Mooney-Rivlin Parameter Determination Model as a Function of Temperature in Vulcanized Rubber Based on Molecular Dynamics Simulations.

The automotive industry is entering a digital revolution, driven by the need to develop new products in less time that are high-quality and environmentally friendly. A proper manufacturing process influences the performance of the door grommet during its lifetime. In this work, uniaxial tensile tests based on molecular dynamics simulations have been performed on an ethylene-propylene-diene monomer (EPDM) material to investigate the effect of the crosslink density and its variation with temperature. The Mooney-Rivlin (MR) model is used to fit the results of molecular dynamics (MD) simulations in this paper and an exponential-type model is proposed to calculate the parameters C1(T) and C2T. The experimental results, confirmed by hardness tests of the cured part according to ASTM 1415-88, show that the free volume fraction and the crosslink density have a significant effect on the stiffness of the EPDM material in a deformed state. The results of molecular dynamics superposition on the MR model agree reasonably well with the macroscopically observed mechanical behavior and tensile stress of the EPDM at the molecular level. This work allows the accurate characterization of the stress-strain behavior of rubber-like materials subjected to deformation and can provide valuable information for their widespread application in the injection molding industry.

Dynamic Simulation and Modeling of a Novel NeuRaiSya for Railway Monitoring System Using Petri Nets.

This research introduces the NeuRaiSya (Neural Railway System Application), an innovative railway signaling system integrating deep learning for passenger analysis. The objectives of this research are to simulate the NeuRaiSya and evaluate its effectiveness using the GreatSPN tool (graphical editor for Petri nets). GreatSPN facilitates evaluations of system behavior, ensuring safety and efficiency. Five models were designed and simulated using the Petri nets model, including the Dynamics of Train Departure model, Train Operations with Passenger Counting model, Timestamp Data Collection model, Train Speed and Location model, and Train Related-Issues model. Through simulations and modeling using Petri nets, the study demonstrates the feasibility of the proposed NeuRaiSya system. The results highlight its potential in enhancing railway operations, ensuring passenger safety, and maintaining service quality amidst the evolving railway landscape in the Philippines.

Application of physiologically based pharmacokinetic modeling of novel drugs approved by the U.S. food and drug administration.

Physiologically based pharmacokinetic (PBPK) models which can leverage preclinical data to predict the pharmacokinetic properties of drugs rapidly became an essential tool to improve the efficiency and quality of novel drug development. In this review, by searching the Application Review Files in Drugs@FDA, we analyzed the current application of PBPK models in novel drugs approved by the U.S. Food and Drug Administration (FDA) in the past five years. According to the results, 243 novel drugs were approved by the FDA from 2019 to 2023. During this period, 74 Application Review Files of novel drugs approved by the FDA that used PBPK models. PBPK models were used in various areas, including drug-drug interactions (DDI), organ impairment (OI) patients, pediatrics, drug-gene interaction (DGI), disease impact, and food effects. DDI was the most widely used area of PBPK models for novel drugs, accounting for 74.2 % of the total. Software platforms with graphical user interfaces (GUI) have reduced the difficulty of PBPK modeling, and Simcyp was the most popular software platform among applicants, with a usage rate of 80.5 %. Despite its challenges, PBPK has demonstrated its potential in novel drug development, and a growing number of successful cases provide experience learned for researchers in the industry.

Drug-drug interactions and synergy: from pharmacological models to clinical application.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts such as concentration-response curves, additive effects, and predictive models are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. While various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected at bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. Significance Statement Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug interaction research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

The Drug-Drug Interaction Between Erlotinib and OSI-930 is Mediated Through Aldehyde Oxidase Inhibition.

