Search

The success of applications of physiologically‐based pharmacokinetic (PBPK) modeling in drug development and drug labeling has triggered regulatory agencies to demand rigorous demonstration of the predictive capability of the specific PBPK platform for a particular intended application purpose. The effort needed to comply with such qualification requirements exceeds the costs for any individual PBPK application. Because changes or updates of a PBPK platform would require (re‐)qualification, a reliable and efficient generic qualification framework is needed. We describe the development and implementation of an agile and sustainable technical framework for automatic PBPK platform (re‐)qualification of PK‐Sim® embedded in the open source and open science GitHub landscape of Open Systems Pharmacology. The qualification approach enables the efficient assessment of all aspects relevant to the qualification of a particular purpose and provides transparency and traceability for all stakeholders. As a showcase example for the power and versatility of the qualification framework, we present the qualification of PK‐Sim® for the intended purpose of predicting cytochrome P450 3A4 (CYP3A4)–mediated drug–drug interactions (DDIs). Several perpetrator PBPK models featuring various degrees of CYP3A4 modulation and different types of mechanisms (competitive inhibition, mechanism‐based inactivation, and induction) were coupled with a set of PBPK models of sensitive CYP3A4 victim drugs. Simulations were compared to a comprehensive data set of 135 observations from published clinical DDI studies. The platform's overall predictive performance showed reasonable accuracy and precision (geometric mean fold error of 1.4 for both area under the plasma concentration‐time curve ratios and peak plasma concentration ratios with/without perpetrator) and suggests that PK‐Sim® can be applied to quantitatively assess CYP3A4‐mediated DDI in clinically untested scenarios.

N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a well-established biomarker in heart failure (HF) but controversially discussed as a potential surrogate marker in HF trials. We analyzed the NT-proBNP/mortality relationship in real-world data (RWD) of 108,330 HF patients from the IBM Watson Health Explorys database and compared it with the NT-proBNP / clinical event end-point relationship in 20 clinical HF studies. With a hierarchical statistical model, we quantified the functional relationship and interstudy variability. To independently qualify the model, we predicted outcome hazard ratios in five phase III HF studies solely based on NT-proBNP measured early in the respective study. In RWD and clinical studies, the relationship between NT-proBNP and clinical outcome is well described by an Emax model. The NT-proBNP independent baseline risk (R0 , RWD/studies median (interstudy interquartile range): 5.5%/3.0% (1.7-4.9%)) is very low compared with the potential NT-proBNP-associated maximum risk (Rmax : 55.2%/79.4% (61.5-89.0%)). The NT-proBNP concentration associated with the half-maximal risk is comparable in RWD and across clinical studies (EC50 : 3,880/2,414 pg/mL (1,460-4,355 pg/mL)). Model-based predictions of phase III outcomes, relying on short-term NT-proBNP data only, match final trial results with comparable confidence intervals. Our analysis qualifies NT-proBNP as a surrogate for clinical outcome in HF trials. NT-proBNP levels after short treatment durations of less than 10 weeks quantitatively predict hazard ratios with confidence levels comparable to final trial readout. Early NT-proBNP measurement can therefore enable shorter and smaller but still reliable HF trials.

Background Finerenone (BAY 94-8862) is a potent non-steroidal, selective mineralocorticoid receptor antagonist being developed for the treatment of patients with type 2 diabetes and chronic kidney disease. Methods We present the population pharmacokinetics and pharmacodynamics (PD) analysis for efficacy and safety markers based on data from two clinical phase IIb studies: ARTS-DN (NCT01874431) and ARTS-DN Japan (NCT01968668). Results The pharmacokinetics of finerenone were adequately characterized, with estimated glomerular filtration rate (eGFR) and body weight as influencing covariates. The area under the plasma concentration–time curve in Japanese patients did not differ from that in the global population, and the investigated pharmacokinetics were dose- and time-linear. In addition, the pharmacokinetic model provided robust individual exposure estimates to study exposure–response. The concentration–effect relationship over time for the efficacy marker urinary albumin:creatinine ratio (UACR) was well-characterized by a maximum effect model indicating saturation at high exposures. For the safety markers, a log-linear model and a power model were identified for serum potassium concentration and eGFR, respectively, indicating attenuation of effect gains at high exposures. There was no apparent ethnic effect on the investigated pharmacokinetic–pharmacodynamic relationships. The model-predicted times to reach the full (99%) steady-state drug effect on UACR, serum potassium, and eGFR were 138, 20, and 85 days, respectively, while the pharmacokinetic half-life was 2–3 h and steady state was achieved after 2 days, indicating timescale separation. Conclusion Our dose–exposure–response modeling and simulation indicates effects were largely saturated at finerenone 20 mg and doses of both 10 and 20 mg once daily appear safe and efficacious at reducing albuminuria. Electronic supplementary material The online version of this article (10.1007/s40262-019-00820-x) contains supplementary material, which is available to authorized users.

