A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug–Drug Interaction Studies

The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug–drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim®, using 45 clinical studies (dosing range 0.1–250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or Ctrough ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development

Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions

Purpose To provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling. Methods PBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration–time curve (AUC) and peak plasma concentrations (Cmax) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies. Results The developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI Cmax ratios within 1.25-fold of the observed values, and all predicted DDI AUC and Cmax ratios within 2.0-fold. Conclusions Whole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug-Drug-Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

Background Metformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug–drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor. Objective The objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug–gene interaction, the cimetidine-metformin drug–drug interaction, and the impact of renal impairment on metformin exposure. Methods Physiologically based pharmacokinetic models were developed in PK-Sim® (version 8.0). Thirty-nine clinical studies (dosing range 0.001–2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug–drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100–800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development. Results The established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug–gene interaction, drug–drug interaction, and drug–drug–gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug–drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species. Conclusions Whole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug–gene interaction, drug–drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients

Significant impact of time-of-day variation on metformin pharmacokinetics

Aims/hypothesis The objective was to investigate if metformin pharmacokinetics is modulated by time-of-day in humans using empirical and mechanistic pharmacokinetic modelling techniques on a large clinical dataset. This study also aimed to generate and test hypotheses on the underlying mechanisms, including evidence for chronotype-dependent interindividual differences in metformin plasma and efficacy-related tissue concentrations. Methods A large clinical dataset consisting of individual metformin plasma and urine measurements was analysed using a newly developed empirical pharmacokinetic model. Causes of daily variation of metformin pharmacokinetics and interindividual variability were further investigated by a literature-informed mechanistic modelling analysis. Results A significant effect of time-of-day on metformin pharmacokinetics was found. Daily rhythms of gastrointestinal, hepatic and renal processes are described in the literature, possibly affecting drug pharmacokinetics. Observed metformin plasma levels were best described by a combination of a rhythm in GFR, renal plasma flow (RPF) and organic cation transporter (OCT) 2 activity. Furthermore, the large interindividual differences in measured metformin concentrations were best explained by individual chronotypes affecting metformin clearance, with impact on plasma and tissue concentrations that may have implications for metformin efficacy. Conclusions/interpretation Metformin’s pharmacology significantly depends on time-of-day in humans, determined with the help of empirical and mechanistic pharmacokinetic modelling, and rhythmic GFR, RPF and OCT2 were found to govern intraday variation. Interindividual variation was found to be partly dependent on individual chronotype, suggesting diurnal preference as an interesting, but so-far underappreciated, topic with regard to future personalised chronomodulated therapy in people with type 2 diabetes

Longitudinal trajectories and predictors of adolescent suicidal ideation and attempts following inpatient hospitalization.

Remarkably little is known regarding the temporal course of adolescent suicidal ideation and behavior, the prediction of suicidal attempts from changes in suicidal ideation, or the prediction of suicidal attempts after accounting for suicidal ideation as a predictor. A sample of 143 adolescents 12–15 years old was assessed during psychiatric inpatient hospitalization and again at 3, 6, 9, 15, and 18 months postdischarge through a series of structured interviews and parent- and adolescent-reported instruments. Symptoms of depression, posttraumatic stress disorder, externalizing psychopathology, hopelessness, and engagement in several forms of self-injurious/suicidal behaviors (i.e., suicide threats/gestures, plans, nonsuicidal self-injury [NSSI]) were assessed. Latent growth curve analyses revealed a period of suicidal ideation remission between baseline and 6 months following discharge, as well as a subtle period of suicidal ideation reemergence between 9 and 18 months postdischarge. Changes in suicidal ideation predicted suicide attempts. After accounting for the effects of suicidal ideation, baseline suicide threats/gestures also predicted future suicide attempts. Higher adolescent-reported depressive symptoms, lower parent-reported externalizing symptoms, and higher frequencies of NSSI predicted weaker suicidal ideation remission slopes. Findings underscore the need for more longitudinal research on the course of adolescent suicidality

Longitudinal trajectories and predictors of adolescent suicidal ideation and attempts following inpatient hospitalization.

