in renal and hepatic function, and by genetic polymorphisms of CYP2C9 and ABCB1. The model is implemented in the Systems Biology Markup Language (SBML) standard. Simulations were performed utilising the libroadrunner library. Here, we demonstrate the computational reproducibility of the key findings from the primary publication, thereby verifying the consistency and reproducibility of the model implementation with the published results.

Keywords: Losartan, PBPK/PD, SBML, Pharmacokinetics, Pharmacodynamics, Computational Model

Canagliflozin is an SGLT2 inhibitor prescribed for the management of type 2 diabetes mellitus, lowering blood glucose by increasing urinary glucose excretion (UGE). However, pharmacokinetic (PK) and pharmacodynamic (PD) responses vary across patient populations, complicating dose selection under altered organ function. Here, we developed a whole-body PBPK/PD digital twin of canagliflozin that integrates absorption, distribution, metabolism, and excretion, and explicitly models renal glucose handling. The model represents canagliflozin and its major metabolites (M5, M7, M9) and was calibrated and evaluated using curated PK/PD data from 22 clinical studies spanning healthy individuals, patients with type 2 diabetes, and cohorts with renal or hepatic impairment. Simulations reproduced dose-dependent exposure and UGE across single- and multiple-dose regimens. Renal impairment produced modest changes in parent drug exposure but markedly reduced UGE and increased metabolite exposure, consistent with reduced glucose filtration and impaired metabolite clearance. Under hepatic impairment, simulations predicted increased canagliflozin exposure with altered metabolite profiles, while PD effects were minimal, although evaluation was limited by sparse clinical PD endpoints. All model files, simulation scripts, and curated datasets are provided in open SBML workflows in accordance with FAIR principles, enabling reproducible simulations and reuse for model-informed analyses of canagliflozin PK/PD variability.

Keywords: Digital twin, Canagliflozin, Type 2 diabetes mellitus, Physiologically based pharmacokinetic/pharmacodynamic model (PBPK/PD), Pharmacokinetics, Pharmacodynamics, Personalized medicine

Introduction: Optimizing glimepiride therapy for type 2 diabetes (T2DM) is challenged by pronounced inter-individual variability in pharmacokinetics. Methods: We developed a whole-body physiologically based pharmacokinetic (PBPK) model as a digital twin of glimepiride, enabling systematic evaluation of how patient-specific factors influence drug disposition. Using curated data from 20 clinical studies, the digital twin mechanistically simulates glimepiride’s absorption, distribution, metabolism, and excretion (ADME). It accounts for key determinants of variability including renal and hepatic function, CYP2C9 genotype, and bodyweight. Results: The model accurately reproduced observed pharmacokinetics and quantified these factors’ impact on drug exposure. Increased glimepiride exposure was predicted in individuals with hepatic dysfunction or specific CYP2C9 variants, highlighting substantial genetic and physiological effects. Discussion: This digital twin provides mechanistic insights into pharmacokinetic variability and serves as an in silico platform for exploring individualized dosing and patient stratification strategies, laying the foundation for clinical decision support tools to improve T2DM management.

Keywords: digital twin, diabetes, Glimepiride, PBPK, physiologically based pharmacokinetic model

A digital twin in the form of a whole-body physiologically based pharmacokinetic (PBPK) model of glimepiride was developed to systematically evaluate the influence of patient-specific factors on drug disposition. Based on curated data from 20 clinical studies, the model simulates the absorption, distribution, metabolism and excretion (ADME) of the drug while accounting for variability in renal and hepatic function, CYP2C9 genetic variants and bodyweight. The model is implemented in the Systems Biology Markup Language (SBML) standard and simulations are per- formed using scripts that utilise the libRoadRunner library to run simulations and generate results. Here, we demonstrate the computational reproducibility of the key findings from the primary publication, thereby verifying the consistency and reproducibility of the model implementation with the published results.

Keywords: digital twin, diabetes, Glimepiride, PBPK, physiologically based pharmacokinetic model

Background: Artificial Intelligence (AI) and Digital Twins (DT) are transforming clinical decision-making, particularly in hepatology and hepatobiliary surgery. While these technologies promise more precise diagnostics and individualised treatment planning, their successful integration depends on clinician acceptance, trust, and regulatory clarity. Methods: We conducted an online survey among hepatologists, hepatobiliary surgeons, and related specialists to assess familiarity, perceived value, adoption of clinical decision support systems (CDSS), concerns regarding automation bias and liability, and attitudes toward patient-centred integration. Data from 18 respondents were analysed using descriptive statistics and visualised in Python. Results: Most clinicians rated AI (80%) and DT (79%) as valuable to very valuable. Adoption of CDSS was high, with 93% reporting current or intended use. However, significant concerns emerged: 71% feared automation bias, and 92% expressed uncertainty about legal protection. Additionally, 77% supported incorporating patient preferences into AI- and DT-assisted decisions. Conclusions: Clinicians demonstrate a strong interest in adopting AI and DT, but remain cautious due to concerns over trust, liability, and regulatory gaps. Clear guidelines, clinical validation, and patient-centred approaches are essential for safe and effective integration into hepatology practice.

Keywords: Digital Twins, Artificial Intelligence, Liver, Hepatology, Hepatobiliary Surgery, Survey

Rivaroxaban, a direct oral anticoagulant (DOAC), is a widely used drug for the prevention and treatment of thromboembolic disorders. While its fixed dosing regimen and predictable anticoagulation profile improve clinical usability, significant inter-individual variability in pharmacokinetics (PK) and pharmacodynamics (PD) exists, particularly in patients with altered physiology or comorbidities. This study established an open database of rivaroxaban PK and PD data from 14 clinical studies. The data were used to develop and validate a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of rivaroxaban that captures its absorption, distribution, metabolism, excretion, and coagulation effects. The model incorporates the effects of dose, food intake, hepatic and renal function, and allows simulation of activated Factor X (Factor Xa) inhibition, prothrombin time (PT), and activated partial thromboplastin time (aPTT). Simulations accurately reproduced observed plasma concentration-time and coagulation profiles across a range of doses, fed/fasting conditions, as well as with different degrees of cirrhosis and renal impairment. This mechanistic PBPK/PD model provides a framework for exploring variability in rivaroxaban response across diverse clinical scenarios and supports its use in personalized dosing strategies, clinical trial design, and regulatory evaluation.