Moxifloxacin is a widely used fluoroquinolone for the treatment of complicated intra‐abdominal infections. We applied physiologically‐based pharmacokinetic (PBPK) and population pharmacokinetic (popPK) modeling to support dose selection in pediatric patients. We scaled an existing adult PBPK model to children based on prior physiological knowledge. The resulting model proposed an age‐dependent dosing regimen that was tested in a phase I study. Refined doses were then tested in a phase III study. A popPK analysis of all clinical pediatric data confirmed the PBPK predictions, including the proposed dosing schedule in children, and supported pharmacokinetics‐related safety/efficacy questions. The pediatric PBPK model adequately predicted the doses necessary to achieve antimicrobial efficacy while maintaining safety in the phase I and III pediatric studies. Altogether, this study retroactively demonstrated the robustness and utility of modeling to support dose finding and confirmation in pediatric drug development for moxifloxacin.

Model building is one of the fundamental knowledge generation concepts in science and medicine. We introduce the concept and its application in Translational Medicine with a series of examples. The Hodgkin & Huxley model for the neural action potential illustrates the power of models in the identification of previously unknown structural properties in biology. Compartmental and Physiology-Based Pharmacokinetics demonstrate how data can be interpreted by mathematical abstraction such that predictions for untested clinical settings are enabled. Based on a computational model of blood coagulation, the mechanistic interaction of different pharmacological principles for anticoagulation is predicted. Quantitative computer simulations are used to inform complex dosing schedules ensuring patient safety when treatment paradigms are switched. Finally, we introduce the framework of model-informed drug development, a novel paradigm in pharmaceutical industry and regulatory practice where direct experimental and clinical evidence is complemented and even substituted by model-based derivations.

Development and guidance of dosing schemes in children have been supported by physiology‐based pharmacokinetic (PBPK) modeling for many years. PBPK models are built on a generic basis, where compound‐ and system‐specific parameters are separated and can be exchanged, allowing the translation of these models from adults to children by accounting for physiological differences. Owing to these features, PBPK modeling is a valuable approach to support clinical decision making for dosing in children. In this analysis, we evaluate pediatric PBPK models for 10 small‐molecule compounds that were applied to support clinical decision processes at Bayer for their predictive power in different age groups. Ratios of PBPK‐predicted to observed PK parameters for the evaluated drugs in different pediatric age groups were estimated. Predictive performance was analyzed on the basis of a 2‐fold error range and the bioequivalence range (ie, 0.8 ≤ predicted/observed ≤ 1.25). For all 10 compounds, all predicted‐to‐observed PK ratios were within a 2‐fold error range (n = 27), with two‐thirds of the ratios within the bioequivalence range (n = 18). The findings demonstrate that the pharmacokinetics of these compounds was successfully and adequately predicted in different pediatric age groups. This illustrates the applicability of PBPK for guiding dosing schemes in the pediatric population.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

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. Electronic supplementary material The online version of this article (10.1007/s10928-017-9559-4) contains supplementary material, which is available to authorized users.

Fur die Wohnungswirtschaft bedeutet Digitalisierung nicht nur Einsatz und Anwendung digitaler Angebote im Gebaude und Quartier, sondern auch Anpassung von operativen Prozessen und strategischen Aufgaben in den Unternehmen. Entsprechend ist die Digitalisierung kein Selbstzweck, sondern muss sich als hilfreiches Werkzeug in der Bewirtschaftung und Weiterentwicklung der Wohnungsbestande erweisen. Digitale Losungen sind attraktiv, wenn sie effizienzsteigernd wirken und flexibel einsetzbar sind. Fur eine echte Wirksamkeit bedarf es der Anderung der Lebens-, Verhaltens- und Arbeitsweisen aller Akteure.

Hormonal contraceptive agents (HCAs) are widely used throughout the world, and women taking HCAs are likely to take other medications. However, little is known about the clinical effect of most drug‐drug interactions (DDIs) associated with HCAs. A team of interdisciplinary outcomes and pharmacometric researchers from academia and industry jointly engage in a research project to (i) quantitatively elucidate DDI impacts on unintended pregnancies and breakthrough bleeding, and (ii) establish a DDI‐prediction framework to inform optimal use of HCAs.