The propensity for aldehyde oxidase (AO) substrates to be implicated in drug-drug interactions (DDI) is not well-understood due to the dearth of potent inhibitors that elicit in vivo inhibition of AO. While there is only one reported instance of DDI that has been ascribed to the inhibition of AO to-date, the supporting evidence for this clinical interaction is rather tenuous and its veracity has been called into question. Our group recently reported that the epidermal growth factor receptor inhibitor erlotinib engendered potent time-dependent inhibition of AO with inactivation kinetic constants in the same order of magnitude as its free circulating plasma concentrations. At the same time, it was previously reported that the concomitant administration of erlotinib with the investigational drug OSI-930 culminated in a ~2-fold increase in its systemic exposure. Although the basis underpinning this interaction remains unclear, the structure of OSI-930 contains a quinoline motif which is amenable to oxidation at the electrophilic carbon adjacent to the nitrogen atom by molybdenum-containing hydroxylases like AO. In this study, we conducted metabolite identification which revealed that OSI-930 undergoes AO metabolism to a mono-oxygenated 2-oxo metabolite and assessed its formation kinetics in human liver cytosol. Additionally, reaction phenotyping in human hepatocytes revealed that AO contributes nearly ~50% to the overall metabolism of OSI-930. Finally, modelling the interaction between erlotinib and OSI-930 using a mechanistic static model projected an ~1.85-fold increase in the systemic exposure of OSI-930 - which accurately recapitulated clinical observations. Significance Statement In this study, we delineate an AO metabolic pathway in the investigational drug OSI-930 for the first time and confirmed that it represented a major route of metabolism through reaction phenotyping in human hepatocytes. Our study provided compelling mechanistic and modelling evidence for the first instance of an AO-mediated clinical DDI stemming from the in vivo inhibition of the AO-mediated quinoline 2-oxidation pathway in OSI-930 by erlotinib.

Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models.

Physiologically-based pharmacokinetic (PBPK) models and quantitative systems pharmacology (QSP) models have contributed to drug development strategies. The parameters of these models are commonly estimated by capturing observed values using the nonlinear least-squares method. Software packages for PBPK and QSP modeling provide a range of parameter estimation algorithms. To choose the most appropriate method, modelers need to understand the basic concept of each approach. This review provides a general introduction to the key points of parameter estimation with a focus on the PBPK and QSP models, and the respective parameter estimation algorithms. The latter part assesses the performance of five parameter estimation algorithms - the quasi-Newton method, Nelder-Mead method, genetic algorithm, particle swarm optimization, and Cluster Gauss-Newton method - using three examples of PBPK and QSP modeling. The assessment revealed that some parameter estimation results were significantly influenced by the initial values. Moreover, the choice of algorithms demonstrating good estimation results heavily depends on factors such as model structure and the parameters to be estimated. To obtain credible parameter estimation results, it is advisable to conduct multiple rounds of parameter estimation under different conditions, employing various estimation algorithms.

Optimizing inferior vena cava filter design: A computational fluid dynamics study on strut configuration for enhanced hemodynamic performance and thrombosis reduction.

Background and objective: Inferior vena cava filters have been shown to be effective in preventing deep vein thrombosis and its secondary complication, pulmonary embolism, thereby reducing the high mortality rate. Although inferior vena cava filters have evolved, specific complications like inferior vena cava thrombosis-induced deep vein thrombosis worsening and recurrent pulmonary embolism continue to pose challenges. This study analyzes the effects of geometric parameter variations of inferior vena cava filters, which have a significant impact on the thrombus formation inside the filter, the capture, dissolution, and hemodynamic flow of thrombus, as well as the shear stress on the filter and vascular wall. Methods: This study used computational fluid dynamic simulations with the carreau model to investigate the impact of varying inferior vena cava filter design parameters (number of struts, strut arm length, and tilt angle) on hemodynamics. Results: Recirculation and stagnation areas due to flow velocity and pressure, along with wall shear stress values, were identified as key factors. It is important to find a balance between wall shear stress high enough to aid thrombolysis and low enough to prevent platelet activation. The results of this paper show that the risk of platelet activation and thrombus filtration may be lowest when the wall shear stress of the filter ranges from 0 to 4 [Pa], minimizing stress concentration within the filter. Conclusion: 16 arm struts with a length of 20 mm and a tilt angle of 0° provide the best balance between thrombus capture and minimization of hemodynamic disturbance. This configuration minimizes the size of the stagnation and recirculation zones while maintaining sufficient wall shear stress for thrombus dissolution.

Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review.

Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other's weaknesses.

A His bundle pacing protocol for suppressing ventricular arrhythmia maintenance and improving defibrillation efficacy.

