Jörg Lippert

Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs.

In clinical development stages, an a priori assessment of the sensitivity of the pharmacokinetic behavior with respect to physiological and anthropometric properties of human (sub-) populations is desirable. A physiology-based pharmacokinetic (PBPK) population model was developed that makes use of known distributions of physiological and anthropometric properties obtained from the literature for realistic populations. As input parameters, the simulation model requires race, gender, age, and two parameters out of body weight, height and body mass index. From this data, the parameters relevant for PBPK modeling such as organ volumes and blood flows are determined for each virtual individual. The resulting parameters were compared to those derived using a previously published model (P3M). Mean organ weights and blood flows were highly correlated between the two models, despite the different methods used to generate these parameters. The inter-individual variability differed greatly especially for organs with a log-normal weight distribution (such as fat and spleen). Two exemplary population pharmacokinetic simulations using ciprofloxacin and paclitaxel as model drugs showed good correlation to observed variability. A sensitivity analysis demonstrated that the physiological differences in the virtual individuals and intrinsic clearance variability were equally influential to the pharmacokinetic variability but were not additive. In conclusion, the new population model is well suited to assess the influence of individual physiological variability on the pharmacokinetics of drugs. It is expected that this new tool can be beneficially applied in the planning of clinical studies.

Risk to the Breast-Fed Neonate From Codeine Treatment to the Mother: A Quantitative Mechanistic Modeling Study

Administering codeine to breast‐feeding mothers had been considered safe until the recent death of a breast‐fed neonate whose mother had been prescribed codeine. We investigated the risk of opioid poisoning to breast‐fed neonates using coupled physiologically based pharmacokinetic models for the mother and child. Neonatal morphine plasma concentrations were simulated for various combinations of cytochrome P450 2D6 (CYP2D6) genotype and morphine clearance, assuming typical breast‐feeding schedules and maternal codeine doses of ≤2.5 mg/kg/day. The simulations demonstrated that the mothers codeine and morphine clearances and the neonates morphine clearance are the most critical determinants of morphine accumulation in the neonate. The cumulative doses ingested by the neonate over 14 days were 0.38 mg/kg codeine and 0.17 mg/kg morphine. Given the added effect of low neonatal elimination capacity for morphine, potentially toxic morphine plasma concentrations can be reached within 4 days in the neonate after repeated codeine dosing to the mother. Importantly, neonates of mothers with the ultrarapid CYP2D6 genotype and neonates of mothers who are extensive metabolizers have comparable risks of opioid poisoning.

From physicochemistry to absorption and distribution: predictive mechanistic modelling and computational tools

During the past decade, the pharmaceutical industry has invested considerably in technologies that have the potential to increase throughput in discovery projects. For large compound libraries, efficacy, availability and safety should be determined as early and as reliably as possible. The latest step in this effort is the implementation of in silico methods that combine and interpret (sometimes replace) experimental in vitro data. For ADME properties (absorption, distribution, metabolism and excretion) rational predictive models have been developed that rely on basic physicochemical input data and on mechanistic descriptions of the underlying biophysical and biochemical processes. Some of these models have become commercially available (e.g., GastroPlusTM: Simulations Plus; PK-MapTM, PK-Sim®: Bayer Technology Services). The contribution of such models to an optimised research and development process will be discussed.

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.

The virtual liver: a multidisciplinary, multilevel challenge for systems biology

The liver is the central metabolic organ in human physiology, with functions that are fundamentally important to the detoxification of xenobiotics (drugs), the maintenance of homeostasis of numerous blood metabolites, and the production of mediators of the acute phase response. Liver toxicity, whether actual or implied is the reason for the failure of a significant proportion of many promising novel medicines that consequently never reach the market, and diseases such as atherosclerosis, diabetes, and fatty liver diseases, that are a major burden on current health resources, are directly linked to functional and structural disorders of the liver. This article presents the concepts and approaches underpinning one of the most exciting and ambitious modeling projects in the field of systems biology and systems medicine. This major multidisciplinary research program is aimed at developing a whole‐organ model of the human liver, representing its central physiological functions under normal and pathological conditions The model will be composed of a larger battery of interconnected submodels representing liver anatomy and physiology, integrating processes across hierarchical levels in space, time, and structural organization. In this article, we outline the general architecture of the liver model and present first step taken to reach this ambitious goal. WIREs Syst Biol Med 2012 doi: 10.1002/wsbm.1158

