Michael Block

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.

Human neutrophil kinetics: modeling of stable isotope labeling data supports short blood neutrophil half-lives

Human neutrophils have traditionally been thought to have a short half-life in blood; estimates vary from 4 to 18 hours. This dogma was recently challenged by stable isotope labeling studies with heavy water, which yielded estimates in excess of 3 days. To investigate this disparity, we generated new stable isotope labeling data in healthy adult subjects using both heavy water (n = 4) and deuterium-labeled glucose (n = 9), a compound with more rapid labeling kinetics. To interpret results, we developed a novel mechanistic model and applied it to previously published (n = 5) and newly generated data. We initially constrained the ratio of the blood neutrophil pool to the marrow precursor pool (ratio = 0.26; from published values). Analysis of heavy water data sets yielded turnover rates consistent with a short blood half-life, but parameters, particularly marrow transit time, were poorly defined. Analysis of glucose-labeling data yielded more precise estimates of half-life (0.79 ± 0.25 days; 19 hours) and marrow transit time (5.80 ± 0.42 days). Substitution of this marrow transit time in the heavy water analysis gave a better-defined blood half-life of 0.77 ± 0.14 days (18.5 hours), close to glucose-derived values. Allowing the ratio of blood neutrophils to mitotic neutrophil precursors (R) to vary yielded a best-fit value of 0.19. Reanalysis of the previously published model and data also revealed the origin of their long estimates for neutrophil half-life: an implicit assumption that R is very large, which is physiologically untenable. We conclude that stable isotope labeling in healthy humans is consistent with a blood neutrophil half-life of less than 1 day.

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.

Defibrillation efficacy comparing a subcutaneous array electrode versus an "active can" implantable cardioverter defibrillator and a subcutaneous array electrode in addition to an "active can" implantable cardioverter defibrillator: results from active can versus array trials I and II.

Defibrillation Efficacy. Introduction: Placement of implantable cardioverter defibrillators (ICDs) has been simplified by using the shell of a pectorally implanted ICD as a defibrillation electrode in combination with an endocardial right ventricular defibrillation lead. However, a sufficiently low defibrillation threshold (DFT) cannot be obtained in a few patients. Therefore, alternative approaches were systematically tested in the Active Can versus Array Trial (ACAT).

Cardiac Output Is Not Affected During Intraoperative Testing of the Automatic Implantable Cardioverter Defibrillator

Cardiac Output and ICD Implantation. Introduction: Perioperative mortality of patients undergoing implantation of automatic implantable cardioverter defibrillators (ICDs) has been reduced dramatically following the availibility of trans venous‐subcutaneous defibrillation leads. However, patients with severely reduced left ventricular function show a substantial rate of nonsudden cardiac mortality within the first year. Whether repeated intraoperative inductions of ventricular tachycardia/fibrillation (VT/VF) during implantation lead to hemodynamic deterioration and thus might contribute to development of end‐stage heart failure in these patients is unknown. The purpose of the present study was to determine cardiac output and hemodynamic performance during transvenous‐subcutaneous ICD implantation in patients with severe left ventricular dysfunction.

Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps

Introduction: Modeling and simulation have become important means of answering questions relevant to the development of a drug, making it possible to assess risks early and to reduce costs. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models contribute to a comprehensive understanding of the drug, covering specific questions from early discovery through lifecycle management stages. As for other disease areas, in oncology, PBPK and PD models are important topics that remain to be addressed. Areas covered: This review describes current PBPK and PD approaches, their applicability in drug development in general and specifically in the area of oncology. It discusses the current status and then focuses on key challenges and the potential for future use. It provides cases in which modeling currently cannot answer the questions and assesses the requirements to close gaps for PBPK/PD in oncology. Expert opinion: PBPK/PD models have led to improvements in identifying risks and reducing costs during the drug development process. Nevertheless, there is a lot of potential, where more rigorous integration of biological knowledge and specific experimental design would result in a more comprehensive biological picture. Ideally, such approaches would reveal the extent to which preclinical work can be extrapolated to clinical settings, thus enabling reliable prediction and, ultimately, reducing failed trials in clinical oncology.

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.