BACKGROUND: The excitable gap (EG), defined as the excitable tissue between two subsequent wavefronts of depolarization, is critical for maintaining reentry that underlies deadly ventricular arrhythmias. EG in the His-Purkinje Network (HPN) plays an important role in the maintenance of electrical wave reentry that underlies these arrhythmias. OBJECTIVE: To determine if rapid His bundle pacing (HBP) during reentry reduces the amount of EG in the HPN and ventricular myocardium to suppress reentry maintenance and/or improve defibrillation efficacy. METHODS: In a virtual human biventricular model, reentry was initiated with rapid line pacing followed by HBP delivered for 3, 6, or 9 s at pacing cycle lengths (PCLs) ranging from 10 to 300 ms (n=30). EG was calculated independently for the HPN and myocardium over each PCL. Defibrillation efficacy was assessed for each PCL by stimulating myocardial surface EG with delays ranging from 0.25 to 9 s (increments of 0.25 s, n=36) after the start of HBP. Defibrillation was successful if reentry terminated within 1 s after EG stimulation. This defibrillation protocol was repeated without HBP. To test the approach under different pathological conditions, all protocols were repeated in the model with right (RBBB) or left (LBBB) bundle branch block. RESULTS: Compared to without pacing, HBP for >3 seconds reduced average EG in the HPN and myocardium across a broad range of PCLs for the default, RBBB, and LBBB models. HBP >6 seconds terminated reentrant arrhythmia by converting HPN activation to a sinus rhythm behavior in the default (6/30 PCLs) and RBBB (7/30 PCLs) models. Myocardial EG stimulation during HBP increased the number of successful defibrillation attempts by 3%-19% for 30/30 PCLs in the default model, 3%-6% for 14/30 PCLs in the RBBB model, and 3%-11% for 27/30 PCLs in the LBBB model. CONCLUSION: HBP can reduce the amount of excitable gap and suppress reentry maintenance in the HPN and myocardium. HBP can also improve the efficacy of low-energy defibrillation approaches targeting excitable myocardium. HBP during reentrant arrhythmias is a promising anti-arrhythmic and defibrillation strategy.

Guiding Dyslipidemia Treatment: A Population Pharmacokinetic-Pharmacodynamic Framework for Obicetrapib.

Obicetrapib is a selective inhibitor of cholesteryl ester transfer protein that is currently in phase 3 of development for the treatment of dyslipidemia as adjunct therapy. The purpose of this study was to comprehensively characterize the pharmacokinetic (PK) and pharmacodynamic (PD) disposition of obicetrapib. Data from 7 clinical trials conducted in healthy adults and those with varying degrees of dyslipidemia were included for model development. The structural model that best described obicetrapib PK was a 3-compartment model with 4-compartment transit absorption and first-order elimination. Body weight was the only covariate found to significantly explain observed variability and was therefore included using allometric scaling on all disposition parameters. For a typical patient weighing 75 kg, the estimated apparent total body clearance and apparent volume of distribution of the central compartment was 0.81 L/h and 36.1 L, respectively. The final PK model parameters were estimated with good precision and were ultimately leveraged to sequentially inform 2 turnover models that describe obicetrapib's effect on low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) concentrations. The maximum stimulatory effect of obicetrapib on LDL-C loss was estimated to be 1.046, while the maximum inhibitory effect of obicetrapib on HDL-C loss was 0.691. This corresponds to a predicted typical maximum percent change from baseline LDL-C and HDL-C of 51.1% and 224%, respectively. The final sequential model described obicetrapib PKPD well and was ultimately able to both demonstrate evidence of internal consistency and support decision-making throughout the development lifecycle.

Drug dissolution and transit in a heterogenous gastric chyme after fed administration: Semi-mechanistic modeling and simulations for an immediate-release and orodispersible tablets containing a poorly soluble drug.