Evolution of a detailed physiological model to simulate the gastrointestinal transit and absorption process in humans, Part 1: Oral solutions

To enable more precise prediction of oral drug absorption, an existing physiologically based absorption model was revised. The revised model reflects detailed knowledge of human gastrointestinal (GI) physiology including fluid secretion and absorption, and comprises an elaborate representation of the intestinal mucosa. The alimentary canal from the stomach to the rectum was divided into 12 compartments. A mucosal compartment was added to each luminal segment of the intestine. A training set of 111 passively absorbed drugs with reported fractions of dose absorbed was used to optimize the semiempirical equation, which calculates intestinal permeability coefficients. The model was subsequently integrated into an established physiologically based pharmacokinetic software and validated by prediction of plasma concentration-time profiles of eight test compounds with diverse physicochemical properties. A good correlation between the simulated and experimental fractions of dose absorbed was established for the 111 compounds in the training set. Subsequently, the concentration-time profiles of six out of eight test compounds were predicted with high accuracy. The detailed model for GI transit and absorption presented in this study can help to understand the complex processes of oral absorption better and will be useful during the drug development process.

Evolution of a Detailed Physiological Model to Simulate the Gastrointestinal Transit and Absorption Process in Humans, Part II: Extension to Describe Performance of Solid Dosage Forms

The physiological absorption model presented in part I of this work is now extended to account for dosage-form-dependent gastrointestinal (GI) transit as well as disintegration and dissolution processes of various immediate-release and modified-release dosage forms. Empirical functions of the Weibull type were fitted to experimental in vitro dissolution profiles of solid dosage forms for eight test compounds (aciclovir, caffeine, cimetidine, diclofenac, furosemide, paracetamol, phenobarbital, and theophylline). The Weibull functions were then implemented into the model to predict mean plasma concentration-time profiles of the various dosage forms. On the basis of these dissolution functions, pharmacokinetics (PK) of six model drugs was predicted well. In the case of diclofenac, deviations between predicted and observed plasma concentrations were attributable to the large variability in gastric emptying time of the enteric-coated tablets. Likewise, oral PK of furosemide was found to be predominantly governed by the gastric emptying patterns. It is concluded that the revised model for GI transit and absorption was successfully integrated with dissolution functions of the Weibull type, enabling prediction of in vivo PK profiles from in vitro dissolution data. It facilitates a comparative analysis of the parameters contributing to oral drug absorption and is thus a powerful tool for formulation design.

Mechanism-based prediction of particle size-dependent dissolution and absorption: Cilostazol pharmacokinetics in dogs

A previously developed physiologically based pharmacokinetic (PBPK) model for gastro-intestinal transit and absorption was combined with a mechanistic dissolution model of the Noyes-Whitney type for spherical particles with a predefined particle size distribution. To validate the combined model, the plasma concentration-time curves for cilostazol obtained in beagle dogs using three different types of suspensions with varying particle diameters were simulated. In vitro dissolution information was also available for different formulations, but this data could only predict the in vivo outcome qualitatively. The mechanistic PBPK model, on the other hand, could predict the influence of the particle size on the rate and extent of absorption under both fasted and fed conditions accurately, and the gap between the in vitro dissolution data and the in vivo outcome could successfully be explained. We conclude that by integrating the processes of particle dissolution, gastro-intestinal transit and permeation across the intestinal epithelium into a mechanistic model, oral drug absorption from suspensions can be predicted quantitatively. The model can be applied readily to typical formulation development data packages to better understand the relative importance of dissolution and permeability and pave the way for successful formulation of solid dosage forms.

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.

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.