Remarkably little is known regarding the temporal course of adolescent suicidal ideation and behavior, the prediction of suicidal attempts from changes in suicidal ideation, or the prediction of suicidal attempts after accounting for suicidal ideation as a predictor. A sample of 143 adolescents 12–15 years old was assessed during psychiatric inpatient hospitalization and again at 3, 6, 9, 15, and 18 months postdischarge through a series of structured interviews and parent- and adolescent-reported instruments. Symptoms of depression, posttraumatic stress disorder, externalizing psychopathology, hopelessness, and engagement in several forms of self-injurious/suicidal behaviors (i.e., suicide threats/gestures, plans, nonsuicidal self-injury [NSSI]) were assessed. Latent growth curve analyses revealed a period of suicidal ideation remission between baseline and 6 months following discharge, as well as a subtle period of suicidal ideation reemergence between 9 and 18 months postdischarge. Changes in suicidal ideation predicted suicide attempts. After accounting for the effects of suicidal ideation, baseline suicide threats/gestures also predicted future suicide attempts. Higher adolescent-reported depressive symptoms, lower parent-reported externalizing symptoms, and higher frequencies of NSSI predicted weaker suicidal ideation remission slopes. Findings underscore the need for more longitudinal research on the course of adolescent suicidality.Psycholog

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug–Drug–Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

Background Metformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug–drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor. Objective The objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug–gene interaction, the cimetidine-metformin drug–drug interaction, and the impact of renal impairment on metformin exposure. Methods Physiologically based pharmacokinetic models were developed in PK-Sim® (version 8.0). Thirty-nine clinical studies (dosing range 0.001–2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug–drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100–800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development. Results The established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug–gene interaction, drug–drug interaction, and drug–drug–gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug–drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species. Conclusions Whole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug–gene interaction, drug–drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients

Significant impact of time-of-day variation on metformin pharmacokinetics

Aims/hypothesis The objective was to investigate if metformin pharmacokinetics is modulated by time-of-day in humans using empirical and mechanistic pharmacokinetic modelling techniques on a large clinical dataset. This study also aimed to generate and test hypotheses on the underlying mechanisms, including evidence for chronotype-dependent interindividual differences in metformin plasma and efficacy-related tissue concentrations. Methods A large clinical dataset consisting of individual metformin plasma and urine measurements was analysed using a newly developed empirical pharmacokinetic model. Causes of daily variation of metformin pharmacokinetics and interindividual variability were further investigated by a literature-informed mechanistic modelling analysis. Results A significant effect of time-of-day on metformin pharmacokinetics was found. Daily rhythms of gastrointestinal, hepatic and renal processes are described in the literature, possibly affecting drug pharmacokinetics. Observed metformin plasma levels were best described by a combination of a rhythm in GFR, renal plasma flow (RPF) and organic cation transporter (OCT) 2 activity. Furthermore, the large interindividual differences in measured metformin concentrations were best explained by individual chronotypes affecting metformin clearance, with impact on plasma and tissue concentrations that may have implications for metformin efficacy. Conclusions/interpretation Metformin’s pharmacology significantly depends on time-of-day in humans, determined with the help of empirical and mechanistic pharmacokinetic modelling, and rhythmic GFR, RPF and OCT2 were found to govern intraday variation. Interindividual variation was found to be partly dependent on individual chronotype, suggesting diurnal preference as an interesting, but so-far underappreciated, topic with regard to future personalised chronomodulated therapy in people with type 2 diabetes

Proliferation und Reifung von Leukozytenvorläuferzellen in vitro und in Krebspatienten und Alterung von Erythrocyten in Diabetes Patienten