Keywords: rivaroxaban, direct oral anticoagulant, DOAC, PBPK/PD modeling, pharmacokinetics, pharmacodynamics

Rivaroxaban, a direct oral anticoagulant (DOAC), is a widely used drug for the prevention and treatment of thromboembolic disorders. While its fixed dosing regimen and predictable anticoagulation profile improve clinical usability, significant inter-individual variability in pharmacokinetics (PK) and pharmacodynamics (PD) exists, particularly in patients with altered physiology or comorbidities. This study established an open database of rivaroxaban PK and PD data from 14 clinical studies. The data were used to develop and validate a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of rivaroxaban that captures its absorption, distribution, metabolism, excretion, and coagulation effects. The model incorporates the effects of dose, food intake, hepatic and renal function, and allows simulation of activated Factor X (Factor Xa) inhibition, prothrombin time (PT), and activated partial thromboplastin time (aPTT). Simulations accurately reproduced observed plasma concentration-time and coagulation profiles across a range of doses, fed/fasting conditions, as well as with different degrees of cirrhosis and renal impairment. This mechanistic PBPK/PD model provides a framework for exploring variability in rivaroxaban response across diverse clinical scenarios and supports its use in personalized dosing strategies, clinical trial design, and regulatory evaluation.

Keywords: rivaroxaban, direct oral anticoagulant, DOAC, PBPK/PD modeling, pharmacokinetics, pharmacodynamics

Losartan is an angiotensin II receptor antagonist commonly used in the treatment of hyperten- sion and heart failure. Its pharmacokinetics and pharmacodynamics are influenced by a range of physiological and genetic factors, which may affect therapeutic efficacy. This thesis presents a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of losartan and its active metabolite E3174 to explore these influences systematically. The modular SBML-based model includes submodels for absorption, hepatic metabolism, renal excretion, and pharmacodynamic effects via the renin-angiotensin-aldosterone system (RAAS). Simulations were conducted to investigate the impact of dose, hepatic and renal impairment, as well as CYP2C9 and ABCB1 polymorphisms. The model successfully reproduced observed pharmacological trends, including a dose-dependent increase in pharmacodynamic response, likely reflecting enhanced receptor blockade at higher systemic exposures. Hepatic dysfunction blunted the pharmacodynamic response to losartan, whereas renal impairment reduced metabolite clearance and lead to modest enhancement in pharmacodynamic effect. Genetic variability in CYP2C9 significantly altered E3174 formation and downstream RAAS inhibition, whereas ABCB1 activity had only minor effects on systemic exposure. The model provides mechanistic insight into inter-individual variability in losartan therapy and supports its potential use in individualized dosing strategies.

Keywords: PK/PD, pharmacokinetics, pharmacodynamics, losartan, hepatorenal impairment, pharmacogenomics, food effect

Dapagliflozin is a SGLT2 inhibitor used in the management of type 2 diabetes. Its effect is based on the reduction of plasma glucose levels by promoting glucose excretion in the urine. The pharmacokinetics and pharmacodynamics of dapagliflozin exhibit considerable interindividual variability, which is influenced by patient-specific factors and potentially affects therapeutic outcomes.
In this study, a physiologically based pharmacokinetic/ pharmacodynamic (PBPK/PD) model was developed to systematically assess how these factors influence dapagliflozin disposition. Using curated data from 27 clinical trials, a modular SBML-based model with sub-models for intestinal absorption, hepatic metabolism and renal excretion was developed.
Parameter optimization resulted in good agreement between simulations and observed clinical PK/PD profiles. The validated model accurately reproduced dose-dependent kinetics within the therapeutic range, captured the effects of physiological variability and functional impairments, and simulated food-drug interactions.
In addition, the model enabled direct comparison of urinary glucose excretion (UGE) under renal and hepatic impairment. Results showed that renal dysfunction substantially reduces UGE, while hepatic impairment has only a minor effect. Furthermore, the simulations revealed that food intake, although not altering total systemic exposure (AUC), significantly affects peak concentrations (Cmax), which in turn influences pharmacodynamic outcomes such as UGE. This suggests a clinically relevant food effect that is often underestimated.
Despite some limitations, the model offers mechanistic insight into the sources of PK/PD variability and supports individualized dosing strategies, contributing to predictive tools for personalized diabetes management and pharmacotherapy optimization.

Keywords: PK/PD, pharmacokinetics, pharmacodynamics, dapagliflozin, hepatorenal impairment, pharmacogenomics, food effect, ODE

Glimepiride is a second-generation sulfonylurea used in the management of type 2 diabetes. Its pharmacokinetics exhibit significant inter-individual variability influenced by patient-specific factors, potentially affecting therapeutic outcomes. This study developed a physiologically based pharmacokinetic (PBPK) model to systematically evaluate how these factors affect glimepiride disposition. Using curated data from 19 clinical studies, a modular SBML-based model was developed with submodels for intestinal absorption, hepatic metabolism, and renal excretion. Parameter optimization achieved good alignment with observed pharmacokinetic profiles. The validated model reproduced dose-proportional kinetics within the therapeutic range and pre- dicted differential impacts of physiological variability. Renal impairment primarily affected metabolite clearance, while hepatic dysfunction led to increased glimepiride exposure via reduced cytochrome P450 2C9 (CYP2C9)-mediated metabolism. Bodyweight and CYP2C9 variants also influenced drug disposition. Despite CYP2C9 genotypes significantly affected individual pharmacokinetics, population-level differences across biogeographical groups were modest. While some limitations remain, particularly regarding data availability in severe organ dysfunction, the model offers mechanistic insights into pharmacokinetic variability. It provides a basis for individualized dosing strategies and supports the ongoing development of clinically applicable decision support tools for type 2 diabetes therapy.

