Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans.

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

In-Depth Analysis of the Selection of PBPK Modeling Tools: Bibliometric and Social Network Analysis of the Open Systems Pharmacology Community.

Since the Open Source Initiative laid the foundation for the open source software environment in 1998, the popularity of free and open source software has been steadily increasing. Model-informed drug discovery and development (MID3), a key component of pharmaceutical research and development, heavily makes use of computational models which can be developed using various software including the Open Systems Pharmacology (OSP) software (PK-Sim/MoBi), a free and open source software tool for physiologically based pharmacokinetic (PBPK) modeling. In this study, we aimed to investigate the impact, application areas, and reach of the OSP software as well as the relationships and collaboration patterns between organizations having published OSP-related articles between 2017 and 2023. Therefore, we conducted a bibliometric analysis of OSP-related publications and a social network analysis of the organizations with which authors of OSP-related publications were affiliated. On several levels, we found evidence for a significant growth in the size of the OSP community as well as its visibility in the MID3 community since OSP's establishment in 2017. Specifically, the annual publication rate of PubMed-indexed PBPK-related articles using the OSP software outpaced that of PBPK-related articles using any software. Our bibliometric analysis and network analysis demonstrated that the expansion of the OSP community was predominantly driven by new authors and organizations without prior connections to the community involving the generation of research clusters de novo and an overall diversification of the network. These findings suggest an ongoing evolution of the OSP community toward a more segmented, diverse, and inclusive network.

Reproductive toxicity in birds predicted by physiologically-based kinetics and bioenergetics modelling.

Effects on the growth and reproduction of birds are important endpoints in the environmental risk assessment (ERA) of pesticides. Toxicokinetic-toxicodynamic models based on dynamic energy budget theory (DEB) are promising tools to predict these effects mechanistically and make extrapolations relevant to ERA. However, before DEB-TKTD models are accepted as part of ERA for birds, ecotoxicological case studies are required so that stakeholders can assess their capabilities. We present such a case-study, modelling the effects of the fluopyram metabolite benzamide on the northern bobwhite quail (Colinus virginianus). We parametrised a DEB-TKTD model for the embryo stage on the basis of an egg injection study, designed to provide data for model development. We found that information on various endpoints, such as survival, growth, and yolk utilisation were needed to clearly distinguish between the performance of model variants with different TKTD assumptions. The calibration data were best explained when it was assumed that chemical uptake occurs via the yolk and that benzamide places stress on energy assimilation and mobilisation. To be able to bridge from the in vitro tests to real-life exposure, we developed a physiologically-based toxicokinetic (PBK) model for the quail and used it to predict benzamide exposure inside the eggs based on dietary exposure in a standard reproductive toxicity study. We then combined the standard DEB model with the TKTD module calibrated to the egg injection studies and used it to predict effects on hatchling and 14-day chick weight based on the exposure predicted by the PBK model. Observed weight reductions, relative to controls, were accurately predicted. Thus, we demonstrate that DEB-TKTD models, in combination with suitable experimental data and, if necessary, with an exposure model, can be used in bird ERA to predict chemical effects on reproduction.

Integration of artificial neural network and physiologically based biopharmaceutic models in the development of sustained-release formulations.

Model-informed drug development is an important area recognized by regulatory authorities and is gaining increasing interest from the generic drug industry. Physiologically based biopharmaceutics modeling (PBBM) is a valuable tool to support drug development and bioequivalence assessments. This study aimed to utilize an artificial neural network (ANN) with a multilayer perceptron (MLP) model to develop a sustained-release matrix tablet of metformin HCl 500 mg, and to test the likelihood of the prototype formulation being bioequivalent to Glucophage® XR, using PBBM modeling and virtual bioequivalence (vBE). The ANN with MLP model was used to simultaneously optimize 735 formulations to determine the optimal formulation for Glucophage® XR release. The optimized formulation was evaluated and compared to Glucophage® XR using PBBM modeling and vBE. The optimized formulation consisted of 228 mg of hydroxypropyl methylcellulose (HPMC) and 151 mg of PVP, and exhibited an observed release rate of 42% at 1 h, 47% at 2 h, 55% at 4 h, and 58% at 8 h. The PBBM modeling was effective in assessing the bioequivalence of two formulations of metformin, and the vBE evaluation demonstrated the utility and relevance of translational modeling for bioequivalence assessments. The study demonstrated the effectiveness of using PBBM modeling and model-informed drug development methodologies, such as ANN and MLP, to optimize drug formulations and evaluate bioequivalence. These tools can be utilized by the generic drug industry to support drug development and biopharmaceutics assessments.