The objective of this thesis was to contribute to the understanding and characterisation of proliferation, maturation and ageing processes of haematopoietic progenitor and blood cells applying the population pharmacokinetic/pharmacodynamic (PK/PD) approach. Impairment and damage of proliferation and maturation of leukocyte progenitor cells in the bone marrow was investigated based on data from a clinical investigation in patients receiving a combination high-dose chemotherapy (HDCT) regimen comprising an autologuous stem cell rescue (ASCR). In Project 1, population PK models for carboplatin, etoposide and thiotepa were developed and individual PK parameter estimates for each drug were utilised to serve as input into a previously published semi-mechanistic population PK/PD model for myelosuppression[138]. This model was adjusted and further extended to account for the special setting of HDCT (Project 2). The implementation of the drugs’ effects and possible drug-drug interactions were explored and the ASCR was integrated into the model. Additionally, concomitant medication influencing the time course of leukopenia was taken into account to describe all observed phases, i.e. an initial increase in leukocyte concentrations most probably attributed to the administration of dexamethasone, followed by a steep decrease caused by the high doses of the cytotoxic drugs and a fast recovery with a pronounced rebound due to ASCR and additional administration of granulocyte colony- stimulating factor. Based on the final PK/PD model, simulations were conducted (Project 3) to investigate the optimal day for the performance of the ASCR with respect to nadir and time below (at least) grade 3 leukopenia, as both are associated with the development of serious infections. The simulations showed that not only the day on which the ASCR was performed but also the amount of retransfused CD34+ cells influenced both parameters. Recommendations for planning of the myelosupportive treatment were derived. The drug effect was further explored in Project 4, in which different PD models for the description of data from an in vitro cytotoxicity assay were investigated. Furthermore, a model for myelosuppression which enabled the estimation of EC50 values from clinical data that were comparable to those obtained from in vitro assays was proposed. This model might prove useful when, in return, in vitro data is used to predict myelosuppression in patients. In Project 5, an existing lifespan model186 for the description of glycated haemoglobin (HbA1c), a long-term biomarker in diabetes mellitus type 2 patients, which incorporates the ageing process of erythrocytes, was extended to describe the influence of a new drug on fasting and postprandial plasma glucose. All developed semi-mechanistic models contributed to the deeper understanding of (patho-) physiological processes involved in cell proliferation and maturation as well as the characterisation of the systems of leukopoiesis, erythrocyte ageing and HbA1c formation. In future, the models can be used to scientifically interpret clinical results, guide the planning of clinical studies and improve existing and future therapy regimens in the indications of oncology and diabetes mellitus type 2.Ziel dieser Arbeit war, unter Verwendung des populationsbasierten pharmakokinetischen/ pharmakodynamischen (PK/PD) Modellierungsansatzes zu einem tieferen Verständnis und der besseren Charakterisierung von Proliferations-, Reifungs- und Alterungsprozessen verschiedener Blutzelllinien beizutragen. Basierend auf Daten, die im Rahmen einer klinischen Untersuchung “Hochdosis-Chemotherapie mit anschließender autologer Stammzellretransfusion in Hodentumorpatienten” erhoben wurden, wurde die Beeinträchtigung und Schädigung der Zellproliferation und -reifung von hämatopoietischen Vorläuferzellen aus dem Knochenmark untersucht. Hierfür wurden in Projekt 1 PK Modelle für die Beschreibung der Plasmakonzentrationen von Carboplatin, Etoposid und Thiotepa entwickelt, individuelle