Keywords: PK/PD, glimepiride, hepatorenal impairment, pharmacogenomics, ODE, CYP2C9

Metabolic syndrome (MetS) and type 2 diabetes mellitus (T2D) are associated with inflammation and the accumulation of macrophages in peripheral nerves, which increases the risk of developing peripheral neuropathy (PN). We have previously investigated that macrophage infiltration in the peripheral nerves of animals with T2D (leptin-deficient ob/ob mice, leptin receptor-deficient db/db) correlated with PN, whereas this process in animals with MetS (Wistar Ottawa Karlsburg W (RT1u) WOKW rat) did not lead to neuropathic changes. Additional data presented in this study suggest an association between increased mRNA expression of the anti-inflammatory marker IL-10 and autophagy in the prevention of neuropathy.

Keywords: Autophagy, Inflammation, Metabolic syndrome;, PDN, T2D, WOKW rat, db/db mice, ob/ob mice

Computational models are essential for studying complex systems which, particularly in clinical settings, need to be quality-approved and transparent. To enhance the communication of a model’s features and capabilities, we propose an adaptation of the Findability, Accessibility, Interoperability and Reusability (FAIR) indicators published by the Research Data Alliance to assess models encoded in domain-specific standards, such as those established by COMBINE. The assessments guide FAIRification and add value to models.

Keywords: FAIR, computational models

Tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonist, represents a novel approach for treating type 2 diabetes mellitus (T2DM) and obesity. By combining the actions of both incretins, it enhances insulin secretion, suppresses glucagon, and reduces appetite, leading to superior glycemic control and weight loss compared to traditional GLP-1 receptor agonists. Despite its efficacy, inter-individual variability in pharmacokinetics necessitates a mechanistic understanding for optimized therapeutic application. This study employs physiologically-based pharmacokinetic (PBPK) modeling to characterize tirzepatide’s absorption, distribution, metabolism, and excretion (ADME). The model integrates physiological parameters to assess dose proportionality, hepatic and renal impairment effects, and body weight dependency. By improving drug exposure predictions, this approach aids personalized treatment strategies and enhances clinical decision-making across diverse patient populations.

Keywords: PKDB, tirzepatide, GLP-1, Pharmacokinetics

Liver sinusoidal endothelial inflammation/dysfunction and fibrosis are a crucial part of Metabolic Dysfunction Associated Steatohepatitis (MASH) development. TRC105 and M1043 are anti-endoglin (ENG) monoclonal antibodies that bind ENG. In this study, we hypothesized that treatment with anti-ENG antibodies would prevent the progression of LSECs inflammation and fibrosis in vivo and in vitro. MASH was induced in male C57BL/6 mice fed a choline-deficient L-amino acid-defined high-fat diet (CDAA-HFD) for 4 or 8 weeks. In the rescue study, mice were divided into three groups: a control group (chow diet), a MASH group (CDAA-HFD + IgG), and a rescue group (CDAA-HFD + M1043). Later, two groups received rat IgG1 (10 mg/kg) and M1043 (10 mg/kg). In vitro experiments, inflammation was induced in human LSECs by ox-LDL (50 μg/mL) and treated with TRC105 (300 μg/mL). Liver sinusoidal endothelial inflammation/dysfunction in MASH animals was characterized by endothelial overexpression of ENG, VCAM-1, and ICAM-1 and reduced VE-cadherin and p-eNOS/eNOS expression. M1043 treatment prevented the overexpression of ENG, VCAM-1, and ICAM-1, the progression of liver fibrosis, and the increase of liver-to-body weight ratio. In vitro experiments with TRC105 confirmed the prevention of LSECs inflammation development by reduced ENG and VCAM-1 expression, as well as decreased THP-1 monocytic cell adhesion in ox-LDL activated LSECs. In conclusion, we demonstrate that anti-ENG antibody treatment can prevent LSECs inflammation and fibrosis progression in a MASH animal model and LSECs inflammation in vitro. Thus, we propose directly targeted ENG may represent a promising pharmacological approach for addressing LSECs inflammation and liver fibrosis.

Keywords: Anti-endoglin antibody, Endoglin, Fibrosis, Liver alteration, Liver sinusoidal endothelial inflammation

Essential prerequisites for the practical application and translation of computational models include: i) reproducibility of results; ii) model reusability and extensibility; iii) data availability; and iv) strategies for model stratification and individualization. Here, we present a modeling workflow built around these foundational prerequisites, with a focus on liver function tests. Despite the paramount significance of liver function assessment in hepatology, reliable quantification remains a clinical challenge. Dynamic liver function tests offer a promising method for non-invasive in vivo assessment of liver function and metabolic phenotyping. By leveraging whole-body physiologically-based pharmacokinetic (PBPK) models, we are simulating these tests and positioning PBPK models as digital twins for metabolic phenotyping and liver function assessment. To develop and validate our models, we established the open pharmacokinetics database, PK-DB, containing curated data from 600+ clinical studies (10.1093/nar/gkaa990, 10.3389/fphar.2021.752826). Our models are individualizable and stratifiable, enabling simulation of lifestyle factors and co-administration effects on drug metabolism. Our models have been instrumental in clinical scenarios: from predicting individual outcomes post-hepatectomy (10.3389/fphys.2021.730418, 10.3389/fphys.2021.757293) to discerning the impact of CYP2D6 gene variants on liver function tests (10.3389/fphar.2022.1029073). These models are constructed hierarchically, describing metabolic and other biological processes in organs like the liver and kidneys, seamlessly integrated with whole-body physiology. Notably, all models and data are readily available and reproducible for reuse, encoded in the Systems Biology Markup Language (SBML) (10.15252/msb.20199110). We will provide an overview of these PBPK models and demonstrate how SBML and FAIR principles can facilitate model development, coupling, and reuse.