Translational learning from clinical studies predicts drug pharmacokinetics across patient populations

Early indication of late-stage failure of novel candidate drugs could be facilitated by continuous integration, assessment, and transfer of knowledge acquired along pharmaceutical development programs. We here present a translational systems pharmacology workflow that combines drug cocktail probing in a specifically designed clinical study, physiologically based pharmacokinetic modeling, and Bayesian statistics to identify and transfer (patho-)physiological and drug-specific knowledge across distinct patient populations. Our work builds on two clinical investigations, one with 103 healthy volunteers and one with 79 diseased patients from which we systematically derived physiological information from pharmacokinetic data for a reference probe drug (midazolam) at the single-patient level. Taking into account the acquired knowledge describing (patho-)physiological alterations in the patient cohort allowed the successful prediction of the population pharmacokinetics of a second, candidate probe drug (torsemide) in the patient population. In addition, we identified significant relations of the acquired physiological processes to patient metadata from liver biopsies. The presented prototypical systems pharmacology approach is a proof of concept for model-based translation across different stages of pharmaceutical development programs. Applied consistently, it has the potential to systematically improve predictivity of pharmacokinetic simulations by incorporating the results of clinical trials and translating them to subsequent studies.Systems pharmacology: predicting population pharmacokinetics in silicoPhysiologically based modeling together with Bayesian statistics allows the prediction of drug pharmacokinetics in specific patient populations. An interdisciplinary group of clinicians and computational scientists led by Dr. Lars Kuepfer from Bayer developed a generic workflow consisting of several consecutive learning steps where knowledge about both individual physiology as well as drug physicochemistry can be efficiently derived from plasma concentration profiles. The acquired information is then be used for the prediction of the pharmacokinetic behavior of a new drug candidate in a diseased population. This allows to simulate the variability in drug exposure virtually before starting clinical investigation in real patients in order to evaluate drug safety or efficacy through the simulation of virtual populations. Further development of this workflow could improve the safety of clinical development programs to assess the risk-benefit ratio of novel drug candidates in silico.

A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim

Proteins are an increasingly important class of drugs used as therapeutic as well as diagnostic agents. A generic physiologically based pharmacokinetic (PBPK) model was developed in order to represent at whole body level the fundamental mechanisms driving the distribution and clearance of large molecules like therapeutic proteins. The model was built as an extension of the PK-Sim model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling as especially relevant for antibodies. For model development and evaluation, PK data was used for compounds with a wide range of solute radii. The model supports the integration of knowledge gained during all development phases of therapeutic proteins, enables translation from pre-clinical species to human and allows predictions of tissue concentration profiles which are of relevance for the analysis of on-target pharmacodynamic effects as well as off-target toxicity. The current implementation of the model replaces the generic protein PBPK model available in PK-Sim since version 4.2 and becomes part of the Open Systems Pharmacology Suite.

PBPK Modelling of Intracellular Drug Delivery Through Active and Passive Transport Processes

Physiologically based pharmacokinetic (PBPK) models describe adsorption, distribution, metabolisation and excretion (ADME) of drugs in the body of an organism based on a large amount of prior anatomical and physiological knowledge. In contrast to compartmental pharmacokinetic modeling which uses rather empirical model structures, PBPK models aim for a detailed mechanistic representation of physiological processes underlying drug pharmacokinetics within the body. That means that the relevant organs or tissues of an organism are explicitly represented in a PBPK model. Organs in PBPK models are usually divided in subcompartments such as plasma, interstitial space, intracellular space and red blood cells. Mass transfer between the different compartments which ultimately governs intracellular drug delivery is quantified either by so-called distribution models for the calculation of organ-plasma partition coefficients or by permeability-surface area products quantifying passive diffusion, respectively. Notably, both types of calculation methods are parameterized based upon the physicochemical properties of the investigated drug, respectively. These properties include amongst others lipophilicity and the molecular weight of the compound. Additional physiological information ranging from the whole body level (e.g. organ volumes, blood flow rates, tissue composition) to relative tissue-specific protein abundance is explicitly provided in the model. PBPK models are nowadays routinely used to analyze pharmacokinetics in drug development Due to the large amount of mechanistic information which is implicitly provided in the structural equations, PBPK models are in particular well-suited for both in-depth analyses of ADME processes underlying drug pharmacokinetics as well as for extrapolation to novel indications, patient populations or treatment designs. In this review we will present and discuss calculation methods used in PBPK model to describe and to quantify intracellular drug delivery.