Considering developmental neurotoxicity in vitro data for human health risk assessment using physiologically-based kinetic modeling: deltamethrin case study.

Developmental neurotoxicity (DNT) is a potential hazard of chemicals. Recently, an in vitro testing battery (DNT IVB) was established to complement existing rodent in vivo approaches. Deltamethrin (DLT), a pyrethroid with a well-characterized neurotoxic mode of action, has been selected as a reference chemical to evaluate the performance of the DNT IVB. The present study provides context for evaluating the relevance of these DNT IVB results for the human health risk assessment of DLT by estimating potential human fetal brain concentrations after maternal exposure to DLT. We developed a physiologically based kinetic (PBK) model for rats which was then translated to humans considering realistic in vivo exposure conditions (acceptable daily intake [ADI] for DLT). To address existing uncertainties, we designed case studies considering the most relevant drivers of DLT uptake and distribution. Calculated human fetal brain concentrations were then compared with the lowest benchmark concentration achieved in the DNT IVB. The developed rat PBK model was validated on in vivo rat toxicokinetic data of DLT over a broad range of doses. The uncertainty based case study evaluation confirmed that repeated exposure to DLT at an ADI level would likely result in human fetal brain concentrations far below the in vitro benchmark. The presented results indicate that DLT concentrations in the human fetal brain are highly unlikely to reach concentrations associated with in vitro findings under realistic exposure conditions. Therefore, the new in vitro DNT results are considered to have no impact on the current risk assessment approach.

A generic avian physiologically-based kinetic (PBK) model and its application in three bird species.

Physiologically-based kinetic (PBK) models are effective tools for designing toxicological studies and conducting extrapolations to inform hazard characterization in risk assessment by filling data gaps and defining safe levels of chemicals. In the present work, a generic avian PBK model for male and female birds was developed using PK-Sim and MoBi from the Open Systems Pharmacology Suite (OSPS). The PBK model includes an ovulation model (egg development) to predict concentrations of chemicals in eggs from dietary exposure. The model was parametrized for chicken (Gallus gallus), bobwhite quail (Colinus virginianus) and mallard duck (Anas platyrhynchos) and was tested with nine chemicals for which in vivo studies were available. Time-concentration profiles of chemicals reaching tissues and egg compartment were simulated and compared to in vivo data. The overall accuracy of the PBK model predictions across the analyzed chemicals was good. Model simulations were found to be in the range of 22-79% within a 3-fold and 41-89% were within 10- fold deviation of the in vivo observed data. However, for some compounds scarcity of in-vivo data and inconsistencies between published studies allowed only a limited goodness of fit evaluation. The generic avian PBK model was developed following a "best practice" workflow describing how to build a PBK model for novel species. The credibility and reproducibility of the avian PBK models were scored by evaluation according to the available guidance documents from WHO (2010), and OECD (2021), to increase applicability, confidence and acceptance of these in silico models in chemical risk assessment.

Quantitative Morphometric, Physiological, and Metabolic Characteristics of Chickens and Mallards for Physiologically Based Kinetic Model Development.

Physiologically based kinetic (PBK) models are a promising tool for xenobiotic environmental risk assessment that could reduce animal testing by predicting in vivo exposure. PBK models for birds could further our understanding of species-specific sensitivities to xenobiotics, but would require species-specific parameterization. To this end, we summarize multiple major morphometric and physiological characteristics in chickens, particularly laying hens (Gallus gallus) and mallards (Anas platyrhynchos) in a meta-analysis of published data. Where such data did not exist, data are substituted from domesticated ducks (Anas platyrhynchos) and, in their absence, from chickens. The distribution of water between intracellular, extracellular, and plasma is similar in laying hens and mallards. Similarly, the lengths of the components of the small intestine (duodenum, jejunum, and ileum) are similar in chickens and mallards. Moreover, not only are the gastrointestinal absorptive areas similar in mallard and chickens but also they are similar to those in mammals when expressed on a log basis and compared to log body weight. In contrast, the following are much lower in laying hens than mallards: cardiac output (CO), hematocrit (Hct), and blood hemoglobin. There are shifts in ovary weight (increased), oviduct weight (increased), and plasma/serum concentrations of vitellogenin and triglyceride between laying hens and sexually immature females. In contrast, reproductive state does not affect the relative weights of the liver, kidneys, spleen, and gizzard.

Quantitative Comparison of Avian and Mammalian Physiologies for Parameterization of Physiologically Based Kinetic Models.