Food and Drug Administration submissions of physiologically based pharmacokinetic (PBPK) modeling and simulation of small-molecule drugs document the relevance of pediatric drug development and, in particular, information on dosing strategies in children. The most relevant prerequisite for reliable PBPK-based translation of adult pharmacokinetics of a small molecule to children is knowledge of the drug-specific absorption, distribution, metabolism, and elimination (ADME) processes in adults together with existing information about ontogeny of ADME processes relevant for the drug. All mechanisms driving a drug's clearance are of specific importance. For other drug modalities, our knowledge of ADME processes and ontogeny is still limited. More research is required, for example, to understand why some therapeutic proteins show complex differences in pharmacokinetics between adults and children, whereas other proteins seem to follow simple allometric scaling rules. Ontogeny information originates from various sources, such as (semi)quantitative mRNA expression, in vitro activity data, and deconvolution of in vivo pharmacokinetic data. The workflow for pediatric predictions is well described in several articles documenting successful translation from adults to children. The technical hurdles for PBPK modeling are low. State-of-the-art PBPK modeling software tools provide integrated pediatric translation workflows. For example, PK-Sim and MoBi are freely available as fully transparent open-source software via Open Systems Pharmacology (OSP). With the latest 2019 software release, version 8.0, OSP even provides a fully integrated technical framework for the qualification (and requalification) of any specific intended PBPK use in line with Food and Drug Administration and European Medicines Agency PBPK guidance. Qualification packages for pediatric translation are available on the OSP platform.

Of the 10,272 currently recognized reptile species, the trade of fewer than 8% are regulated by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the European Wildlife Trade Regulations (EWTR). However, the International Union for Conservation of Nature (IUCN) Red List has assessed 45% of the world's reptile species and determined that at least 1390 species are threatened by “biological resource use”. Of these, 355 species are intentionally targeted by collectors, including 194 non-CITES-listed species. Herein we review the global reptile pet trade, its impacts, and its contribution to the over-harvesting of species and populations, in light of current international law. Findings are based on an examination of relevant professional observations, online sources, and literature (e.g., applicable policies, taxonomy [reptile database], trade statistics [EUROSTAT], and conservation status [IUCN Red List]). Case studies are presented from the following countries and regions: Australia, Central America, China, Galapagos Islands (Ecuador), Germany, Europe, India, Indonesia (Kalimantan), Islamic Republic of Iran, Japan, Madagascar, Mexico, New Zealand, the Philippines, South Africa, Sri Lanka, Vietnam, Western Africa, and Western Asia. The European Union (EU) plays a major role in reptile trade. Between 2004 and 2014 (the period under study), the EU member states officially reported the import of 20,788,747 live reptiles. This review suggests that illegal trade activities involve species regulated under CITES, as well as species that are not CITES-regulated but nationally protected in their country of origin and often openly offered for sale in the EU. Further, these case studies demonstrate that regulations and enforcement in several countries are inadequate to prevent the overexploitation of species and to halt illegal trade activities.

The therapeutic effect of a drug is governed by its pharmacokinetics which determine the downstream pharmacodynamic response within the cellular network. A complete understanding of the drug-effect relationship therefore requires multi-scale models which integrate the properties of the different physiological scales. Computational modelling of these individual scales has been successfully established in the past. However, coupling of the scales remains challenging, although it will provide a unique possibility of mechanistic and holistic analyses of therapeutic outcomes for varied treatment scenarios. We present a methodology to combine whole-body physiologically-based pharmacokinetic (PBPK) models with mechanistic intracellular models of signal transduction in the liver for therapeutic proteins. To this end, we developed a whole-body distribution model of IFN-α in human and a detailed intracellular model of the JAK/STAT signalling cascade in hepatocytes and coupled them at the liver of the whole-body human model. This integrated model infers the time-resolved concentration of IFN-α arriving at the liver after intravenous injection while simultaneously estimates the effect of this dose on the intracellular signalling behaviour in the liver. In our multi-scale physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) model, receptor saturation is seen at low doses, thus giving mechanistic insights into the pharmacodynamic (PD) response. This model suggests a fourfold lower intracellular response after administration of a typical IFN-α dose to an individual as compared to the experimentally observed responses in in vitro setups. In conclusion, this work highlights clear differences between the observed in vitro and in vivo drug effects and provides important suggestions for future model-based study design.