Keywords: digital twin, reproducibility, liver, FAIR, pharmacokinetics

The Systems Biology Markup Language (SBML) (Keating et al., 2020) is recognized as the standard framework for representing and exchanging complex mathematical models in biological systems research. SBML facilitates the depiction of a diverse array of biological phenomena, encom- passing metabolic networks, signaling pathways, and regulatory networks. It is versatile enough to handle models ranging from simple individual processes to intricate multi-scale representations. One of the primary challenges faced by newcomers in computational biology is the encoding and development of ordinary differential equation (ODE) models within the SBML framework. Addressing this hurdle, we introduce two innovative Python tools: sbmlutils (https://github. com/matthiaskoenig/sbmlutils), sbml4humans (https://sbml4humans.de), and the Cytoscape application cy3sbml (https://github.com/matthiaskoenig/cy3sbml). These tools collectively streamline the process of SBML model creation, enhancing both the programmatic aspect and the user experience. Specifically, sbmlutils facilitates the programmatic construction of SBML models, while sbml4humans generates user-friendly reports for model interpretation. Furthermore, cy3sbml integrates with Cytoscape to offer advanced visualization capabilities, thereby augmenting the comprehension and analysis of SBML-encoded models. These advancements significantly contribute to the ease of SBML model development and interpretation, fostering greater accessibility and understanding for those entering the field of computational systems biology.

Keywords: digital twin, reproducibility, liver

In this thesis, an extensive dataset of aliskiren pharmacokinetics and pharmacodynamics was compiled and used to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model. This model was applied to address three primary research questions: I. How does hepatorenal impairment influence the pharmacokinetics of aliskiren?; II. What effects do co-administered medications have on the pharmacokinetics of aliskiren?; III. What are the consequences of these pharmacokinetic changes for the pharmacodynamics of aliskiren, particularly its ability to modulate the renin-angiotensin-aldosterone system (RAAS)? The PBPK/PD model enables the prediction of aliskiren concentration-time profiles following oral and intravenous administration and provides detailed insights into the factors that affect its pharmacological efficacy. By exploring these influences, the study offers a foundation for developing optimized therapeutic strategies tailored to individual patients.

Keywords: PK/PD, aliskiren, hepatorenal impairment, ODE

Genome scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses including Flux variability, Reaction deletion, Objective function, and Gene deletion to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

Keywords: COMBINE, FROG, FBC

The mammalian liver is organized into three-dimensional structures called lobules that are integral to its function. According to the widely accepted hexagonal model, the cross-section of each lobule is characterized by a central vein and portal triads at the corners. These portal triads consist of a hepatic portal vein, a hepatic arteriole, and a bile duct. Blood is delivered through the hepatic artery and portal vein and flows inward from the outer periportal region toward the central vein through sinusoids lined with hepatocytes. These hepatocytes express a wide variety of metabolic enzymes, including cytochrome P450 (CYP) isoforms that are critical for xenobiotic metabolism. Notably, many hepatic enzymes, including CYPs, exhibit differential expression along the periportal-to-perivenous axis, a spatial variation known as liver zonation. Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases characterized by the accumulation of fat in the liver (steatosis) in the absence of significant alcohol consumption. Histopathologically, NAFLD is characterized by the presence of microvesicular fat droplets within hepatocytes. Although liver zonation has been extensively studied, the protein gradients of CYPs and their alterations in response to steatosis have not been systematically quantified or compared across species. This study aimed to develop an image analysis workflow to accurately derive these lobular expression gradients and quantify macrosteatosis in whole slide images (WSI) of liver histology. A comprehensive analysis was performed on whole slide images (WSIs) of mouse, rat, pig, and human liver specimens. These images included hematoxylin and eosin (H\&E) stained slides and immunohistochemically stained slides for glutamine synthetase (GS) and cytochrome P450 isoforms 1A2, 2E1, 2D6, and 3A4. To achieve the study objectives, two image analysis workflows were developed: one using classical methods and the other using convolutional neural networks (CNN). These workflows were designed to perform two key tasks: (1) segmentation of liver lobules and (2) detection of macrosteatosis droplets. Key findings were that there was significant variability in lobular geometry within subjects, with strong consistency across species, with inter-species differences observed primarily in lobular size. A slight increase in lobular size was observed in steatotic samples, with a moderate to strong correlation between lobular size and steatosis content. In addition, interspecies differences in zonation patterns were observed for CYP1A2 in pigs, CYP2E1 in rats, and CYP3A4 in humans. Importantly, zonation patterns in steatotic subjects were not qualitatively different from those in non-steatotic subjects across species. In summary, I have successfully developed and implemented an image analysis workflow for whole slide images (WSIs) that allows detailed examination of liver lobule geometry and macrosteatosis droplets. Furthermore, the resulting datasets were integrated to facilitate a comprehensive analysis of the zonal distribution of CYP protein expression and macrosteatosis within the lobules.

Keywords: liver, image analysis, steatosis, machine learning, whole slide image (WSI)

Background and Aims: NASH, a growing liver disease worldwide,requires an understanding of its molecular progression for effective treatment.Endoglin,a TGFβ-superfamily coreceptor,has two forms:membrane endoglin (ENG)and soluble endoglin (sENG).While ENG is associated with liver sinusoidal endothelial dysfunction (LSED)and liver fibrosis, its direct involvement in the progression of NASH remains unclear.Our aim was to investigate the relationship between NASH-progression,ENG expression,and LSED development.We hypothesized that the anti-endoglin monoclonal antibody M1043 would affect LSED development by directly affecting ENG expression and signaling with respect to NASH development. Methods: NASH was induced in two separated groups of male C57BL/6 mice using the CDAA-HFD diet(n=7),while two control groups(n=7)were fed a standard diet.Mice were sacrificed after four and eight weeks to evaluate ENG protein changes during NASH. Then,24 mice were grouped into the control group(n=8),CDAA+rat IgG(n=8),and CDAA+M1043 group (n=8).After four weeks,CDAA+IgG and CDAA+M1043 groups received intraperitoneal injections of rat IgG (10 mg/kg)and M1043(10 mg/kg) twice a week.The experiment lasted eight weeks,with blood and liver samples collected for analysis.To better understand the role of ENG antibody and its cleavage,a computational model of ENG modulation of TGFβ signaling was developed. Results: Liver injury was confirmed by elevated liver enzymes,increased fibrosis,and inflammatory biomarkers in both CDAA-HFD groups.LSEC development was confirmed by increased ENG,VCAM-1,and ICAM-1 expression in endothelial cells.We showed increased expression of MMP-14 and higher levels of sENG levels in 8 weeks fed mice.M1043 treatment significantly prevented the increase in ENG,VCAM-1,and ICAM-1 protein expression in liver sinusoidal endothelial cells when compared to non-treated mice.Computational modeling simulations showed the ENG-dependent switch between Smad2/3 and Smad1/5/8 signaling,along with changes in sENG levels,which was reversed by ENG antibodies. Conclusions: Our findings indicate that NASH development is associated with LSED development,increased ENG and MMP-14 expression,and elevated sENG levels,suggesting ENG role in LSED progression.Blocking of ENG expression and signaling might be a potential target to affect LSED development and possibly prevent NASH aggravation.