Physiologically based kinetic (PBK) models facilitate chemical risk assessment by predicting in vivo exposure while reducing the need for animal testing. PBK models for mammals have seen significant progress, which has yet to be achieved for avian systems. Here, we quantitatively compare physiological, metabolic and anatomical characteristics between birds and mammals, with the aim of facilitating bird PBK model development. For some characteristics, there is considerable complementarity between avian and mammalian species with identical values for the following: blood hemoglobin and hemoglobin concentrations per unit erythrocyte volume together with relative weights of the liver, heart, and lungs. There are also systematic differences for some major characteristics between avian and mammalian species including erythrocyte volume, plasma concentrations of albumin, total protein and triglyceride together with liver cell size and relative weights of the kidney, spleen, and ovary. There are also major differences between characteristics between sexually mature and sexually immature female birds. For example, the relative weights of the ovary and oviduct are greater in sexually mature females compared to immature birds as are the plasma concentrations of triglyceride and vitellogenin. Both these sets of differences reflect the genetic "blue print" inherited from ancestral archosaurs such as the production of large eggs with yolk filled oocytes surrounded by egg white proteins, membranes and a calciferous shell together with adaptions for flight in birds or ancestrally in flightless birds.

Evaluation of the drug-drug interaction potential of treosulfan using a physiologically-based pharmacokinetic modelling approach.

AIMS: The aim of this work is the development of a mechanistic physiologically-based pharmacokinetic (PBPK) model using in vitro to in vivo extrapolation to conduct a drug-drug interaction (DDI) assessment of treosulfan against two cytochrome p450 (CYP) isoenzymes and P-glycoprotein (P-gp) substrates. METHODS: A PBPK model for treosulfan was developed de novo based on literature and unpublished clinical data. The PBPK DDI analysis was conducted using the U.S. Food and Drug Administration (FDA) DDI index drugs (probe substrates) midazolam, omeprazole and digoxin for CYP3A4, CYP2C19 and P-gp, respectively. Qualified and documented PBPK models of the probe substrates have been adopted from an open-source online model database. RESULTS: The PBPK model for treosulfan, based on both in vitro and in vivo data, was able to predict the plasma concentration-time profiles and exposure levels of treosulfan applied for a standard conditioning treatment. Medium and low potentials for DDI on CYP3A4 (maximum area under the concentration-time curve ratio (AUCRmax = 2.23) and CYP2C19 (AUCRmax = 1.6) were predicted, respectively, using probe substrates midazolam and omeprazole. Treosulfan was not predicted to cause a DDI on P-gp. CONCLUSION: Medicinal products with a narrow therapeutic index (eg, digoxin) that are substrates for CYP3A4, CYP2C19 or P-gp should not be given during treatment with treosulfan. However, considering the comprehensive treosulfan-based conditioning treatment schedule and the respective pharmacokinetic properties of the concomitantly used drugs (eg, half-life), the potential for interaction on all evaluated mechanisms would be low (AUCR < 1.25), if concomitantly administered drugs are dosed either 2 hours before or 8 hours after the 2-hour intravenous infusion of treosulfan.

Safer chemicals using less animals: kick-off of the European ONTOX project.

The 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, is receiving increasing attention around the world, and has found its way to legislation, in particular in the European Union. This is aligned by continuing high-level efforts of the European Commission to support development and implementation of 3Rs methods. In this respect, the European project called "ONTOX: ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment" was recently initiated with the goal to provide a functional and sustainable solution for advancing human risk assessment of chemicals without the use of animals in line with the principles of 21st century toxicity testing and next generation risk assessment. ONTOX will deliver a generic strategy to create new approach methodologies (NAMs) in order to predict systemic repeated dose toxicity effects that, upon combination with tailored exposure assessment, will enable human risk assessment. For proof-of-concept purposes, focus is put on NAMs addressing adversities in the liver, kidneys and developing brain induced by a variety of chemicals. The NAMs each consist of a computational system based on artificial intelligence and are fed by biological, toxicological, chemical and kinetic data. Data are consecutively integrated in physiological maps, quantitative adverse outcome pathway networks and ontology frameworks. Supported by artificial intelligence, data gaps are identified and are filled by targeted in vitro and in silico testing. ONTOX is anticipated to have a deep and long-lasting impact at many levels, in particular by consolidating Europe's world-leading position regarding the development, exploitation, regulation and application of animal-free methods for human risk assessment of chemicals.

A Physiologically-Based Quantitative Systems Pharmacology Model of the Incretin Hormones GLP-1 and GIP and the DPP4 Inhibitor Sitagliptin.

Incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) play a major role in regulation of postprandial glucose and the development of type 2 diabetes mellitus. The incretins are rapidly metabolized, primarily by the enzyme dipeptidyl-peptidase 4 (DPP4), and the neutral endopeptidase (NEP), although the exact metabolization pathways are unknown. We developed a physiologically-based (PB) quantitative systems pharmacology model of GLP-1 and GIP and their metabolites that describes the secretion of the incretins in response to intraduodenal glucose infusions and their degradation by DPP4 and NEP. The model describes the observed data and suggests that NEP significantly contributes to the metabolization of GLP-1, and the traditional assays for the total GLP-1 and GIP forms measure yet unknown entities produced by NEP. We further extended the model with a PB pharmacokinetics/pharmacodynamics model of the DPP4 inhibitor sitagliptin that allows predictions of the effects of this medication class on incretin concentrations.

A Quantitative Systems Pharmacology Kidney Model of Diabetes Associated Renal Hyperfiltration and the Effects of SGLT Inhibitors.

The early stage of diabetes mellitus is characterized by increased glomerular filtration rate (GFR), known as hyperfiltration, which is believed to be one of the main causes leading to renal injury in diabetes. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been shown to be able to reverse hyperfiltration in some patients. We developed a mechanistic computational model of the kidney that explains the interplay of hyperglycemia and hyperfiltration and integrates the pharmacokinetics/pharmacodynamics (PK/PD) of the SGLT2i dapagliflozin. Based on simulation results, we propose kidney growth as the necessary process for hyperfiltration progression. Further, the model indicates that renal SGLT1i could significantly improve hyperfiltration when added to SGTL2i. Integrated into a physiologically based PK/PD (PBPK/PD) Diabetes Platform, the model presents a powerful tool for aiding drug development, prediction of hyperfiltration risk, and allows the assessment of the outcomes of individualized treatments with SGLT1-inhibitors and SGLT2-inhibitors and their co-administration with insulin.

Physiologically Based Simulations of Deuterated Glucose for Quantifying Cell Turnover in Humans.

In vivo [6,6-2H2]-glucose labeling is a state-of-the-art technique for quantifying cell proliferation and cell disappearance in humans. However, there are discrepancies between estimates of T cell proliferation reported in short (1-day) versus long (7-day) 2H2-glucose studies and very-long (9-week) 2H2O studies. It has been suggested that these discrepancies arise from underestimation of true glucose exposure from intermittent blood sampling in the 1-day study. Label availability in glucose studies is normally approximated by a "square pulse" (Sq pulse). Since the body glucose pool is small and turns over rapidly, the availability of labeled glucose can be subject to large fluctuations and the Sq pulse approximation may be very inaccurate. Here, we model the pharmacokinetics of exogenous labeled glucose using a physiologically based pharmacokinetic (PBPK) model to assess the impact of a more complete description of label availability as a function of time on estimates of CD4+ and CD8+ T cell proliferation and disappearance. The model enabled us to predict the exposure to labeled glucose during the fasting and de-labeling phases, to capture the fluctuations of labeled glucose availability caused by the intake of food or high-glucose beverages, and to recalculate the proliferation and death rates of immune cells. The PBPK model was used to reanalyze experimental data from three previously published studies using different labeling protocols. Although using the PBPK enrichment profile decreased the 1-day proliferation estimates by about 4 and 7% for CD4 and CD8+ T cells, respectively, differences with the 7-day and 9-week studies remained significant. We conclude that the approximations underlying the "square pulse" approach-recently suggested as the most plausible hypothesis-only explain a component of the discrepancy in published T cell proliferation rate estimates.

Modeling and Simulation of In Vivo Drug Effects.

The concept of a pharmacokinetics-pharmacodynamics (PK/PD) assessment of drug development candidates is well established in pharmaceutical research and development, and PK/PD modeling is common practice in all pharmaceutical companies. A recent analysis (Morgan et al., Drug Discov Today 17(9-10):419-424, 2012) revealed however that insufficient certainty in the integrity of the causal chain of fundamental pharmacological steps from drug dosing through systemic exposure, target tissue exposure, and engagement of molecular target to pharmacological response is still the major driver of failure in phase II of clinical drug development. Despite the rise of molecular biomarkers, ethical, scientific, and practical constraints very often still prevent a direct assessment of each necessary step ultimately leading to an intended drug effect or an unintended adverse reaction. Yet, incomplete investigation of the causality of drug responses is a major risk for translational assessments and the prediction of drug responses in different species or other populations. Mechanism-based modeling and simulation (M&S) offers a means to investigate complex physiological and pharmacological processes and to complement experimental data for non-accessible steps in the pharmacological causal chain. With the help of two examples, it is illustrated, what level of physiological detail, state-of-the-art models can represent, how predictive these models are and how mechanism-based approaches can be combined with empirical correlation-based concepts.

