Thomas Eissing

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.

Bistable Biological Systems: A Characterization Through Local Compact Input-to-State Stability

Many biological systems have the capacity to operate in two distinct modes, in a stable manner. Typically, the system can switch from one stable mode to the other in response to a specific external input. Mathematically, these bistable systems are usually described by models that exhibit (at least) two distinct stable steady states. On the other hand, to capture biological variability, it seems more natural to associate to each stable mode of operation an appropriate invariant set in the state space rather than a single fixed point. A general formulation is proposed in this paper, which allows freedom in the form of kinetic interactions, and is suitable for establishing conditions on the existence of one or more disjoint forward-invariant sets for the given system. Stability with respect to each set is studied in terms of a local notion of input-to-state stability with respect to compact sets. Two well known systems that exhibit bistability are analyzed in this framework: the lac operon and an apoptosis network. For the first example, the question of designing an input that drives the system to switch between modes is also considered.

A Generic Integrated Physiologically based Whole-body Model of the Glucose-Insulin-Glucagon Regulatory System.

Models of glucose metabolism are a valuable tool for fundamental and applied medical research in diabetes. Use cases range from pharmaceutical target selection to automatic blood glucose control. Standard compartmental models represent little biological detail, which hampers the integration of multiscale data and confines predictive capabilities. We developed a detailed, generic physiologically based whole‐body model of the glucose‐insulin‐glucagon regulatory system, reflecting detailed physiological properties of healthy populations and type 1 diabetes individuals expressed in the respective parameterizations. The model features a detailed representation of absorption models for oral glucose, subcutaneous insulin and glucagon, and an insulin receptor model relating pharmacokinetic properties to pharmacodynamic effects. Model development and validation is based on literature data. The quality of predictions is high and captures relevant observed inter‐ and intra‐individual variability. In the generic form, the model can be applied to the development and validation of novel diabetes treatment strategies.

Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model

The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.

Physiologically Based Pharmacokinetic Modeling of Tamoxifen and its Metabolites in Women of Different CYP2D6 Phenotypes Provides New Insight into the Tamoxifen Mass Balance

Tamoxifen is a first-line endocrine agent in the mechanism-based treatment of estrogen receptor positive (ER+) mammary carcinoma and applied to breast cancer patients all over the world. Endoxifen is a secondary and highly active metabolite of tamoxifen that is formed among others by the polymorphic cytochrome P450 2D6 (CYP2D6). It is widely accepted that CYP2D6 poor metabolizers exert a pronounced decrease in endoxifen steady-state plasma concentrations compared to CYP2D6 extensive metabolizers. Nevertheless, an in-depth understanding of the chain of cause and effect between CYP2D6 genotype, endoxifen steady-state plasma concentration, and subsequent tamoxifen treatment benefit still remains to be evolved. In this study, physiologically based pharmacokinetic (PBPK)-modeling was applied to mechanistically investigate the impact of CYP2D6 phenotype on endoxifen formation in female breast cancer patients undergoing tamoxifen therapy. A PBPK-model of tamoxifen and its pharmacologically important metabolites N-desmethyltamoxifen (NDM-TAM), 4-hydroxytamoxifen (4-OH-TAM), and endoxifen was developed and validated. This model is able to simulate the pharmacokinetics (PK) after single and repeated oral tamoxifen doses in female breast cancer patients in dependence of the CYP2D6 phenotype. A detailed model-based analysis of the mass balance offered support for a recent hypothesis stating a more prominent role for endoxifen formation from 4-OH-TAM. In the future this model provides a good basis to further investigate the linkage of PK, mode of action, and treatment outcome in dependence of factors such as phenotype, ethnicity, or co-treatment with CYP2D6 inhibitors.

Pharmacogenomics of Codeine, Morphine, and Morphine-6-Glucuronide

AbstractBackground and Objective: The analgesic effect of codeine depends on the formation of the opioid metabolites morphine and morphine-6-glucuronide. Different factors have been shown or suspected to affect the safety and efficacy of codeine treatment. The objective of the current study is to assess and quantify the impact of important pharmacokinetic factors, using a mechanistic modeling approach. Methods: By means of a generic modeling approach integrating prior physiologic knowledge, we systematically investigated the complex dependence of opioid exposure on cytochrome P450 2D6 and 3A4 (CYP2D6 and CYP3A4), and uridine diphosphate glucuronosyltransferase 2B7 (UGT2B7) activity, as well as renal function, by means of a virtual clinical trial. Results: First, the known dominant role of CYP2D6 activity for morphine exposure was reproduced. Second, the model demonstrated that mild and moderate renal impairment and co-administration of CYP3A4 inhibitors have only minor influences on opioid exposure. Third, the model showed — in contrast to current opinion — that increased UGT2B7 activity is associated with a decrease in active opioid exposure. Conclusion: Overall, the model-based analysis predicts a wide range of morphine levels after codeine administration and supports recent doubts about safe and efficacious use of codeine for analgesia in non-genotyped individuals.