Keywords: endoglin, modeling

The “COmputational Modeling in BIology NEtwork” (COMBINE) initiative aims to harmonise the development of diverse community standards for computational models in biology. It coordinates standard development to support the associated projects towards establishing a suite of compatible, interoperable and comprehensive standards that address the full spectrum of modeling in systems and synthetic biology.

Keywords: COMBINE, reproducibility, standards

There is a lack of systematic research exploring cross-species variation in liver lobular geometry and zonation patterns of critical drug-metabolizing enzymes, a knowledge gap essential for translational studies. This study investigated the critical interplay between lobular geometry and key cytochrome P450 (CYP) zonation in four species: mouse, rat, pig, and human. We developed an automated pipeline based on whole slide images (WSI) of hematoxylin-eosin-stained liver sections and immunohistochemistry. This pipeline allows accurate quantification of both lobular geometry and zonation patterns of essential CYP proteins. Our analysis of CYP zonal expression shows that all CYP enzymes (besides CYP2D6 with panlobular expression) were observed in the pericentral region in all species, but with distinct differences. Comparison of normalized gradient intensity shows a high similarity between mice and humans, followed by rats. Specifically, CYP1A2 was expressed throughout the pericentral region in mice and humans, whereas it was restricted to a narrow pericentral rim in rats and showed a panlobular pattern in pigs. Similarly, CYP3A4 is present in the pericentral region, but its extent varies considerably in rats and appears panlobular in pigs. CYP2D6 zonal expression consistently shows a panlobular pattern in all species, although the intensity varies. CYP2E1 zonal expression covered the entire pericentral region with extension into the midzone in all four species, suggesting its potential for further cross-species analysis. Analysis of lobular geometry revealed an increase in lobular size with increasing species size, whereas lobular compactness was similar. Based on our results, zonated CYP expression in mice is most similar to humans. Therefore, mice appear to be the most appropriate species for drug metabolism studies unless larger species are required for other purposes, e.g., surgical reasons. CYP selection should be based on species, with CYP2E1 and CYP2D6 being the most preferable to compare four species. CYP1A2 could be considered as an additional CYP for rodent versus human comparisons, and CYP3A4 for mouse/human comparisons. In conclusion, our image analysis pipeline together with suggestions for species and CYP selection can serve to improve future cross-species and translational drug metabolism studies.

Keywords: liver, zonation, cytochrome P450, lobule geometry, drug metabolism, species

Enalapril is a medication used to treat high blood pressure and a num- ber of other cardiovascular conditions. The renin-angiotensin-aldosterone system (RAAS) is the main component involved in the regulation of blood pressure in the body. One of the key steps in this process is the conver- sion of angiotensin I to angiotensin II by angiotensin converting enzyme (ACE). Enalapril is an ACE inhibitor and therefore helps to lower blood pressure. Pharmacokinetics is the branch of pharmacology that studies how the body absorbs, distributes, metabolizes, and eliminates drugs over time. After administration as enalapril maleate, it is absorbed in the intestine and converted by carboxylesterase 1 (CES1) in the liver to the active metabolite, enalaprilat. Enalapril and enalaprilat are eliminated from the body by re- nal clearance. A physiologically based pharmacokinetic (PBPK) model was developed to investigate the pharmacokinetics of enalapril and the factors influencing it. As part of the work, an extensive database of enalapril phar- macokinetics consisting of data from 49 clinical trials was established and used to parameterize and validate the computational model. The model was used to investigate the effect of renal impairment, hepatic impairment and changes in CES1 activity on the pharmacokinetics of enalapril and enalapri- lat, as these three factors are known to have a major influence on the phar- macokinetics of enalapril. The model shows good agreement with a wide range of enalapril and enalaprilat data in serum, plasma and urine under different conditions (healthy, renal impairment, hepatic impairment, CES1 mutations). The model is available in SBML under a CC-BY 4.0 license with all data freely available from the PK-DB pharmacokinetics database.

Keywords: hypertension, enalapril, PBPK, pharmacokinetics, CES1, liver function, renal function, hepatic impairment, renal impairment

Cytochrome P450 (CYP450) and UDP-glucuronosyltransferase (UGT) enzymes play key roles in the metabolism of a wide variety of endogenous and exogenous compounds, including drugs and toxins. CYP450 enzymes are involved in phase I metabolic reactions that typically introduce or unmask functional groups on molecules, often leading to their activation or inactivation. These reactions include hydroxylation, oxidation, and reduction. UGT enzymes, on the other hand, are essential for phase II metabolism, where they catalyze the conjugation of glucuronic acid to various substrates, making the compounds more water soluble and therefore easier to excrete. Both CYP450 and UGT are critical in determining drug pharmacokinetics, drug efficacy, drug interactions and potential toxicity. A variety of CYP450 and UGT isoforms exist in the human liver, and different isoforms are responsible for detoxifying different substances. For example, CYP1A2 is involved in the metabolism of caffeine, CYP2D6 in dextromethorphan, CYP2E1 in chlorzoxazone, and CYP3A4 in midazolam. There are large inter-individual differences in the metabolism of drugs by the different CYP450 and UGT isoforms. Important factors for these differences are differences in activity, e.g. due to genetic polymorphisms, and the amount of protein of the respective enzyme in the liver. The aim of this study was to provide a systematic overview of the variability in protein levels of CYP450 and UGT isoforms in the human liver. To this end, a manually curated database of protein abundance information was established and used to answer the following questions: (i) What are the protein distributions of the different CYP450 and UGT isoforms? (ii) What factors affect these protein levels (age, sex, body mass index, smoking, alcohol consumption)? (iii) How do CYP and UGT isoforms correlate among each other? Key results include: (i) A free and open database of CYP and UGT protein levels in the human liver has been established. (ii) CYP and UGT isoforms show large inter-individual variability in protein levels. (iii) Different CYP and UGT isoforms have different protein levels. (iv) Factors such as age, smoking and alcohol consumption may affect the protein levels of CYP and UGT enzymes. (v) There are significant correlations in protein levels between CYP isoforms and between UGT isoforms. This work has established an important resource for the study of variability in drug detoxification due to variability in protein levels in the human liver.