Robust PBPK/PD-Based Model Predictive Control of Blood Glucose.

GOAL: Automated glucose control (AGC) has not yet reached the point where it can be applied clinically [3]. Challenges are accuracy of subcutaneous (SC) glucose sensors, physiological lag times, and both inter- and intraindividual variability. To address above issues, we developed a novel scheme for MPC that can be applied to AGC. RESULTS: An individualizable generic whole-body physiology-based pharmacokinetic and dynamics (PBPK/PD) model of the glucose, insulin, and glucagon metabolism has been used as the predictive kernel. The high level of mechanistic detail represented by the model takes full advantage of the potential of MPC and may make long-term prediction possible as it captures at least some relevant sources of variability [4]. Robustness against uncertainties was increased by a control cascade relying on proportional-integrative derivative-based offset control. The performance of this AGC scheme was evaluated in silico and retrospectively using data from clinical trials. This analysis revealed that our approach handles sensor noise with a MARD of 10%-14%, and model uncertainties and disturbances. CONCLUSION: The results suggest that PBPK/PD models are well suited for MPC in a glucose control setting, and that their predictive power in combination with the integrated database-driven (a priori individualizable) model framework will help overcome current challenges in the development of AGC systems. SIGNIFICANCE: This study provides a new, generic, and robust mechanistic approach to AGC using a PBPK platform with extensive a priori (database) knowledge for individualization.

A multiscale, model-based analysis of the multi-tissue interplay underlying blood glucose regulation in type I diabetes.

A multiscale model for blood glucose regulation in diabetes type I patients is constructed by integrating detailed metabolic network models for fat, liver and muscle cells into a whole body physiologically-based pharmacokinetic/pharmacodynamic (pBPK/PD) model. The blood glucose regulation PBPK/PD model simulates the distribution and metabolization of glucose, insulin and glucagon on an organ and whole body level. The genome-scale metabolic networks in contrast describe intracellular reactions. The developed multiscale model is fitted to insulin, glucagon and glucose measurements of a 48h clinical trial featuring 6 subjects and is subsequently used to simulate (in silico) the influence of geneknockouts and drug-induced enzyme inhibitions on whole body blood glucose levels. Simulations of diabetes associated gene knockouts and impaired cellular glucose metabolism, resulted in elevated whole body blood-glucose levels, but also in a metabolic shift within the cell's reaction network. Such multiscale models have the potential to be employed in the exploration of novel drug-targets or to be integrated into control algorithms for artificial pancreas systems.

Electromyographic permutation entropy quantifies diaphragmatic denervation and reinnervation.

Spontaneous reinnervation after diaphragmatic paralysis due to trauma, surgery, tumors and spinal cord injuries is frequently observed. A possible explanation could be collateral reinnervation, since the diaphragm is commonly double-innervated by the (accessory) phrenic nerve. Permutation entropy (PeEn), a complexity measure for time series, may reflect a functional state of neuromuscular transmission by quantifying the complexity of interactions across neural and muscular networks. In an established rat model, electromyographic signals of the diaphragm after phrenicotomy were analyzed using PeEn quantifying denervation and reinnervation. Thirty-three anesthetized rats were unilaterally phrenicotomized. After 1, 3, 9, 27 and 81 days, diaphragmatic electromyographic PeEn was analyzed in vivo from sternal, mid-costal and crural areas of both hemidiaphragms. After euthanasia of the animals, both hemidiaphragms were dissected for fiber type evaluation. The electromyographic incidence of an accessory phrenic nerve was 76%. At day 1 after phrenicotomy, PeEn (normalized values) was significantly diminished in the sternal (median: 0.69; interquartile range: 0.66-0.75) and mid-costal area (0.68; 0.66-0.72) compared to the non-denervated side (0.84; 0.78-0.90) at threshold p<0.05. In the crural area, innervated by the accessory phrenic nerve, PeEn remained unchanged (0.79; 0.72-0.86). During reinnervation over 81 days, PeEn normalized in the mid-costal area (0.84; 0.77-0.86), whereas it remained reduced in the sternal area (0.77; 0.70-0.81). Fiber type grouping, a histological sign for reinnervation, was found in the mid-costal area in 20% after 27 days and in 80% after 81 days. Collateral reinnervation can restore diaphragm activity after phrenicotomy. Electromyographic PeEn represents a new, distinctive assessment characterizing intramuscular function following denervation and reinnervation.

Integrating cellular metabolism into a multiscale whole-body model.

Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.