Mathematical models that treat the fed stomach content as a uniform entity emptied with a constant rate may not suffice to explain pharmacokinetic profiles recorded in clinical trials. In reality, phenomena such as the Magenstrasse or chyme areas of different pH and viscosity, play an important role in the intragastric drug dissolution and its transfer to the intestine. In this study, we investigated the data gathered in the bioequivalence trial between an immediate-release tablet (Reference) and an orally dispersible tablet (Test) with a poorly soluble weak base drug administered with or without water after a high-fat high-calorie breakfast. Maximum concentrations (Cmax) were significantly greater after administering the Reference product than the Test tablets, despite similar in vitro dissolution profiles. To explain this difference, we constructed a novel semi-mechanistic IVIVP model including a heterogeneous gastric chyme. The drug dissolution in vivo was modeled from the in vitro experiments in biorelevant media simulating gastric and intestinal fluids in the fed state (FEDGAS and FeSSIF). The key novelty of the model was separating the stomach contents into two compartments: isolated chyme (the viscous food content) that carries the drug slowly, and aq_chyme open for rapid Magenstrasse-like routes of drug transit. Drug distribution between these two compartments was both formulation- and administration-dependent, and recognized the respective drug fractions from the clinical pharmacokinetic data. The model's assumption about the nonuniform mixing of the API with the chyme, influencing differential drug dissolution and transit kinetics, led to simulating plasma concentration profiles that reflected well the variability observed in the clinical trial. The model indicated that, after administration, the Reference product mixes to a greater extent with aq_chyme, where the released drug dissolves better and transfers faster to the intestine. In conclusion, this novel approach underlines that diverse gastric emptying of different oral dosage forms may significantly impact pharmacokinetics and affect the outcomes of bioequivalence trials.

The hybrid (physical-computational) cardiovascular simulator to study valvular diseases.

To better understand the impact of valvular heart disease (VHD) on the hemodynamics of the circulatory system, investigations can be carried out using a model of the cardiovascular system. In this study, a previously developed hybrid (hydro-numerical) simulator of the cardiovascular system (HCS) was adapted and used. In our HCS Björk-Shiley mechanical heart valves were used, playing the role of mitral and aortic ones. In order to simulate aortic stenosis (AS) and mitral regurgitation (MR), special mechanical devices have been developed and integrated with the HCS. The simulation results proved that the system works correctly. Namely, in the case of AS - the mean pulmonary arterial pressure was increased due to increased preload of the left ventricle and the decrease in right ventricular preload was caused by a decrease in systemic arterial pressure. The severity of AS was performed based on the transaortic pressure gradient as well as using the Gorlin and Aaslid equations. In the case of severe AS, when the mean gradient was above 40 mmHg, the aortic valve orifice area was 0.5 cm2, which is in line with ACC/AHA guidelines. For the case of MR - with increasing severity of MR, there was a decrease in the left ventricular pressure and an increase in left atrial pressure. Using mechanical heart valves to simulate VHD by the HCS can be a valuable tool for biomedical research, providing a safe and controlled environment to study and understand the pathophysiology of VHD.

Concentration-QTc Modeling of the DPP-4 Inhibitor HSK7653 in a First-in-Human Study of Chinese Healthy Volunteers.

Cofrogliptin (HSK7653) is a long-acting dipeptidyl peptidase-4 inhibitor for the treatment of type 2 diabetes mellitus with a twice-monthly dosing regimen. This study included 62 participants (48 without food effect, 14 with food effect) receiving single doses of HSK7653 (5, 10, 25, 50, 100, and 150 mg) or placebo. Pharmacokinetic samples were collected over 24 hours postdosing and sampling times are aligned with 12-lead electrocardiograms (ECGs) which were derived from continuous ECG recordings. For the concentration-QT interval corrected for heart rate (C-QTc) analysis, we used linear mixed-effects modeling to characterize the correlation between plasma concentrations of HSK7653 and the change from baseline in the QT interval which was corrected by Fridericia's formula (ΔQTcF). The result showed that a placebo-corrected Fridericia corrected QT interval (ΔΔQTcF) prolongation higher than 10 milliseconds is unlikely at the mean maximum observed concentration (Cmax) (411 ng/mL) associated with the recommended therapeutic doses (25 mg twice-monthly), even at the highest supratherapeutic concentration (2425 ng/mL). Thus, HSK7653 does not significantly affect QT prolongation at either recommended doses or the highest supratherapeutic concentration.

Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine.

Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to apply ML on actual clinical data. First, we simulated datasets based on clinical data to compare the performance of various ML and traditional pharmacometrics (PMX) techniques with and without accounting for highly-correlated covariates. This simulation step identified the ML algorithm and the number of top covariates to select when using the actual clinical data. A previously developed desipramine population-pharmacokinetic model was used to simulate virtual subjects. Fifteen covariates were considered with four having an effect included. Based on the F1 score (an accuracy measure), ridge regression was the most accurate ML technique on 200 simulated datasets (F1 score = 0.475 ± 0.231), a performance which almost doubled when highly-correlated covariates were accounted for (F1 score = 0.860 ± 0.158). These performances were better than forwards selection with SCM (F1 score = 0.251 ± 0.274 and 0.499 ± 0.381 without/with correlations respectively). In terms of computational cost, ridge regression (0.42 ± 0.07 seconds/simulated dataset, 1 thread) was ~20,000 times faster than SCM (2.30 ± 2.29 hours, 15 threads). On the clinical dataset, prescreening with the selected ML algorithm reduced SCM runtime by 42.86% (from 1.75 to 1.00 days) and produced the same final model as SCM only. In conclusion, we have demonstrated that accounting for highly-correlated covariates improves ML prescreening accuracy. The choice of ML method and the proportion of important covariates (unknown a priori) can be guided by simulations.

Evaluating Drug Interactions between Ritonavir and Opioid Analgesics: Implications from Physiologically Based Pharmacokinetic Simulation.

Several commonly used opioid analgesics, such as fentanyl, sufentanil, alfentanil, and hydrocodone, are by report primarily metabolized by the CYP3A4 enzyme. The concurrent use of ritonavir, a potent CYP3A4 inhibitor, can lead to significant drug interactions. Using physiologically based pharmacokinetic (PBPK) modeling and simulation, this study examines the effects of different dosing regimens of ritonavir on the pharmacokinetics of these opioids. The findings reveal that co-administration of ritonavir significantly increases the exposure of fentanyl analogs, with over a 10-fold increase in the exposure of alfentanil and sufentanil when given with ritonavir. Conversely, the effect of ritonavir on fentanyl exposure is modest, likely due to additional metabolism pathways. Additionally, the study demonstrates that the steady-state exposure of hydrocodone and its active metabolite hydromorphone can be increased by up to 87% and 95%, respectively, with concurrent use of ritonavir. The extended-release formulation of hydrocodone is particularly affected. These insights from PBPK modeling provide valuable guidance for optimizing opioid dosing and minimizing the risk of toxicity when used in combination with ritonavir-containing prescriptions.

Modeling the heterogeneous apoptotic response of caspase-mediated signaling in tumor cells.

Resisting apoptosis is a hallmark of cancer. For this reason, it may be possible to force cancer cells to die by targeting components along the apoptotic signaling pathway. However, apoptosis signaling is challenging to understand due to dynamic and complex behaviors of ligands, receptors, and intracellular signaling components in response to cancer therapy. In this work, we forecast the apoptotic response based on the combined impact of these features. We expanded a previously established mathematical model of caspase-mediated apoptosis to include extracellular activation and receptor dynamics. In addition, three potential threshold values of caspase-3 necessary for the activation of apoptosis were selected to forecast which cells become apoptotic over time. We first vary ligand and receptor levels with the number of intracellular signaling proteins remaining consistent. Then, we vary the intracellular protein molecules in each simulated tumor cell to forecast the response of a heterogeneous population. By leveraging the benefits of computational modeling, we investigate the combined effect of several factors on the onset of apoptosis. This work provides quantitative insights for how the apoptotic signaling response can be forecasted, and precisely triggered, amongst heterogeneous cells via extracellular activation.

Non-equilibrium structural and dynamic behaviors of active polymers in complex and crowded environments.

Active matter systems, which convert internal chemical energy or energy from the environment into directed motion, are ubiquitous in nature and exhibit a range of emerging non-equilibrium behaviors. However, most of the current works on active matter have been devoted to particles, and the study of active polymers has only recently come into the spotlight due to their prevalence within living organisms. The intricate interplay between activity and conformational degrees of freedom gives rise to novel structural and dynamical behaviors of active polymers. Research in active polymers remarkably broadens diverse concepts of polymer physics, such as molecular architecture, dynamics, scaling and so on, which is of significant importance for the development of new polymer materials with unique performance. Furthermore, active polymers are often found in strongly interacting and crowded systems and in complex environments, so that the understanding of this behavior is essential for future developments of novel polymer-based biomaterials. This review thereby focuses on the study of active polymers in complex and crowded environments, and aims to provide insights into the fundamental physics underlying the adaptive and collective behaviors far from equilibrium, as well as the open challenges that the field is currently facing.

Leveraging Modeling and Simulation to Enhance the Efficiency of Bioequivalence Approaches for Generic Drugs: Highlights from the 2023 Generic Drug Science and Research Initiatives Public Workshop.

