User-Centered Development and Testing of the Online Patient-Reported Outcomes, Burdens, and Experiences (PROBE) Survey and the myPROBE App and Integration With the Canadian Bleeding Disorder Registry: Mixed Methods Study.

BACKGROUND: The Patient-Reported Outcomes, Burdens, and Experiences (PROBE) questionnaire is a tool for assessing the quality of life and disease burden in people living with hemophilia. OBJECTIVE: The objectives of our study were (1) to assess the needs of relevant stakeholders involved in the use of PROBE, (2) to develop the software infrastructure needed to meet these needs, and (3) to test the usability of the final product. METHODS: We conducted a series of semistructured interviews of relevant stakeholders, including PROBE investigators, people with hemophilia, and representatives of the sponsor. Based on these, we developed an online survey and a mobile app for iOS and Android. A user group evaluated the final product using the System Usability Scale (SUS) and an open feedback framework. RESULTS: The online survey was updated, and the myPROBE app for mobile devices and a new application programming interface were developed. The app was tested and modified according to user feedback over multiple cycles. The final version of the app was released in July 2019. Seventeen users aged 23 to 67 years evaluated the final version of the app using the SUS. The median (first, third quartile) SUS score for the app was 85 (68, 88) out of 100. The newly introduced functionalities were as follows: (1) capability to longitudinally track repeated fillings of the questionnaire at different time points by the same participant (as opposed to anonymous completion); (2) linking of the questionnaire with hemophilia registries, starting with the Canadian Bleeding Disorders Registry as a proof of concept; (3) removing or adding questions as needed; and (4) sending notifications to the users (eg, reminders). A new secure database was built for securely storing personal information separately from the questionnaire data. The PROBE online survey is currently available in 96 countries and 34 languages. CONCLUSIONS: The online survey was updated successfully, and the myPROBE app was developed, with a SUS score of 85 (out of 100). The app has been released in 81 countries and 34 languages. This will facilitate data collection for research and advocacy purposes, and the use of this tool in everyday clinical practice.

Physiologically Based Pharmacokinetic Approach to Determine Dosing on Extracorporeal Life Support: Fluconazole in Children on ECMO.

Extracorporeal life support (e.g., dialysis, extracorporeal membrane oxygenation (ECMO)) can affect drug disposition, placing patients at risk for therapeutic failure. In this population, dose selection to achieve safe and effective drug exposure is difficult. We developed a novel and flexible approach that uses physiologically based pharmacokinetic (PBPK) modeling to translate results from ECMO ex vivo experiments into bedside dosing recommendations. To determine fluconazole dosing in children on ECMO, we developed a PBPK model, which was validated using fluconazole pharmacokinetic (PK) data in adults and critically ill infants. Next, an ECMO compartment was added to the PBPK model and parameterized using data from a previously published ex vivo study. Simulations using the final ECMO PBPK model reasonably characterized observed PK data in infants on ECMO, and the model was used to derive dosing in children on ECMO across the pediatric age spectrum. This approach can be generalized to other forms of extracorporeal life support (ECLS), such as dialysis.

The integration of allometry and virtual populations to predict clearance and clearance variability in pediatric populations over the age of 6 years.