Keywords: cytochrome P450, UDP-glucuronosyltransferase, CYP, UGT, protein, liver

Motivation: Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties. Results: We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection. Availability and implementation: The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.

Keywords: Animals, Bayes Theorem, Humans, Liver metabolism, Magnetic Resonance Imaging, Mice, Rats, Software, Systems Biology

Modern biological research is increasingly informed by computational simulation experiments, which necessitate the development of methods for annotating, archiving, sharing, and reproducing the conducted experiments. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. Level 1 Version 5 of SED-ML expands the ability of modelers to define simulations in SED-ML using the Kinetic Simulation Algorithm Onotoloy (KiSAO). While it was possible in Version 4 to define a simulation entirely using KiSAO, Version 5 now allows users to define tasks, model changes, ranges, and outputs using the ontology as well. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including various languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, and many simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

Keywords: SED-ML, Standardization

Multiscale modeling requires the coupling of models on different scales, often based on different mathematical approaches and developed by different research teams. This poses many challenges, such as defining interfaces for coupling, reproducible exchange of submodels, efficient simulation of the models, or reproducibility of results. Here, we present a multiscale digital twin of the liver that couples a partial differential equation (PDE)-based porous media approach for the hepatic lobule with cellular-scale ordinary differential equation (ODE)-based models. The models based on the theory of porous media describe transport, tissue mechanical properties, and deformations at the lobular scale, while the cellular models describe hepatic metabolism in terms of drug metabolism and damage in terms of necrosis. The resulting multiscale model of the liver was used to simulate perfusion-zonation-function relationships in the liver spanning scales from single cell to the lobulus. The model was applied to study the effects of (i) protein zonation patterns (metabolic zonation) and (ii) drug concentration dependence on spatially heterogeneous liver damage in the form of necrosis. Depending on the zonation pattern, different liver damage patterns could be reproduced, including periportal and pericentral necrosis as seen in drug-induced liver injury (DILI). Increasing the drug concentration led to an increase in the observed damage pattern. A key point for the success was the integration of domain-specific simulators based on standard exchange formats, i.e., libroadrunner for the high-performance simulation of ODE-based systems and FEBio for the simulation of the continuum-biomechanical part. This allows a standardized and reproducible exchange of cellular scale models in the Systems Biology Markup Language (SBML) between research groups.

Keywords: liver, computational model, biomechanics, porous media, drug metabolism, digital twin, DILI, cytochrome P450

Metabolic zonation refers to the spatial separation of metabolic functions along the sinusoidal axes of the liver. This phenomenon forms the foundation for adjusting hepatic metabolism to physiological requirements in health and disease (e.g., metabolic dysfunction-associated steatotic liver disease/MASLD). Zonated metabolic functions are influenced by zonal morphological abnormalities in the liver, such as periportal fibrosis and pericentral steatosis. We aim to analyze the interplay between microperfusion, oxygen gradient, fat metabolism and resulting zonated fat accumulation in a liver lobule. Therefore we developed a continuum biomechanical, tri-phasic, bi-scale, and multicomponent in silico model, which allows to numerically simulate coupled perfusion-function-growth interactions two-dimensionally in liver lobules. The developed homogenized model has the following specifications: (i) thermodynamically consistent, (ii) tri-phase model (tissue, fat, blood), (iii) penta-substances (glycogen, glucose, lactate, FFA, and oxygen), and (iv) bi-scale approach (lobule, cell). Our presented in silico model accounts for the mutual coupling between spatial and time-dependent liver perfusion, metabolic pathways and fat accumulation. The model thus allows the prediction of fat development in the liver lobule, depending on perfusion, oxygen and plasma concentration of free fatty acids (FFA), oxidative processes, the synthesis and the secretion of triglycerides (TGs). The use of a bi-scale approach allows in addition to focus on scale bridging processes. Thus, we will investigate how changes at the cellular scale affect perfusion at the lobular scale and vice versa. This allows to predict the zonation of fat distribution (periportal or pericentral) depending on initial conditions, as well as external and internal boundary value conditions.

Keywords: Multiscale Approach, Homogenization, Theory of Porous Media (TPM), Growth Kinematics, Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD), FEM boundary problem

The SIMulation supported LIVer Assessment for donor organs (SimLivA) project aims to develop a mathematical model to accurately simulate the influence of mechanical alterations in marginal liver grafts (specifically steatotic ones) and cold ischemia on early ischemia-reperfusion injury (IRI) during liver transplantation. Our project tackles significant research challenges, including the co-development of computational methodologies, experimental studies, clinical processes, and technical workflows. We aim to refine a continuum-biomechanical model for enhanced IRI prediction, collect pivotal experimental and clinical data, and assess the clinical applicability of our model. Our efforts involve augmenting and tailoring a coupled continuum-biomechanical, multiphase, and multi-scale partial differential equation-ordinary differential equation (PDE-ODE) model of the liver lobule, allowing us to numerically simulate IRI depending on the degree of steatosis and the duration of ischemia. The envisaged model will intertwine the structure, perfusion, and function of the liver, serving as a crucial aid in clinical decision-making processes. We view this as the initial step towards an in-silico clinical decision support tool aimed at enhancing the outcomes of liver transplantation. In this paper, we provide an overview of the SimLivA project and our preliminary findings, which include: a cellular model that delineates critical processes in the context of IRI during transplantation; and the integration of this model into a multi-scale PDE-ODE model using a homogenized, multi-scale, multi-component approach within the Theory of Porous Media (TPM) framework. The model has successfully simulated the interconnected relationship between structure, perfusion, and function—all of which are integral to IRI. Initial results show simulations at the cellular scale that describe critical processes related to IRI during transplantation. After integrating this model into a multiscale PDE-ODE model, first simulations were performed on the spatial distribution of key functions during warm and cold ischaemia. In addition, we were able to study the effect of tissue perfusion and temperature, two critical parameters in the context of liver transplantation and IRI.