The 2023 Generic Drug Science and Research Initiative Public Workshop organized by the U.S. Food and Drug Administration (FDA) discussed the research needs to improve and enhance bioequivalence (BE) approaches for generic drug development. FDA takes such research needs and panel discussions into account to develop its Generic Drug User Fee Amendments III (GDUFA III) Science and Research Initiatives specific to generics. During the five workshop sessions, presentations and panel discussions focused on identifying and addressing scientific gaps and research needs related to nitrosamine impurity issues, BE assessment for oral products, innovative BE approaches for long-acting injectable products, alternative BE approaches for orally inhaled products, and advanced BE methods for topical products. Specifically, this report highlights the discussions on how to improve BE assessment for developing generic drug products based on research priorities for leveraging quantitative methods and modeling, as well as artificial intelligence/machine learning (AI/ML).

The myeloperoxidase inhibitor mitiperstat (AZD4831) does not prolong the QT interval at expected therapeutic doses.

Mitiperstat is a myeloperoxidase inhibitor in clinical development for treatment of patients with heart failure and preserved or mildly reduced ejection fraction, non-alcoholic steatohepatits and chronic obstructive pulmonary disease. We aimed to assess the risk of QT-interval prolongation with mitiperstat using concentration-QT (C-QT) modeling. Healthy male volunteers were randomized to receive single oral doses of mitiperstat 5, 15, 45, 135, or 405 mg (n = 6 per dose) or matching placebo (n = 10) in a phase 1 study (NCT02712372). Time-matched pharmacokinetic and digital electrocardiogram data were collected at the baseline (pre-dose) and at 11 time-points up to 48 h post-dose. C-QT analysis was prespecified as an exploratory objective. The prespecified linear mixed effects model used baseline-adjusted QT interval corrected for the heart rate by Fridericia's formula (ΔQTcF) as a dependent variable and plasma mitiperstat concentration as an independent variable. Initial exploratory analyses indicated that all model assumptions were met (no effect on heart rate; appropriate use of QTcF; no hysteresis; linear concentration-response relationship). Model-predicted mean baseline-corrected and placebo-adjusted ΔΔQTcF was +0.73 ms (90% confidence interval [CI]: -1.73, +3.19) at the highest anticipated clinical exposure (0.093 µmol/L) during treatment with mitiperstat 5 mg once daily. The upper 90% CI was below the established threshold of regulatory concern. The 16-fold margin to the highest observed exposure was high enough to mean that a positive control was not needed. Mitiperstat is not associated with risk of QT-interval prolongation at expected therapeutic concentrations.

Model-based exploration of the rationality of off-label use of cetirizine in Chinese pediatric patients: a prospective cohort study.

Aims: Cetirizine is frequently administered at an increased dosage in clinical practice and recommended by several guidelines. Nonetheless, the pharmacokinetic (PK) profile and real-world safety data remain insufficient in the Chinese pediatric population. The objective of the current study is to develop a population pharmacokinetic (PPK) model for cetirizine in Chinese pediatric patients and to investigate the rationale behind its off-label usage. Methods: A prospective cohort study was conducted, enrolling children who had been diagnosed with allergic diseases and prescribed cetirizine. The outcomes were safety and pharmacokinetic (PK) parameters. Cetirizine concentrations were measured using a pre-established analytical method. Subsequently, a PK model was developed, followed by model evaluation and simulation. The developed PK model was employed to investigate the drug exposure differences across various age groups and to simulate scenarios of potential overdose. Results: Sixty-three children were enrolled, and 24 of them received a cetirizine dose exceeding the recommended dosage. A PPK model, based on published literature, served as the basis of our analysis, with adjustment made to estimate certain parameters. The final model evaluation and validation indicated accurate predictive performance and robust parameter estimation. Simulations conducted for the label-dose among age 1-12 indicated median maximum concentration at steady state (Cmax,ss) of 7 year old children could be the highest. The model was also used to predict the off-label dose scenarios and overdose patient to support the clinical decision. There were no adverse drug reactions in either group. Conclusion: This study provides evidence-based and model-based exploration for optimizing cetirizine usage in Chinese pediatric patients. The cetirizine PPK model showed accurate predictive performance and could be utilized to simulate individual patient exposure in real-world clinical scenarios.