This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of "good practice" recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit RD ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. “Smart Medical Information Technology for Healthcare (SMITH)” is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH’s goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach. Objectives: To give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented. Governance and Policies: SMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals’ Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage. Methodology and Architectural Framework: To share medical and research data, SMITH’s information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM2 for enterprise architecture modeling supports a consistent development process.The DIC reference architecture determines the services, applications and the standards-based communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability. Use Cases: The methodological use case “Phenotype Pipeline” (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case “Algorithmic Surveillance of ICU Patients” (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a “hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections” (HELP). ASIC and HELP use the PheP. The clinical benefit of the use cases ASIC and HELP will be demonstrated in a change of care clinical trial based on a step wedge design. Discussion: SMITH’s strength is the modular, reusable IT architecture based on interoperability standards, the integration of the hospitals’ information management departments and the public-private partnership. The project aims at sustainability beyond the first 4-year funding period.

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.

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.

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.

International audience; 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.

To evaluate the ability of a physiology-based pharmacokinetic (PBPK) model to predict the systemic drug exposure of high- and low-dose etoposide in children from a model developed with adult data.Simulations were performed with PK-Sim(®) (Bayer Technology Services). Model development was done using data from adult patients receiving etoposide in a conventional and high-dose polychemotherapy regimen before stem cell transplantation. Michaelis-Menten parameters from in vitro experiments reported in the literature were applied to describe the metabolism and excretion processes by P450 enzymes and transporters. The model was scaled down to children and compared to etoposide plasma concentrations in this age group.Simulated plasma concentration-time courses of protein-bound and free etoposide in adults for high- and low-dose schedules agreed with the observed data. Mean simulated total clearance of high- and low-dose etoposide was 0.70 ml/min/kg (Cl(observed): 0.70 ml/min/kg) versus 0.50 ml/min/kg (Cl(observed): 0.60 ml/min/kg), respectively. Integrated Michaelis-Menten kinetics was adequately transformed to age-related pharmacokinetics in children. Predictions of the pharmacokinetics in different age groups were also in good agreement with observed data. Drug interactions triggered by P-glycoprotein inhibitors or nephrotoxic drugs can also be elucidated.The PBPK model matched the pharmacokinetics in different dosing regimens in adults. Furthermore, the scaling procedure from the adult model to children provides useful predictions for paediatric patients. Comedication with drugs influencing the metabolism and excretion has to be taken into account. This approach could be useful for planning pharmacokinetic studies in children.

Delayed absorption of nifedipine when administered as a 20 mg immediate release soft gelatin capsule to fasted volunteers has been reported. Physiologically based pharmacokinetic (PBPK) modeling and in vitro dissolution data were used to explore our hypothesis that at high doses of nifedipine it precipitates in the stomach. Plasma concentration-time profiles following different doses of nifedipine were simulated using commercial PBPK software and compared to in vivo data. In vitro dissolution tests were performed with Adalat 10 mg capsules in different volumes of fasted state simulated gastric fluid (FaSSGF). The discrepancy in plasma concentration-time profiles between the different nifedipine doses could be well simulated, assuming protracted dissolution for the 20 mg dose. Nifedipine release from one Adalat 10 capsule in 250 or 500 mL FaSSGF was completed within 15 min whereas when release from two capsules, corresponding to 20 mg nifedipine, was studied in 250 mL FaSSGF, a maximum of about 75% drug dissolved was observed after 15 min followed by a decline in the % dissolved to a final value of approximately 40%. Based on the in silico and in vitro results it can be concluded that the observed prolongation in nifedipine absorption following the 20 mg dose was likely caused by nifedipine precipitation in human stomach.

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 (P(3)M). 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.

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 (MS) 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.

Among pediatric patients, preterm neonates and newborns are the most vulnerable subpopulation. Rapid developmental changes of physiological factors affecting the pharmacokinetics of drug substances in newborns require extreme care in dose and dose regimen decisions. These decisions could be supported by in silico methods such as physiologically-based pharmacokinetic (PBPK) modeling. In a comprehensive literature search, the physiological information of preterm neonates that is required to establish a PBPK model has been summarized and implemented into the database of a generic PBPK software. Physiological parameters include the organ weights and blood flow rates, tissue composition, as well as ontogeny information about metabolic and elimination processes in the liver and kidney. The aim of this work is to evaluate the model's accuracy in predicting the pharmacokinetics following intravenous administration of two model drugs with distinct physicochemical properties and elimination pathways based on earlier reported in vivo data. To this end, PBPK models of amikacin and paracetamol have been set up to predict their plasma levels in preterm neonates. Predicted plasma concentration-time profiles were compared to experimentally obtained in vivo data. For both drugs, plasma concentration time profiles following single and multiple dosing were appropriately predicted for a large range gestational and postnatal ages. In summary, PBPK simulations in preterm neonates appear feasible and might become a useful tool in the future to support dosing decisions in this special patient population. ispartof: Current Pharmaceutical Design vol:21 issue:39 pages:5688-5698 ispartof: location:United Arab Emirates status: published

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.