pharmakokinetische Parameter geschätzt und diese in ein PK/PD Modell zur Beschreibung der Myelosuppression nach einer Chemotherapie[138] implementiert. Dieses Modell wurde dahingehend erweitert, dass es der besonderen Situation eines Hochdosis- Chemotherapieregimes gerecht wurde (Projekt 2). Verschiedene Möglichkeiten der Implementierung der Arzneistoffeffekte auf die proliferierenden Zellen im Knochenmark und eine mögliche Interaktion der Arzneistoffe wurden untersucht und der Prozess der autologen Retransfusion von Stammzellen erfolgreich in das Modell integriert. Des Weiteren wurde der Einfluss der verabreichten Komedikation auf den Verlauf der Leukopenie untersucht und gegebenenfalls im Modell berücksichtigt. Der typische Verlauf der Leukopenie in der untersuchten Population war durch einen initialen Anstieg der Leukozytenkonzentrationen, der wahrscheinlich auf die Applikation von Dexamethason zurückzuführen war, gekennzeichnet. Daran anschließend war ein steiler Abfall der Leukozytenkonzentration zu beobachten, welcher auf die hohe Dosis der Arzneistoffe zurückzuführen war, gefolgt von einer schnellen Erholung hin zu physiologischen Leukozytenkonzentrationen. Diese war gekennzeichnet durch ein deutliches Überschießen der Leukozytenkonzentrationen auf Grund eines Rebound- Effekts der u.a. in der autologen Stammzellretransfusion und der Applikation von Granulozyten Kolonie stimulierendem Faktor begründet war. Basierend auf dem finalen PK/PD Modell wurden Simulationen durchgeführt, mittels derer der bestmögliche Tag für die Durchführung der autologen Stammzellretransfusion untersucht wurde (Projekt 3). Für die Bewertung wurden der Nadir und die Dauer einer Leukopenie (mindestens) dritten Grades herangezogen, da beide mit dem Auftreten von schwerwiegenden Infektionen in Zusammenhang stehen. Die Simulationen zeigten, dass nicht nur der Tag an dem die Retransfusion stattfand, sondern auch die Anzahl an retransfundierten CD34+ Zellen einen Einfluss auf den Nadir und die Dauer der Leukopenie hatten und beide bei der Planung der Durchführung einer autologen Stammzellretransfusion berücksichtigt werden sollten. Der Effekt von zytotoxischen Wirkstoffen auf das Überleben von peripheren Mononukleären Zellen wurde in Projekt 4 anhand von In-vitro Daten charakterisiert. Hierfür wurden verschiedene PD Modelle für die Beschreibung des zytotoxischen Effekts untersucht. In einem weiteren Teilprojekt wurde ein Modell für die Abschätzung von EC50 Werten, basierend auf Neutrophilenkonzentrationen entwickelt, die im Rahmen einer klinischen Studie in Krebspatienten erhoben wurden. Dieses Modell ermöglichte die Abschätzung von EC50 Werten, die vergleichbar mit denen aus In-vitro Assays waren. Künftig könnte dieses Modell dazu genutzt werden, den Verlauf einer Myelosuppression in Patienten basierend auf In-vitro Daten vorherzusagen. In Projekt 5 wurde ein Modell zur Beschreibung von glykiertem Hämoglobin (HbA1c) in Patienten mit Diabetes mellitus Typ 2186 untersucht, welches die Abschätzung der Lebensdauer von Erythrozyten in der Zirkulation ermöglicht. Das Modell wurde dahingehend erweitert, dass der Einfluss eines neuen Arzneistoffs auf Nüchtern und Postprandiale Glukosekonzentrationen im Plasma und deren Beitrag zur Inhibition der Bildung von HbA1c beschrieben werden konnte. Die Entwicklung der semi-mechanistischen Modelle konnte zu einem tieferen Verständnis von (patho-)physiologischen Vorgängen, die in der Zellproliferation und -reifung eine Rolle spielen, sowie zur Beschreibung der Lebensdauer von Zellen in der Zirkulation beitragen und die physiologischen Vorgänge der Leukopoiese unter Hochdosis-Chemotherapie und der HbA1c-Bildung beschreiben. Die entwickelten Modelle können dazu beitragen, Ergebnisse klinischer Studien oder Messungen aus dem klinischen Alltag wissenschaftlich zu interpretieren, die Planung von klinischen Studien zu unterstützen und bereits bestehende oder künftige Therapieregime sowohl in der Hochdosis-Chemotherapie als auch in der Therapie von Diabetes mellitus Typ 2 zu verbessern