Quantitative systems pharmacology of the eye: Tools and data for ocular QSP.

Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant healthcare burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PKs) and pharmacodynamics (PDs). Ocular quantitative systems pharmacology models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based PK (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models using molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eyes and discuss cellular systems, data repositories, as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for postoperative fibrosis after glaucoma surgery.

Spatio-temporal visualization of the distribution of acetaminophen as well as its metabolites and adducts in mouse livers by MALDI MSI.

Acetaminophen (APAP) is one of the most intensively studied compounds that causes hepatotoxicity in the pericentral region of the liver lobules. However, spatio-temporal information on the distribution of APAP, its metabolites and GSH adducts in the liver tissue is not yet available. Here, we addressed the question, whether APAP-GSH adducts and GSH depletion show a zonated pattern and whether the distribution of APAP and its glucuronide as well as sulfate conjugates in liver lobules are zonated. For this purpose, a matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) technique was established, where the MSI images were superimposed onto CYP2E1 immunostained tissue. A time-dependent analysis (5, 15, 30, 60, 120, 240, 480 min) after intraperitoneal administration of 300 mg/kg APAP and a dose-dependent analysis (56 up to 500 mg APAP/kg) at 30 min were performed. The results demonstrate that the MALDI MSI technique allows the assignment of compounds and their metabolites to specific lobular zones. APAP-GSH adducts and GSH depletion occurred predominantly in the CYP2E1-positive zone of the liver, although GSH also decreased in the periportal region. In contrast, the parent compound, APAP sulfate and APAP glucuronide did not show a zonated pattern and tissue concentrations showed a similar time course as the corresponding analyses were performed with blood from the portal and liver veins. In conclusion, the present study is in agreement with the concept that pericentral CYPs form NAPQI that in the same cell binds to and depletes GSH but a lower level of GSH adducts is also observed in the periportal region. The results also provide further evidence of the recently published concept of 'aggravated loss of clearance capacity' according to which also liver tissue that survives intoxication may transiently show decreased metabolic capacity.

Bringing in vitro analysis closer to in vivo: Studying doxorubicin toxicity and associated mechanisms in 3D human microtissues with PBPK-based dose modelling.

Doxorubicin (DOX) is a chemotherapeutic agent of which the medical use is limited due to cardiotoxicity. While acute cardiotoxicity is reversible, chronic cardiotoxicity is persistent or progressive, dose-dependent and irreversible. While DOX mechanisms of action are not fully understood yet, 3 toxicity processes are known to occur in vivo: cardiomyocyte dysfunction, mitochondrial dysfunction and cell death. We present an in vitro experimental design aimed at detecting DOX-induced cardiotoxicity by obtaining a global view of the induced molecular mechanisms through RNA-sequencing. To better reflect the in vivo situation, human 3D cardiac microtissues were exposed to physiologically-based pharmacokinetic (PBPK) relevant doses of DOX for 2 weeks. We analysed a therapeutic and a toxic dosing profile. Transcriptomics analysis revealed significant gene expression changes in pathways related to "striated muscle contraction" and "respiratory electron transport", thus suggesting mitochondrial dysfunction as an underlying mechanism for cardiotoxicity. Furthermore, expression changes in mitochondrial processes differed significantly between the doses. Therapeutic dose reflects processes resembling the phenotype of delayed chronic cardiotoxicity, while toxic doses resembled acute cardiotoxicity. Overall, these results demonstrate the capability of our innovative in vitro approach to detect the three known mechanisms of DOX leading to toxicity, thus suggesting its potential relevance for reflecting the patient situation. Our study also demonstrated the importance of applying physiologically relevant doses during toxicological research, since mechanisms of acute and chronic toxicity differ.

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.

Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling.

Active processes involved in drug metabolization and distribution mediated by enzymes, transporters, or binding partners mostly occur simultaneously in various organs. However, a quantitative description of active processes is difficult because of limited experimental accessibility of tissue-specific protein activity in vivo. In this work, we present a novel approach to estimate in vivo activity of such enzymes or transporters that have an influence on drug pharmacokinetics. Tissue-specific mRNA expression is used as a surrogate for protein abundance and activity and is integrated into physiologically based pharmacokinetic (PBPK) models that already represent detailed anatomical and physiological information. The new approach was evaluated using three publicly available databases: whole-genome expression microarrays from ArrayExpress, reverse transcription-polymerase chain reaction-derived gene expression estimates collected from the literature, and expressed sequence tags from UniGene. Expression data were preprocessed and stored in a customized database that was then used to build PBPK models for pravastatin in humans. These models represented drug uptake by organic anion-transporting polypeptide 1B1 and organic anion transporter 3, active efflux by multidrug resistance protein 2, and metabolization by sulfotransferases in liver, kidney, and/or intestine. Benchmarking of PBPK models based on gene expression data against alternative models with either a less complex model structure or randomly assigned gene expression values clearly demonstrated the superior model performance of the former. Besides accurate prediction of drug pharmacokinetics, integration of relative gene expression data in PBPK models offers the unique possibility to simultaneously investigate drug-drug interactions in all relevant organs because of the physiological representation of protein-mediated processes.