Disparity-sensitive neurons respond to contrast-inverted stereograms (aRDS) that do not evoke depth percepts. This is in conflict with the idea that such neurons are the direct correlate of depth perception. However, the output of neurons responding to aRDS may be further processed: neurons at later processing stages show weaker responses to aRDS than early stage neurons. Here, we show that such a response hierarchy emerges in a three-layered neural network. A numerical analysis demonstrates that threshold operations can largely explain the network's behavior as well as the electrophysiological data. An extension of the energy neuron model for disparity-sensitive neurons predicts increased responses to aRDS for an identifiable sub-class of cells and can thus be tested in electrophysiological experiments.

Background Venous thromboembolism has been increasingly recognised as a clinical problem in the paediatric population. Guideline recommendations for antithrombotic therapy in paediatric patients are based mainly on extrapolation from adult clinical trial data, owing to the limited number of clinical trials in paediatric populations. The oral, direct Factor Xa inhibitor rivaroxaban has been approved in adult patients for several thromboembolic disorders, and its well-defined pharmacokinetic and pharmacodynamic characteristics and efficacy and safety profiles in adults warrant further investigation of this agent in the paediatric population. Objective The objective of this study was to develop and qualify a physiologically based pharmacokinetic (PBPK) model for rivaroxaban doses of 10 and 20 mg in adults and to scale this model to the paediatric population (0–18 years) to inform the dosing regimen for a clinical study of rivaroxaban in paediatric patients. Methods Experimental data sets from phase I studies supported the development and qualification of an adult PBPK model. This adult PBPK model was then scaled to the paediatric population by including anthropometric and physiological information, age-dependent clearance and age-dependent protein binding. The pharmacokinetic properties of rivaroxaban in virtual populations of children were simulated for two body weight-related dosing regimens equivalent to 10 and 20 mg once daily in adults. The quality of the model was judged by means of a visual predictive check. Subsequently, paediatric simulations of the area under the plasma concentration–time curve (AUC), maximum (peak) plasma drug concentration (Cmax) and concentration in plasma after 24 h (C24h) were compared with the adult reference simulations. Results Simulations for AUC, Cmax and C24h throughout the investigated age range largely overlapped with values obtained for the corresponding dose in the adult reference simulation for both body weight-related dosing regimens. However, pharmacokinetic values in infants and preschool children (body weight 70 kg body weight, the simulated 90 % prediction interval values of AUC and C24h were much higher than the 90 % confidence interval of the adult reference population, owing to the weight-based simulation approach, but for these patients rivaroxaban would be administered at adult fixed doses of 10 and 20 mg. Conclusion The paediatric PBPK model developed here allowed an exploratory analysis of the pharmacokinetics of rivaroxaban in children to inform the dosing regimen for a clinical study in paediatric patients. Electronic supplementary material The online version of this article (doi:10.1007/s40262-013-0090-5) contains supplementary material, which is available to authorized users.

Objectives In-silico methods are a cost-effective possibility to support decision making at different stages of the drug development process. Among the various computational methods available, physiologically-based pharmacokinetic (PBPK) modelling represents a well-established tool for mechanistically predicting the pharmacokinetics of drugs and drug candidates. PK-Sim, a component of the Computational Systems Biology Software Suite of Bayer Technology Services GmbH (Leverkusen, Germany) is a commercial PBPK software tool. It is based on a generic model structure for typical animal species from mice to monkey and humans, and allows simultaneous simulation of drug liberation, absorption, distribution, metabolism, and excretion in one model. In this study PK-Sim has been used for the prediction of the in-vivo pharmacokinetics of drugs with a particular focus on the integration of dissolution properties and, due to its leading role in the drug development process, for the performance of different dosage forms administered via the oral route. Methods Three real life case studies have been presented to exemplify the benefits of using PBPK absorption modelling. Key findings In the first example, the in-vivo dissolution rate was directly predicted from the physical properties of different particle formulations using a mechanistic dissolution model of the Noyes–Whitney type. In the second case study, the PBPK tool was successfully used to predict the food effect in humans based on data obtained in Beagle dogs. In the third example, the utilization of the software for the support of the development of a combined immediate release–controlled release formulation has been described. Conclusions Future perspectives of the use of PBPK modelling have been discussed, with a special focus on the integration of in-vitro dissolution data into PBPK models for oral and non-oral administration of drugs.

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 (RD). 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 RD 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.

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.

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.