Keywords: ischemia-reperfusion injury, IRI, liver transplantation, ODE, PDE, porous media

Liver cancer ranks second in cancer-related deaths thereby making timely diagnosis and personalized treatment crucial. Hepatocellular carcinoma (HCC) patients often get diagnosed at advanced stages, hindering curative options like resection and ablation. Systemic treatment has become the standard therapy and sorafenib has been the primary systemic treatment for advanced HCC for the last ten years, being the first approved drug for this purpose. Sorafenib (Nexavar), is a multi-kinase inhibitor medication approved for primarily treating advanced renal cell carcinoma, hepatocellular carcinoma or other cancers. After oral administration, sorafenib enters hepatocytes via OATP1B-type carriers. It undergoes CYP3A4-mediated oxidative metabolism to sorafenib-N-oxide (M2) and UGT1A9-mediated glucuronidation forming sorafenib-glucuronide (SG). SG is secreted into bile mainly by ABCC2, with a fraction reuptaken by ABCC3, preventing ABCC2 saturation. In bile, SG can be excreted via the feces or converted back to sorafenib by a bacterial β-glucuronidase. Sorafenib is absorbed in the intestine, reentering the systemic circulation, contributing to elimination and detoxification. Sorafenib is excreted mainly by the feces (77%) and urine (19%). Hepatic and renal impairment could have a large impact on the pharmacokinetics of sorafenib. Within this work the pharmacokinetics of sorafenib were analyzed by developing a physiologically-based pharmacokinetic (PBPK) model based on extensive data curation of sorafenib data. The model allowed to simulate the time-concentration courses of sorafenib in various tissues and to calculate the pharmacokinetic parameters for sorafenib under single and multiple dosing regimes. The model was applied to study the dose-dependency of sorafenib treatment and to investigate the effect of hepatic and renal impairment on sorafenib pharmacokinetics.

Keywords: sorafenib, PBPK, pharmacokinetics, HCC, liver function, renal function

Reproducible research and open science practices have the potential to accelerate scientific progress by allowing others to reuse research outputs, and by promoting rigorous research that is more likely to yield trustworthy results. However, these practices are uncommon in many fields, so there is a clear need for training that helps and encourages researchers to integrate reproducible research and open science practices into their daily work. Here, we outline eleven strategies for making training in these practices the norm at research institutions. The strategies, which emerged from a virtual brainstorming event organized in collaboration with the German Reproducibility Network, are concentrated in three areas: (i) adapting research assessment criteria and program requirements; (ii) training; (iii) building communities. We provide a brief overview of each strategy, offer tips for implementation, and provide links to resources. We also highlight the importance of allocating resources and monitoring impact. Our goal is to encourage researchers – in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees – to think creatively about the many ways they can promote reproducible research and open science practices in their institutions.

Keywords: reproducible research, open science, training

Talinolol is a cardioselective beta-blocker used in the treatment of various cardiovascular diseases and tachyarrhythmias. Its intestinal absorption is determined by uptake by the organic anion transporting polypeptide 2B1 (OATP2B1) and efflux via P-glycoprotein (P-gp). Talinolol can be taken up via OATP1B1 in the liver, where it enters the enterohepatic circulation. Talinolol is excreted unchanged in the urine and feces. Talinolol is widely used as a probe drug for the intestinal efflux transporter P-gp, which plays a critical role in protecting against potentially toxic substances and facilitating the elimination of xenobiotics. In this work, an extensive database of talinolol pharmacokinetics was established and used to develop and validate a physiologically based pharmacokinetic (PBPK) model of talinolol for P-gp phenotyping. The model was used to investigate the influence of several factors on talinolol pharmacokinetics: (i) inhibition of P-gp via drug-drug interaction; (i) genetic polymorphisms of P-gp; (iii) activity of OATP2B1 and OATP1B1; (iv) effect of disease, namely hepatic and renal impairment; and (v) site-specific distribution of P-gp and OATP2B1 in the intestine. The model accurately predicts the concentration-time profile of talinolol after oral and intravenous administration of single and multiple dosing. Furthermore, the model accurately describes the effect of genetic variants of P-gp on the pharmacokinetics of talinolol, the effect of inhibition of P-gp, the effect of renal impairment, as well as site-specific infusion of talinolol in the intestine. The detailed description of the intestinal absorption of talinolol and the predictions of talinolol pharmacokinetics as a function of hepatorenal impairment provide valuable clinical insights for metabolic phenotyping with talinolol. Both the model and the database are freely available for reuse.

Keywords: PBPK, talinolol, P-glycoprotein, pharmacokinetics, OATP2B1, OATB1B1, renal impairment, cirrhosis

The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic β-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct a novel data-driven kinetic model of GSIS in the pancreatic β-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and β-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and phenomenological equations for insulin secretion coupled to cellular energy state, ATP dynamics and (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the β-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and biphasic insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic β-cell.