Multiscale mechanistic modeling in pharmaceutical research and development.

Discontinuation of drug development projects due to lack of efficacy or adverse events is one of the main cost drivers in pharmaceutical research and development (R&D). Investments have to be written-off and contribute to the total costs of a successful drug candidate receiving marketing authorization and allowing return on invest. A vital risk for pharmaceutical innovator companies is late stage clinical failure since costs for individual clinical trials may exceed the one billion Euro threshold. To guide investment decisions and to safeguard maximum medical benefit and safety for patients recruited in clinical trials, it is therefore essential to understand the clinical consequences of all information and data generated. The complexity of the physiological and pathophysiological processes and the sheer amount of information available overcharge the mental capacity of any human being and prevent a prediction of the success in clinical development. A rigorous integration of knowledge, assumption, and experimental data into computational models promises a significant improvement of the rationalization of decision making in pharmaceutical industry. We here give an overview of the current status of modeling and simulation in pharmaceutical R&D and outline the perspectives of more recent developments in mechanistic modeling. Specific modeling approaches for different biological scales ranging from intracellular processes to whole organism physiology are introduced and an example for integrative multiscale modeling of therapeutic efficiency in clinical oncology trials is showcased.

Evaluation of the efficacy and safety of rivaroxaban using a computer model for blood coagulation.

Rivaroxaban is an oral, direct Factor Xa inhibitor approved in the European Union and several other countries for the prevention of venous thromboembolism in adult patients undergoing elective hip or knee replacement surgery and is in advanced clinical development for the treatment of thromboembolic disorders. Its mechanism of action is antithrombin independent and differs from that of other anticoagulants, such as warfarin (a vitamin K antagonist), enoxaparin (an indirect thrombin/Factor Xa inhibitor) and dabigatran (a direct thrombin inhibitor). A blood coagulation computer model has been developed, based on several published models and preclinical and clinical data. Unlike previous models, the current model takes into account both the intrinsic and extrinsic pathways of the coagulation cascade, and possesses some unique features, including a blood flow component and a portfolio of drug action mechanisms. This study aimed to use the model to compare the mechanism of action of rivaroxaban with that of warfarin, and to evaluate the efficacy and safety of different rivaroxaban doses with other anticoagulants included in the model. Rather than reproducing known standard clinical measurements, such as the prothrombin time and activated partial thromboplastin time clotting tests, the anticoagulant benchmarking was based on a simulation of physiologically plausible clotting scenarios. Compared with warfarin, rivaroxaban showed a favourable sensitivity for tissue factor concentration inducing clotting, and a steep concentration-effect relationship, rapidly flattening towards higher inhibitor concentrations, both suggesting a broad therapeutic window. The predicted dosing window is highly accordant with the final dose recommendation based upon extensive clinical studies.

A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks.

Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim(®) and MoBi(®) capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug-drug, or drug-metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli.

To which extent can optimality principles describe the operation of metabolic networks? By explicitly considering experimental errors and in silico alternate optima in flux balance analysis, we systematically evaluate the capacity of 11 objective functions combined with eight adjustable constraints to predict (13)C-determined in vivo fluxes in Escherichia coli under six environmental conditions. While no single objective describes the flux states under all conditions, we identified two sets of objectives for biologically meaningful predictions without the need for further, potentially artificial constraints. Unlimited growth on glucose in oxygen or nitrate respiring batch cultures is best described by nonlinear maximization of the ATP yield per flux unit. Under nutrient scarcity in continuous cultures, in contrast, linear maximization of the overall ATP or biomass yields achieved the highest predictive accuracy. Since these particular objectives predict the system behavior without preconditioning of the network structure, the identified optimality principles reflect, to some extent, the evolutionary selection of metabolic network regulation that realizes the various flux states.