BACKGROUND AND OBJECTIVES: Pharmacokinetics play an integral role in the pediatric drug development process. The determination of pharmacokinetic parameters, particularly clearance, in different age groups directly informs dosing strategies for subsequent efficacy trials. Allometric scaling for prediction of pediatric clearance from the observed clearance in adults has been used in this effort. Clinical trial simulation, a powerful tool used to inform clinical trial design, requires both an estimate of clearance along with an estimate of the expected pharmacokinetic variability. The standard deviations (SD) of individual clearance values for adults are typically used and may lead to inaccurate predictions by not taking into account the more widespread distribution of factors such as body weight in the pediatric population. The objective of this study was to assess the accuracy of allometric prediction of drug clearance as well as methods for predicting clearance variability in children 6 years of age and older. METHODS: US Food and Drug Administration (FDA) clinical pharmacology reviews of pediatric studies conducted from 2002 onwards were reviewed to collate adult and pediatric clearance and clearance variability for studies including children 6 years of age and older. A set of 1,000 virtual adults {A} and a set of 5,000 virtual children (aged 2-17) {P} were generated on the basis of the White American NHANES database. Pediatric clearances were predicted in method 1 by using the geometric mean adult clearance from the in vivo study and calculating pediatric clearance for each virtual child within a subset {P'} of {P} that contained only those children that were within the age range of the in vivo pediatric study. In method 2, adult clearance values were randomly generated from the geometric mean adult clearance and standard deviation and assigned to each virtual adult in {A}. For each adult, allometric clearance scaling was completed with each virtual child within {P'}. The prediction error for the predicted and observed clearance and the clearance variability metric, coefficient of variation (CV), was calculated. The prediction accuracy as a function of the lowest age range (2 years and older) included in the study was also assessed. RESULTS: Thirty-nine unique drugs were included in the study. For both method 1 and method 2, 100 % of predicted pediatric mean clearances were within 2-fold of the observed values and approximately 82 % were within a 30 % prediction error. There was a significant increase in the prediction accuracy of CV using method 2 vs. method 1. There was a major bias towards underprediction of pediatric CV in method 1 whereas method 2 was precise and not biased. Clearance and CV prediction accuracy were not a function of the age range included in the in vivo studies. The observed CV between the adult and pediatric study groups was not significantly different although, on average, the observed pediatric CV was 32 % greater than that from adult studies. CONCLUSIONS: Allometric scaling may be a useful tool during pediatric drug development to predict drug clearance and dosing requirements in children 6 years of age and older. A novel methodology is reported that employs virtual adult and pediatric populations and adult pharmacokinetic data to accurately predict clearance variability in specific pediatric subpopulations. This approach has important implications for both clinical trial simulations and sample size determination for pediatric pharmacokinetic studies.

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.

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 (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.

Dynamically simulating the interaction of midazolam and the CYP3A4 inhibitor itraconazole using individual coupled whole-body physiologically-based pharmacokinetic (WB-PBPK) models.

BACKGROUND: Drug-drug interactions resulting from the inhibition of an enzymatic process can have serious implications for clinical drug therapy. Quantification of the drugs internal exposure increase upon administration with an inhibitor requires understanding to avoid the drug reaching toxic thresholds. In this study, we aim to predict the effect of the CYP3A4 inhibitors, itraconazole (ITZ) and its primary metabolite, hydroxyitraconazole (OH-ITZ) on the pharmacokinetics of the anesthetic, midazolam (MDZ) and its metabolites, 1' hydroxymidazolam (1OH-MDZ) and 1' hydroxymidazolam glucuronide (1OH-MDZ-Glu) using mechanistic whole body physiologically-based pharmacokinetic simulation models. The model is build on MDZ, 1OH-MDZ and 1OH-MDZ-Glu plasma concentration time data experimentally determined in 19 CYP3A5 genotyped adult male individuals, who received MDZ intravenously in a basal state. The model is then used to predict MDZ, 1OH-MDZ and 1OH-MDZ-Glu concentrations in an CYP3A-inhibited state following ITZ administration. RESULTS: For the basal state model, three linked WB-PBPK models (MDZ, 1OH-MDZ, 1OH-MDZ-Glu) for each individual were elimination optimized that resulted in MDZ and metabolite plasma concentration time curves that matched individual observed clinical data. In vivo Km and Vmax optimized values for MDZ hydroxylation were similar to literature based in vitro measures. With the addition of the ITZ/OH-ITZ model to each individual coupled MDZ + metabolite model, the plasma concentration time curves were predicted to greatly increase the exposure of MDZ as well as to both increase exposure and significantly alter the plasma concentration time curves of the MDZ metabolites in comparison to the basal state curves. As compared to the observed clinical data, the inhibited state curves were generally well described although the simulated concentrations tended to exceed the experimental data between approximately 6 to 12 hours following MDZ administration. This deviations appeared to be greater in the CYP3A5 *1/*1 and CYP3A5 *1/*3 group than in the CYP3A5 *3/*3 group and was potentially the result of assuming that ITZ/OH-ITZ inhibits both CYP3A4 and CYP3A5, whereas in vitro inhibition is due to CYP3A4. CONCLUSION: This study represents the first attempt to dynamically simulate metabolic enzymatic drug-drug interactions via coupled WB-PBPK models. The workflow described herein, basal state optimization followed by inhibition prediction, is novel and will provide a basis for the development of other inhibitor models that can be used to guide, interpret, and potentially replace clinical drug-drug interaction trials.