Keywords: glucose-stimulated insulin secretion, GSIS, glycolysis, pancreas, kinetic model, systems biology

Talinolol is a cardioselective beta-blocker used for the treatment of various cardiovascular diseases and tachyarrhythmia. The gastrointestinal absorption of talinolol is determined via its uptake in the intestine via the organic anion transporting polypeptide 2B1 (OATP2B1) and its efflux via the P-glycoprotein (P-gp). After intestinal absorption talinolol can be transported into the liver via OATP1B1 talinolol where it undergoes enterohepatic circulation. Talinolol is excreted unchanged in the urine and feces. In addition to its clinical application, talinolol is widely used as a probe drug for the intestinal efflux transporter P-glycoprotein. P-gp plays a crucial role in the human body as it is expressed in various tissues to protect against potentially toxic substances, facilitating the elimination of xenobiotics. The application of talinolol for P-gp phenotyping enables evaluation of factors influencing P-gp-mediated transport in vivo such as genetic polymorphisms of P-gp as well as the distribution of P-gp along the intestine. Within this thesis, an extensive dataset of talinolol pharmacokinetics was established and used to develop a physiologically based pharmacokinetic (PBPK) model for talinolol. The model was applied to investigate the influence of various factors on the pharmacokinetics of talinolol, including: (i) genetic variants of P-gp; (ii) enzymatic activity of the transporters OATP2B1 and OATP1B1; (iii) site-specific distribution of P-gp and OATP2B1 proteins in the intestine, and (iv) the impact of diseases such as liver cirrhosis and renal dysfunction. The model enables precise predictions of the concentration-time profile of talinolol in various tissues following oral or intravenous administration. Furthermore, the model accurately describes the effect of genetic variants of P-gp on the pharmacokinetics of talinolol. The detailed description of the limiting intestinal absorption of intestinal provided by the model, along with the precise prediction of talinolol`s pharmacokinetics in different renal functions, holds significant clinical relevance.

Keywords: talinolol, PBPK, pharmacokinetics, OATP2B1, OATP1B1, P-glycoprotein, liver function, renal function

The cytochrome P450 (CYP) superfamily of enzymes plays a critical role in the metabolism of drugs, toxins, and endogenous and exogenous compounds. The activity of CYP enzymes can be influenced by a variety of factors, including genetics, diet, age, environmental factors, and disease. Among the major isoforms, CYP2E1 is of particular interest due to its involvement in the metabolism of various low molecular weight chemicals, including alcohols, pharmaceuticals, industrial solvents, and halogenated anesthetics. Metabolic phenotyping of CYPs based on the elimination of test compounds is a useful method for assessing in vivo activity, with chlorzoxazone being the primary probe drug for phenotyping of CYP2E1. The aim of this work was to investigate the effect of changes in CYP2E1 level and activity, ethanol consumption, ethanol abstinence, and liver impairment on the results of metabolic phenotyping with chlorzoxazone. To accomplish this, an extensive pharmacokinetic dataset for chlorzoxazone was established and a physiologically based pharmacokinetic (PBPK) model of chlorzoxazone and its metabolites, 6-hydroxychlorzoxazone and chlorzoxazone-O-glucuronide, was developed and validated. The model incorporates the effect of ethanol consumption on CYP2E1 levels and activity by extending the model with a core ethanol pharmacokinetic model and a CYP2E1 turnover model. The model accurately predicts pharmacokinetic data from several clinical studies and is able to estimate the effect of changes in CYP2E1 levels and activity on chlorzoxazone pharmacokinetics. Regular ethanol consumption induces CYP2E1 over two to three weeks, resulting in increased conversion of chlorzoxazone to 6-hydroxychlorzoxazone and a higher 6-hydroxychlorzoxazone/chlorzoxazone metabolic ratio. After ethanol withdrawal, CYP2E1 levels return to baseline within one week. Importantly, liver impairment has an opposite effect, resulting in reduced liver function via CYP2E1. In alcoholics with liver impairment who also consume ethanol, these factors will have opposite confounding effects on metabolic phenotyping with chlorzoxazone.

Keywords: PBPK, chlorzoxazone, CYP2E1, alcohol, ethanol, pharmacokinetics

This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2022 special issue presents three updates to the standards: CellML 2.0.1, SBML Level 3 Package: Spatial Processes, Version 1, Release 1, and Synthetic Biology Open Language (SBOL) Version 3.1.0. This document can also be used to identify the latest specifications for all COMBINE standards. In addition, this editorial provides a brief overview of the COMBINE 2022 meeting in Berlin.

Keywords: COMBINE, standardization, SBML, SEDML, OMEX

Hypercholesterolemia is a multifaceted plasma lipid disorder with heterogeneous causes including lifestyle and genetic factors. A key feature of hypercholesterolemia is elevated plasma levels of low-density lipoprotein cholesterol (LDL-C). Several genetic variants have been reported to be associated with hypercholesterolemia, known as familial hypercholesterolemia (FH). Important variants affect the LDL receptor (LDLR), which mediates the uptake of LDL-C from the plasma, apoliporotein B (APOB), which is involved in the binding of LDL-C to the LDLR, and proprotein convertase subtilisin/kexin type 9 (PCSK9), which modulates the degradation of the LDLR. A typical treatment for hypercholesterolemia is statin medication, with simvastatin being one of the most commonly prescribed statins. In this work, the LDL-C lowering therapy with simvastatin in hypercholesterolemia was investigated using a computational modeling approach. A physiologically based pharmacokinetic model of simvastatin integrated with a pharmacodynamic model of plasma LDL-C (PBPK/PD) was developed based on extensive data curation. A key component of the model is LDL-C turnover by the liver, consisting of: hepatic cholesterol synthesis with the key enzymes HMG-CoA reductase and HMG-CoA synthase; cholesterol export from the liver as VLDL-C; de novo synthesis of LDLR; transport of LDLR to the membrane; binding of LDL-C by LDLR via APOB; endocytosis of the LDLR-LDL-C complex; recycling of LDLR from the complex. The model was applied to study the effects of simvastatin therapy in hypercholesterolemia due to different causes in the LDLR pathway corresponding to different subtypes of hypercholesterolemia. Model predictions of LDL-C lowering therapy were validated with independent clinical data sets. Key findings are: (i) hepatic LDLR turnover is highly heterogeneous among FH classes; (ii) despite this heterogeneity, simvastatin therapy results in a consistent reduction in plasma LDL-C regardless of class