Pharmacogenomics of codeine, morphine, and morphine-6-glucuronide: model-based analysis of the influence of CYP2D6 activity, UGT2B7 activity, renal impairment, and CYP3A4 inhibition.

BACKGROUND AND OBJECTIVE The analgesic effect of codeine depends on the formation of the opioid metabolites morphine and morphine-6-glucuronide. Different factors have been shown or suspected to affect the safety and efficacy of codeine treatment. The objective of the current study is to assess and quantify the impact of important pharmacokinetic factors, using a mechanistic modeling approach. METHODS By means of a generic modeling approach integrating prior physiologic knowledge, we systematically investigated the complex dependence of opioid exposure on cytochrome P450 2D6 and 3A4 (CYP2D6 and CYP3A4), and uridine diphosphate glucuronosyltransferase 2B7 (UGT2B7) activity, as well as renal function, by means of a virtual clinical trial. RESULTS First, the known dominant role of CYP2D6 activity for morphine exposure was reproduced. Second, the model demonstrated that mild and moderate renal impairment and co-administration of CYP3A4 inhibitors have only minor influences on opioid exposure. Third, the model showed - in contrast to current opinion - that increased UGT2B7 activity is associated with a decrease in active opioid exposure. CONCLUSION Overall, the model-based analysis predicts a wide range of morphine levels after codeine administration and supports recent doubts about safe and efficacious use of codeine for analgesia in non-genotyped individuals.

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.

Regulation of Apoptosis via the NFκB Pathway: Modeling and Analysis

Programmed cell death (or apoptosis) has an essential biological function, enabling successful embryonic development, as well as maintenance of a healthy living organism [6]. Apoptosis is a physiological process which enables an organism to remove unwanted or damaged cells. Malfunctioning apoptotic pathways can lead to many diseases, including cancer and inflammatory or immune system related problems. A family of proteins called caspases are primarily responsible for execution of the apoptotic process: basically, in response to appropriate stimuli, initiator caspases (for instance, caspases 8, 9) activate effector caspases (for instance, caspases 3, 7), which will then cleave various cellular substrates to accomplish the cell death process [22]. Nuclear factor κB (NFκB) is a transcription factor for a large group of genes which are involved in several different pathways. For instance, NFκB activates its own inhibitor (IκB) [14] as well as groups of pro-apoptotic and anti-apoptotic genes [21]. Among the latter, NFκB activates transcription of a gene encoding for inhibitor of apoptosis protein (IAP). This protein in turn contributes to downregulate the activity of the caspase cascade which forms the core of the apoptotic pathway [6, 8]. The canonical NFκB pathway is induced, among other stimuli, by the cytokine tumor necrosis factor α (TNFα) [21]. Binding of TNFα to death receptor TNFR1 forms a first complex which eventually activates NFκB. A second complex is later formed, which will activate the initiator caspase 8 [6], and hence activate the apoptotic process. The same signal (TNFα stimulation) thus triggers two parallel but contrary pathways: the pro-apoptotic caspase cascade and the anti-apoptotic NFκB-IκB-IAP pathway. These two pathways, together with the interactions among their components, form a

A Detailed Physiologically Based Model to Simulate the Pharmacokinetics and Hormonal Pharmacodynamics of Enalapril on the Circulating Endocrine Renin-Angiotensin-Aldosterone System

The renin-angiotensin-aldosterone system (RAAS) plays a key role in the pathogenesis of cardiovascular disorders including hypertension and is one of the most important targets for drugs. A whole body physiologically based pharmacokinetic (wb PBPK) model integrating this hormone circulation system and its inhibition can be used to explore the influence of drugs that interfere with this system, and thus to improve the understanding of interactions between drugs and the target system. In this study, we describe the development of a mechanistic RAAS model and exemplify drug action by a simulation of enalapril administration. Enalapril and its metabolite enalaprilat are potent inhibitors of the angiotensin-converting-enzyme (ACE). To this end, a coupled dynamic parent-metabolite PBPK model was developed and linked with the RAAS model that consists of seven coupled PBPK models for aldosterone, ACE, angiotensin 1, angiotensin 2, angiotensin 2 receptor type 1, renin, and prorenin. The results indicate that the model represents the interactions in the RAAS in response to the pharmacokinetics (PK) and pharmacodynamics (PD) of enalapril and enalaprilat in an accurate manner. The full set of RAAS-hormone profiles and interactions are consistently described at pre- and post-administration steady state as well as during their dynamic transition and show a good agreement with literature data. The model allows a simultaneous representation of the parent-metabolite conversion to the active form as well as the effect of the drug on the hormone levels, offering a detailed mechanistic insight into the hormone cascade and its inhibition. This model constitutes a first major step to establish a PBPK-PD-model including the PK and the mode of action (MoA) of a drug acting on a dynamic RAAS that can be further used to link to clinical endpoints such as blood pressure.