Evaluation of the cardiac safety of parsaclisib, a selective PI3Kδ inhibitor, in patients with previously treated B-cell malignancies: Results from the CITADEL-101 study.

Parsaclisib, a potent and selective phosphatidylinositol 3 kinase δ inhibitor, has been investigated for the treatment of B-cell malignancies and studied in patients with autoimmune diseases and myelofibrosis. The CITADEL-101 study (NCT02018861) assessed safety, tolerability, and preliminary efficacy of parsaclisib in patients with relapsed or refractory non-Hodgkin lymphoma. This study evaluated the cardiac safety of parsaclisib as monotherapy based on data from 72 patients enrolled in the CITADEL-101 study. Time-matched pharmacokinetic and ECG measurements were collected at specified times for 69 patients receiving monotherapy in doses of 5, 10, 15, 20, 30, and 45 mg once daily. Based on the categorical outlier analysis, no dose-dependent effect was observed on the incidence of outliers in QT interval corrected for heart rate (HR) by Fridericia's method (QTcF), HR, or cardiac conduction. Based on central tendency analysis, the least square means (LSMs) (90% confidence interval [CI]) of ΔQTcF from the central tendency analysis ranged from -6.83 (-18.8 to 5.19) to 4.75 ms (0.410-9.09) across dose groups (below 20 ms, the threshold of large QT effects) and was not considered dose dependent. Moreover, the LSMs of ΔHR, ΔPR interval, and ΔQRS interval were minor. From the concentration-ΔQTcF analyses, the predicted ΔQTcF (90% CI) for all dose levels was between 0.365 (-1.75 to 2.48) and 7.87 ms (0.921-14.8), with the highest upper limit of CIs well below 20 ms, and therefore, a large QT/QTc effect was ruled out up to the highest dose level (45 mg) investigated. Overall, parsaclisib at the dose ranges studied did not reveal concentration-dependent effects on change in QTcF and did not have a significant effect on HR or cardiac conduction.

PBPK Modeling Approach to Predict the Behavior of Drugs Cleared by Metabolism in Pregnant Subjects and Fetuses.

This study aimed to develop a physiologically based pharmacokinetic (PBPK) model that simulates metabolically cleared compounds' pharmacokinetics (PK) in pregnant subjects and fetuses. This model accounts for the differences in tissue sizes, blood flow rates, enzyme expression levels, plasma protein binding, and other physiological factors affecting the drugs' PK in both the pregnant woman and the fetus. The PBPKPlus™ module in GastroPlus® was used to model the PK of metoprolol, midazolam, and metronidazole for both non-pregnant and pregnant groups. For each of the three compounds, the model was first developed and validated against PK data in healthy non-pregnant volunteers and then applied to predict the PK in the pregnant groups. The model accurately described the PK in both the non-pregnant and pregnant groups and explained well the differences in the plasma concentration due to pregnancy. When available, the fetal plasma concentration, placenta, and fetal tissue concentrations were also predicted reasonably well at different stages of pregnancy. The work described the use of a PBPK approach for drug development and demonstrates the ability to predict differences in PK in pregnant subjects and fetal exposure for metabolically cleared compounds.

PBPK Modeling Approach to Predict the Behavior of Drugs Cleared by Kidney in Pregnant Subjects and Fetus.

The purpose of this study was to develop a physiologically based pharmacokinetic (PBPK) model predicting the pharmacokinetics (PK) of different compounds in pregnant subjects. This model considers the differences in tissue sizes, blood flow rates, enzyme expression levels, glomerular filtration rates, plasma protein binding, and other factors affected during pregnancy in both the maternal and fetal models. The PBPKPlus™ module in GastroPlus® was used to model the PK of cefuroxime and cefazolin. For both compounds, the model was first validated against PK data in healthy non-pregnant volunteers and then applied to predict pregnant groups PK. The model accurately described the PK in both non-pregnant and pregnant groups and explained well differences in the plasma concentration due to pregnancy. The fetal plasma and amniotic fluid concentrations were also predicted reasonably well at different stages of pregnancy. This work describes the use of a PBPK approach for drug development and demonstrates the ability to predict differences in PK in pregnant subjects and fetal exposure for compounds excreted renally. The prediction for pregnant groups is also improved when the model is calibrated with postpartum or non-pregnant female group if such data are available.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies.

Oral drug absorption is a complex process depending on many factors, including the physicochemical properties of the drug, formulation characteristics and their interplay with gastrointestinal physiology and biology. Physiological-based pharmacokinetic (PBPK) models integrate all available information on gastro-intestinal system with drug and formulation data to predict oral drug absorption. The latter together with in vitro-in vivo extrapolation and other preclinical data on drug disposition can be used to predict plasma concentration-time profiles in silico. Despite recent successes of PBPK in many areas of drug development, an improvement in their utility for evaluating oral absorption is much needed. Current status of predictive performance, within the confinement of commonly available in vitro data on drugs and formulations alongside systems information, were tested using 3 PBPK software packages (GI-Sim (ver.4.1), Simcyp® Simulator (ver.15.0.86.0), and GastroPlus™ (ver.9.0.00xx)). This was part of the Innovative Medicines Initiative (IMI) Oral Biopharmaceutics Tools (OrBiTo) project. Fifty eight active pharmaceutical ingredients (APIs) were qualified from the OrBiTo database to be part of the investigation based on a priori set criteria on availability of minimum necessary information to allow modelling exercise. The set entailed over 200 human clinical studies with over 700 study arms. These were simulated using input parameters which had been harmonised by a panel of experts across different software packages prior to conduct of any simulation. Overall prediction performance and software packages comparison were evaluated based on performance indicators (Fold error (FE), Average fold error (AFE) and absolute average fold error (AAFE)) of pharmacokinetic (PK) parameters. On average, PK parameters (Area Under the Concentration-time curve (AUC0-tlast), Maximal concentration (Cmax), half-life (t1/2)) were predicted with AFE values between 1.11 and 1.97. Variability in FEs of these PK parameters was relatively high with AAFE values ranging from 2.08 to 2.74. Around half of the simulations were within the 2-fold error for AUC0-tlast and around 90% of the simulations were within 10-fold error for AUC0-tlast. Oral bioavailability (Foral) predictions, which were limited to 19 APIs having intravenous (i.v.) human data, showed AFE and AAFE of values 1.37 and 1.75 respectively. Across different APIs, AFE of AUC0-tlast predictions were between 0.22 and 22.76 with 70% of the APIs showing an AFE > 1. When compared across different formulations and routes of administration, AUC0-tlast for oral controlled release and i.v. administration were better predicted than that for oral immediate release formulations. Average predictive performance did not clearly differ between software packages but some APIs showed a high level of variability in predictive performance across different software packages. This variability could be related to several factors such as compound specific properties, the quality and availability of information, and errors in scaling from in vitro and preclinical in vivo data to human in vivo behaviour which will be explored further. Results were compared with previous similar exercise when the input data selection was carried by the modeller rather than a panel of experts on each in vitro test. Overall, average predictive performance was increased as reflected in smaller AAFE value of 2.8 as compared to AAFE value of 3.8 in case of previous exercise.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 3: Identifying gaps in system parameters by analysing In Silico performance across different compound classes.

Three Physiologically Based Pharmacokinetic software packages (GI-Sim, Simcyp® Simulator, and GastroPlus™) were evaluated as part of the Innovative Medicine Initiative Oral Biopharmaceutics Tools project (OrBiTo) during a blinded "bottom-up" anticipation of human pharmacokinetics. After data analysis of the predicted vs. measured pharmacokinetics parameters, it was found that oral bioavailability (Foral) was underpredicted for compounds with low permeability, suggesting improper estimates of intestinal surface area, colonic absorption and/or lack of intestinal transporter information. Foral was also underpredicted for acidic compounds, suggesting overestimation of impact of ionisation on permeation, lack of information on intestinal transporters, or underestimation of solubilisation of weak acids due to less than optimal intestinal model pH settings or underestimation of bile micelle contribution. Foral was overpredicted for weak bases, suggesting inadequate models for precipitation or lack of in vitro precipitation information to build informed models. Relative bioavailability was underpredicted for both high logP compounds as well as poorly water-soluble compounds, suggesting inadequate models for solubility/dissolution, underperforming bile enhancement models and/or lack of biorelevant solubility measurements. These results indicate areas for improvement in model software, modelling approaches, and generation of applicable input data. However, caution is required when interpreting the impact of drug-specific properties in this exercise, as the availability of input parameters was heterogeneous and highly variable, and the modellers generally used the data "as is" in this blinded bottom-up prediction approach.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 2: An introduction to the simulation exercise and overview of results.

Orally administered drugs are subject to a number of barriers impacting bioavailability (Foral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters. Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details. 43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.). Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, Foral and relative AUC (Frel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations. A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.